1
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Podda M, Bonechi S, Palladino A, Scaramuzzino M, Brozzi A, Roma G, Muzzi A, Priami C, Sîrbu A, Bodini M. Classification of Neisseria meningitidis genomes with a bag-of-words approach and machine learning. iScience 2024; 27:109257. [PMID: 38439962 PMCID: PMC10910294 DOI: 10.1016/j.isci.2024.109257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 12/13/2023] [Accepted: 02/13/2024] [Indexed: 03/06/2024] Open
Abstract
Whole genome sequencing of bacteria is important to enable strain classification. Using entire genomes as an input to machine learning (ML) models would allow rapid classification of strains while using information from multiple genetic elements. We developed a "bag-of-words" approach to encode, using SentencePiece or k-mer tokenization, entire bacterial genomes and analyze these with ML. Initial model selection identified SentencePiece with 8,000 and 32,000 words as the best approach for genome tokenization. We then classified in Neisseria meningitidis genomes the capsule B group genotype with 99.6% accuracy and the multifactor invasive phenotype with 90.2% accuracy, in an independent test set. Subsequently, in silico knockouts of 2,808 genes confirmed that the ML model predictions aligned with our current understanding of the underlying biology. To our knowledge, this is the first ML method using entire bacterial genomes to classify strains and identify genes considered relevant by the classifier.
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Affiliation(s)
- Marco Podda
- Vaccines Discovery Data Sciences, GSK Vaccines, GSK, 53100 Siena, Italy
| | - Simone Bonechi
- Vaccines Discovery Data Sciences, GSK Vaccines, GSK, 53100 Siena, Italy
- Department of Computer Science, University of Pisa, 56127 Pisa, Italy
| | - Andrea Palladino
- Vaccines Discovery Data Sciences, GSK Vaccines, GSK, 53100 Siena, Italy
| | | | - Alessandro Brozzi
- Vaccines Discovery Data Sciences, GSK Vaccines, GSK, 53100 Siena, Italy
| | - Guglielmo Roma
- Vaccines Discovery Data Sciences, GSK Vaccines, GSK, 53100 Siena, Italy
| | - Alessandro Muzzi
- Vaccines Discovery Data Sciences, GSK Vaccines, GSK, 53100 Siena, Italy
| | - Corrado Priami
- Department of Computer Science, University of Pisa, 56127 Pisa, Italy
| | - Alina Sîrbu
- Department of Computer Science, University of Pisa, 56127 Pisa, Italy
| | - Margherita Bodini
- Vaccines Discovery Data Sciences, GSK Vaccines, GSK, 53100 Siena, Italy
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2
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Gravina A, Wilson JL, Bacciu D, Grimes KJ, Priami C. Controlling astrocyte-mediated synaptic pruning signals for schizophrenia drug repurposing with deep graph networks. PLoS Comput Biol 2022; 18:e1009531. [PMID: 35507580 PMCID: PMC9109907 DOI: 10.1371/journal.pcbi.1009531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 05/16/2022] [Accepted: 03/29/2022] [Indexed: 11/19/2022] Open
Abstract
Schizophrenia is a debilitating psychiatric disorder, leading to both physical and social morbidity. Worldwide 1% of the population is struggling with the disease, with 100,000 new cases annually only in the United States. Despite its importance, the goal of finding effective treatments for schizophrenia remains a challenging task, and previous work conducted expensive large-scale phenotypic screens. This work investigates the benefits of Machine Learning for graphs to optimize drug phenotypic screens and predict compounds that mitigate abnormal brain reduction induced by excessive glial phagocytic activity in schizophrenia subjects. Given a compound and its concentration as input, we propose a method that predicts a score associated with three possible compound effects, i.e., reduce, increase, or not influence phagocytosis. We leverage a high-throughput screening to prove experimentally that our method achieves good generalization capabilities. The screening involves 2218 compounds at five different concentrations. Then, we analyze the usability of our approach in a practical setting, i.e., prioritizing the selection of compounds in the SWEETLEAD library. We provide a list of 64 compounds from the library that have the most potential clinical utility for glial phagocytosis mitigation. Lastly, we propose a novel approach to computationally validate their utility as possible therapies for schizophrenia.
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Affiliation(s)
- Alessio Gravina
- Department of Computer Science, University of Pisa, Pisa, Italy
- * E-mail:
| | - Jennifer L. Wilson
- Department of Chemical & Systems Biology, Stanford University, Stanford, California, United States of America
| | - Davide Bacciu
- Department of Computer Science, University of Pisa, Pisa, Italy
| | - Kevin J. Grimes
- Department of Chemical & Systems Biology, Stanford University, Stanford, California, United States of America
| | - Corrado Priami
- Department of Computer Science, University of Pisa, Pisa, Italy
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3
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Leonardelli L, Lofano G, Selvaggio G, Parolo S, Giampiccolo S, Tomasoni D, Domenici E, Priami C, Song H, Medini D, Marchetti L, Siena E. Literature Mining and Mechanistic Graphical Modelling to Improve mRNA Vaccine Platforms. Front Immunol 2021; 12:738388. [PMID: 34557200 PMCID: PMC8454234 DOI: 10.3389/fimmu.2021.738388] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Accepted: 08/23/2021] [Indexed: 12/25/2022] Open
Abstract
RNA vaccines represent a milestone in the history of vaccinology. They provide several advantages over more traditional approaches to vaccine development, showing strong immunogenicity and an overall favorable safety profile. While preclinical testing has provided some key insights on how RNA vaccines interact with the innate immune system, their mechanism of action appears to be fragmented amid the literature, making it difficult to formulate new hypotheses to be tested in clinical settings and ultimately improve this technology platform. Here, we propose a systems biology approach, based on the combination of literature mining and mechanistic graphical modeling, to consolidate existing knowledge around mRNA vaccines mode of action and enhance the translatability of preclinical hypotheses into clinical evidence. A Natural Language Processing (NLP) pipeline for automated knowledge extraction retrieved key biological evidences that were joined into an interactive mechanistic graphical model representing the chain of immune events induced by mRNA vaccines administration. The achieved mechanistic graphical model will help the design of future experiments, foster the generation of new hypotheses and set the basis for the development of mathematical models capable of simulating and predicting the immune response to mRNA vaccines.
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Affiliation(s)
- Lorena Leonardelli
- Fondazione The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
| | | | - Gianluca Selvaggio
- Fondazione The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
| | - Silvia Parolo
- Fondazione The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
| | - Stefano Giampiccolo
- Fondazione The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
| | - Danilo Tomasoni
- Fondazione The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
| | - Enrico Domenici
- Fondazione The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy.,Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Povo, Italy
| | - Corrado Priami
- Fondazione The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy.,Department of Computer Science, University of Pisa, Pisa, Italy
| | | | | | - Luca Marchetti
- Fondazione The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy.,Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Povo, Italy
| | - Emilio Siena
- Data Science and Computational Vaccinology, GSK, Siena, Italy
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4
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Sîrbu A, Barbieri G, Faita F, Ferragina P, Gargani L, Ghiadoni L, Priami C. Early outcome detection for COVID-19 patients. Sci Rep 2021; 11:18464. [PMID: 34531473 PMCID: PMC8446000 DOI: 10.1038/s41598-021-97990-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Accepted: 08/11/2021] [Indexed: 02/08/2023] Open
Abstract
With the outbreak of COVID-19 exerting a strong pressure on hospitals and health facilities, clinical decision support systems based on predictive models can help to effectively improve the management of the pandemic. We present a method for predicting mortality for COVID-19 patients. Starting from a large number of clinical variables, we select six of them with largest predictive power, using a feature selection method based on genetic algorithms and starting from a set of COVID-19 patients from the first wave. The algorithm is designed to reduce the impact of missing values in the set of variables measured, and consider only variables that show good accuracy on validation data. The final predictive model provides accuracy larger than 85% on test data, including a new patient cohort from the second COVID-19 wave, and on patients with imputed missing values. The selected clinical variables are confirmed to be relevant by recent literature on COVID-19.
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Affiliation(s)
- Alina Sîrbu
- grid.5395.a0000 0004 1757 3729Department of Computer Science, University of Pisa, Pisa, Italy
| | - Greta Barbieri
- grid.5395.a0000 0004 1757 3729Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Francesco Faita
- grid.5326.20000 0001 1940 4177Institute of Clinical Physiology, National Research Council, Pisa, Italy
| | - Paolo Ferragina
- grid.5395.a0000 0004 1757 3729Department of Computer Science, University of Pisa, Pisa, Italy
| | - Luna Gargani
- grid.5326.20000 0001 1940 4177Institute of Clinical Physiology, National Research Council, Pisa, Italy
| | - Lorenzo Ghiadoni
- grid.5395.a0000 0004 1757 3729Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Corrado Priami
- grid.5395.a0000 0004 1757 3729Department of Computer Science, University of Pisa, Pisa, Italy
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5
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Tomasoni D, Paris A, Giampiccolo S, Reali F, Simoni G, Marchetti L, Kaddi C, Neves-Zaph S, Priami C, Azer K, Lombardo R. QSPcc reduces bottlenecks in computational model simulations. Commun Biol 2021; 4:1022. [PMID: 34471226 PMCID: PMC8410852 DOI: 10.1038/s42003-021-02553-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Accepted: 08/09/2021] [Indexed: 01/13/2023] Open
Abstract
Mathematical models have grown in size and complexity becoming often computationally intractable. In sensitivity analysis and optimization phases, critical for tuning, validation and qualification, these models may be run thousands of times. Scientific programming languages popular for prototyping, such as MATLAB and R, can be a bottleneck in terms of performance. Here we show a compiler-based approach, designed to be universal at handling engineering and life sciences modeling styles, that automatically translates models into fast C code. At first QSPcc is demonstrated to be crucial in enabling the research on otherwise intractable Quantitative Systems Pharmacology models, such as in rare Lysosomal Storage Disorders. To demonstrate the full value in seamlessly accelerating, or enabling, the R&D efforts in natural sciences, we then benchmark QSPcc against 8 solutions on 24 real-world projects from different scientific fields. With speed-ups of 22000x peak, and 1605x arithmetic mean, our results show consistent superior performances. Lombardo and colleagues present QSPcc, a computational code compiler designed to convert code from popular scientific programming languages, such as MATLAB or R, into fast-running C code. This reduces the computational load required for complex modelling approaches and reduces user investment learning additional complex languages.
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Affiliation(s)
- Danilo Tomasoni
- Fondazione the Microsoft Research, University of Trento Centre for Computational and Systems Biology, Rovereto, Italy
| | - Alessio Paris
- Fondazione the Microsoft Research, University of Trento Centre for Computational and Systems Biology, Rovereto, Italy
| | - Stefano Giampiccolo
- Fondazione the Microsoft Research, University of Trento Centre for Computational and Systems Biology, Rovereto, Italy
| | - Federico Reali
- Fondazione the Microsoft Research, University of Trento Centre for Computational and Systems Biology, Rovereto, Italy
| | - Giulia Simoni
- Fondazione the Microsoft Research, University of Trento Centre for Computational and Systems Biology, Rovereto, Italy
| | - Luca Marchetti
- Fondazione the Microsoft Research, University of Trento Centre for Computational and Systems Biology, Rovereto, Italy
| | - Chanchala Kaddi
- Data and Data Science - Translational Disease Modeling, Sanofi, Bridgewater, NJ, USA
| | - Susana Neves-Zaph
- Data and Data Science - Translational Disease Modeling, Sanofi, Bridgewater, NJ, USA
| | - Corrado Priami
- Fondazione the Microsoft Research, University of Trento Centre for Computational and Systems Biology, Rovereto, Italy.,Department of Computer Science, University of Pisa, Pisa, Italy
| | - Karim Azer
- Data and Data Science - Translational Disease Modeling, Sanofi, Bridgewater, NJ, USA.,Axcella Health, Cambridge, MA, USA
| | - Rosario Lombardo
- Fondazione the Microsoft Research, University of Trento Centre for Computational and Systems Biology, Rovereto, Italy.
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6
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Simoni G, Kaddi C, Tao M, Reali F, Tomasoni D, Priami C, Azer K, Neves-Zaph S, Marchetti L. A robust computational pipeline for model-based and data-driven phenotype clustering. Bioinformatics 2021; 37:1269-1277. [PMID: 33225350 DOI: 10.1093/bioinformatics/btaa948] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Revised: 10/06/2020] [Accepted: 10/28/2020] [Indexed: 01/23/2023] Open
Abstract
MOTIVATION Precision medicine is a promising field that proposes, in contrast to a one-size-fits-all approach, the tailoring of medical decisions, treatments or products. In this context, it is crucial to introduce innovative methods to stratify a population of patients on the basis of an accurate system-level knowledge of the disease. This is particularly important in very challenging conditions, where the use of standard statistical methods can be prevented by poor data availability or by the need of oversimplifying the processes regulating a complex disease. RESULTS We define an innovative method for phenotype classification that combines experimental data and a mathematical description of the disease biology. The methodology exploits the mathematical model for inferring additional subject features relevant for the classification. Finally, the algorithm identifies the optimal number of clusters and classifies the samples on the basis of a subset of the features estimated during the model fit. We tested the algorithm in two test cases: an in silico case in the context of dyslipidemia, a complex disease for which a large population of patients has been generated, and a clinical test case, in the context of a lysosomal rare disorder, for which the amount of available data was limited. In both the scenarios, our methodology proved to be accurate and robust, and allowed the inference of an additional phenotype division that the experimental data did not show. AVAILABILITY AND IMPLEMENTATION The code to reproduce the in silico results has been implemented in MATLAB v.2017b and it is available in the Supplementary Material. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Giulia Simoni
- Fondazione The Microsoft Research-University of Trento Centre for Computational and Systems Biology (COSBI), 38068 Rovereto, Italy
| | - Chanchala Kaddi
- Digital Data Science - Translational Disease Modeling, Sanofi, Bridgewater, NJ 08807, USA
| | - Mengdi Tao
- Digital Data Science - Translational Disease Modeling, Sanofi, Bridgewater, NJ 08807, USA
| | - Federico Reali
- Fondazione The Microsoft Research-University of Trento Centre for Computational and Systems Biology (COSBI), 38068 Rovereto, Italy
| | - Danilo Tomasoni
- Fondazione The Microsoft Research-University of Trento Centre for Computational and Systems Biology (COSBI), 38068 Rovereto, Italy
| | - Corrado Priami
- Fondazione The Microsoft Research-University of Trento Centre for Computational and Systems Biology (COSBI), 38068 Rovereto, Italy.,Department of Computer Science, University of Pisa, 56127 Pisa, Italy
| | - Karim Azer
- Digital Data Science - Translational Disease Modeling, Sanofi, Bridgewater, NJ 08807, USA
| | - Susana Neves-Zaph
- Digital Data Science - Translational Disease Modeling, Sanofi, Bridgewater, NJ 08807, USA
| | - Luca Marchetti
- Fondazione The Microsoft Research-University of Trento Centre for Computational and Systems Biology (COSBI), 38068 Rovereto, Italy
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7
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Tini G, Varma V, Lombardo R, Nolen GT, Lefebvre G, Descombes P, Métairon S, Priami C, Kaput J, Scott-Boyer MP. DNA methylation during human adipogenesis and the impact of fructose. Genes Nutr 2020; 15:21. [PMID: 33243154 PMCID: PMC7691080 DOI: 10.1186/s12263-020-00680-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Accepted: 11/10/2020] [Indexed: 01/12/2023]
Abstract
BACKGROUND Increased adipogenesis and altered adipocyte function contribute to the development of obesity and associated comorbidities. Fructose modified adipocyte metabolism compared to glucose, but the regulatory mechanisms and consequences for obesity are unknown. Genome-wide methylation and global transcriptomics in SGBS pre-adipocytes exposed to 0, 2.5, 5, and 10 mM fructose, added to a 5-mM glucose-containing medium, were analyzed at 0, 24, 48, 96, 192, and 384 h following the induction of adipogenesis. RESULTS Time-dependent changes in DNA methylation compared to baseline (0 h) occurred during the final maturation of adipocytes, between 192 and 384 h. Larger percentages (0.1% at 192 h, 3.2% at 384 h) of differentially methylated regions (DMRs) were found in adipocytes differentiated in the glucose-containing control media compared to adipocytes differentiated in fructose-supplemented media (0.0006% for 10 mM, 0.001% for 5 mM, and 0.005% for 2.5 mM at 384 h). A total of 1437 DMRs were identified in 5237 differentially expressed genes at 384 h post-induction in glucose-containing (5 mM) control media. The majority of them inversely correlated with the gene expression, but 666 regions were positively correlated to the gene expression. CONCLUSIONS Our studies demonstrate that DNA methylation regulates or marks the transformation of morphologically differentiating adipocytes (seen at 192 h), to the more mature and metabolically robust adipocytes (as seen at 384 h) in a genome-wide manner. Lower (2.5 mM) concentrations of fructose have the most robust effects on methylation compared to higher concentrations (5 and 10 mM), suggesting that fructose may be playing a signaling/regulatory role at lower concentrations of fructose and as a substrate at higher concentrations.
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Affiliation(s)
- Giulia Tini
- The Microsoft Research - University of Trento Centre for Computational and Systems Biology, Piazza Manifattura 1, 38068, Rovereto, Italy.,Department of Mathematics, University of Trento, Via Sommarive 14, 38050, Povo, Italy.,Present address: Department of Experimental Oncology, IEO European Institute of Oncology IRCSS, Milan, Italy
| | - Vijayalakshmi Varma
- Division of Systems Biology, National Center for Toxicological Research, FDA, 3900 NCTR Road, Jefferson, AR, 72079, USA.,Present Address: Cardiovascular Renal and Metabolism Division of MedImmune, Astrazeneca, Gaithersburg, MD, 20878, USA
| | - Rosario Lombardo
- The Microsoft Research - University of Trento Centre for Computational and Systems Biology, Piazza Manifattura 1, 38068, Rovereto, Italy
| | - Greg T Nolen
- Division of Systems Biology, National Center for Toxicological Research, FDA, 3900 NCTR Road, Jefferson, AR, 72079, USA
| | | | | | | | - Corrado Priami
- The Microsoft Research - University of Trento Centre for Computational and Systems Biology, Piazza Manifattura 1, 38068, Rovereto, Italy.,Department of Computer Science, University of Pisa, Pisa, Italy
| | - Jim Kaput
- Nestlé Institute of Health Science, Lausanne, Switzerland.,Present Addresses: Vydiant Inc., Folsom, CA, 95630, USA
| | - Marie-Pier Scott-Boyer
- The Microsoft Research - University of Trento Centre for Computational and Systems Biology, Piazza Manifattura 1, 38068, Rovereto, Italy. .,Present Address: CRCHU de Québec-Université Laval, Quebec City, Québec, Canada.
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8
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Priami C. Computational approaches to understanding nutrient metabolism and metabolic disorders. Curr Opin Biotechnol 2020; 70:7-14. [PMID: 33038781 DOI: 10.1016/j.copbio.2020.09.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 09/01/2020] [Accepted: 09/06/2020] [Indexed: 10/23/2022]
Abstract
Computational methods are becoming more and more essential to elucidate biological systems. Many different approaches exist with pros and cons. This paper reviews the most useful technologies focusing on nutrient metabolism and metabolic disorders. Space limitation prevents from exploring the examples in details, but pointers to the relevant papers are reported.
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Affiliation(s)
- Corrado Priami
- Dipartimento di Informatica, Università di Pisa, Largo Pontecorvo, 56124 Pisa, Italy.
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9
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Tini G, Marchetti L, Priami C, Scott-Boyer MP. Multi-omics integration-a comparison of unsupervised clustering methodologies. Brief Bioinform 2020; 20:1269-1279. [PMID: 29272335 DOI: 10.1093/bib/bbx167] [Citation(s) in RCA: 69] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2017] [Revised: 11/06/2017] [Indexed: 12/19/2022] Open
Abstract
With the recent developments in the field of multi-omics integration, the interest in factors such as data preprocessing, choice of the integration method and the number of different omics considered had increased. In this work, the impact of these factors is explored when solving the problem of sample classification, by comparing the performances of five unsupervised algorithms: Multiple Canonical Correlation Analysis, Multiple Co-Inertia Analysis, Multiple Factor Analysis, Joint and Individual Variation Explained and Similarity Network Fusion. These methods were applied to three real data sets taken from literature and several ad hoc simulated scenarios to discuss classification performance in different conditions of noise and signal strength across the data types. The impact of experimental design, feature selection and parameter training has been also evaluated to unravel important conditions that can affect the accuracy of the result.
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10
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Misselbeck K, Parolo S, Lorenzini F, Savoca V, Leonardelli L, Bora P, Morine MJ, Mione MC, Domenici E, Priami C. A network-based approach to identify deregulated pathways and drug effects in metabolic syndrome. Nat Commun 2019; 10:5215. [PMID: 31740673 PMCID: PMC6861239 DOI: 10.1038/s41467-019-13208-z] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2018] [Accepted: 10/25/2019] [Indexed: 12/11/2022] Open
Abstract
Metabolic syndrome is a pathological condition characterized by obesity, hyperglycemia, hypertension, elevated levels of triglycerides and low levels of high-density lipoprotein cholesterol that increase cardiovascular disease risk and type 2 diabetes. Although numerous predisposing genetic risk factors have been identified, the biological mechanisms underlying this complex phenotype are not fully elucidated. Here we introduce a systems biology approach based on network analysis to investigate deregulated biological processes and subsequently identify drug repurposing candidates. A proximity score describing the interaction between drugs and pathways is defined by combining topological and functional similarities. The results of this computational framework highlight a prominent role of the immune system in metabolic syndrome and suggest a potential use of the BTK inhibitor ibrutinib as a novel pharmacological treatment. An experimental validation using a high fat diet-induced obesity model in zebrafish larvae shows the effectiveness of ibrutinib in lowering the inflammatory load due to macrophage accumulation.
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Affiliation(s)
- Karla Misselbeck
- Fondazione The Microsoft Research University of Trento, Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
- Department of Mathematics, University of Trento, Trento, Italy
| | - Silvia Parolo
- Fondazione The Microsoft Research University of Trento, Centre for Computational and Systems Biology (COSBI), Rovereto, Italy.
| | - Francesca Lorenzini
- Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento, Italy
| | - Valeria Savoca
- Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento, Italy
| | - Lorena Leonardelli
- Fondazione The Microsoft Research University of Trento, Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
| | - Pranami Bora
- Fondazione The Microsoft Research University of Trento, Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
| | - Melissa J Morine
- Fondazione The Microsoft Research University of Trento, Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
| | - Maria Caterina Mione
- Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento, Italy
| | - Enrico Domenici
- Fondazione The Microsoft Research University of Trento, Centre for Computational and Systems Biology (COSBI), Rovereto, Italy.
- Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento, Italy.
| | - Corrado Priami
- Fondazione The Microsoft Research University of Trento, Centre for Computational and Systems Biology (COSBI), Rovereto, Italy.
- Department of Computer Science, University of Pisa, Pisa, Italy.
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11
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Nassiri I, Inga A, Meškytė EM, Alessandrini F, Ciribilli Y, Priami C. Regulatory Crosstalk of Doxorubicin, Estradiol and TNFα Combined Treatment in Breast Cancer-derived Cell Lines. Sci Rep 2019; 9:15172. [PMID: 31645610 PMCID: PMC6811586 DOI: 10.1038/s41598-019-51349-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2018] [Accepted: 09/28/2019] [Indexed: 11/10/2022] Open
Abstract
We present a new model of ESR1 network regulation based on analysis of Doxorubicin, Estradiol, and TNFα combination treatment in MCF-7. We used Doxorubicin as a therapeutic agent, TNFα as marker and mediator of an inflammatory microenvironment and 17β-Estradiol (E2) as an agonist of Estrogen Receptors, known predisposing factor for hormone-driven breast cancer, whose pharmacological inhibition reduces the risk of breast cancer recurrence. Based on the results of transcriptomics analysis, we found 71 differentially expressed genes that are specific for the combination treatment with Doxorubicin + Estradiol + TNFα in comparison with single or double treatments. The responsiveness to the triple treatment was examined for seven genes by qPCR, of which six were validated, and then extended to four additional cell lines differing for p53 and/or ER status. The results of differential regulation enrichment analysis highlight the role of the ESR1 network that included 36 of 71 specific differentially expressed genes. We propose that the combined activation of p53 and NF-kB transcription factors significantly influences ligand-dependent, ER-driven transcriptional responses, also of the ESR1 gene itself. These results provide a model of coordinated interaction of TFs to explain the Doxorubicin, E2 and TNFα induced repression mechanisms.
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Affiliation(s)
- Isar Nassiri
- Department of Oncology, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK.,The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, TN, Italy
| | - Alberto Inga
- Laboratory of Transcriptional Networks, Department CIBIO, University of Trento, 38123, Trento, Italy
| | - Erna Marija Meškytė
- Laboratory of Molecular Cancer Genetics, Department CIBIO, University of Trento, 38123, Trento, Italy.,Department of Biological Models, Life Sciences Centre, Institute of Biochemistry, Vilnius University, Vilnius, Lithuania
| | - Federica Alessandrini
- Laboratory of Molecular Cancer Genetics, Department CIBIO, University of Trento, 38123, Trento, Italy
| | - Yari Ciribilli
- Laboratory of Molecular Cancer Genetics, Department CIBIO, University of Trento, 38123, Trento, Italy
| | - Corrado Priami
- The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, TN, Italy. .,Dipartimento di Informatica, Università di Pisa, Pisa, Italy.
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Simoni G, Reali F, Priami C, Marchetti L. Stochastic simulation algorithms for computational systems biology: Exact, approximate, and hybrid methods. Wiley Interdiscip Rev Syst Biol Med 2019; 11:e1459. [PMID: 31260191 DOI: 10.1002/wsbm.1459] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/18/2019] [Revised: 05/28/2019] [Accepted: 05/31/2019] [Indexed: 12/19/2022]
Abstract
Nowadays, mathematical modeling is playing a key role in many different research fields. In the context of system biology, mathematical models and their associated computer simulations constitute essential tools of investigation. Among the others, they provide a way to systematically analyze systems perturbations, develop hypotheses to guide the design of new experimental tests, and ultimately assess the suitability of specific molecules as novel therapeutic targets. To these purposes, stochastic simulation algorithms (SSAs) have been introduced for numerically simulating the time evolution of a well-stirred chemically reacting system by taking proper account of the randomness inherent in such a system. In this work, we review the main SSAs that have been introduced in the context of exact, approximate, and hybrid stochastic simulation. Specifically, we will introduce the direct method (DM), the first reaction method (FRM), the next reaction method (NRM) and the rejection-based SSA (RSSA) in the area of exact stochastic simulation. We will then present the τ-leaping method and the chemical Langevin method in the area of approximate stochastic simulation and an implementation of the hybrid RSSA (HRSSA) in the context of hybrid stochastic-deterministic simulation. Finally, we will consider the model of the sphingolipid metabolism to provide an example of application of SSA to computational system biology by exemplifying how different simulation strategies may unveil different insights into the investigated biological phenomenon. This article is categorized under: Models of Systems Properties and Processes > Mechanistic Models Analytical and Computational Methods > Computational Methods.
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Affiliation(s)
- Giulia Simoni
- The Microsoft Research-University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, TN, Italy
| | - Federico Reali
- The Microsoft Research-University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, TN, Italy
| | - Corrado Priami
- The Microsoft Research-University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, TN, Italy.,Department of Computer Science, University of Pisa, Pisa, Italy
| | - Luca Marchetti
- The Microsoft Research-University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, TN, Italy
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Caberlotto L, Nguyen TP, Lauria M, Priami C, Rimondini R, Maioli S, Cedazo-Minguez A, Sita G, Morroni F, Corsi M, Carboni L. Cross-disease analysis of Alzheimer's disease and type-2 Diabetes highlights the role of autophagy in the pathophysiology of two highly comorbid diseases. Sci Rep 2019; 9:3965. [PMID: 30850634 PMCID: PMC6408545 DOI: 10.1038/s41598-019-39828-5] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2018] [Accepted: 01/29/2019] [Indexed: 12/24/2022] Open
Abstract
Evidence is accumulating that the main chronic diseases of aging Alzheimer's disease (AD) and type-2 diabetes mellitus (T2DM) share common pathophysiological mechanisms. This study aimed at applying systems biology approaches to increase the knowledge of the shared molecular pathways underpinnings of AD and T2DM. We analysed transcriptomic data of post-mortem AD and T2DM human brains to obtain disease signatures of AD and T2DM and combined them with protein-protein interaction information to construct two disease-specific networks. The overlapping AD/T2DM network proteins were then used to extract the most representative Gene Ontology biological process terms. The expression of genes identified as relevant was studied in two AD models, 3xTg-AD and ApoE3/ApoE4 targeted replacement mice. The present transcriptomic data analysis revealed a principal role for autophagy in the molecular basis of both AD and T2DM. Our experimental validation in mouse AD models confirmed the role of autophagy-related genes. Among modulated genes, Cyclin-Dependent Kinase Inhibitor 1B, Autophagy Related 16-Like 2, and insulin were highlighted. In conclusion, the present investigation revealed autophagy as the central dys-regulated pathway in highly co-morbid diseases such as AD and T2DM allowing the identification of specific genes potentially involved in disease pathophysiology which could become novel targets for therapeutic intervention.
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Affiliation(s)
- Laura Caberlotto
- The Microsoft Research, University of Trento Centre for Computational Systems Biology (COSBI), Rovereto, Italy.
- Aptuit an Evotec company Drug Design and Discovery, Verona, Italy.
| | - T-Phuong Nguyen
- The Microsoft Research, University of Trento Centre for Computational Systems Biology (COSBI), Rovereto, Italy
- Life Sciences Research Unit, University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Megeno S.A.6A, avenue des Hauts-FourneauxL-4362 Esch-sur-Alzette, Esch-sur-Alzette, Luxembourg
| | - Mario Lauria
- The Microsoft Research, University of Trento Centre for Computational Systems Biology (COSBI), Rovereto, Italy
- Department of Mathematics, University of Trento, Povo, Trento, Italy
| | - Corrado Priami
- The Microsoft Research, University of Trento Centre for Computational Systems Biology (COSBI), Rovereto, Italy
| | - Roberto Rimondini
- Department of Medical and Surgical Science, Alma Mater Studiorum University of Bologna, Bologna, Italy
| | - Silvia Maioli
- Department of Neurobiology, Care Sciences and Society, Division of Neurogeriatrics, Karolinska Institutet, Stockholm, Sweden
| | - Angel Cedazo-Minguez
- Department of Neurobiology, Care Sciences and Society, Division of Neurogeriatrics, Karolinska Institutet, Stockholm, Sweden
| | - Giulia Sita
- Department of Pharmacy and Biotechnology, Alma Mater Studiorum University of Bologna, Bologna, Italy
| | - Fabiana Morroni
- Department of Pharmacy and Biotechnology, Alma Mater Studiorum University of Bologna, Bologna, Italy
| | - Mauro Corsi
- Aptuit, an Evotec company, Drug Design and Discovery, Verona, Italy
| | - Lucia Carboni
- Department of Pharmacy and Biotechnology, Alma Mater Studiorum University of Bologna, Bologna, Italy
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14
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Simoni G, Vo HT, Priami C, Marchetti L. A comparison of deterministic and stochastic approaches for sensitivity analysis in computational systems biology. Brief Bioinform 2019; 21:527-540. [DOI: 10.1093/bib/bbz014] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2018] [Revised: 11/29/2018] [Accepted: 01/16/2019] [Indexed: 11/13/2022] Open
Abstract
AbstractWith the recent rising application of mathematical models in the field of computational systems biology, the interest in sensitivity analysis methods had increased. The stochastic approach, based on chemical master equations, and the deterministic approach, based on ordinary differential equations (ODEs), are the two main approaches for analyzing mathematical models of biochemical systems. In this work, the performance of these approaches to compute sensitivity coefficients is explored in situations where stochastic and deterministic simulation can potentially provide different results (systems with unstable steady states, oscillators with population extinction and bistable systems). We consider two methods in the deterministic approach, namely the direct differential method and the finite difference method, and five methods in the stochastic approach, namely the Girsanov transformation, the independent random number method, the common random number method, the coupled finite difference method and the rejection-based finite difference method. The reviewed methods are compared in terms of sensitivity values and computational time to identify differences in outcome that can highlight conditions in which one approach performs better than the other.
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Affiliation(s)
- Giulia Simoni
- The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Piazza Manifattura, Rovereto (TN), Italy
| | - Hong Thanh Vo
- The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Piazza Manifattura, Rovereto (TN), Italy
- Department of Computer Science, Aalto University, Finland
| | - Corrado Priami
- The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Piazza Manifattura, Rovereto (TN), Italy
- Department of Computer Science, University of Pisa, Pisa, Italy
| | - Luca Marchetti
- The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Piazza Manifattura, Rovereto (TN), Italy
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Matone A, Derlindati E, Marchetti L, Spigoni V, Dei Cas A, Montanini B, Ardigò D, Zavaroni I, Priami C, Bonadonna RC. Correction: Identification of an early transcriptomic signature of insulin resistance and related diseases in lymphomonocytes of healthy subjects. PLoS One 2019; 14:e0211394. [PMID: 30673781 PMCID: PMC6343918 DOI: 10.1371/journal.pone.0211394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
[This corrects the article DOI: 10.1371/journal.pone.0182559.].
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Abstract
Sensitivity analysis of biochemical reactions aims at quantifying the dependence of the reaction dynamics on the reaction rates. The computation of the parameter sensitivities, however, poses many computational challenges when taking stochastic noise into account. This paper proposes a new finite-difference method for efficiently computing sensitivities of biochemical reactions. We employ propensity bounds of reactions to couple the simulation of the nominal and perturbed processes. The exactness of the simulation is preserved by applying the rejection-based mechanism. For each simulation step, the nominal and perturbed processes under our coupling strategy are synchronized and often jump together, increasing their positive correlation and hence reducing the variance of the estimator. The distinctive feature of our approach in comparison with existing coupling approaches is that it only needs to maintain a single data structure storing propensity bounds of reactions during the simulation of the nominal and perturbed processes. Our approach allows to compute sensitivities of many reaction rates simultaneously. Moreover, the data structure does not require to be updated frequently, hence improving the computational cost. This feature is especially useful when applied to large reaction networks. We benchmark our method on biological reaction models to prove its applicability and efficiency.
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Affiliation(s)
- Vo Hong Thanh
- Department of Computer Science, Aalto University, Espoo, Finland
| | - Roberto Zunino
- Department of Mathematics, University of Trento, Rovereto, Italy
| | - Corrado Priami
- Department of Computer Science, The Microsoft Research—University of Trento Centre for Computational and Systems Biology (COSBI), Italy and University of Pisa, Pisa, Italy
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17
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Michelini S, Balakrishnan B, Parolo S, Matone A, Mullaney JA, Young W, Gasser O, Wall C, Priami C, Lombardo R, Kussmann M. A reverse metabolic approach to weaning: in silico identification of immune-beneficial infant gut bacteria, mining their metabolism for prebiotic feeds and sourcing these feeds in the natural product space. Microbiome 2018; 6:171. [PMID: 30241567 PMCID: PMC6151060 DOI: 10.1186/s40168-018-0545-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2018] [Accepted: 08/30/2018] [Indexed: 05/13/2023]
Abstract
BACKGROUND Weaning is a period of marked physiological change. The introduction of solid foods and the changes in milk consumption are accompanied by significant gastrointestinal, immune, developmental, and microbial adaptations. Defining a reduced number of infections as the desired health benefit for infants around weaning, we identified in silico (i.e., by advanced public domain mining) infant gut microbes as potential deliverers of this benefit. We then investigated the requirements of these bacteria for exogenous metabolites as potential prebiotic feeds that were subsequently searched for in the natural product space. RESULTS Using public domain literature mining and an in silico reverse metabolic approach, we constructed probiotic-prebiotic-food associations, which can guide targeted feeding of immune health-beneficial microbes by weaning food; analyzed competition and synergy for (prebiotic) nutrients between selected microbes; and translated this information into designing an experimental complementary feed for infants enrolled in a pilot clinical trial ( http://www.nourishtoflourish.auckland.ac.nz/ ). CONCLUSIONS In this study, we applied a benefit-oriented microbiome research strategy for enhanced early-life immune health. We extended from "classical" to molecular nutrition aiming to identify nutrients, bacteria, and mechanisms that point towards targeted feeding to improve immune health in infants around weaning. Here, we present the systems biology-based approach we used to inform us on the most promising prebiotic combinations known to support growth of beneficial gut bacteria ("probiotics") in the infant gut, thereby favorably promoting development of the immune system.
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Affiliation(s)
- Samanta Michelini
- The Microsoft Research–University of Trento Centre for Computational and Systems Biology, Rovereto, Italy
| | - Biju Balakrishnan
- The Liggins Institute, the University of Auckland, Auckland, New Zealand
| | - Silvia Parolo
- The Microsoft Research–University of Trento Centre for Computational and Systems Biology, Rovereto, Italy
| | - Alice Matone
- The Microsoft Research–University of Trento Centre for Computational and Systems Biology, Rovereto, Italy
| | - Jane A. Mullaney
- AgResearch, Food & Bio-based Products, Palmerston North, New Zealand
- Riddet Institute, Palmerston North, New Zealand
| | - Wayne Young
- AgResearch, Food & Bio-based Products, Palmerston North, New Zealand
- Riddet Institute, Palmerston North, New Zealand
| | - Olivier Gasser
- Malaghan Institute of Medical Research, Wellington, New Zealand
| | - Clare Wall
- Discipline of Nutrition, School of Medical Science, University of Auckland, Auckland, New Zealand
| | - Corrado Priami
- The Microsoft Research–University of Trento Centre for Computational and Systems Biology, Rovereto, Italy
- Department of Computer Science, University of Pisa, Pisa, Italy
| | - Rosario Lombardo
- The Microsoft Research–University of Trento Centre for Computational and Systems Biology, Rovereto, Italy
| | - Martin Kussmann
- The Liggins Institute, the University of Auckland, Auckland, New Zealand
- National Science Challenge “High Value Nutrition”, Auckland, New Zealand
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18
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Thanh VH, Marchetti L, Reali F, Priami C. Incorporating extrinsic noise into the stochastic simulation of biochemical reactions: A comparison of approaches. J Chem Phys 2018; 148:064111. [PMID: 29448774 DOI: 10.1063/1.5016338] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023] Open
Abstract
The stochastic simulation algorithm (SSA) has been widely used for simulating biochemical reaction networks. SSA is able to capture the inherently intrinsic noise of the biological system, which is due to the discreteness of species population and to the randomness of their reciprocal interactions. However, SSA does not consider other sources of heterogeneity in biochemical reaction systems, which are referred to as extrinsic noise. Here, we extend two simulation approaches, namely, the integration-based method and the rejection-based method, to take extrinsic noise into account by allowing the reaction propensities to vary in time and state dependent manner. For both methods, new efficient implementations are introduced and their efficiency and applicability to biological models are investigated. Our numerical results suggest that the rejection-based method performs better than the integration-based method when the extrinsic noise is considered.
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Affiliation(s)
- Vo Hong Thanh
- The Microsoft Research-University of Trento Centre for Computational and Systems Biology (COSBI), Piazza Manifattura 1, 38068 Rovereto (TN), Italy
| | - Luca Marchetti
- The Microsoft Research-University of Trento Centre for Computational and Systems Biology (COSBI), Piazza Manifattura 1, 38068 Rovereto (TN), Italy
| | - Federico Reali
- The Microsoft Research-University of Trento Centre for Computational and Systems Biology (COSBI), Piazza Manifattura 1, 38068 Rovereto (TN), Italy
| | - Corrado Priami
- The Microsoft Research-University of Trento Centre for Computational and Systems Biology (COSBI), Piazza Manifattura 1, 38068 Rovereto (TN), Italy
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19
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Parolo S, Marchetti L, Lauria M, Misselbeck K, Scott-Boyer MP, Caberlotto L, Priami C. Combined use of protein biomarkers and network analysis unveils deregulated regulatory circuits in Duchenne muscular dystrophy. PLoS One 2018. [PMID: 29529088 PMCID: PMC5846794 DOI: 10.1371/journal.pone.0194225] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Although the genetic basis of Duchenne muscular dystrophy has been known for almost thirty years, the cellular and molecular mechanisms characterizing the disease are not completely understood and an efficacious treatment remains to be developed. In this study we analyzed proteomics data obtained with the SomaLogic technology from blood serum of a cohort of patients and matched healthy subjects. We developed a workflow based on biomarker identification and network-based pathway analysis that allowed us to describe different deregulated pathways. In addition to muscle-related functions, we identified other biological processes such as apoptosis, signaling in the immune system and neurotrophin signaling as significantly modulated in patients compared with controls. Moreover, our network-based analysis identified the involvement of FoxO transcription factors as putative regulators of different pathways. On the whole, this study provided a global view of the molecular processes involved in Duchenne muscular dystrophy that are decipherable from serum proteome.
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Affiliation(s)
- Silvia Parolo
- The Microsoft Research—University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto (TN), Italy
- * E-mail:
| | - Luca Marchetti
- The Microsoft Research—University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto (TN), Italy
| | - Mario Lauria
- The Microsoft Research—University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto (TN), Italy
- Department of Mathematics, University of Trento, Povo (TN), Italy
| | - Karla Misselbeck
- The Microsoft Research—University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto (TN), Italy
- Department of Mathematics, University of Trento, Povo (TN), Italy
| | - Marie-Pier Scott-Boyer
- The Microsoft Research—University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto (TN), Italy
| | - Laura Caberlotto
- The Microsoft Research—University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto (TN), Italy
| | - Corrado Priami
- The Microsoft Research—University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto (TN), Italy
- Department of Computer Science, University of Pisa, Pisa (PI), Italy
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20
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Mathias MG, Coelho‐Landell CDA, Scott‐Boyer M, Lacroix S, Morine MJ, Salomão RG, Toffano RBD, Almada MORDV, Camarneiro JM, Hillesheim E, de Barros TT, Camelo‐Junior JS, Campos Giménez E, Redeuil K, Goyon A, Bertschy E, Lévêques A, Oberson J, Giménez C, Carayol J, Kussmann M, Descombes P, Métairon S, Draper CF, Conus N, Mottaz SC, Corsini GZ, Myoshi SKB, Muniz MM, Hernandes LC, Venâncio VP, Antunes LMG, da Silva RQ, Laurito TF, Rossi IR, Ricci R, Jorge JR, Fagá ML, Quinhoneiro DCG, Reche MC, Silva PVS, Falquetti LL, da Cunha THA, Deminice TMM, Tambellini TH, de Souza GCA, de Oliveira MM, Nogueira‐Pileggi V, Matsumoto MT, Priami C, Kaput J, Monteiro JP. Clinical and Vitamin Response to a Short-Term Multi-Micronutrient Intervention in Brazilian Children and Teens: From Population Data to Interindividual Responses. Mol Nutr Food Res 2018; 62:e1700613. [PMID: 29368422 PMCID: PMC6120145 DOI: 10.1002/mnfr.201700613] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2017] [Revised: 12/02/2017] [Indexed: 12/11/2022]
Abstract
SCOPE Micronutrients are in small amounts in foods, act in concert, and require variable amounts of time to see changes in health and risk for disease. These first principles are incorporated into an intervention study designed to develop new experimental strategies for setting target recommendations for food bioactives for populations and individuals. METHODS AND RESULTS A 6-week multivitamin/mineral intervention is conducted in 9-13 year olds. Participants (136) are (i) their own control (n-of-1); (ii) monitored for compliance; (iii) measured for 36 circulating vitamin forms, 30 clinical, anthropometric, and food intake parameters at baseline, post intervention, and following a 6-week washout; and (iv) had their ancestry accounted for as modifier of vitamin baseline or response. The same intervention is repeated the following year (135 participants). Most vitamins respond positively and many clinical parameters change in directions consistent with improved metabolic health to the intervention. Baseline levels of any metabolite predict its own response to the intervention. Elastic net penalized regression models are identified, and significantly predict response to intervention on the basis of multiple vitamin/clinical baseline measures. CONCLUSIONS The study design, computational methods, and results are a step toward developing recommendations for optimizing vitamin levels and health parameters for individuals.
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Affiliation(s)
| | | | - Marie‐Pier Scott‐Boyer
- The Microsoft Research, Centre for Computational and Systems Biology (COSBI)University of TrentoRoveretoItaly
| | - Sébastien Lacroix
- The Microsoft Research, Centre for Computational and Systems Biology (COSBI)University of TrentoRoveretoItaly
| | - Melissa J. Morine
- The Microsoft Research, Centre for Computational and Systems Biology (COSBI)University of TrentoRoveretoItaly
- Department of MathematicsUniversity of TrentoTrentoItaly
| | - Roberta Garcia Salomão
- Department of PediatricsFaculty of MedicineNutrition and MetabolismUniversity of São Paulo
| | | | | | | | - Elaine Hillesheim
- Department of PediatricsFaculty of MedicineNutrition and MetabolismUniversity of São Paulo
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Nelly Conus
- Nestlé Institute of Health SciencesLausanneSwitzerland
| | | | | | | | - Mariana Mendes Muniz
- Department of PediatricsFaculty of MedicineNutrition and MetabolismUniversity of São Paulo
| | | | - Vinícius Paula Venâncio
- School of Pharmaceutical Science of Ribeirao PretoUniversity of São PauloRibeirao PretoBrazil
| | | | | | - Taís Fontellas Laurito
- Department of PediatricsFaculty of MedicineNutrition and MetabolismUniversity of São Paulo
| | - Isabela Ribeiro Rossi
- Department of PediatricsFaculty of MedicineNutrition and MetabolismUniversity of São Paulo
| | - Raquel Ricci
- Department of PediatricsFaculty of MedicineNutrition and MetabolismUniversity of São Paulo
| | - Jéssica Ré Jorge
- Department of PediatricsFaculty of MedicineNutrition and MetabolismUniversity of São Paulo
| | - Mayara Leite Fagá
- Department of PediatricsFaculty of MedicineNutrition and MetabolismUniversity of São Paulo
| | | | | | | | - Letícia Lima Falquetti
- Department of PediatricsFaculty of MedicineNutrition and MetabolismUniversity of São Paulo
| | | | | | | | | | | | - Vicky Nogueira‐Pileggi
- Department of PediatricsFaculty of MedicineNutrition and MetabolismUniversity of São Paulo
| | | | - Corrado Priami
- The Microsoft Research, Centre for Computational and Systems Biology (COSBI)University of TrentoRoveretoItaly
- Department of MathematicsUniversity of TrentoTrentoItaly
| | - Jim Kaput
- Nestlé Institute of Health SciencesLausanneSwitzerland
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21
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Lacroix S, Klicic Badoux J, Scott-Boyer MP, Parolo S, Matone A, Priami C, Morine MJ, Kaput J, Moco S. A computationally driven analysis of the polyphenol-protein interactome. Sci Rep 2018; 8:2232. [PMID: 29396566 PMCID: PMC5797150 DOI: 10.1038/s41598-018-20625-5] [Citation(s) in RCA: 49] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2017] [Accepted: 01/22/2018] [Indexed: 01/08/2023] Open
Abstract
Polyphenol-rich foods are part of many nutritional interventions aimed at improving health and preventing cardiometabolic diseases (CMDs). Polyphenols have oxidative, inflammatory, and/or metabolic effects. Research into the chemistry and biology of polyphenol bioactives is prolific but knowledge of their molecular interactions with proteins is limited. We mined public data to (i) identify proteins that interact with or metabolize polyphenols, (ii) mapped these proteins to pathways and networks, and (iii) annotated functions enriched within the resulting polyphenol-protein interactome. A total of 1,395 polyphenols and their metabolites were retrieved (using Phenol-Explorer and Dictionary of Natural Products) of which 369 polyphenols interacted with 5,699 unique proteins in 11,987 interactions as annotated in STITCH, Pathway Commons, and BindingDB. Pathway enrichment analysis using the KEGG repository identified a broad coverage of significant pathways of low specificity to particular polyphenol (sub)classes. When compared to drugs or micronutrients, polyphenols have pleiotropic effects across many biological processes related to metabolism and CMDs. These systems-wide effects were also found in the protein interactome of the polyphenol-rich citrus fruits, used as a case study. In sum, these findings provide a knowledgebase for identifying polyphenol classes (and polyphenol-rich foods) that individually or in combination influence metabolism.
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Affiliation(s)
- Sébastien Lacroix
- The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto (TN), Italy
- Institute of Nutrition and Functional Foods (INAF), Québec, Canada
| | | | - Marie-Pier Scott-Boyer
- The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto (TN), Italy
- Centre de Recherche du Centre Hospitalier Universitaire de Québec (CRCHUQ), Québec, Canada
| | - Silvia Parolo
- The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto (TN), Italy
| | - Alice Matone
- The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto (TN), Italy
| | - Corrado Priami
- The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto (TN), Italy
- Department of Computer Science, University of Pisa, Pisa (PI), Italy
| | - Melissa J Morine
- The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto (TN), Italy
| | - Jim Kaput
- Nestle Institute of Health Sciences, Lausanne, Switzerland
| | - Sofia Moco
- Nestle Institute of Health Sciences, Lausanne, Switzerland.
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22
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Matone A, Derlindati E, Marchetti L, Spigoni V, Dei Cas A, Montanini B, Ardigò D, Zavaroni I, Priami C, Bonadonna RC. Identification of an early transcriptomic signature of insulin resistance and related diseases in lymphomonocytes of healthy subjects. PLoS One 2017; 12:e0182559. [PMID: 28777829 PMCID: PMC5544197 DOI: 10.1371/journal.pone.0182559] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2016] [Accepted: 07/20/2017] [Indexed: 12/26/2022] Open
Abstract
Insulin resistance is considered to be a pathogenetic mechanism in several and diverse diseases (e.g. type 2 diabetes, atherosclerosis) often antedating them in apparently healthy subjects. The aim of this study is to investigate with a microarray based approach whether IR per se is characterized by a specific pattern of gene expression. For this purpose we analyzed the transcriptomic profile of peripheral blood mononuclear cells in two groups (10 subjects each) of healthy individuals, with extreme insulin resistance or sensitivity, matched for BMI, age and gender, selected within the MultiKnowledge Study cohort (n = 148). Data were analyzed with an ad-hoc rank-based classification method. 321 genes composed the gene set distinguishing the insulin resistant and sensitive groups, within which the "Adrenergic signaling in cardiomyocytes" KEGG pathway was significantly represented, suggesting a pattern of increased intracellular cAMP and Ca2+, and apoptosis in the IR group. The same pathway allowed to discriminate between insulin resistance and insulin sensitive subjects with BMI >25, supporting his role as a biomarker of IR. Moreover, ASCM pathway harbored biomarkers able to distinguish healthy and diseased subjects (from publicly available data sets) in IR-related diseases involving excitable cells: type 2 diabetes, chronic heart failure, and Alzheimer's disease. The altered gene expression profile of the ASCM pathway is an early molecular signature of IR and could provide a common molecular pathogenetic platform for IR-related disorders, possibly representing an important aid in the efforts aiming at preventing, early detecting and optimally treating IR-related diseases.
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Affiliation(s)
- Alice Matone
- The Microsoft Research—University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
| | | | - Luca Marchetti
- The Microsoft Research—University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
| | - Valentina Spigoni
- Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Alessandra Dei Cas
- Department of Medicine and Surgery, University of Parma, Parma, Italy
- Division of Endocrinology and Metabolic Diseases, Azienda Ospedaliero-Universitaria of Parma, Parma, Italy
| | - Barbara Montanini
- Department of Chemistry, Life Sciences and Environmental Sustainability, University of Parma, Parma, Italy
| | - Diego Ardigò
- Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Ivana Zavaroni
- Department of Medicine and Surgery, University of Parma, Parma, Italy
- Division of Endocrinology and Metabolic Diseases, Azienda Ospedaliero-Universitaria of Parma, Parma, Italy
| | - Corrado Priami
- The Microsoft Research—University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
- Department of Mathematics, University of Trento, Trento, Italy
| | - Riccardo C. Bonadonna
- Department of Medicine and Surgery, University of Parma, Parma, Italy
- Division of Endocrinology and Metabolic Diseases, Azienda Ospedaliero-Universitaria of Parma, Parma, Italy
- * E-mail:
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23
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Thanh VH, Zunino R, Priami C. Efficient Constant-Time Complexity Algorithm for Stochastic Simulation of Large Reaction Networks. IEEE/ACM Trans Comput Biol Bioinform 2017; 14:657-667. [PMID: 26890923 DOI: 10.1109/tcbb.2016.2530066] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Exact stochastic simulation is an indispensable tool for a quantitative study of biochemical reaction networks. The simulation realizes the time evolution of the model by randomly choosing a reaction to fire and update the system state according to a probability that is proportional to the reaction propensity. Two computationally expensive tasks in simulating large biochemical networks are the selection of next reaction firings and the update of reaction propensities due to state changes. We present in this work a new exact algorithm to optimize both of these simulation bottlenecks. Our algorithm employs the composition-rejection on the propensity bounds of reactions to select the next reaction firing. The selection of next reaction firings is independent of the number reactions while the update of propensities is skipped and performed only when necessary. It therefore provides a favorable scaling for the computational complexity in simulating large reaction networks. We benchmark our new algorithm with the state of the art algorithms available in literature to demonstrate its applicability and efficiency.
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24
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Misselbeck K, Marchetti L, Field MS, Scotti M, Priami C, Stover PJ. A hybrid stochastic model of folate-mediated one-carbon metabolism: Effect of the common C677T MTHFR variant on de novo thymidylate biosynthesis. Sci Rep 2017; 7:797. [PMID: 28400561 PMCID: PMC5429759 DOI: 10.1038/s41598-017-00854-w] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2016] [Accepted: 03/13/2017] [Indexed: 11/19/2022] Open
Abstract
Folate-mediated one-carbon metabolism (FOCM) is an interconnected network of metabolic pathways, including those required for the de novo synthesis of dTMP and purine nucleotides and for remethylation of homocysteine to methionine. Mouse models of folate-responsive neural tube defects (NTDs) indicate that impaired de novo thymidylate (dTMP) synthesis through changes in SHMT expression is causative in folate-responsive NTDs. We have created a hybrid computational model comprised of ordinary differential equations and stochastic simulation. We investigated whether the de novo dTMP synthesis pathway was sensitive to perturbations in FOCM that are known to be associated with human NTDs. This computational model shows that de novo dTMP synthesis is highly sensitive to the common MTHFR C677T polymorphism and that the effect of the polymorphism on FOCM is greater in folate deficiency. Computational simulations indicate that the MTHFR C677T polymorphism and folate deficiency interact to increase the stochastic behavior of the FOCM network, with the greatest instability observed for reactions catalyzed by serine hydroxymethyltransferase (SHMT). Furthermore, we show that de novo dTMP synthesis does not occur in the cytosol at rates sufficient for DNA replication, supporting empirical data indicating that impaired nuclear de novo dTMP synthesis results in uracil misincorporation into DNA.
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Affiliation(s)
- Karla Misselbeck
- The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Piazza Manifattura, 1, 38068, Rovereto (TN), Italy
- Department of Mathematics, University of Trento, Trento, Italy
| | - Luca Marchetti
- The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Piazza Manifattura, 1, 38068, Rovereto (TN), Italy
| | - Martha S Field
- Division of Nutritional Sciences, Cornell University, Ithaca, New York, 14853, USA
| | - Marco Scotti
- GEOMAR Helmholtz Centre for Ocean Research Kiel, Düsternbrooker Weg 20, 24105, Kiel, Germany
| | - Corrado Priami
- The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Piazza Manifattura, 1, 38068, Rovereto (TN), Italy.
- Department of Mathematics, University of Trento, Trento, Italy.
| | - Patrick J Stover
- Division of Nutritional Sciences, Cornell University, Ithaca, New York, 14853, USA.
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25
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Abstract
A main source of failures in systems projects (including systems pharmacology) is poor communication level and different expectations among the stakeholders. A common and not ambiguous language that is naturally comprehensible by all the involved players is a boost to success. We present bStyle, a modeling tool that adopts a graphical language close enough to cartoons to be a common media to exchange ideas and data and that it is at the same time formal enough to enable modeling, analysis, and dynamic simulations of a system. Data analysis and simulation integrated in the same application are fundamental to understand the mechanisms of actions of drugs: a core aspect of systems pharmacology.
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Affiliation(s)
- Rosario Lombardo
- The Microsoft Research, University of Trento Centre for Computational and Systems Biology (COSBI), Trento, Italy
| | - Corrado Priami
- The Microsoft Research, University of Trento Centre for Computational and Systems Biology (COSBI), Trento, Italy.,Department of Mathematics, University of Trento, Trento, Italy.,Department of Computer Science, Stanford University, Stanford, CA, USA
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26
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Affiliation(s)
- Vo Hong Thanh
- The Microsoft Research—University of Trento Centre for Computational and Systems Biology, Piazza Manifattura 1, Rovereto 38068, Italy
| | - Roberto Zunino
- Department of Mathematics, University of Trento, Trento, Italy
| | - Corrado Priami
- The Microsoft Research—University of Trento Centre for Computational and Systems Biology, Piazza Manifattura 1, Rovereto 38068, Italy and Department of Mathematics, University of Trento, Trento, Italy and Department of Computer Science, Stanford University, Stanford, California 94305, USA
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27
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Marchetti L, Reali F, Dauriz M, Brangani C, Boselli L, Ceradini G, Bonora E, Bonadonna RC, Priami C. A Novel Insulin/Glucose Model after a Mixed-Meal Test in Patients with Type 1 Diabetes on Insulin Pump Therapy. Sci Rep 2016; 6:36029. [PMID: 27824066 PMCID: PMC5099899 DOI: 10.1038/srep36029] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2016] [Accepted: 10/10/2016] [Indexed: 11/30/2022] Open
Abstract
Current closed-loop insulin delivery methods stem from sophisticated models of the glucose-insulin (G/I) system, mostly based on complex studies employing glucose tracer technology. We tested the performance of a new minimal model (GLUKINSLOOP 2.0) of the G/I system to characterize the glucose and insulin dynamics during multiple mixed meal tests (MMT) of different sizes in patients with type 1 diabetes (T1D) on insulin pump therapy (continuous subcutaneous insulin infusion, CSII). The GLUKINSLOOP 2.0 identified the G/I system, provided a close fit of the G/I time-courses and showed acceptable reproducibility of the G/I system parameters in repeated studies of identical and double-sized MMTs. This model can provide a fairly good and reproducible description of the G/I system in T1D patients on CSII, and it may be applied to create a bank of “virtual” patients. Our results might be relevant at improving the architecture of upcoming closed-loop CSII systems.
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Affiliation(s)
- Luca Marchetti
- The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto (TN), Italy
| | - Federico Reali
- The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto (TN), Italy.,Department of Mathematics, University of Trento, Trento, Italy
| | - Marco Dauriz
- Department of Medicine, Section of Endocrinology, University of Verona School of Medicine, Verona, Italy
| | - Corinna Brangani
- Department of Medicine, Section of Endocrinology, University of Verona School of Medicine, Verona, Italy
| | - Linda Boselli
- Department of Medicine, Section of Endocrinology, University of Verona School of Medicine, Verona, Italy
| | - Giulia Ceradini
- Department of Medicine, Section of Endocrinology, University of Verona School of Medicine, Verona, Italy
| | - Enzo Bonora
- Department of Medicine, Section of Endocrinology, University of Verona School of Medicine, Verona, Italy.,Division of Endocrinology and Metabolic Diseases, Azienda Ospedaliera Universitaria Integrata, Verona, Italy
| | - Riccardo C Bonadonna
- Department of Clinical and Experimental Medicine, University of Parma, Parma, Italy.,Division of Endocrinology, Azienda Ospedaliera Universitaria of Parma, Italy
| | - Corrado Priami
- The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto (TN), Italy.,Department of Mathematics, University of Trento, Trento, Italy
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28
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Thanh VH, Priami C, Zunino R. Accelerating rejection-based simulation of biochemical reactions with bounded acceptance probability. J Chem Phys 2016; 144:224108. [DOI: 10.1063/1.4953559] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
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29
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Abstract
We address the problem of simulating biochemical reaction networks with time-dependent rates and propose a new algorithm based on our rejection-based stochastic simulation algorithm (RSSA) [Thanh et al., J. Chem. Phys. 141(13), 134116 (2014)]. The computation for selecting next reaction firings by our time-dependent RSSA (tRSSA) is computationally efficient. Furthermore, the generated trajectory is exact by exploiting the rejection-based mechanism. We benchmark tRSSA on different biological systems with varying forms of reaction rates to demonstrate its applicability and efficiency. We reveal that for nontrivial cases, the selection of reaction firings in existing algorithms introduces approximations because the integration of reaction rates is very computationally demanding and simplifying assumptions are introduced. The selection of the next reaction firing by our approach is easier while preserving the exactness.
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Affiliation(s)
- Vo Hong Thanh
- The Microsoft Research - University of Trento Centre for Computational and Systems Biology, Piazza Manifattura 1, Rovereto 38068, Italy
| | - Corrado Priami
- The Microsoft Research - University of Trento Centre for Computational and Systems Biology, Piazza Manifattura 1, Rovereto 38068, Italy
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30
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Abstract
Stochastic simulation for in silico studies of large biochemical networks requires a great amount of computational time. We recently proposed a new exact simulation algorithm, called the rejection-based stochastic simulation algorithm (RSSA) [Thanh et al., J. Chem. Phys. 141(13), 134116 (2014)], to improve simulation performance by postponing and collapsing as much as possible the propensity updates. In this paper, we analyze the performance of this algorithm in detail, and improve it for simulating large-scale biochemical reaction networks. We also present a new algorithm, called simultaneous RSSA (SRSSA), which generates many independent trajectories simultaneously for the analysis of the biochemical behavior. SRSSA improves simulation performance by utilizing a single data structure across simulations to select reaction firings and forming trajectories. The memory requirement for building and storing the data structure is thus independent of the number of trajectories. The updating of the data structure when needed is performed collectively in a single operation across the simulations. The trajectories generated by SRSSA are exact and independent of each other by exploiting the rejection-based mechanism. We test our new improvement on real biological systems with a wide range of reaction networks to demonstrate its applicability and efficiency.
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Affiliation(s)
- Vo Hong Thanh
- The Microsoft Research-University of Trento Centre for Computational and Systems Biology, Piazza Manifattura 1, Rovereto 38068, Italy
| | - Roberto Zunino
- Department of Mathematics, University of Trento, Trento, Italy
| | - Corrado Priami
- The Microsoft Research-University of Trento Centre for Computational and Systems Biology, Piazza Manifattura 1, Rovereto 38068, Italy
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31
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Allen GI, Amoroso N, Anghel C, Balagurusamy V, Bare CJ, Beaton D, Bellotti R, Bennett DA, Boehme KL, Boutros PC, Caberlotto L, Caloian C, Campbell F, Chaibub Neto E, Chang YC, Chen B, Chen CY, Chien TY, Clark T, Das S, Davatzikos C, Deng J, Dillenberger D, Dobson RJB, Dong Q, Doshi J, Duma D, Errico R, Erus G, Everett E, Fardo DW, Friend SH, Fröhlich H, Gan J, St George-Hyslop P, Ghosh SS, Glaab E, Green RC, Guan Y, Hong MY, Huang C, Hwang J, Ibrahim J, Inglese P, Iyappan A, Jiang Q, Katsumata Y, Kauwe JSK, Klein A, Kong D, Krause R, Lalonde E, Lauria M, Lee E, Lin X, Liu Z, Livingstone J, Logsdon BA, Lovestone S, Ma TW, Malhotra A, Mangravite LM, Maxwell TJ, Merrill E, Nagorski J, Namasivayam A, Narayan M, Naz M, Newhouse SJ, Norman TC, Nurtdinov RN, Oyang YJ, Pawitan Y, Peng S, Peters MA, Piccolo SR, Praveen P, Priami C, Sabelnykova VY, Senger P, Shen X, Simmons A, Sotiras A, Stolovitzky G, Tangaro S, Tateo A, Tung YA, Tustison NJ, Varol E, Vradenburg G, Weiner MW, Xiao G, Xie L, Xie Y, Xu J, Yang H, Zhan X, Zhou Y, Zhu F, Zhu H, Zhu S. Crowdsourced estimation of cognitive decline and resilience in Alzheimer's disease. Alzheimers Dement 2016; 12:645-53. [PMID: 27079753 DOI: 10.1016/j.jalz.2016.02.006] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2015] [Revised: 01/15/2016] [Accepted: 02/18/2016] [Indexed: 10/22/2022]
Abstract
Identifying accurate biomarkers of cognitive decline is essential for advancing early diagnosis and prevention therapies in Alzheimer's disease. The Alzheimer's disease DREAM Challenge was designed as a computational crowdsourced project to benchmark the current state-of-the-art in predicting cognitive outcomes in Alzheimer's disease based on high dimensional, publicly available genetic and structural imaging data. This meta-analysis failed to identify a meaningful predictor developed from either data modality, suggesting that alternate approaches should be considered for prediction of cognitive performance.
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Affiliation(s)
- Genevera I Allen
- Department of Statistics and Electrical and Computer Engineering, Rice University, Houston, TX, USA
| | - Nicola Amoroso
- Dipartimento di Fisica "M. Merlin", Università degli studi di Bari "A. Moro", Bari, Italy; Sezione di Bari, Istituto Nazionale di Fisica Nucleare, Bari, Italy
| | - Catalina Anghel
- Ontario Institute for Cancer Research, Informatics and Bio-computing Program, MaRS Centre, Toronto, ON, Canada
| | | | | | - Derek Beaton
- School of Behavioral and Brain Sciences, The University of Texas at Dallas, Richardson, TX, USA
| | - Roberto Bellotti
- Dipartimento di Fisica "M. Merlin", Università degli studi di Bari "A. Moro", Bari, Italy; Sezione di Bari, Istituto Nazionale di Fisica Nucleare, Bari, Italy
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Kevin L Boehme
- Department of Biology, Brigham Young University, Provo, UT, USA
| | - Paul C Boutros
- Ontario Institute for Cancer Research, Informatics and Bio-computing Program, MaRS Centre, Toronto, ON, Canada; Department of Medical Biophysics, University of Toronto, Toronto, Canada; Department of Pharmacology & Toxicology, University of Toronto, Toronto, Canada
| | - Laura Caberlotto
- The Microsoft Research, University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
| | - Cristian Caloian
- Ontario Institute for Cancer Research, Informatics and Bio-computing Program, MaRS Centre, Toronto, ON, Canada
| | - Frederick Campbell
- Department of Statistics and Electrical and Computer Engineering, Rice University, Houston, TX, USA
| | | | - Yu-Chuan Chang
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan
| | - Beibei Chen
- Quantitative Biomedical Research Center, The University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Chien-Yu Chen
- Department of Bio-Industrial Mechatronics Engineering, National Taiwan University, Taipei, Taiwan
| | - Ting-Ying Chien
- Innovation Center for Big Data and Digital Convergence, Yuan Ze University, Taoyuan, Taiwan
| | - Tim Clark
- Department of Neurology, Massachusetts General Hospital, Cambridge, MA, USA; Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Sudeshna Das
- Department of Neurology, Massachusetts General Hospital, Cambridge, MA, USA; Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | - Jieyao Deng
- School of Computer Science, Fudan University, Shanghai, Shanghai, China; Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai, Shanghai, China
| | | | - Richard J B Dobson
- NIHR Biomedical Research Centre for Mental Health, Kings College London, London, UK; Institute of Psychiatry, Psychology and Neuroscience, MRC Social, Genetic and Developmental Psychiatry Centre, Kings College London, London, UK; Farr Institute of Health Informatics Research, UCL Institute of Health Informatics, University College London, London WC1E 6BT, UK
| | - Qilin Dong
- School of Computer Science, Fudan University, Shanghai, Shanghai, China; Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai, Shanghai, China
| | - Jimit Doshi
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | - Denise Duma
- Department of Pediatrics-Neurology, Baylor College of Medicine, Houston, TX, USA
| | | | - Guray Erus
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | - Evan Everett
- Department of Statistics and Electrical and Computer Engineering, Rice University, Houston, TX, USA
| | - David W Fardo
- Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY, USA; Department of Biostatistics, University of Kentucky, Lexington, KY, USA
| | | | - Holger Fröhlich
- Bonn-Aachen International Center for IT, University of Bonn, Bonn, Germany
| | - Jessica Gan
- Department of Statistics and Electrical and Computer Engineering, Rice University, Houston, TX, USA
| | - Peter St George-Hyslop
- Cambridge Institute for Medical Research, University of Cambridge and University of Toronto, Cambridge, CB2, UK
| | - Satrajit S Ghosh
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Otology and Laryngology, Harvard Medical School, Boston, MA, USA
| | - Enrico Glaab
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Robert C Green
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Broad Institute and Harvard Medical School, Boston, MA, USA
| | - Yuanfang Guan
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA; Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA; Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Ming-Yi Hong
- Department of Bio-Industrial Mechatronics Engineering, National Taiwan University, Taipei, Taiwan
| | - Chao Huang
- Department of Biostatistics, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jinseub Hwang
- Department of Computer science and Statistics, Daegu University, Gyeongsan-si, Gyeongsangbuk-do, Republic of Korea
| | - Joseph Ibrahim
- Department of Biostatistics, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Paolo Inglese
- Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK
| | - Anandhi Iyappan
- Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Department for Bioinformatics, Schloss Birlinghoven, Sankt Augustin, Germany; Bonn-Aachen International Center for IT, University of Bonn, Bonn, Germany
| | - Qijia Jiang
- Department of Statistics and Electrical and Computer Engineering, Rice University, Houston, TX, USA
| | - Yuriko Katsumata
- Department of Biostatistics, University of Kentucky, Lexington, KY, USA
| | - John S K Kauwe
- Department of Biology, Brigham Young University, Provo, UT, USA.
| | | | - Dehan Kong
- Department of Biostatistics, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Roland Krause
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Emilie Lalonde
- Ontario Institute for Cancer Research, Informatics and Bio-computing Program, MaRS Centre, Toronto, ON, Canada
| | - Mario Lauria
- The Microsoft Research, University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
| | - Eunjee Lee
- Department of Biostatistics, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Xihui Lin
- Ontario Institute for Cancer Research, Informatics and Bio-computing Program, MaRS Centre, Toronto, ON, Canada
| | - Zhandong Liu
- Department of Statistics and Electrical and Computer Engineering, Rice University, Houston, TX, USA
| | - Julie Livingstone
- Ontario Institute for Cancer Research, Informatics and Bio-computing Program, MaRS Centre, Toronto, ON, Canada
| | | | - Simon Lovestone
- Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, UK
| | - Tsung-Wei Ma
- Quantitative Biomedical Research Center, The University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Ashutosh Malhotra
- Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Department for Bioinformatics, Schloss Birlinghoven, Sankt Augustin, Germany; Bonn-Aachen International Center for IT, University of Bonn, Bonn, Germany
| | | | - Taylor J Maxwell
- Computational Biology Institute, The George Washington University, Ashburn, VA, USA
| | - Emily Merrill
- Department of Neurology, Massachusetts General Hospital, Cambridge, MA, USA
| | - John Nagorski
- Department of Statistics and Electrical and Computer Engineering, Rice University, Houston, TX, USA
| | - Aishwarya Namasivayam
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Manjari Narayan
- Department of Statistics and Electrical and Computer Engineering, Rice University, Houston, TX, USA
| | - Mufassra Naz
- Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Department for Bioinformatics, Schloss Birlinghoven, Sankt Augustin, Germany; Bonn-Aachen International Center for IT, University of Bonn, Bonn, Germany
| | - Stephen J Newhouse
- NIHR Biomedical Research Centre for Mental Health, Kings College London, London, UK; Department of Biostatistics, Kings College London, London, UK
| | | | - Ramil N Nurtdinov
- Department of Neuroimmunology, Foundation Institut de Recerca, Hospital Universitari Vall d'Hebron, Barcelona, Spain
| | - Yen-Jen Oyang
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan
| | - Yudi Pawitan
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Shengwen Peng
- School of Computer Science, Fudan University, Shanghai, Shanghai, China; Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai, Shanghai, China
| | | | | | - Paurush Praveen
- The Microsoft Research, University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy; Bonn-Aachen International Center for IT, University of Bonn, Bonn, Germany
| | - Corrado Priami
- The Microsoft Research, University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
| | - Veronica Y Sabelnykova
- Ontario Institute for Cancer Research, Informatics and Bio-computing Program, MaRS Centre, Toronto, ON, Canada
| | - Philipp Senger
- Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Department for Bioinformatics, Schloss Birlinghoven, Sankt Augustin, Germany
| | - Xia Shen
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden; Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK; MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Andrew Simmons
- NIHR Biomedical Research Centre for Mental Health, Kings College London, London, UK
| | - Aristeidis Sotiras
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | - Gustavo Stolovitzky
- Genetics and Genomics Sciences Department, Icahn School of Medicine at Mount Sinai, New York, NY, USA; IBM Computational Biology Center, IBM Research, NY, USA
| | - Sabina Tangaro
- Sezione di Bari, Istituto Nazionale di Fisica Nucleare, Bari, Italy
| | - Andrea Tateo
- Dipartimento di Fisica "M. Merlin", Università degli studi di Bari "A. Moro", Bari, Italy
| | - Yi-An Tung
- Genome and systems biology degree program, National Taiwan University, Taipei, Taiwan
| | - Nicholas J Tustison
- Department of Radiology and Medical Imaging, The University of Virginia, Charlottesville, VA, USA
| | - Erdem Varol
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Michael W Weiner
- Radiology, Medicine, Psychiatry, and Neurology, UCSF, SFVAMC, San Francisco, CA, USA
| | - Guanghua Xiao
- Quantitative Biomedical Research Center, The University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Lei Xie
- Department of Computer Science, Hunter College, The City University of New York, New York, NY, USA
| | - Yang Xie
- Quantitative Biomedical Research Center, The University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Jia Xu
- Quantitative Biomedical Research Center, The University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Hojin Yang
- Department of Biostatistics, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Xiaowei Zhan
- Quantitative Biomedical Research Center, The University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Yunyun Zhou
- Quantitative Biomedical Research Center, The University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Fan Zhu
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Hongtu Zhu
- Department of Biostatistics, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Shanfeng Zhu
- School of Computer Science, Fudan University, Shanghai, Shanghai, China; Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai, Shanghai, China; Centre for Computational Systems Biology, Fudan University, Shanghai, China
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Scott-Boyer MP, Lacroix S, Scotti M, Morine MJ, Kaput J, Priami C. A network analysis of cofactor-protein interactions for analyzing associations between human nutrition and diseases. Sci Rep 2016; 6:19633. [PMID: 26777674 PMCID: PMC4726080 DOI: 10.1038/srep19633] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2015] [Accepted: 12/14/2015] [Indexed: 11/09/2022] Open
Abstract
The involvement of vitamins and other micronutrients in intermediary metabolism was elucidated in the mid 1900's at the level of individual biochemical reactions. Biochemical pathways remain the foundational knowledgebase for understanding how micronutrient adequacy modulates health in all life stages. Current daily recommended intakes were usually established on the basis of the association of a single nutrient to a single, most sensitive adverse effect and thus neglect interdependent and pleiotropic effects of micronutrients on biological systems. Hence, the understanding of the impact of overt or sub-clinical nutrient deficiencies on biological processes remains incomplete. Developing a more complete view of the role of micronutrients and their metabolic products in protein-mediated reactions is of importance. We thus integrated and represented cofactor-protein interaction data from multiple and diverse sources into a multi-layer network representation that links cofactors, cofactor-interacting proteins, biological processes, and diseases. Network representation of this information is a key feature of the present analysis and enables the integration of data from individual biochemical reactions and protein-protein interactions into a systems view, which may guide strategies for targeted nutritional interventions aimed at improving health and preventing diseases.
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Affiliation(s)
- Marie Pier Scott-Boyer
- The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto (TN), Italy
| | - Sébastien Lacroix
- The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto (TN), Italy
| | - Marco Scotti
- The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto (TN), Italy.,GEOMAR Helmholtz Centre for Ocean Research Kiel, Kiel, Germany
| | - Melissa J Morine
- The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto (TN), Italy
| | - Jim Kaput
- Nestlé Institute of Health Sciences, Lausanne, Switzerland
| | - Corrado Priami
- The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto (TN), Italy.,Department of Mathematics, University of Trento, Italy
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Lacroix S, Lauria M, Scott-Boyer MP, Marchetti L, Priami C, Caberlotto L. Systems biology approaches to study the molecular effects of caloric restriction and polyphenols on aging processes. Genes Nutr 2015; 10:58. [PMID: 26608884 PMCID: PMC4659783 DOI: 10.1007/s12263-015-0508-9] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/17/2015] [Accepted: 11/13/2015] [Indexed: 12/17/2022]
Abstract
Worldwide population is aging, and a large part of the growing burden associated with age-related conditions can be prevented or delayed by promoting healthy lifestyle and normalizing metabolic risk factors. However, a better understanding of the pleiotropic effects of available nutritional interventions and their influence on the multiple processes affected by aging is needed to select and implement the most promising actions. New methods of analysis are required to tackle the complexity of the interplay between nutritional interventions and aging, and to make sense of a growing amount of -omics data being produced for this purpose. In this paper, we review how various systems biology-inspired methods of analysis can be applied to the study of the molecular basis of nutritional interventions promoting healthy aging, notably caloric restriction and polyphenol supplementation. We specifically focus on the role that different versions of network analysis, molecular signature identification and multi-omics data integration are playing in elucidating the complex mechanisms underlying nutrition, and provide some examples on how to extend the application of these methods using available microarray data.
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Affiliation(s)
- Sébastien Lacroix
- The Microsoft Research, University of Trento Centre for Computational Systems Biology (COSBI), Piazza Manifattura 1, 38068, Rovereto, Italy
| | - Mario Lauria
- The Microsoft Research, University of Trento Centre for Computational Systems Biology (COSBI), Piazza Manifattura 1, 38068, Rovereto, Italy
| | - Marie-Pier Scott-Boyer
- The Microsoft Research, University of Trento Centre for Computational Systems Biology (COSBI), Piazza Manifattura 1, 38068, Rovereto, Italy
| | - Luca Marchetti
- The Microsoft Research, University of Trento Centre for Computational Systems Biology (COSBI), Piazza Manifattura 1, 38068, Rovereto, Italy
| | - Corrado Priami
- The Microsoft Research, University of Trento Centre for Computational Systems Biology (COSBI), Piazza Manifattura 1, 38068, Rovereto, Italy
- Department of Mathematics, University of Trento, Via Sommarive 14, 38123, Povo, Italy
| | - Laura Caberlotto
- The Microsoft Research, University of Trento Centre for Computational Systems Biology (COSBI), Piazza Manifattura 1, 38068, Rovereto, Italy.
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Jordán F, Lauria M, Scotti M, Nguyen TP, Praveen P, Morine M, Priami C. Diversity of key players in the microbial ecosystems of the human body. Sci Rep 2015; 5:15920. [PMID: 26514870 PMCID: PMC4626846 DOI: 10.1038/srep15920] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2015] [Accepted: 09/21/2015] [Indexed: 01/06/2023] Open
Abstract
Coexisting bacteria form various microbial communities in human body parts. In these ecosystems they interact in various ways and the properties of the interaction network can be related to the stability and functional diversity of the local bacterial community. In this study, we analyze the interaction network among bacterial OTUs in 11 locations of the human body. These belong to two major groups. One is the digestive system and the other is the female genital tract. In each local ecosystem we determine the key species, both the ones being in key positions in the interaction network and the ones that dominate by frequency. Beyond identifying the key players and discussing their biological relevance, we also quantify and compare the properties of the 11 networks. The interaction networks of the female genital system and the digestive system show totally different architecture. Both the topological properties and the identity of the key groups differ. Key groups represent four phyla of prokaryotes. Some groups appear in key positions in several locations, while others are assigned only to a single body part. The key groups of the digestive and the genital tracts are totally different.
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Affiliation(s)
- Ferenc Jordán
- The Microsoft Research - University of Trento Centre for Computational and Systems Biology, Piazza Manifattura 1, Rovereto, TN, 38068, Italy.,MTA Centre for Ecological Research, Karolina út 29, 1113, Budapest, Hungary
| | - Mario Lauria
- The Microsoft Research - University of Trento Centre for Computational and Systems Biology, Piazza Manifattura 1, Rovereto, TN, 38068, Italy
| | - Marco Scotti
- The Microsoft Research - University of Trento Centre for Computational and Systems Biology, Piazza Manifattura 1, Rovereto, TN, 38068, Italy.,GEOMAR Helmholtz Centre for Ocean Research Kiel, Duesternbrooker Weg 20, 24105 Kiel, Germany
| | - Thanh-Phuong Nguyen
- The Microsoft Research - University of Trento Centre for Computational and Systems Biology, Piazza Manifattura 1, Rovereto, TN, 38068, Italy.,Life Sciences Research Unit, University of Luxembourg, 162 A, avenue de la Faïencerie, L-1511 Luxembourg
| | - Paurush Praveen
- The Microsoft Research - University of Trento Centre for Computational and Systems Biology, Piazza Manifattura 1, Rovereto, TN, 38068, Italy
| | - Melissa Morine
- The Microsoft Research - University of Trento Centre for Computational and Systems Biology, Piazza Manifattura 1, Rovereto, TN, 38068, Italy.,Department of Mathematics, University of Trento, Via Sommarive 14, Povo, TN, 38123, Italy
| | - Corrado Priami
- The Microsoft Research - University of Trento Centre for Computational and Systems Biology, Piazza Manifattura 1, Rovereto, TN, 38068, Italy.,Department of Mathematics, University of Trento, Via Sommarive 14, Povo, TN, 38123, Italy
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Rizzetto S, Priami C, Csikász-Nagy A. Qualitative and Quantitative Protein Complex Prediction Through Proteome-Wide Simulations. PLoS Comput Biol 2015; 11:e1004424. [PMID: 26492574 PMCID: PMC4619657 DOI: 10.1371/journal.pcbi.1004424] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2014] [Accepted: 06/22/2015] [Indexed: 12/18/2022] Open
Abstract
Despite recent progress in proteomics most protein complexes are still unknown. Identification of these complexes will help us understand cellular regulatory mechanisms and support development of new drugs. Therefore it is really important to establish detailed information about the composition and the abundance of protein complexes but existing algorithms can only give qualitative predictions. Herein, we propose a new approach based on stochastic simulations of protein complex formation that integrates multi-source data--such as protein abundances, domain-domain interactions and functional annotations--to predict alternative forms of protein complexes together with their abundances. This method, called SiComPre (Simulation based Complex Prediction), achieves better qualitative prediction of yeast and human protein complexes than existing methods and is the first to predict protein complex abundances. Furthermore, we show that SiComPre can be used to predict complexome changes upon drug treatment with the example of bortezomib. SiComPre is the first method to produce quantitative predictions on the abundance of molecular complexes while performing the best qualitative predictions. With new data on tissue specific protein complexes becoming available SiComPre will be able to predict qualitative and quantitative differences in the complexome in various tissue types and under various conditions.
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Affiliation(s)
- Simone Rizzetto
- The Microsoft Research-University of Trento Centre for Computational Systems Biology, Rovereto, Italy
| | - Corrado Priami
- The Microsoft Research-University of Trento Centre for Computational Systems Biology, Rovereto, Italy
- Department of Mathematics, University of Trento, Povo (TN), Italy
- * E-mail: (CP); (ACN)
| | - Attila Csikász-Nagy
- Department of Computational Biology, Research and Innovation Centre, Fondazione Edmund Mach, San Michele all'Adige, Italy
- Randall Division of Cell and Molecular Biophysics and Institute for Mathematical and Molecular Biomedicine, King's College London, London, United Kingdom
- * E-mail: (CP); (ACN)
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Abstract
We propose a new exact stochastic rejection-based simulation algorithm for biochemical reactions and extend it to systems with delays. Our algorithm accelerates the simulation by pre-computing reaction propensity bounds to select the next reaction to perform. Exploiting such bounds, we are able to avoid recomputing propensities every time a (delayed) reaction is initiated or finished, as is typically necessary in standard approaches. Propensity updates in our approach are still performed, but only infrequently and limited for a small number of reactions, saving computation time and without sacrificing exactness. We evaluate the performance improvement of our algorithm by experimenting with concrete biological models.
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Affiliation(s)
- Vo Hong Thanh
- The Microsoft Research - University of Trento Centre for Computational and Systems Biology, Piazza Manifattura 1, Rovereto 38068, Italy
| | - Corrado Priami
- The Microsoft Research - University of Trento Centre for Computational and Systems Biology, Piazza Manifattura 1, Rovereto 38068, Italy
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Praveen P, Jordan F, Priami C, Morine MJ. The role of breast-feeding in infant immune system: a systems perspective on the intestinal microbiome. Microbiome 2015; 3:41. [PMID: 26399409 PMCID: PMC4581423 DOI: 10.1186/s40168-015-0104-7] [Citation(s) in RCA: 65] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/23/2015] [Accepted: 08/26/2015] [Indexed: 05/09/2023]
Abstract
BACKGROUND The human intestinal microbiota changes from being sparsely populated and variable to possessing a mature, adult-like stable microbiome during the first 2 years of life. This assembly process of the microbiota can lead to either negative or positive effects on health, depending on the colonization sequence and diet. An integrative study on the diet, the microbiota, and genomic activity at the transcriptomic level may give an insight into the role of diet in shaping the human/microbiome relationship. This study aims at better understanding the effects of microbial community and feeding mode (breast-fed and formula-fed) on the immune system, by comparing intestinal metagenomic and transcriptomic data from breast-fed and formula-fed babies. RESULTS We re-analyzed a published metagenomics and host gene expression dataset from a systems biology perspective. Our results show that breast-fed samples co-express genes associated with immunological, metabolic, and biosynthetic activities. The diversity of the microbiota is higher in formula-fed than breast-fed infants, potentially reflecting the weaker dependence of infants on maternal microbiome. We mapped the microbial composition and the expression patterns for host systems and studied their relationship from a systems biology perspective, focusing on the differences. CONCLUSIONS Our findings revealed that there is co-expression of more genes in breast-fed samples but lower microbial diversity compared to formula-fed. Applying network-based systems biology approach via enrichment of microbial species with host genes revealed the novel key relationships of the microbiota with immune and metabolic activity. This was supported statistically by data and literature.
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Affiliation(s)
- Paurush Praveen
- The Microsoft Research-University of Trento Centre for Computational and Systems Biology, 38068, Rovereto, Italy.
| | - Ferenc Jordan
- The Microsoft Research-University of Trento Centre for Computational and Systems Biology, 38068, Rovereto, Italy.
| | - Corrado Priami
- The Microsoft Research-University of Trento Centre for Computational and Systems Biology, 38068, Rovereto, Italy.
- Department of Mathematics, University of Trento, 38100, Povo, Italy.
| | - Melissa J Morine
- The Microsoft Research-University of Trento Centre for Computational and Systems Biology, 38068, Rovereto, Italy.
- Department of Mathematics, University of Trento, 38100, Povo, Italy.
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Nguyen TP, Priami C, Caberlotto L. Novel drug target identification for the treatment of dementia using multi-relational association mining. Sci Rep 2015; 5:11104. [PMID: 26154857 PMCID: PMC4495601 DOI: 10.1038/srep11104] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2015] [Accepted: 05/13/2015] [Indexed: 12/12/2022] Open
Abstract
Dementia is a neurodegenerative condition of the brain in which there is a progressive and permanent loss of cognitive and mental performance. Despite the fact that the number of people with dementia worldwide is steadily increasing and regardless of the advances in the molecular characterization of the disease, current medical treatments for dementia are purely symptomatic and hardly effective. We present a novel multi-relational association mining method that integrates the huge amount of scientific data accumulated in recent years to predict potential novel targets for innovative therapeutic treatment of dementia. Owing to the ability of processing large volumes of heterogeneous data, our method achieves a high performance and predicts numerous drug targets including several serine threonine kinase and a G-protein coupled receptor. The predicted drug targets are mainly functionally related to metabolism, cell surface receptor signaling pathways, immune response, apoptosis, and long-term memory. Among the highly represented kinase family and among the G-protein coupled receptors, DLG4 (PSD-95), and the bradikynin receptor 2 are highlighted also for their proposed role in memory and cognition, as described in previous studies. These novel putative targets hold promises for the development of novel therapeutic approaches for the treatment of dementia.
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Affiliation(s)
- Thanh-Phuong Nguyen
- 1] The Microsoft Research, University of Trento Centre for Computational Systems Biology (COSBI), Piazza Manifattura 1, 38068, Rovereto, Italy [2] Life Sciences Research Unit, University of Luxembourg, 162 A, avenue de la Faïencerie, L-1511 Luxembourg
| | - Corrado Priami
- 1] The Microsoft Research, University of Trento Centre for Computational Systems Biology (COSBI), Piazza Manifattura 1, 38068, Rovereto, Italy [2] Department of Mathematics, University of Trento, Via Sommarive, 14-38123 Povo, Italy
| | - Laura Caberlotto
- The Microsoft Research, University of Trento Centre for Computational Systems Biology (COSBI), Piazza Manifattura 1, 38068, Rovereto, Italy
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Lauria M, Moyseos P, Priami C. SCUDO: a tool for signature-based clustering of expression profiles. Nucleic Acids Res 2015; 43:W188-92. [PMID: 25958391 PMCID: PMC4489218 DOI: 10.1093/nar/gkv449] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2015] [Accepted: 04/24/2015] [Indexed: 01/23/2023] Open
Abstract
SCUDO (Signature-based ClUstering for DiagnOstic purposes) is an online tool for the analysis of gene expression profiles for diagnostic and classification purposes. The tool is based on a new method for the clustering of profiles based on a subject-specific, as opposed to disease-specific, signature. Our approach relies on construction of a reference map of transcriptional signatures, from both healthy and affected subjects, derived from their respective mRNA or miRNA profiles. A diagnosis for a new individual can then be performed by determining the position of the individual's transcriptional signature on the map. The diagnostic power of our method has been convincingly demonstrated in an open scientific competition (SBV Improver Diagnostic Signature Challenge), scoring second place overall and first place in one of the sub-challenges.
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Affiliation(s)
- Mario Lauria
- The Microsoft Research-University of Trento Centre for Computational and Systems Biology (COSBI), Piazza Manifattura 1, 38068 Rovereto (TN), Italy
| | - Petros Moyseos
- The Microsoft Research-University of Trento Centre for Computational and Systems Biology (COSBI), Piazza Manifattura 1, 38068 Rovereto (TN), Italy
| | - Corrado Priami
- The Microsoft Research-University of Trento Centre for Computational and Systems Biology (COSBI), Piazza Manifattura 1, 38068 Rovereto (TN), Italy Department of Mathematics, University of Trento, via Sommarive, 14, 38123 Povo (TN), Italy
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Morine MJ, Monteiro JP, Wise C, Teitel C, Pence L, Williams A, Ning B, McCabe-Sellers B, Champagne C, Turner J, Shelby B, Bogle M, Beger RD, Priami C, Kaput J. Genetic associations with micronutrient levels identified in immune and gastrointestinal networks. Genes Nutr 2014; 9:408. [PMID: 24879315 PMCID: PMC4169061 DOI: 10.1007/s12263-014-0408-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2014] [Accepted: 05/12/2014] [Indexed: 01/05/2023]
Abstract
The discovery of vitamins and clarification of their role in preventing frank essential nutrient deficiencies occurred in the early 1900s. Much vitamin research has understandably focused on public health and the effects of single nutrients to alleviate acute conditions. The physiological processes for maintaining health, however, are complex systems that depend upon interactions between multiple nutrients, environmental factors, and genetic makeup. To analyze the relationship between these factors and nutritional health, data were obtained from an observational, community-based participatory research program of children and teens (age 6–14) enrolled in a summer day camp in the Delta region of Arkansas. Assessments of erythrocyte S-adenosylmethionine (SAM) and S-adenosylhomocysteine (SAH), plasma homocysteine (Hcy) and 6 organic micronutrients (retinol, 25-hydroxy vitamin D3, pyridoxal, thiamin, riboflavin, and vitamin E), and 1,129 plasma proteins were performed at 3 time points in each of 2 years. Genetic makeup was analyzed with 1 M SNP genotyping arrays, and nutrient status was assessed with 24-h dietary intake questionnaires. A pattern of metabolites (met_PC1) that included the ratio of erythrocyte SAM/SAH, Hcy, and 5 vitamins were identified by principal component analysis. Met_PC1 levels were significantly associated with (1) single-nucleotide polymorphisms, (2) levels of plasma proteins, and (3) multilocus genotypes coding for gastrointestinal and immune functions, as identified in a global network of metabolic/protein–protein interactions. Subsequent mining of data from curated pathway, network, and genome-wide association studies identified genetic and functional relationships that may be explained by gene–nutrient interactions. The systems nutrition strategy described here has thus associated a multivariate metabolite pattern in blood with genes involved in immune and gastrointestinal functions.
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Affiliation(s)
- Melissa J Morine
- The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
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Monteiro JP, Wise C, Morine MJ, Teitel C, Pence L, Williams A, McCabe-Sellers B, Champagne C, Turner J, Shelby B, Ning B, Oguntimein J, Taylor L, Toennessen T, Priami C, Beger RD, Bogle M, Kaput J. Methylation potential associated with diet, genotype, protein, and metabolite levels in the Delta Obesity Vitamin Study. Genes Nutr 2014; 9:403. [PMID: 24760553 PMCID: PMC4026438 DOI: 10.1007/s12263-014-0403-9] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2014] [Accepted: 04/06/2014] [Indexed: 12/28/2022]
Abstract
Micronutrient research typically focuses on analyzing the effects of single or a few nutrients on health by analyzing a limited number of biomarkers. The observational study described here analyzed micronutrients, plasma proteins, dietary intakes, and genotype using a systems approach. Participants attended a community-based summer day program for 6-14 year old in 2 years. Genetic makeup, blood metabolite and protein levels, and dietary differences were measured in each individual. Twenty-four-hour dietary intakes, eight micronutrients (vitamins A, D, E, thiamin, folic acid, riboflavin, pyridoxal, and pyridoxine) and 3 one-carbon metabolites [homocysteine (Hcy), S-adenosylmethionine (SAM), and S-adenosylhomocysteine (SAH)], and 1,129 plasma proteins were analyzed as a function of diet at metabolite level, plasma protein level, age, and sex. Cluster analysis identified two groups differing in SAM/SAH and differing in dietary intake patterns indicating that SAM/SAH was a potential marker of nutritional status. The approach used to analyze genetic association with the SAM/SAH metabolites is called middle-out: SNPs in 275 genes involved in the one-carbon pathway (folate, pyridoxal/pyridoxine, thiamin) or were correlated with SAM/SAH (vitamin A, E, Hcy) were analyzed instead of the entire 1M SNP data set. This procedure identified 46 SNPs in 25 genes associated with SAM/SAH demonstrating a genetic contribution to the methylation potential. Individual plasma metabolites correlated with 99 plasma proteins. Fourteen proteins correlated with body mass index, 49 with group age, and 30 with sex. The analytical strategy described here identified subgroups for targeted nutritional interventions.
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Affiliation(s)
- Jacqueline Pontes Monteiro
- />Department of Pediatrics, Faculty of Medicine, Faculty of Nutrition and Metabolism, University of São Paulo, Ribeirão Prêto, SP Brazil
| | - Carolyn Wise
- />Division of Personalized Nutrition and Medicine, National Center for Toxicological Research (NCTR), Food and Drug Administration (FDA), Jefferson, AR USA
| | - Melissa J. Morine
- />Department of Mathematics, University of Trento, Trento, Italy
- />The Microsoft Research, University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
| | - Candee Teitel
- />Division of Personalized Nutrition and Medicine, National Center for Toxicological Research (NCTR), Food and Drug Administration (FDA), Jefferson, AR USA
| | - Lisa Pence
- />Division of Systems Biology, NCTR/FDA, Jefferson, AR USA
| | - Anna Williams
- />Division of Personalized Nutrition and Medicine, National Center for Toxicological Research (NCTR), Food and Drug Administration (FDA), Jefferson, AR USA
| | - Beverly McCabe-Sellers
- />Delta Obesity Prevention Research Unit, United States Department of Agriculture, Agricultural Research Service, Little Rock, AR USA
| | - Catherine Champagne
- />Dietary Assessment and Nutrition Counseling, Pennington Biomedical Research Center, Baton Rouge, LA USA
| | - Jerome Turner
- />Boys, Girls, Adults Community Development Center & The Phillips County Community Partners, Marvell, AR USA
| | - Beatrice Shelby
- />Boys, Girls, Adults Community Development Center & The Phillips County Community Partners, Marvell, AR USA
| | - Baitang Ning
- />Division of Personalized Nutrition and Medicine, National Center for Toxicological Research (NCTR), Food and Drug Administration (FDA), Jefferson, AR USA
| | - Joan Oguntimein
- />Shepherd Program for the Interdisciplinary Study of Poverty and Human Capability, Washington and Lee University, Lexington, VA USA
- />Medical School, Drexel University, Philadelphia, PA USA
| | - Lauren Taylor
- />Shepherd Program for the Interdisciplinary Study of Poverty and Human Capability, Washington and Lee University, Lexington, VA USA
- />Emory School of Public Health, Atlanta, GA USA
| | - Terri Toennessen
- />Division of Personalized Nutrition and Medicine, National Center for Toxicological Research (NCTR), Food and Drug Administration (FDA), Jefferson, AR USA
| | - Corrado Priami
- />Department of Mathematics, University of Trento, Trento, Italy
- />The Microsoft Research, University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
| | | | - Margaret Bogle
- />Delta Obesity Prevention Research Unit, United States Department of Agriculture, Agricultural Research Service, Little Rock, AR USA
| | - Jim Kaput
- />Systems Nutrition and Health Unit, Nestle Institute of Health Sciences, Innovation Square, EPFL Campus, 1015 Lausanne, Switzerland
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Kaput J, van Ommen B, Kremer B, Priami C, Monteiro JP, Morine M, Pepping F, Diaz Z, Fenech M, He Y, Albers R, Drevon CA, Evelo CT, Hancock REW, Ijsselmuiden C, Lumey LH, Minihane AM, Muller M, Murgia C, Radonjic M, Sobral B, West KP. Consensus statement understanding health and malnutrition through a systems approach: the ENOUGH program for early life. Genes Nutr 2014; 9:378. [PMID: 24363221 PMCID: PMC3896628 DOI: 10.1007/s12263-013-0378-y] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/19/2013] [Accepted: 12/02/2013] [Indexed: 12/20/2022]
Abstract
Nutrition research, like most biomedical disciplines, adopted and often uses experimental approaches based on Beadle and Tatum's one gene-one polypeptide hypothesis, thereby reducing biological processes to single reactions or pathways. Systems thinking is needed to understand the complexity of health and disease processes requiring measurements of physiological processes, as well as environmental and social factors, which may alter the expression of genetic information. Analysis of physiological processes with omics technologies to assess systems' responses has only become available over the past decade and remains costly. Studies of environmental and social conditions known to alter health are often not connected to biomedical research. While these facts are widely accepted, developing and conducting comprehensive research programs for health are often beyond financial and human resources of single research groups. We propose a new research program on essential nutrients for optimal underpinning of growth and health (ENOUGH) that will use systems approaches with more comprehensive measurements and biostatistical analysis of the many biological and environmental factors that influence undernutrition. Creating a knowledge base for nutrition and health is a necessary first step toward developing solutions targeted to different populations in diverse social and physical environments for the two billion undernourished people in developed and developing economies.
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Affiliation(s)
- Jim Kaput
- Clinical Translation Unit, Nestle Institute of Health Sciences, Lausanne, Switzerland,
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Kahramanoğullari O, Fantaccini G, Lecca P, Morpurgo D, Priami C. Algorithmic modeling quantifies the complementary contribution of metabolic inhibitions to gemcitabine efficacy. PLoS One 2012; 7:e50176. [PMID: 23239976 PMCID: PMC3519828 DOI: 10.1371/journal.pone.0050176] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2012] [Accepted: 10/22/2012] [Indexed: 01/19/2023] Open
Abstract
Gemcitabine (2,2-difluorodeoxycytidine, dFdC) is a prodrug widely used for treating various carcinomas. Gemcitabine exerts its clinical effect by depleting the deoxyribonucleotide pools, and incorporating its triphosphate metabolite (dFdC-TP) into DNA, thereby inhibiting DNA synthesis. This process blocks the cell cycle in the early S phase, eventually resulting in apoptosis. The incorporation of gemcitabine into DNA takes place in competition with the natural nucleoside dCTP. The mechanisms of indirect competition between these cascades for common resources are given with the race for DNA incorporation; in clinical studies dedicated to singling out mechanisms of resistance, ribonucleotide reductase (RR) and deoxycytidine kinase (dCK) and human equilibrative nucleoside transporter1 (hENT1) have been associated to efficacy of gemcitabine with respect to their roles in the synthesis cascades of dFdC-TP and dCTP. However, the direct competition, which manifests itself in terms of inhibitions between these cascades, remains to be quantified. We propose an algorithmic model of gemcitabine mechanism of action, verified with respect to independent experimental data. We performed in silico experiments in different virtual conditions, otherwise difficult in vivo, to evaluate the contribution of the inhibitory mechanisms to gemcitabine efficacy. In agreement with the experimental data, our model indicates that the inhibitions due to the association of dCTP with dCK and the association of gemcitabine diphosphate metabolite (dFdC-DP) with RR play a key role in adjusting the efficacy. While the former tunes the catalysis of the rate-limiting first phosphorylation of dFdC, the latter is responsible for depletion of dCTP pools, thereby contributing to gemcitabine efficacy with a dependency on nucleoside transport efficiency. Our simulations predict the existence of a continuum of non-efficacy to high-efficacy regimes, where the levels of dFdC-TP and dCTP are coupled in a complementary manner, which can explain the resistance to this drug in some patients.
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Affiliation(s)
- Ozan Kahramanoğullari
- The Microsoft Research-University of Trento Centre for Computational and Systems Biology, Rovereto (Trento), Italy.
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Abstract
A better understanding of the pathophysiology should help deliver drugs whose targets are involved in the causative processes underlying a disease. Biological network inference uses computational methods for deducing from high-throughput experimental data, the topology and the causal structure of the interactions among the drugs and their targets. Therefore, biological network inference can support and contribute to the experimental identification of both gene and protein networks causing a disease as well as the biochemical networks of drugs metabolism and mechanisms of action. The resulting high-level networks serve as a foundational basis for more detailed mechanistic models and are increasingly used in drug discovery by pharmaceutical and biotechnology companies. We review and compare recent computational technologies for network inference applied to drug discovery.
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Affiliation(s)
- Paola Lecca
- The Microsoft Research, University of Trento, Centre for Computational and Systems Biology, Piazza Manifattura 1 - 38068 Rovereto, Italy.
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Lecca P, Morpurgo D, Fantaccini G, Casagrande A, Priami C. Inferring biochemical reaction pathways: the case of the gemcitabine pharmacokinetics. BMC Syst Biol 2012; 6:51. [PMID: 22640931 PMCID: PMC3536593 DOI: 10.1186/1752-0509-6-51] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/19/2011] [Accepted: 04/23/2012] [Indexed: 11/17/2022]
Abstract
BACKGROUND The representation of a biochemical system as a network is the precursor of any mathematical model of the processes driving the dynamics of that system. Pharmacokinetics uses mathematical models to describe the interactions between drug, and drug metabolites and targets and through the simulation of these models predicts drug levels and/or dynamic behaviors of drug entities in the body. Therefore, the development of computational techniques for inferring the interaction network of the drug entities and its kinetic parameters from observational data is raising great interest in the scientific community of pharmacologists. In fact, the network inference is a set of mathematical procedures deducing the structure of a model from the experimental data associated to the nodes of the network of interactions. In this paper, we deal with the inference of a pharmacokinetic network from the concentrations of the drug and its metabolites observed at discrete time points. RESULTS The method of network inference presented in this paper is inspired by the theory of time-lagged correlation inference with regard to the deduction of the interaction network, and on a maximum likelihood approach with regard to the estimation of the kinetic parameters of the network. Both network inference and parameter estimation have been designed specifically to identify systems of biotransformations, at the biochemical level, from noisy time-resolved experimental data. We use our inference method to deduce the metabolic pathway of the gemcitabine. The inputs to our inference algorithm are the experimental time series of the concentration of gemcitabine and its metabolites. The output is the set of reactions of the metabolic network of the gemcitabine. CONCLUSIONS Time-lagged correlation based inference pairs up to a probabilistic model of parameter inference from metabolites time series allows the identification of the microscopic pharmacokinetics and pharmacodynamics of a drug with a minimal a priori knowledge. In fact, the inference model presented in this paper is completely unsupervised. It takes as input the time series of the concetrations of the parent drug and its metabolites. The method, applied to the case study of the gemcitabine pharmacokinetics, shows good accuracy and sensitivity.
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Affiliation(s)
- Paola Lecca
- The Microsoft Research - University of Trento Centre for Computational and Systems Biology, , 38068 Rovereto, Italy
| | - Daniele Morpurgo
- The Microsoft Research - University of Trento Centre for Computational and Systems Biology, , 38068 Rovereto, Italy
| | - Gianluca Fantaccini
- The Microsoft Research - University of Trento Centre for Computational and Systems Biology, , 38068 Rovereto, Italy
| | - Alessandro Casagrande
- The Microsoft Research - University of Trento Centre for Computational and Systems Biology, , 38068 Rovereto, Italy
- Department of Information Engineering and Computer Science - University of Trento, , Trento, Italy
| | - Corrado Priami
- The Microsoft Research - University of Trento Centre for Computational and Systems Biology, , 38068 Rovereto, Italy
- Department of Information Engineering and Computer Science - University of Trento, , Trento, Italy
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Nguyen TP, Scotti M, Morine MJ, Priami C. Model-based clustering reveals vitamin D dependent multi-centrality hubs in a network of vitamin-related proteins. BMC Syst Biol 2011; 5:195. [PMID: 22136443 PMCID: PMC3264545 DOI: 10.1186/1752-0509-5-195] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/04/2011] [Accepted: 12/02/2011] [Indexed: 01/20/2023]
Abstract
Background Nutritional systems biology offers the potential for comprehensive predictions that account for all metabolic changes with the intricate biological organization and the multitudinous interactions between the cellular proteins. Protein-protein interaction (PPI) networks can be used for an integrative description of molecular processes. Although widely adopted in nutritional systems biology, these networks typically encompass a single category of functional interaction (i.e., metabolic, regulatory or signaling) or nutrient. Incorporating multiple nutrients and functional interaction categories under an integrated framework represents an informative approach for gaining system level insight on nutrient metabolism. Results We constructed a multi-level PPI network starting from the interactions of 200 vitamin-related proteins. Its final size was 1,657 proteins, with 2,700 interactions. To characterize the role of the proteins we computed 6 centrality indices and applied model-based clustering. We detected a subgroup of 22 proteins that were highly central and significantly related to vitamin D. Immune system and cancer-related processes were strongly represented among these proteins. Clustering of the centralities revealed a degree of redundancy among the indices; a repeated analysis using subsets of the centralities performed well in identifying the original set of 22 most central proteins. Conclusions Hierarchical and model-based clustering revealed multi-centrality hubs in a vitamin PPI network and redundancies among the centrality indices. Vitamin D-related proteins were strongly represented among network hubs, highlighting the pervasive effects of this nutrient. Our integrated approach to network construction identified promiscuous transcription factors, cytokines and enzymes - primarily related to immune system and cancer processes - representing potential gatekeepers linking vitamin intake to disease.
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Affiliation(s)
- Thanh-Phuong Nguyen
- The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Piazza Manifattura 1, 38068 Rovereto (Trento), Italy
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Lecca P, Ihekwaba AEC, Dematté L, Priami C. Stochastic simulation of the spatio-temporal dynamics of reaction-diffusion systems: the case for the bicoid gradient. J Integr Bioinform 2010. [DOI: 10.1515/jib-2010-150] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
SummaryReaction-diffusion systems are mathematical models that describe how the concentrations of substances distributed in space change under the influence of local chemical reactions, and diffusion which causes the substances to spread out in space. The classical representation of a reaction-diffusion system is given by semi-linear parabolic partial differential equations, whose solution predicts how diffusion causes the concentration field to change with time. This change is proportional to the diffusion coefficient. If the solute moves in a homogeneous system in thermal equilibrium, the diffusion coefficients are constants that do not depend on the local concentration of solvent and solute. However, in nonhomogeneous and structured media the assumption of constant intracellular diffusion coefficient is not necessarily valid, and, consequently, the diffusion coefficient is a function of the local concentration of solvent and solutes. In this paper we propose a stochastic model of reaction-diffusion systems, in which the diffusion coefficients are function of the local concentration, viscosity and frictional forces. We then describe the software tool Redi (REaction-DIffusion simulator) which we have developed in order to implement this model into a Gillespie-like stochastic simulation algorithm. Finally, we show the ability of our model implemented in the Redi tool to reproduce the observed gradient of the bicoid protein in the Drosophila Melanogaster embryo. With Redi, we were able to simulate with an accuracy of 1% the experimental spatio-temporal dynamics of the bicoid protein, as recorded in time-lapse experiments obtained by direct measurements of transgenic bicoidenhanced green fluorescent protein.
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Abstract
BACKGROUND Networks are widely recognized as key determinants of structure and function in systems that span the biological, physical, and social sciences. They are static pictures of the interactions among the components of complex systems. Often, much effort is required to identify networks as part of particular patterns as well as to visualize and interpret them.From a pure dynamical perspective, simulation represents a relevant way-out. Many simulator tools capitalized on the "noisy" behavior of some systems and used formal models to represent cellular activities as temporal trajectories. Statistical methods have been applied to a fairly large number of replicated trajectories in order to infer knowledge.A tool which both graphically manipulates reactive models and deals with sets of simulation time-course data by aggregation, interpretation and statistical analysis is missing and could add value to simulators. RESULTS We designed and implemented Snazer, the simulations and networks analyzer. Its goal is to aid the processes of visualizing and manipulating reactive models, as well as to share and interpret time-course data produced by stochastic simulators or by any other means. CONCLUSIONS Snazer is a solid prototype that integrates biological network and simulation time-course data analysis techniques.
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Affiliation(s)
- Tommaso Mazza
- The Microsoft Research University of Trento, CoSBi, Trento, Italy
| | | | - Corrado Priami
- The Microsoft Research University of Trento, CoSBi, Trento, Italy
- DISI - University of Trento, Trento, Italy
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Ihekwaba AEC, Nguyen PT, Priami C. Elucidation of functional consequences of signalling pathway interactions. BMC Bioinformatics 2009; 10:370. [PMID: 19895694 PMCID: PMC2778660 DOI: 10.1186/1471-2105-10-370] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2009] [Accepted: 11/06/2009] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND A great deal of data has accumulated on signalling pathways. These large datasets are thought to contain much implicit information on their molecular structure, interaction and activity information, which provides a picture of intricate molecular networks believed to underlie biological functions. While tremendous advances have been made in trying to understand these systems, how information is transmitted within them is still poorly understood. This ever growing amount of data demands we adopt powerful computational techniques that will play a pivotal role in the conversion of mined data to knowledge, and in elucidating the topological and functional properties of protein - protein interactions. RESULTS A computational framework is presented which allows for the description of embedded networks, and identification of common shared components thought to assist in the transmission of information within the systems studied. By employing the graph theories of network biology - such as degree distribution, clustering coefficient, vertex betweenness and shortest path measures - topological features of protein-protein interactions for published datasets of the p53, nuclear factor kappa B (NF-kappaB) and G1/S phase of the cell cycle systems were ascertained. Highly ranked nodes which in some cases were identified as connecting proteins most likely responsible for propagation of transduction signals across the networks were determined. The functional consequences of these nodes in the context of their network environment were also determined. These findings highlight the usefulness of the framework in identifying possible combination or links as targets for therapeutic responses; and put forward the idea of using retrieved knowledge on the shared components in constructing better organised and structured models of signalling networks. CONCLUSION It is hoped that through the data mined reconstructed signal transduction networks, well developed models of the published data can be built which in the end would guide the prediction of new targets based on the pathway's environment for further analysis. Source code is available upon request.
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Affiliation(s)
- Adaoha E C Ihekwaba
- The Microsoft Research-University of Trento, Centre for Computational Systems Biology, Povo (Trento), Italy.
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