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Arduino Soft Sensor for Monitoring Schizochytrium sp. Fermentation, a Proof of Concept for the Industrial Application of Genome-Scale Metabolic Models in the Context of Pharma 4.0. Processes (Basel) 2022. [DOI: 10.3390/pr10112226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Schizochytrium sp. is a microorganism cultured for producing docosahexaenoic acid (DHA). Genome-scale metabolic modeling (GEM) is a promising technique for describing gen-protein-reactions in cells, but with still limited industrial application due to its complexity and high computation requirements. In this work, we simplified GEM results regarding the relationship between the specific oxygen uptake rate (−rO2), the specific growth rate (µ), and the rate of lipid synthesis (rL) using an evolutionary algorithm for developing a model that can be used by a soft sensor for fermentation monitoring. The soft sensor estimated the concentration of active biomass (X), glutamate (N), lipids (L), and DHA in a Schizochytrium sp. fermentation using the dissolved oxygen tension (DO) and the oxygen mass transfer coefficient (kLa) as online input variables. The soft sensor model described the biomass concentration response of four reported experiments characterized by different kLa values. The average range normalized root-mean-square error for X, N, L, and DHA were equal to 1.1, 1.3, 1.1, and 3.2%, respectively, suggesting an acceptable generalization capacity. The feasibility of implementing the soft sensor over a low-cost electronic board was successfully tested using an Arduino UNO, showing a novel path for applying GEM-based soft sensors in the context of Pharma 4.0.
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2
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Ramos JRC, Oliveira GP, Dumas P, Oliveira R. Genome-scale modeling of Chinese hamster ovary cells by hybrid semi-parametric flux balance analysis. Bioprocess Biosyst Eng 2022; 45:1889-1904. [DOI: 10.1007/s00449-022-02795-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 09/30/2022] [Indexed: 11/28/2022]
Abstract
AbstractFlux balance analysis (FBA) is currently the standard method to compute metabolic fluxes in genome-scale networks. Several FBA extensions employing diverse objective functions and/or constraints have been published. Here we propose a hybrid semi-parametric FBA extension that combines mechanistic-level constraints (parametric) with empirical constraints (non-parametric) in the same linear program. A CHO dataset with 27 measured exchange fluxes obtained from 21 reactor experiments served to evaluate the method. The mechanistic constraints were deduced from a reduced CHO-K1 genome-scale network with 686 metabolites, 788 reactions and 210 degrees of freedom. The non-parametric constraints were obtained by principal component analysis of the flux dataset. The two types of constraints were integrated in the same linear program showing comparable computational cost to standard FBA. The hybrid FBA is shown to significantly improve the specific growth rate prediction under different constraints scenarios. A metabolically efficient cell growth feed targeting minimal byproducts accumulation was designed by hybrid FBA. It is concluded that integrating parametric and nonparametric constraints in the same linear program may be an efficient approach to reduce the solution space and to improve the predictive power of FBA methods when critical mechanistic information is missing.
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3
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Gouda G, Gupta MK, Donde R, Behera L, Vadde R. Metabolic pathway-based target therapy to hepatocellular carcinoma: a computational approach. THERANOSTICS AND PRECISION MEDICINE FOR THE MANAGEMENT OF HEPATOCELLULAR CARCINOMA, VOLUME 2 2022:83-103. [DOI: 10.1016/b978-0-323-98807-0.00003-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/06/2023]
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4
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Hu LF, Lan HR, Huang D, Li XM, Jin KT. Personalized Immunotherapy in Colorectal Cancers: Where Do We Stand? Front Oncol 2021; 11:769305. [PMID: 34888246 PMCID: PMC8649954 DOI: 10.3389/fonc.2021.769305] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 10/26/2021] [Indexed: 12/17/2022] Open
Abstract
Colorectal cancer (CRC) is the second leading cause of cancer death in the world. Immunotherapy using monoclonal antibodies, immune-checkpoint inhibitors, adoptive cell therapy, and cancer vaccines has raised great hopes for treating poor prognosis metastatic CRCs that are resistant to the conventional therapies. However, high inter-tumor and intra-tumor heterogeneity hinder the success of immunotherapy in CRC. Patients with a similar tumor phenotype respond differently to the same immunotherapy regimen. Mutation-based classification, molecular subtyping, and immunoscoring of CRCs facilitated the multi-aspect grouping of CRC patients and improved immunotherapy. Personalized immunotherapy using tumor-specific neoantigens provides the opportunity to consider each patient as an independent group deserving of individualized immunotherapy. In the recent decade, the development of sequencing and multi-omics techniques has helped us classify patients more precisely. The expansion of such advanced techniques along with the neoantigen-based immunotherapy could herald a new era in treating heterogeneous tumors such as CRC. In this review article, we provided the latest findings in immunotherapy of CRC. We elaborated on the heterogeneity of CRC patients as a bottleneck of CRC immunotherapy and reviewed the latest advances in personalized immunotherapy to overcome CRC heterogeneity.
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Affiliation(s)
- Li-Feng Hu
- Department of Colorectal Surgery, Shaoxing People’s Hospital (Shaoxing Hospital, Zhejiang University School of Medicine), Shaoxing, China
| | - Huan-Rong Lan
- Department of Breast and Thyroid Surgery, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, China
| | - Dong Huang
- Department of Colorectal Surgery, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, China
| | - Xue-Min Li
- Department of Hepatobiliary Surgery, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, China
| | - Ke-Tao Jin
- Department of Colorectal Surgery, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, China
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5
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Cheng K, Martin‐Sancho L, Pal LR, Pu Y, Riva L, Yin X, Sinha S, Nair NU, Chanda SK, Ruppin E. Genome-scale metabolic modeling reveals SARS-CoV-2-induced metabolic changes and antiviral targets. Mol Syst Biol 2021; 17:e10260. [PMID: 34709707 PMCID: PMC8552660 DOI: 10.15252/msb.202110260] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Revised: 09/29/2021] [Accepted: 09/30/2021] [Indexed: 12/15/2022] Open
Abstract
Tremendous progress has been made to control the COVID-19 pandemic caused by the SARS-CoV-2 virus. However, effective therapeutic options are still rare. Drug repurposing and combination represent practical strategies to address this urgent unmet medical need. Viruses, including coronaviruses, are known to hijack host metabolism to facilitate viral proliferation, making targeting host metabolism a promising antiviral approach. Here, we describe an integrated analysis of 12 published in vitro and human patient gene expression datasets on SARS-CoV-2 infection using genome-scale metabolic modeling (GEM), revealing complicated host metabolism reprogramming during SARS-CoV-2 infection. We next applied the GEM-based metabolic transformation algorithm to predict anti-SARS-CoV-2 targets that counteract the virus-induced metabolic changes. We successfully validated these targets using published drug and genetic screen data and by performing an siRNA assay in Caco-2 cells. Further generating and analyzing RNA-sequencing data of remdesivir-treated Vero E6 cell samples, we predicted metabolic targets acting in combination with remdesivir, an approved anti-SARS-CoV-2 drug. Our study provides clinical data-supported candidate anti-SARS-CoV-2 targets for future evaluation, demonstrating host metabolism targeting as a promising antiviral strategy.
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Affiliation(s)
- Kuoyuan Cheng
- Cancer Data Science Laboratory (CDSL)National Cancer Institute (NCI)National Institutes of Health (NIH)BethesdaMDUSA
- Biological Sciences Graduate Program (BISI)University of MarylandCollege ParkMDUSA
| | - Laura Martin‐Sancho
- Immunity and Pathogenesis Program, Infectious and Inflammatory Disease CenterSanford Burnham Prebys Medical Discovery InstituteLa JollaCAUSA
| | - Lipika R Pal
- Cancer Data Science Laboratory (CDSL)National Cancer Institute (NCI)National Institutes of Health (NIH)BethesdaMDUSA
| | - Yuan Pu
- Immunity and Pathogenesis Program, Infectious and Inflammatory Disease CenterSanford Burnham Prebys Medical Discovery InstituteLa JollaCAUSA
| | - Laura Riva
- Immunity and Pathogenesis Program, Infectious and Inflammatory Disease CenterSanford Burnham Prebys Medical Discovery InstituteLa JollaCAUSA
- Present address:
Calibr, a Division of The Scripps Research InstituteLa JollaCAUSA
| | - Xin Yin
- Immunity and Pathogenesis Program, Infectious and Inflammatory Disease CenterSanford Burnham Prebys Medical Discovery InstituteLa JollaCAUSA
- State Key Laboratory of Veterinary BiotechnologyHarbin Veterinary Research InstituteChinese Academy of Agricultural SciencesHarbinChina
| | - Sanju Sinha
- Cancer Data Science Laboratory (CDSL)National Cancer Institute (NCI)National Institutes of Health (NIH)BethesdaMDUSA
- Biological Sciences Graduate Program (BISI)University of MarylandCollege ParkMDUSA
| | - Nishanth Ulhas Nair
- Cancer Data Science Laboratory (CDSL)National Cancer Institute (NCI)National Institutes of Health (NIH)BethesdaMDUSA
| | - Sumit K Chanda
- Immunity and Pathogenesis Program, Infectious and Inflammatory Disease CenterSanford Burnham Prebys Medical Discovery InstituteLa JollaCAUSA
| | - Eytan Ruppin
- Cancer Data Science Laboratory (CDSL)National Cancer Institute (NCI)National Institutes of Health (NIH)BethesdaMDUSA
- Department of Computer ScienceUniversity of MarylandCollege ParkMDUSA
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6
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Cheng K, Martin-Sancho L, Pal LR, Pu Y, Riva L, Yin X, Sinha S, Nair NU, Chanda SK, Ruppin E. Genome-scale metabolic modeling reveals SARS-CoV-2-induced metabolic changes and antiviral targets. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2021:2021.01.27.428543. [PMID: 33532779 PMCID: PMC7852273 DOI: 10.1101/2021.01.27.428543] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Tremendous progress has been made to control the COVID-19 pandemic caused by the SARS-CoV-2 virus. However, effective therapeutic options are still rare. Drug repurposing and combination represent practical strategies to address this urgent unmet medical need. Viruses, including coronaviruses, are known to hijack host metabolism to facilitate viral proliferation, making targeting host metabolism a promising antiviral approach. Here, we describe an integrated analysis of 12 published in vitro and human patient gene expression datasets on SARS-CoV-2 infection using genome-scale metabolic modeling (GEM), revealing complicated host metabolism reprogramming during SARS-CoV-2 infection. We next applied the GEM-based metabolic transformation algorithm to predict anti-SARS-CoV-2 targets that counteract the virus-induced metabolic changes. We successfully validated these targets using published drug and genetic screen data and by performing an siRNA assay in Caco-2 cells. Further generating and analyzing RNA-sequencing data of remdesivir-treated Vero E6 cell samples, we predicted metabolic targets acting in combination with remdesivir, an approved anti-SARS-CoV-2 drug. Our study provides clinical data-supported candidate anti-SARS-CoV-2 targets for future evaluation, demonstrating host metabolism-targeting as a promising antiviral strategy.
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Affiliation(s)
- Kuoyuan Cheng
- Cancer Data Science Laboratory (CDSL), National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD, USA
- Biological Sciences Graduate Program (BISI), University of Maryland, College Park, MD, USA
| | - Laura Martin-Sancho
- Immunity and Pathogenesis Program, Infectious and Inflammatory Disease Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
| | - Lipika R. Pal
- Cancer Data Science Laboratory (CDSL), National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD, USA
| | - Yuan Pu
- Immunity and Pathogenesis Program, Infectious and Inflammatory Disease Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
| | - Laura Riva
- Immunity and Pathogenesis Program, Infectious and Inflammatory Disease Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
| | - Xin Yin
- Immunity and Pathogenesis Program, Infectious and Inflammatory Disease Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
- State Key Laboratory of Veterinary Biotechnology, Harbin Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Harbin, China
| | - Sanju Sinha
- Cancer Data Science Laboratory (CDSL), National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD, USA
- Biological Sciences Graduate Program (BISI), University of Maryland, College Park, MD, USA
| | - Nishanth Ulhas Nair
- Cancer Data Science Laboratory (CDSL), National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD, USA
| | - Sumit K. Chanda
- Immunity and Pathogenesis Program, Infectious and Inflammatory Disease Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
| | - Eytan Ruppin
- Cancer Data Science Laboratory (CDSL), National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD, USA
- Department of Computer Science, University of Maryland, College Park, MD, USA
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7
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Trujillo-Montenegro JH, Rodríguez Cubillos MJ, Loaiza CD, Quintero M, Espitia-Navarro HF, Salazar Villareal FA, Viveros Valens CA, González Barrios AF, De Vega J, Duitama J, Riascos JJ. Unraveling the Genome of a High Yielding Colombian Sugarcane Hybrid. FRONTIERS IN PLANT SCIENCE 2021; 12:694859. [PMID: 34484261 PMCID: PMC8414525 DOI: 10.3389/fpls.2021.694859] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Accepted: 06/07/2021] [Indexed: 05/04/2023]
Abstract
Recent developments in High Throughput Sequencing (HTS) technologies and bioinformatics, including improved read lengths and genome assemblers allow the reconstruction of complex genomes with unprecedented quality and contiguity. Sugarcane has one of the most complicated genomes among grassess with a haploid length of 1Gbp and a ploidies between 8 and 12. In this work, we present a genome assembly of the Colombian sugarcane hybrid CC 01-1940. Three types of sequencing technologies were combined for this assembly: PacBio long reads, Illumina paired short reads, and Hi-C reads. We achieved a median contig length of 34.94 Mbp and a total genome assembly of 903.2 Mbp. We annotated a total of 63,724 protein coding genes and performed a reconstruction and comparative analysis of the sucrose metabolism pathway. Nucleotide evolution measurements between orthologs with close species suggest that divergence between Saccharum officinarum and Saccharum spontaneum occurred <2 million years ago. Synteny analysis between CC 01-1940 and the S. spontaneum genome confirms the presence of translocation events between the species and a random contribution throughout the entire genome in current sugarcane hybrids. Analysis of RNA-Seq data from leaf and root tissue of contrasting sugarcane genotypes subjected to water stress treatments revealed 17,490 differentially expressed genes, from which 3,633 correspond to genes expressed exclusively in tolerant genotypes. We expect the resources presented here to serve as a source of information to improve the selection processes of new varieties of the breeding programs of sugarcane.
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Affiliation(s)
- Jhon Henry Trujillo-Montenegro
- Centro de Investigación de la Caña de Azúcar de Colombia (CENICAÑA), Cali, Colombia
- Research Group in Bioinformatics, Department of Computer Science, Faculty of Engineering, Universidad Del Valle,Cali, Colombia
| | - María Juliana Rodríguez Cubillos
- Grupo de Diseño de Productos y Procesos, Department of Chemical and Food Engineering, Faculty of Engineering, Universidad de los Andes, Bogotá, Colombia
| | | | - Manuel Quintero
- Centro de Investigación de la Caña de Azúcar de Colombia (CENICAÑA), Cali, Colombia
| | | | | | | | - Andrés Fernando González Barrios
- Grupo de Diseño de Productos y Procesos, Department of Chemical and Food Engineering, Faculty of Engineering, Universidad de los Andes, Bogotá, Colombia
| | - José De Vega
- Earlham Institute, Norwich Research Park, Norwich, United Kingdom
| | - Jorge Duitama
- Systems and Computing Engineering Department, Universidad de los Andes, Bogotá, Colombia
| | - John J. Riascos
- Centro de Investigación de la Caña de Azúcar de Colombia (CENICAÑA), Cali, Colombia
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8
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Patra P, Das M, Kundu P, Ghosh A. Recent advances in systems and synthetic biology approaches for developing novel cell-factories in non-conventional yeasts. Biotechnol Adv 2021; 47:107695. [PMID: 33465474 DOI: 10.1016/j.biotechadv.2021.107695] [Citation(s) in RCA: 70] [Impact Index Per Article: 23.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2020] [Revised: 12/14/2020] [Accepted: 01/09/2021] [Indexed: 12/14/2022]
Abstract
Microbial bioproduction of chemicals, proteins, and primary metabolites from cheap carbon sources is currently an advancing area in industrial research. The model yeast, Saccharomyces cerevisiae, is a well-established biorefinery host that has been used extensively for commercial manufacturing of bioethanol from myriad carbon sources. However, its Crabtree-positive nature often limits the use of this organism for the biosynthesis of commercial molecules that do not belong in the fermentative pathway. To avoid extensive strain engineering of S. cerevisiae for the production of metabolites other than ethanol, non-conventional yeasts can be selected as hosts based on their natural capacity to produce desired commodity chemicals. Non-conventional yeasts like Kluyveromyces marxianus, K. lactis, Yarrowia lipolytica, Pichia pastoris, Scheffersomyces stipitis, Hansenula polymorpha, and Rhodotorula toruloides have been considered as potential industrial eukaryotic hosts owing to their desirable phenotypes such as thermotolerance, assimilation of a wide range of carbon sources, as well as ability to secrete high titers of protein and lipid. However, the advanced metabolic engineering efforts in these organisms are still lacking due to the limited availability of systems and synthetic biology methods like in silico models, well-characterised genetic parts, and optimized genome engineering tools. This review provides an insight into the recent advances and challenges of systems and synthetic biology as well as metabolic engineering endeavours towards the commercial usage of non-conventional yeasts. Particularly, the approaches in emerging non-conventional yeasts for the production of enzymes, therapeutic proteins, lipids, and metabolites for commercial applications are extensively discussed here. Various attempts to address current limitations in designing novel cell factories have been highlighted that include the advances in the fields of genome-scale metabolic model reconstruction, flux balance analysis, 'omics'-data integration into models, genome-editing toolkit development, and rewiring of cellular metabolisms for desired chemical production. Additionally, the understanding of metabolic networks using 13C-labelling experiments as well as the utilization of metabolomics in deciphering intracellular fluxes and reactions have also been discussed here. Application of cutting-edge nuclease-based genome editing platforms like CRISPR/Cas9, and its optimization towards efficient strain engineering in non-conventional yeasts have also been described. Additionally, the impact of the advances in promising non-conventional yeasts for efficient commercial molecule synthesis has been meticulously reviewed. In the future, a cohesive approach involving systems and synthetic biology will help in widening the horizon of the use of unexplored non-conventional yeast species towards industrial biotechnology.
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Affiliation(s)
- Pradipta Patra
- School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Manali Das
- School of Bioscience, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Pritam Kundu
- School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Amit Ghosh
- School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India; P.K. Sinha Centre for Bioenergy and Renewables, Indian Institute of Technology Kharagpur, West Bengal 721302, India.
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9
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Methanothermobacter thermautotrophicus strain ΔH as a potential microorganism for bioconversion of CO2 to methane. J CO2 UTIL 2020. [DOI: 10.1016/j.jcou.2020.101210] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
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10
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Ahmad A, Pathania R, Srivastava S. Biochemical Characteristics and a Genome-Scale Metabolic Model of an Indian Euryhaline Cyanobacterium with High Polyglucan Content. Metabolites 2020; 10:metabo10050177. [PMID: 32365713 PMCID: PMC7281201 DOI: 10.3390/metabo10050177] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2020] [Revised: 01/28/2020] [Accepted: 02/05/2020] [Indexed: 12/16/2022] Open
Abstract
Marine cyanobacteria are promising microbes to capture and convert atmospheric CO2 and light into biomass and valuable industrial bio-products. Yet, reports on metabolic characteristics of non-model cyanobacteria are scarce. In this report, we show that an Indian euryhaline Synechococcus sp. BDU 130192 has biomass accumulation comparable to a model marine cyanobacterium and contains approximately double the amount of total carbohydrates, but significantly lower protein levels compared to Synechococcus sp. PCC 7002 cells. Based on its annotated chromosomal genome sequence, we present a genome scale metabolic model (GSMM) of this cyanobacterium, which we have named as iSyn706. The model includes 706 genes, 908 reactions, and 900 metabolites. The difference in the flux balance analysis (FBA) predicted flux distributions between Synechococcus sp. PCC 7002 and Synechococcus sp. BDU130192 strains mimicked the differences in their biomass compositions. Model-predicted oxygen evolution rate for Synechococcus sp. BDU130192 was found to be close to the experimentally-measured value. The model was analyzed to determine the potential of the strain for the production of various industrially-useful products without affecting growth significantly. This model will be helpful to researchers interested in understanding the metabolism as well as to design metabolic engineering strategies for the production of industrially-relevant compounds.
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Affiliation(s)
- Ahmad Ahmad
- DBT-ICGEB Center for Advanced Bioenergy Research, International Centre for Genetic Engineering and Biotechnology, New Delhi 110067, India;
- Department of Biotechnology, Noida International University, Noida, U.P. 203201, India
| | - Ruchi Pathania
- Systems Biology for Biofuels Group, International Centre for Genetic Engineering and Biotechnology, New Delhi 110067, India;
| | - Shireesh Srivastava
- DBT-ICGEB Center for Advanced Bioenergy Research, International Centre for Genetic Engineering and Biotechnology, New Delhi 110067, India;
- Systems Biology for Biofuels Group, International Centre for Genetic Engineering and Biotechnology, New Delhi 110067, India;
- Correspondence: ; Tel.: +91-11-26741361 (ext. 450)
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11
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Széliová D, Schoeny H, Knez Š, Troyer C, Coman C, Rampler E, Koellensperger G, Ahrends R, Hann S, Borth N, Zanghellini J, Ruckerbauer DE. Robust Analytical Methods for the Accurate Quantification of the Total Biomass Composition of Mammalian Cells. Methods Mol Biol 2020; 2088:119-160. [PMID: 31893373 DOI: 10.1007/978-1-0716-0159-4_7] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Biomass composition is an important input for genome-scale metabolic models and has a big impact on their predictive capabilities. However, researchers often rely on generic data for biomass composition, e.g. collected from similar organisms. This leads to inaccurate predictions, because biomass composition varies between different cell lines, conditions, and growth phases. In this chapter we present protocols for the determination of the biomass composition of Chinese Hamster Ovary (CHO) cells. These methods can easily be adapted to other types of mammalian cells. The protocols include the quantification of cell dry mass and of the main biomass components, namely protein, lipid, DNA, RNA, and carbohydrates. Cell dry mass is determined gravimetrically by weighing a defined number of cells. Amino acid composition and protein content are measured by gas chromatography mass spectrometry. Lipids are quantified by shotgun mass spectrometry, which provides quantities for the different lipid classes and also the distribution of fatty acids. RNA is purified and then quantified spectrophotometrically. The methods for DNA and carbohydrates are simple fluorometric and colorimetric assays adapted to a 96-well plate format. To ensure quantitative results, internal standards or spike-in controls are used in all methods, e.g. to account for possible matrix effects or loss of material. Finally, the last section provides a guide on how to convert the measured data into biomass equations, which can then be integrated into a metabolic model.
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Affiliation(s)
- Diana Széliová
- Austrian Centre of Industrial Biotechnology, Vienna, Austria
- University of Natural Resources and Life Sciences, Vienna, Austria
| | | | - Špela Knez
- University of Ljubljana, Ljubljana, Slovenia
| | - Christina Troyer
- University of Natural Resources and Life Sciences, Vienna, Austria
| | - Cristina Coman
- Leibniz Institut für Analytische Wissenschaften - e.V., Dortmund, Germany
| | | | | | - Robert Ahrends
- Leibniz Institut für Analytische Wissenschaften - e.V., Dortmund, Germany
| | - Stephen Hann
- Austrian Centre of Industrial Biotechnology, Vienna, Austria
- University of Natural Resources and Life Sciences, Vienna, Austria
| | - Nicole Borth
- Austrian Centre of Industrial Biotechnology, Vienna, Austria
- University of Natural Resources and Life Sciences, Vienna, Austria
| | - Jürgen Zanghellini
- University of Natural Resources and Life Sciences, Vienna, Austria
- Austrian Biotech University of Applied Sciences, Tulln, Austria
- Austrian Centre of Industrial Biotechnology, Vienna, Austria
| | - David E Ruckerbauer
- Austrian Centre of Industrial Biotechnology, Vienna, Austria.
- University of Natural Resources and Life Sciences, Vienna, Austria.
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12
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Chaudhary T, Shukla P. Bioinoculant capability enhancement through metabolomics and systems biology approaches. Brief Funct Genomics 2019; 18:159-168. [PMID: 31232454 DOI: 10.1093/bfgp/elz011] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2019] [Revised: 03/30/2019] [Accepted: 04/26/2019] [Indexed: 02/07/2023] Open
Abstract
Bioinoculants are eco-friendly microorganisms, and their products are utilized for improving the potential of soil and fulfill the nutrients requirement for the host plant. The agricultural yield has increased due to the use of bioinoculants over chemical-based fertilizers, and thus it generates interest in understanding the innovation process by various methods. By gene-editing tool, the desired gene product can be changed for engineered microbial inoculants. We have also described various modern biotechnological tools like constraint-based modeling, OptKnock, flux balance analysis and modeling of the biological network for enhancing the bioinoculant capability. These fluxes give the fascinating perception of the metabolic network in the absence of comprehensive kinetic information. These tools also help in the stimulation of the metabolic networks by incorporation of enzyme-encoding genes. The present review explains the use of systems biology and gene-editing tools for improving the capability of bioinoculants. Moreover, this review also emphasizes on the challenges and future perspective of systems biology and its multidisciplinary facets.
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Affiliation(s)
- Twinkle Chaudhary
- Enzyme Technology and Protein Bioinformatics Laboratory, Department of Microbiology, Maharshi Dayanand University, Rohtak, India
| | - Pratyoosh Shukla
- Enzyme Technology and Protein Bioinformatics Laboratory, Department of Microbiology, Maharshi Dayanand University, Rohtak, India
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Jayachandran M, Chung SSM, Xu B. A critical review of the relationship between dietary components, the gut microbe Akkermansia muciniphila, and human health. Crit Rev Food Sci Nutr 2019; 60:2265-2276. [DOI: 10.1080/10408398.2019.1632789] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Affiliation(s)
- Muthukumaran Jayachandran
- Food Science and Technology Programme, Beijing Normal University-Hong Kong Baptist University United International College, Zhuhai, China
| | - Stephen Sum Man Chung
- Food Science and Technology Programme, Beijing Normal University-Hong Kong Baptist University United International College, Zhuhai, China
| | - Baojun Xu
- Food Science and Technology Programme, Beijing Normal University-Hong Kong Baptist University United International College, Zhuhai, China
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14
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Granata I, Troiano E, Sangiovanni M, Guarracino MR. Integration of transcriptomic data in a genome-scale metabolic model to investigate the link between obesity and breast cancer. BMC Bioinformatics 2019; 20:162. [PMID: 30999849 PMCID: PMC6471692 DOI: 10.1186/s12859-019-2685-9] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Obesity is a complex disorder associated with an increased risk of developing several comorbid chronic diseases, including postmenopausal breast cancer. Although many studies have investigated this issue, the link between body weight and either risk or poor outcome of breast cancer is still to characterize. Systems biology approaches, based on the integration of multiscale models and data from a wide variety of sources, are particularly suitable for investigating the underlying molecular mechanisms of complex diseases. In this scenario, GEnome-scale metabolic Models (GEMs) are a valuable tool, since they represent the metabolic structure of cells and provide a functional scaffold for simulating and quantifying metabolic fluxes in living organisms through constraint-based mathematical methods. The integration of omics data into the structural information described by GEMs allows to build more accurate descriptions of metabolic states. RESULTS In this work, we exploited gene expression data of postmenopausal breast cancer obese and lean patients to simulate a curated GEM of the human adipocyte, available in the Human Metabolic Atlas database. To this aim, we used a published algorithm which exploits a data-driven approach to overcome the limitation of defining a single objective function to simulate the model. The flux solutions were used to build condition-specific graphs to visualise and investigate the reaction networks and their properties. In particular, we performed a network topology differential analysis to search for pattern differences and identify the principal reactions associated with significant changes across the two conditions under study. CONCLUSIONS Metabolic network models represent an important source to study the metabolic phenotype of an organism in different conditions. Here we demonstrate the importance of exploiting Next Generation Sequencing data to perform condition-specific GEM analyses. In particular, we show that the qualitative and quantitative assessment of metabolic fluxes modulated by gene expression data provides a valuable method for investigating the mechanisms associated with the phenotype under study, and can foster our interpretation of biological phenomena.
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Affiliation(s)
- Ilaria Granata
- High Performance Computing and Networking Institute, National Research Council of Italy, Via P. Castellino, 111, Napoli, 80131, Italy.
| | - Enrico Troiano
- High Performance Computing and Networking Institute, National Research Council of Italy, Via P. Castellino, 111, Napoli, 80131, Italy
| | - Mara Sangiovanni
- Stazione Zoologica Anton Dohrn, Villa Comunale, Napoli, 80121, Italy
| | - Mario Rosario Guarracino
- High Performance Computing and Networking Institute, National Research Council of Italy, Via P. Castellino, 111, Napoli, 80131, Italy
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15
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Pfau T, Christian N, Masakapalli SK, Sweetlove LJ, Poolman MG, Ebenhöh O. The intertwined metabolism during symbiotic nitrogen fixation elucidated by metabolic modelling. Sci Rep 2018; 8:12504. [PMID: 30131500 PMCID: PMC6104047 DOI: 10.1038/s41598-018-30884-x] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2018] [Accepted: 08/07/2018] [Indexed: 11/09/2022] Open
Abstract
Genome-scale metabolic network models can be used for various analyses including the prediction of metabolic responses to changes in the environment. Legumes are well known for their rhizobial symbiosis that introduces nitrogen into the global nutrient cycle. Here, we describe a fully compartmentalised, mass and charge-balanced, genome-scale model of the clover Medicago truncatula, which has been adopted as a model organism for legumes. We employed flux balance analysis to demonstrate that the network is capable of producing biomass components in experimentally observed proportions, during day and night. By connecting the plant model to a model of its rhizobial symbiont, Sinorhizobium meliloti, we were able to investigate the effects of the symbiosis on metabolic fluxes and plant growth and could demonstrate how oxygen availability influences metabolic exchanges between plant and symbiont, thus elucidating potential benefits of inter organism amino acid cycling. We thus provide a modelling framework, in which the interlinked metabolism of plants and nodules can be studied from a theoretical perspective.
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Affiliation(s)
- Thomas Pfau
- Institute of Complex Systems and Mathematical Biology, University of Aberdeen, Aberdeen, UK
- Life Sciences Research Unit, University of Luxembourg, Belvaux, Luxembourg
| | - Nils Christian
- Institute of Complex Systems and Mathematical Biology, University of Aberdeen, Aberdeen, UK
| | - Shyam K Masakapalli
- School of Basic Sciences, Indian Institute of Technology Mandi, Mandi, India
| | - Lee J Sweetlove
- Department of Plant Sciences, University of Oxford, Oxford, UK
| | - Mark G Poolman
- Department Biological and Medical Sciences, Oxford Brookes University, Oxford, UK
| | - Oliver Ebenhöh
- Institute of Quantitative and Theoretical Biology, Cluster of Excellence on Plant Sciences CEPLAS, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany.
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16
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Cortopassi WA, Celmar Costa Franca T, Krettli AU. A systems biology approach to antimalarial drug discovery. Expert Opin Drug Discov 2018; 13:617-626. [DOI: 10.1080/17460441.2018.1471056] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Affiliation(s)
- Wilian Augusto Cortopassi
- Department of Pharmaceutical Chemistry, University of California, San Francisco (UCSF), San Francisco, CA, USA
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17
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Cvitanović T, Reichert MC, Moškon M, Mraz M, Lammert F, Rozman D. Large-scale computational models of liver metabolism: How far from the clinics? Hepatology 2017; 66:1323-1334. [PMID: 28520105 DOI: 10.1002/hep.29268] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/02/2016] [Revised: 03/31/2017] [Accepted: 05/11/2017] [Indexed: 12/17/2022]
Abstract
Understanding the dynamics of human liver metabolism is fundamental for effective diagnosis and treatment of liver diseases. This knowledge can be obtained with systems biology/medicine approaches that account for the complexity of hepatic responses and their systemic consequences in other organs. Computational modeling can reveal hidden principles of the system by classification of individual components, analyzing their interactions and simulating the effects that are difficult to investigate experimentally. Herein, we review the state-of-the-art computational models that describe liver dynamics from metabolic, gene regulatory, and signal transduction perspectives. We focus especially on large-scale liver models described either by genome scale metabolic networks or an object-oriented approach. We also discuss the benefits and limitations of each modeling approach and their value for clinical applications in diagnosis, therapy, and prevention of liver diseases as well as precision medicine in hepatology. (Hepatology 2017;66:1323-1334).
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Affiliation(s)
- Tanja Cvitanović
- Centre for Functional Genomics and Bio-Chips, Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Matthias C Reichert
- Department of Medicine II, Saarland University Medical Center, Saarland University, Homburg, Germany
| | - Miha Moškon
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
| | - Miha Mraz
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
| | - Frank Lammert
- Department of Medicine II, Saarland University Medical Center, Saarland University, Homburg, Germany
| | - Damjana Rozman
- Centre for Functional Genomics and Bio-Chips, Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
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18
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Aziz MA, Yousef Z, Saleh AM, Mohammad S, Al Knawy B. Towards personalized medicine of colorectal cancer. Crit Rev Oncol Hematol 2017; 118:70-78. [PMID: 28917272 DOI: 10.1016/j.critrevonc.2017.08.007] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2016] [Revised: 04/18/2017] [Accepted: 08/21/2017] [Indexed: 02/07/2023] Open
Abstract
Efforts in colorectal cancer (CRC) research aim to improve early detection and treatment for metastatic stages which could translate into better prognosis of this disease. One of the major challenges that hinder these efforts is the heterogeneous nature of CRC and involvement of diverse molecular pathways. New large-scale 'omics' technologies are making it possible to generate, analyze and interpret biological data from molecular determinants of CRC. The developments of sophisticated computational analyses would allow information from different omics platforms to be integrated, thus providing new insights into the biology of CRC. Together, these technological advances and an improved mechanistic understanding might allow CRC to be clinically managed at the level of the individual patient. This review provides an account of the current challenges in CRC management and an insight into how new technologies could allow the development of personalized medicine for CRC.
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Affiliation(s)
- Mohammad Azhar Aziz
- King Abdullah International Medical Research Center [KAIMRC], King Saud Bin Abdulaziz University for Health Sciences, Colorectal Cancer Research Program, National Guard Health Affairs, P.O. Box 22490, Riyadh 11426, Saudi Arabia.
| | - Zeyad Yousef
- King Abdullah International Medical Research Center [KAIMRC], King Saud Bin Abdulaziz University for Health Sciences, Department of Surgery, National Guard Health Affairs, P.O. Box 22490, Riyadh 11426, Saudi Arabia.
| | - Ayman M Saleh
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, King Saud Bin Abdulaziz University for Health Sciences, King Abdulaziz Medical City, National Guard Health Affairs, Mail Code 6610, P. O. Box 9515 Jeddah 21423, Saudi Arabia; King Abdullah International Medical Research Center [KAIMRC], King Abdulaziz Medical City, National Guard Health Affairs, P. O. Box 9515, Jeddah 21423, Saudi Arabia.
| | - Sameer Mohammad
- King Abdullah International Medical Research Center [KAIMRC], King Saud Bin Abdulaziz University for Health Sciences, Department of Experimental Medicine, National Guard Health Affairs, P.O. Box 22490, Riyadh 11426, Saudi Arabia.
| | - Bandar Al Knawy
- King Abdullah International Medical Research Center [KAIMRC], King Saud Bin Abdulaziz University for Health Sciences, Office of the Chief Executive Officer, National Guard Health Affairs, P.O. Box 22490, Riyadh 11426, Saudi Arabia.
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Rejc Ž, Magdevska L, Tršelič T, Osolin T, Vodopivec R, Mraz J, Pavliha E, Zimic N, Cvitanović T, Rozman D, Moškon M, Mraz M. Computational modelling of genome-scale metabolic networks and its application to CHO cell cultures. Comput Biol Med 2017; 88:150-160. [PMID: 28732234 DOI: 10.1016/j.compbiomed.2017.07.005] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2017] [Revised: 07/04/2017] [Accepted: 07/05/2017] [Indexed: 01/30/2023]
Abstract
Genome-scale metabolic models (GEMs) have become increasingly important in recent years. Currently, GEMs are the most accurate in silico representation of the genotype-phenotype link. They allow us to study complex networks from the systems perspective. Their application may drastically reduce the amount of experimental and clinical work, improve diagnostic tools and increase our understanding of complex biological phenomena. GEMs have also demonstrated high potential for the optimisation of bio-based production of recombinant proteins. Herein, we review the basic concepts, methods, resources and software tools used for the reconstruction and application of GEMs. We overview the evolution of the modelling efforts devoted to the metabolism of Chinese Hamster Ovary (CHO) cells. We present a case study on CHO cell metabolism under different amino acid depletions. This leads us to the identification of the most influential as well as essential amino acids in selected CHO cell lines.
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Affiliation(s)
- Živa Rejc
- Faculty of Chemistry and Chemical Technology, University of Ljubljana, Ljubljana, Slovenia
| | - Lidija Magdevska
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
| | - Tilen Tršelič
- Faculty of Chemistry and Chemical Technology, University of Ljubljana, Ljubljana, Slovenia
| | - Timotej Osolin
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
| | - Rok Vodopivec
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
| | - Jakob Mraz
- Biotechnical Faculty, University of Ljubljana, Ljubljana, Slovenia
| | - Eva Pavliha
- Biotechnical Faculty, University of Ljubljana, Ljubljana, Slovenia
| | - Nikolaj Zimic
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
| | - Tanja Cvitanović
- Centre for Functional Genomics and Bio-Chips, Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Damjana Rozman
- Centre for Functional Genomics and Bio-Chips, Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Miha Moškon
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia.
| | - Miha Mraz
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
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20
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Cashman M, Catlett JL, Cohen MB, Buan NR, Sakkaff Z, Pierobon M, Kelley CA. BioSIMP: Using Software Testing Techniques for Sampling and Inference in Biological Organisms. SE4SCIENCE 2017 : 2017 IEEE/ACM 12TH INTERNATIONAL WORKSHOP ON SOFTWARE ENGINEERING FOR SCIENCE : PROCEEDINGS : 22 MAY 2017, BUENOS AIRES, ARGENTINA. INTERNATIONAL WORKSHOP ON SOFTWARE ENGINEERING FOR SCIENCE (2017 : BUENOS AIRES, ARGEN... 2017; 2017:2-8. [PMID: 36848304 PMCID: PMC9949343 DOI: 10.1109/se4science.2017.9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Years of research in software engineering has given us novel ways to reason about, test, and predict the behavior of complex software systems that contain hundreds of thousands of lines of code. Many of these techniques have been inspired by nature such as genetic algorithms, swarm intelligence, and ant colony optimization. In this paper we reverse the direction and present BioSIMP, a process that models and predicts the behavior of biological organisms to aid in the emerging field of systems biology. It utilizes techniques from testing and modeling of highly-configurable software systems. Using both experimental and simulation data we show that BioSIMP can find important environmental factors in two microbial organisms. However, we learn that in order to fully reason about the complexity of biological systems, we will need to extend existing or create new software engineering techniques.
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Affiliation(s)
- Mikaela Cashman
- Dept. of Computer Science & Engineering, University of Nebraksa-Lincoln, Lincoln, NE, USA
| | - Jennie L Catlett
- Dept. of Biochemistry, University of Nebraksa-Lincoln, Lincoln, NE, USA
| | - Myra B Cohen
- Dept. of Computer Science & Engineering, University of Nebraksa-Lincoln, Lincoln, NE, USA
| | - Nicole R Buan
- Dept. of Biochemistry, University of Nebraksa-Lincoln, Lincoln, NE, USA
| | - Zahmeeth Sakkaff
- Dept. of Computer Science & Engineering, University of Nebraksa-Lincoln, Lincoln, NE, USA
| | - Massimiliano Pierobon
- Dept. of Computer Science & Engineering, University of Nebraksa-Lincoln, Lincoln, NE, USA
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21
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Martín-Jiménez CA, Salazar-Barreto D, Barreto GE, González J. Genome-Scale Reconstruction of the Human Astrocyte Metabolic Network. Front Aging Neurosci 2017; 9:23. [PMID: 28243200 PMCID: PMC5303712 DOI: 10.3389/fnagi.2017.00023] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2016] [Accepted: 01/27/2017] [Indexed: 12/22/2022] Open
Abstract
Astrocytes are the most abundant cells of the central nervous system; they have a predominant role in maintaining brain metabolism. In this sense, abnormal metabolic states have been found in different neuropathological diseases. Determination of metabolic states of astrocytes is difficult to model using current experimental approaches given the high number of reactions and metabolites present. Thus, genome-scale metabolic networks derived from transcriptomic data can be used as a framework to elucidate how astrocytes modulate human brain metabolic states during normal conditions and in neurodegenerative diseases. We performed a Genome-Scale Reconstruction of the Human Astrocyte Metabolic Network with the purpose of elucidating a significant portion of the metabolic map of the astrocyte. This is the first global high-quality, manually curated metabolic reconstruction network of a human astrocyte. It includes 5,007 metabolites and 5,659 reactions distributed among 8 cell compartments, (extracellular, cytoplasm, mitochondria, endoplasmic reticle, Golgi apparatus, lysosome, peroxisome and nucleus). Using the reconstructed network, the metabolic capabilities of human astrocytes were calculated and compared both in normal and ischemic conditions. We identified reactions activated in these two states, which can be useful for understanding the astrocytic pathways that are affected during brain disease. Additionally, we also showed that the obtained flux distributions in the model, are in accordance with literature-based findings. Up to date, this is the most complete representation of the human astrocyte in terms of inclusion of genes, proteins, reactions and metabolic pathways, being a useful guide for in-silico analysis of several metabolic behaviors of the astrocyte during normal and pathologic states.
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Affiliation(s)
- Cynthia A Martín-Jiménez
- Departamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad Javeriana Bogotá, Colombia
| | - Diego Salazar-Barreto
- Departamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad Javeriana Bogotá, Colombia
| | - George E Barreto
- Departamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad JaverianaBogotá, Colombia; Instituto de Ciencias Biomédicas, Universidad Autónoma de ChileSantiago, Chile
| | - Janneth González
- Departamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad Javeriana Bogotá, Colombia
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22
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Reconstruction of the Fatty Acid Biosynthetic Pathway of Exiguobacterium antarcticum B7 Based on Genomic and Bibliomic Data. BIOMED RESEARCH INTERNATIONAL 2016; 2016:7863706. [PMID: 27595107 PMCID: PMC4993939 DOI: 10.1155/2016/7863706] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/10/2015] [Accepted: 06/16/2016] [Indexed: 11/23/2022]
Abstract
Exiguobacterium antarcticum B7 is extremophile Gram-positive bacteria able to survive in cold environments. A key factor to understanding cold adaptation processes is related to the modification of fatty acids composing the cell membranes of psychrotrophic bacteria. In our study we show the in silico reconstruction of the fatty acid biosynthesis pathway of E. antarcticum B7. To build the stoichiometric model, a semiautomatic procedure was applied, which integrates genome information using KEGG and RAST/SEED. Constraint-based methods, namely, Flux Balance Analysis (FBA) and elementary modes (EM), were applied. FBA was implemented in the sense of hexadecenoic acid production maximization. To evaluate the influence of the gene expression in the fluxome analysis, FBA was also calculated using the log2FC values obtained in the transcriptome analysis at 0°C and 37°C. The fatty acid biosynthesis pathway showed a total of 13 elementary flux modes, four of which showed routes for the production of hexadecenoic acid. The reconstructed pathway demonstrated the capacity of E. antarcticum B7 to de novo produce fatty acid molecules. Under the influence of the transcriptome, the fluxome was altered, promoting the production of short-chain fatty acids. The calculated models contribute to better understanding of the bacterial adaptation at cold environments.
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23
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Klanchui A, Raethong N, Prommeenate P, Vongsangnak W, Meechai A. Cyanobacterial Biofuels: Strategies and Developments on Network and Modeling. ADVANCES IN BIOCHEMICAL ENGINEERING/BIOTECHNOLOGY 2016; 160:75-102. [PMID: 27783135 DOI: 10.1007/10_2016_42] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Cyanobacteria, the phototrophic microorganisms, have attracted much attention recently as a promising source for environmentally sustainable biofuels production. However, barriers for commercial markets of cyanobacteria-based biofuels concern the economic feasibility. Miscellaneous strategies for improving the production performance of cyanobacteria have thus been developed. Among these, the simple ad hoc strategies resulting in failure to optimize fully cell growth coupled with desired product yield are explored. With the advancement of genomics and systems biology, a new paradigm toward systems metabolic engineering has been recognized. In particular, a genome-scale metabolic network reconstruction and modeling is a crucial systems-based tool for whole-cell-wide investigation and prediction. In this review, the cyanobacterial genome-scale metabolic models, which offer a system-level understanding of cyanobacterial metabolism, are described. The main process of metabolic network reconstruction and modeling of cyanobacteria are summarized. Strategies and developments on genome-scale network and modeling through the systems metabolic engineering approach are advanced and employed for efficient cyanobacterial-based biofuels production.
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Affiliation(s)
- Amornpan Klanchui
- Biological Engineering Program, Faculty of Engineering, King Mongkut's University of Technology Thonburi, Bangkok, 10140, Thailand
| | - Nachon Raethong
- Interdisciplinary Graduate Program in Bioscience, Faculty of Science, Kasetsart University, Bangkok, 10900, Thailand
| | - Peerada Prommeenate
- Biochemical Engineering and Pilot Plant Research and Development (BEC) Unit, National Center for Genetic Engineering and Biotechnology, National Science and Technology Development Agency, King Mongkut's University of Technology Thonburi, Bangkok, 10150, Thailand
| | - Wanwipa Vongsangnak
- Department of Zoology, Faculty of Science, Kasetsart University, Bangkok, 10900, Thailand.,Computational Biomodelling Laboratory for Agricultural Science and Technology (CBLAST), Faculty of Science, Kasetsart University, Bangkok, 10900, Thailand
| | - Asawin Meechai
- Department of Chemical Engineering, Faculty of Engineering, King Mongkut's University of Technology Thonburi, Bangkok, 10140, Thailand.
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24
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Ates O. Systems Biology of Microbial Exopolysaccharides Production. Front Bioeng Biotechnol 2015; 3:200. [PMID: 26734603 PMCID: PMC4683990 DOI: 10.3389/fbioe.2015.00200] [Citation(s) in RCA: 160] [Impact Index Per Article: 17.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2015] [Accepted: 11/30/2015] [Indexed: 11/23/2022] Open
Abstract
Exopolysaccharides (EPSs) produced by diverse group of microbial systems are rapidly emerging as new and industrially important biomaterials. Due to their unique and complex chemical structures and many interesting physicochemical and rheological properties with novel functionality, the microbial EPSs find wide range of commercial applications in various fields of the economy such as food, feed, packaging, chemical, textile, cosmetics and pharmaceutical industry, agriculture, and medicine. EPSs are mainly associated with high-value applications, and they have received considerable research attention over recent decades with their biocompatibility, biodegradability, and both environmental and human compatibility. However, only a few microbial EPSs have achieved to be used commercially due to their high production costs. The emerging need to overcome economic hurdles and the increasing significance of microbial EPSs in industrial and medical biotechnology call for the elucidation of the interrelations between metabolic pathways and EPS biosynthesis mechanism in order to control and hence enhance its microbial productivity. Moreover, a better understanding of biosynthesis mechanism is a significant issue for improvement of product quality and properties and also for the design of novel strains. Therefore, a systems-based approach constitutes an important step toward understanding the interplay between metabolism and EPS biosynthesis and further enhances its metabolic performance for industrial application. In this review, primarily the microbial EPSs, their biosynthesis mechanism, and important factors for their production will be discussed. After this brief introduction, recent literature on the application of omics technologies and systems biology tools for the improvement of production yields will be critically evaluated. Special focus will be given to EPSs with high market value such as xanthan, levan, pullulan, and dextran.
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Affiliation(s)
- Ozlem Ates
- Department of Medical Services and Techniques, Nisantasi University, Istanbul, Turkey
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25
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Polettini M, Wachtel A, Esposito M. Dissipation in noisy chemical networks: The role of deficiency. J Chem Phys 2015; 143:184103. [DOI: 10.1063/1.4935064] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Affiliation(s)
- M. Polettini
- Complex Systems and Statistical Mechanics, Physics and Materials Science Research Unit, University of Luxembourg, 162a Avenue de la Faïencerie, Luxembourg L-1511, Luxembourg
| | - A. Wachtel
- Complex Systems and Statistical Mechanics, Physics and Materials Science Research Unit, University of Luxembourg, 162a Avenue de la Faïencerie, Luxembourg L-1511, Luxembourg
| | - M. Esposito
- Complex Systems and Statistical Mechanics, Physics and Materials Science Research Unit, University of Luxembourg, 162a Avenue de la Faïencerie, Luxembourg L-1511, Luxembourg
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26
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Rolfsson Ó, Palsson BO. Decoding the jargon of bottom-up metabolic systems biology. Bioessays 2015; 37:588-91. [PMID: 25761171 DOI: 10.1002/bies.201400187] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Óttar Rolfsson
- Center for Systems Biology, University of Iceland, Reykjavik, Iceland.,University of Iceland Biomedical Center, Reykjavik, Iceland
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27
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Gomes D, Aguiar TQ, Dias O, Ferreira EC, Domingues L, Rocha I. Genome-wide metabolic re-annotation of Ashbya gossypii: new insights into its metabolism through a comparative analysis with Saccharomyces cerevisiae and Kluyveromyces lactis. BMC Genomics 2014; 15:810. [PMID: 25253284 PMCID: PMC4190384 DOI: 10.1186/1471-2164-15-810] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2014] [Accepted: 08/15/2014] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Ashbya gossypii is an industrially relevant microorganism traditionally used for riboflavin production. Despite the high gene homology and gene order conservation comparatively with Saccharomyces cerevisiae, it presents a lower level of genomic complexity. Its type of growth, placing it among filamentous fungi, questions how close it really is from the budding yeast, namely in terms of metabolism, therefore raising the need for an extensive and thorough study of its entire metabolism. This work reports the first manual enzymatic genome-wide re-annotation of A. gossypii as well as the first annotation of membrane transport proteins. RESULTS After applying a developed enzymatic re-annotation pipeline, 847 genes were assigned with metabolic functions. Comparatively to KEGG's annotation, these data corrected the function for 14% of the common genes and increased the information for 52 genes, either completing existing partial EC numbers or adding new ones. Furthermore, 22 unreported enzymatic functions were found, corresponding to a significant increase in the knowledge of the metabolism of this organism. The information retrieved from the metabolic re-annotation and transport annotation was used for a comprehensive analysis of A. gossypii's metabolism in comparison to the one of S. cerevisiae (post-WGD - whole genome duplication) and Kluyveromyces lactis (pre-WGD), suggesting some relevant differences in several parts of their metabolism, with the majority being found for the metabolism of purines, pyrimidines, nitrogen and lipids. A considerable number of enzymes were found exclusively in A. gossypii comparatively with K. lactis (90) and S. cerevisiae (13). In a similar way, 176 and 123 enzymatic functions were absent on A. gossypii comparatively to K. lactis and S. cerevisiae, respectively, confirming some of the well-known phenotypes of this organism. CONCLUSIONS This high quality metabolic re-annotation, together with the first membrane transporters annotation and the metabolic comparative analysis, represents a new important tool for the study and better understanding of A. gossypii's metabolism.
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Affiliation(s)
- Daniel Gomes
- CEB - Centre of Biological Engineering, Universidade do Minho, Campus de Gualtar, 4710-057 Braga, Portugal
| | - Tatiana Q Aguiar
- CEB - Centre of Biological Engineering, Universidade do Minho, Campus de Gualtar, 4710-057 Braga, Portugal
| | - Oscar Dias
- CEB - Centre of Biological Engineering, Universidade do Minho, Campus de Gualtar, 4710-057 Braga, Portugal
| | - Eugénio C Ferreira
- CEB - Centre of Biological Engineering, Universidade do Minho, Campus de Gualtar, 4710-057 Braga, Portugal
| | - Lucília Domingues
- CEB - Centre of Biological Engineering, Universidade do Minho, Campus de Gualtar, 4710-057 Braga, Portugal
| | - Isabel Rocha
- CEB - Centre of Biological Engineering, Universidade do Minho, Campus de Gualtar, 4710-057 Braga, Portugal
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28
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Price AH, Norton GJ, Salt DE, Ebenhoeh O, Meharg AA, Meharg C, Islam MR, Sarma RN, Dasgupta T, Ismail AM, McNally KL, Zhang H, Dodd IC, Davies WJ. Alternate wetting and drying irrigation for rice in Bangladesh: Is it sustainable and has plant breeding something to offer? Food Energy Secur 2013. [DOI: 10.1002/fes3.29] [Citation(s) in RCA: 57] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Affiliation(s)
- Adam H. Price
- Institute of Biological and Environmental Science University of Aberdeen AB24 3UU Aberdeen U.K
| | - Gareth J. Norton
- Institute of Biological and Environmental Science University of Aberdeen AB24 3UU Aberdeen U.K
| | - David E. Salt
- Institute of Biological and Environmental Science University of Aberdeen AB24 3UU Aberdeen U.K
| | - Oliver Ebenhoeh
- Institute of Complex Systems and Mathematical Biology Department of Physics University of Aberdeen Aberdeen AB24 3UE U.K
| | - Andrew A. Meharg
- Institute for Global Food Security Queen's University Belfast David Keir Building Malone Road Belfast BT9 5BN U.K
| | - Caroline Meharg
- Institute for Global Food Security Queen's University Belfast David Keir Building Malone Road Belfast BT9 5BN U.K
| | - M. Rafiqul Islam
- Department of Soil Science Bangladesh Agricultural University Mymensingh Bangladesh
| | - Ramen N. Sarma
- Department of Plant Breeding and Genetics Assam Agricultural University Jorhat 785013 Assam India
| | - Tapash Dasgupta
- Department of Genetics and Plant Breeding Calcutta University 35 B.C. Road Kolkata 700 019 West Bengal India
| | - Abdelbagi M. Ismail
- International Rice Research Institute (IRRI) DAPO 7777 Metro Manila 1031 The Philippines
| | - Kenneth L. McNally
- International Rice Research Institute (IRRI) DAPO 7777 Metro Manila 1031 The Philippines
| | - Hao Zhang
- Lancaster Environment Centre Lancaster University Lancaster LA1 4YQ U.K
| | - Ian C. Dodd
- Centre for Sustainable Agriculture Lancaster Environment Centre Lancaster University Lancaster LA1 4YQ U.K
| | - William J. Davies
- Centre for Sustainable Agriculture Lancaster Environment Centre Lancaster University Lancaster LA1 4YQ U.K
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29
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Arakawa K, Tomita M. Merging multiple omics datasets in silico: statistical analyses and data interpretation. Methods Mol Biol 2013; 985:459-70. [PMID: 23417818 DOI: 10.1007/978-1-62703-299-5_23] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
By the combinations of high-throughput analytical technologies in the fields of transcriptomics, proteomics, and metabolomics, we are now able to gain comprehensive and quantitative snapshots of the intracellular processes. Dynamic intracellular activities and their regulations can be elucidated by systematic observation of these multi-omics data. On the other hand, careful statistical analysis is necessary for such integration, since each of the omics layers as well as the specific analytical methodologies harbor different levels of noise and variations. Moreover, interpretation of such multitude of data requires an intuitive pathway context. Here we describe such statistical methods for the integration and comparison of multi-omics data, as well as the computational methods for pathway reconstruction, ID conversion, mapping, and visualization that play key roles for the efficient study of multi-omics information.
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Affiliation(s)
- Kazuharu Arakawa
- Institute for Advanced Biosciences, Keio University, Fujisawa, Kanagawa, Japan.
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30
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Xu C, Liu L, Zhang Z, Jin D, Qiu J, Chen M. Genome-scale metabolic model in guiding metabolic engineering of microbial improvement. Appl Microbiol Biotechnol 2012. [DOI: 10.1007/s00253-012-4543-9] [Citation(s) in RCA: 35] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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31
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Bernard T, Bridge A, Morgat A, Moretti S, Xenarios I, Pagni M. Reconciliation of metabolites and biochemical reactions for metabolic networks. Brief Bioinform 2012; 15:123-35. [PMID: 23172809 PMCID: PMC3896926 DOI: 10.1093/bib/bbs058] [Citation(s) in RCA: 50] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Genome-scale metabolic network reconstructions are now routinely used in the study of metabolic pathways, their evolution and design. The development of such reconstructions involves the integration of information on reactions and metabolites from the scientific literature as well as public databases and existing genome-scale metabolic models. The reconciliation of discrepancies between data from these sources generally requires significant manual curation, which constitutes a major obstacle in efforts to develop and apply genome-scale metabolic network reconstructions. In this work, we discuss some of the major difficulties encountered in the mapping and reconciliation of metabolic resources and review three recent initiatives that aim to accelerate this process, namely BKM-react, MetRxn and MNXref (presented in this article). Each of these resources provides a pre-compiled reconciliation of many of the most commonly used metabolic resources. By reducing the time required for manual curation of metabolite and reaction discrepancies, these resources aim to accelerate the development and application of high-quality genome-scale metabolic network reconstructions and models.
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32
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Chagoyen M, Pazos F. Tools for the functional interpretation of metabolomic experiments. Brief Bioinform 2012; 14:737-44. [DOI: 10.1093/bib/bbs055] [Citation(s) in RCA: 47] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
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