1
|
Bai J, Wu H, Cao J. Topology identification for fractional complex networks with synchronization in finite time based on adaptive observers and event-triggered control. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.07.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
|
2
|
Jang H, Kim KKK, Braatz RD, Gopaluni RB, Lee JH. Regularized maximum likelihood estimation of sparse stochastic monomolecular biochemical reaction networks. Comput Chem Eng 2016. [DOI: 10.1016/j.compchemeng.2016.03.018] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
|
3
|
Guner U, Jang H, Realff MJ, Lee JH. An Extended Constrained Total Least-Squares Method for the Identification of Genetic Networks from Noisy Measurements. Ind Eng Chem Res 2015. [DOI: 10.1021/acs.iecr.5b01418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Ugur Guner
- School
of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Hong Jang
- Department
of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 305-701, Republic of Korea
| | - Matthew J. Realff
- School
of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Jay H. Lee
- Department
of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 305-701, Republic of Korea
| |
Collapse
|
4
|
Lo LY, Wong ML, Lee KH, Leung KS. Time Delayed Causal Gene Regulatory Network Inference with Hidden Common Causes. PLoS One 2015; 10:e0138596. [PMID: 26394325 PMCID: PMC4578777 DOI: 10.1371/journal.pone.0138596] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2015] [Accepted: 09/01/2015] [Indexed: 01/07/2023] Open
Abstract
Inferring the gene regulatory network (GRN) is crucial to understanding the working of the cell. Many computational methods attempt to infer the GRN from time series expression data, instead of through expensive and time-consuming experiments. However, existing methods make the convenient but unrealistic assumption of causal sufficiency, i.e. all the relevant factors in the causal network have been observed and there are no unobserved common cause. In principle, in the real world, it is impossible to be certain that all relevant factors or common causes have been observed, because some factors may not have been conceived of, and therefore are impossible to measure. In view of this, we have developed a novel algorithm named HCC-CLINDE to infer an GRN from time series data allowing the presence of hidden common cause(s). We assume there is a sparse causal graph (possibly with cycles) of interest, where the variables are continuous and each causal link has a delay (possibly more than one time step). A small but unknown number of variables are not observed. Each unobserved variable has only observed variables as children and parents, with at least two children, and the children are not linked to each other. Since it is difficult to obtain very long time series, our algorithm is also capable of utilizing multiple short time series, which is more realistic. To our knowledge, our algorithm is far less restrictive than previous works. We have performed extensive experiments using synthetic data on GRNs of size up to 100, with up to 10 hidden nodes. The results show that our algorithm can adequately recover the true causal GRN and is robust to slight deviation from Gaussian distribution in the error terms. We have also demonstrated the potential of our algorithm on small YEASTRACT subnetworks using limited real data.
Collapse
Affiliation(s)
- Leung-Yau Lo
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, Hong Kong
- * E-mail:
| | - Man-Leung Wong
- Department of Computing and Decision Sciences, Lingnan University, Tuen Mun, Hong Kong
| | - Kin-Hong Lee
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, Hong Kong
| | - Kwong-Sak Leung
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, Hong Kong
| |
Collapse
|
5
|
Lo LY, Leung KS, Lee KH. Inferring Time-Delayed Causal Gene Network Using Time-Series Expression Data. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2015; 12:1169-1182. [PMID: 26451828 DOI: 10.1109/tcbb.2015.2394442] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Inferring gene regulatory network (GRN) from the microarray expression data is an important problem in Bioinformatics, because knowing the GRN is an essential first step in understanding the inner workings of the cell and the related diseases. Time delays exist in the regulatory effects from one gene to another due to the time needed for transcription, translation, and to accumulate a sufficient number of needed proteins. Also, it is known that the delays are important for oscillatory phenomenon. Therefore, it is crucial to develop a causal gene network model, preferably as a function of time. In this paper, we propose an algorithm CLINDE to infer causal directed links in GRN with time delays and regulatory effects in the links from time-series microarray gene expression data. It is one of the most comprehensive in terms of features compared to the state-of-the-art discrete gene network models. We have tested CLINDE on synthetic data, the in vivo IRMA (On and Off) datasets and the [1] yeast expression data validated using KEGG pathways. Results show that CLINDE can effectively recover the links, the time delays and the regulatory effects in the synthetic data, and outperforms other algorithms in the IRMA in vivo datasets.
Collapse
|
6
|
Hagen DR, Tidor B. Efficient Bayesian estimates for discrimination among topologically different systems biology models. MOLECULAR BIOSYSTEMS 2014; 11:574-84. [PMID: 25460000 DOI: 10.1039/c4mb00276h] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
A major effort in systems biology is the development of mathematical models that describe complex biological systems at multiple scales and levels of abstraction. Determining the topology-the set of interactions-of a biological system from observations of the system's behavior is an important and difficult problem. Here we present and demonstrate new methodology for efficiently computing the probability distribution over a set of topologies based on consistency with existing measurements. Key features of the new approach include derivation in a Bayesian framework, incorporation of prior probability distributions of topologies and parameters, and use of an analytically integrable linearization based on the Fisher information matrix that is responsible for large gains in efficiency. The new method was demonstrated on a collection of four biological topologies representing a kinase and phosphatase that operate in opposition to each other with either processive or distributive kinetics, giving 8-12 parameters for each topology. The linearization produced an approximate result very rapidly (CPU minutes) that was highly accurate on its own, as compared to a Monte Carlo method guaranteed to converge to the correct answer but at greater cost (CPU weeks). The Monte Carlo method developed and applied here used the linearization method as a starting point and importance sampling to approach the Bayesian answer in acceptable time. Other inexpensive methods to estimate probabilities produced poor approximations for this system, with likelihood estimation showing its well-known bias toward topologies with more parameters and the Akaike and Schwarz Information Criteria showing a strong bias toward topologies with fewer parameters. These results suggest that this linear approximation may be an effective compromise, providing an answer whose accuracy is near the true Bayesian answer, but at a cost near the common heuristics.
Collapse
Affiliation(s)
- David R Hagen
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
| | | |
Collapse
|
7
|
Studham ME, Tjärnberg A, Nordling TEM, Nelander S, Sonnhammer ELL. Functional association networks as priors for gene regulatory network inference. ACTA ACUST UNITED AC 2014; 30:i130-8. [PMID: 24931976 PMCID: PMC4058914 DOI: 10.1093/bioinformatics/btu285] [Citation(s) in RCA: 55] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Motivation: Gene regulatory network (GRN) inference reveals the influences genes have on one another in cellular regulatory systems. If the experimental data are inadequate for reliable inference of the network, informative priors have been shown to improve the accuracy of inferences. Results: This study explores the potential of undirected, confidence-weighted networks, such as those in functional association databases, as a prior source for GRN inference. Such networks often erroneously indicate symmetric interaction between genes and may contain mostly correlation-based interaction information. Despite these drawbacks, our testing on synthetic datasets indicates that even noisy priors reflect some causal information that can improve GRN inference accuracy. Our analysis on yeast data indicates that using the functional association databases FunCoup and STRING as priors can give a small improvement in GRN inference accuracy with biological data. Contact:matthew.studham@scilifelab.se Supplementary information:Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Matthew E Studham
- Stockholm Bioinformatics Centre, Science for Life Laboratory, SE-171 65 Solna, Sweden, Department of Biochemistry and Biophysics, Stockholm University, SE-106 91 Stockholm, Sweden, Department of Immunology, Genetics and Pathology, Uppsala University, Rudbeck Laboratory, SE-751 05 Uppsala, Sweden and Swedish eScience Research Center, SE-100 44 Stockholm, SwedenStockholm Bioinformatics Centre, Science for Life Laboratory, SE-171 65 Solna, Sweden, Department of Biochemistry and Biophysics, Stockholm University, SE-106 91 Stockholm, Sweden, Department of Immunology, Genetics and Pathology, Uppsala University, Rudbeck Laboratory, SE-751 05 Uppsala, Sweden and Swedish eScience Research Center, SE-100 44 Stockholm, Sweden
| | - Andreas Tjärnberg
- Stockholm Bioinformatics Centre, Science for Life Laboratory, SE-171 65 Solna, Sweden, Department of Biochemistry and Biophysics, Stockholm University, SE-106 91 Stockholm, Sweden, Department of Immunology, Genetics and Pathology, Uppsala University, Rudbeck Laboratory, SE-751 05 Uppsala, Sweden and Swedish eScience Research Center, SE-100 44 Stockholm, SwedenStockholm Bioinformatics Centre, Science for Life Laboratory, SE-171 65 Solna, Sweden, Department of Biochemistry and Biophysics, Stockholm University, SE-106 91 Stockholm, Sweden, Department of Immunology, Genetics and Pathology, Uppsala University, Rudbeck Laboratory, SE-751 05 Uppsala, Sweden and Swedish eScience Research Center, SE-100 44 Stockholm, Sweden
| | - Torbjörn E M Nordling
- Stockholm Bioinformatics Centre, Science for Life Laboratory, SE-171 65 Solna, Sweden, Department of Biochemistry and Biophysics, Stockholm University, SE-106 91 Stockholm, Sweden, Department of Immunology, Genetics and Pathology, Uppsala University, Rudbeck Laboratory, SE-751 05 Uppsala, Sweden and Swedish eScience Research Center, SE-100 44 Stockholm, SwedenStockholm Bioinformatics Centre, Science for Life Laboratory, SE-171 65 Solna, Sweden, Department of Biochemistry and Biophysics, Stockholm University, SE-106 91 Stockholm, Sweden, Department of Immunology, Genetics and Pathology, Uppsala University, Rudbeck Laboratory, SE-751 05 Uppsala, Sweden and Swedish eScience Research Center, SE-100 44 Stockholm, Sweden
| | - Sven Nelander
- Stockholm Bioinformatics Centre, Science for Life Laboratory, SE-171 65 Solna, Sweden, Department of Biochemistry and Biophysics, Stockholm University, SE-106 91 Stockholm, Sweden, Department of Immunology, Genetics and Pathology, Uppsala University, Rudbeck Laboratory, SE-751 05 Uppsala, Sweden and Swedish eScience Research Center, SE-100 44 Stockholm, Sweden
| | - Erik L L Sonnhammer
- Stockholm Bioinformatics Centre, Science for Life Laboratory, SE-171 65 Solna, Sweden, Department of Biochemistry and Biophysics, Stockholm University, SE-106 91 Stockholm, Sweden, Department of Immunology, Genetics and Pathology, Uppsala University, Rudbeck Laboratory, SE-751 05 Uppsala, Sweden and Swedish eScience Research Center, SE-100 44 Stockholm, SwedenStockholm Bioinformatics Centre, Science for Life Laboratory, SE-171 65 Solna, Sweden, Department of Biochemistry and Biophysics, Stockholm University, SE-106 91 Stockholm, Sweden, Department of Immunology, Genetics and Pathology, Uppsala University, Rudbeck Laboratory, SE-751 05 Uppsala, Sweden and Swedish eScience Research Center, SE-100 44 Stockholm, SwedenStockholm Bioinformatics Centre, Science for Life Laboratory, SE-171 65 Solna, Sweden, Department of Biochemistry and Biophysics, Stockholm University, SE-106 91 Stockholm, Sweden, Department of Immunology, Genetics and Pathology, Uppsala University, Rudbeck Laboratory, SE-751 05 Uppsala, Sweden and Swedish eScience Research Center, SE-100 44 Stockholm, Sweden
| |
Collapse
|
8
|
Lee TH, Lakshmanan S, Park JH, Balasubramaniam P. State estimation for genetic regulatory networks with mode-dependent leakage delays, time-varying delays, and Markovian jumping parameters. IEEE Trans Nanobioscience 2014; 12:363-75. [PMID: 25003168 DOI: 10.1109/tnb.2013.2294478] [Citation(s) in RCA: 66] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
This paper considers the state estimation problem for Markovian jumping genetic regulatory networks (GRNs) with mode-dependent leakage and time-varying delays. In order to approximate the true concentrations of the mRNA and protein, the state estimator is designed using available measurement outputs. The GRNs are composed of N modes. The system switches from one mode to another according to a Markovian chain with known transition probabilities. Based on the Lyapunov functionals, including triple integral terms, some inequalities, and a time-varying delay partitioning approach, delay-dependent criteria which employ all upper bounds of time delays of each mode are obtained in terms of linear matrix inequalities (LMIs). This guarantees that the estimation error dynamics can be globally asymptotically stable from solutions of LMIs. Finally, a numerical example is presented to demonstrate the efficiency of the proposed estimation scheme.
Collapse
|
9
|
Molinelli EJ, Korkut A, Wang W, Miller ML, Gauthier NP, Jing X, Kaushik P, He Q, Mills G, Solit DB, Pratilas CA, Weigt M, Braunstein A, Pagnani A, Zecchina R, Sander C. Perturbation biology: inferring signaling networks in cellular systems. PLoS Comput Biol 2013; 9:e1003290. [PMID: 24367245 PMCID: PMC3868523 DOI: 10.1371/journal.pcbi.1003290] [Citation(s) in RCA: 111] [Impact Index Per Article: 10.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2012] [Accepted: 08/26/2013] [Indexed: 12/16/2022] Open
Abstract
We present a powerful experimental-computational technology for inferring network models that predict the response of cells to perturbations, and that may be useful in the design of combinatorial therapy against cancer. The experiments are systematic series of perturbations of cancer cell lines by targeted drugs, singly or in combination. The response to perturbation is quantified in terms of relative changes in the measured levels of proteins, phospho-proteins and cellular phenotypes such as viability. Computational network models are derived de novo, i.e., without prior knowledge of signaling pathways, and are based on simple non-linear differential equations. The prohibitively large solution space of all possible network models is explored efficiently using a probabilistic algorithm, Belief Propagation (BP), which is three orders of magnitude faster than standard Monte Carlo methods. Explicit executable models are derived for a set of perturbation experiments in SKMEL-133 melanoma cell lines, which are resistant to the therapeutically important inhibitor of RAF kinase. The resulting network models reproduce and extend known pathway biology. They empower potential discoveries of new molecular interactions and predict efficacious novel drug perturbations, such as the inhibition of PLK1, which is verified experimentally. This technology is suitable for application to larger systems in diverse areas of molecular biology. Drugs that target specific effects of signaling proteins are promising agents for treating cancer. One of the many obstacles facing optimal drug design is inadequate quantitative understanding of the coordinated interactions between signaling proteins. De novo model inference of network or pathway models refers to the algorithmic construction of mathematical predictive models from experimental data without dependence on prior knowledge. De novo inference is difficult because of the prohibitively large number of possible sets of interactions that may or may not be consistent with observations. Our new method overcomes this difficulty by adapting a method from statistical physics, called Belief Propagation, which first calculates probabilistically the most likely interactions in the vast space of all possible solutions, then derives a set of individual, highly probable solutions in the form of executable models. In this paper, we test this method on artificial data and then apply it to model signaling pathways in a BRAF-mutant melanoma cancer cell line based on a large set of rich output measurements from a systematic set of perturbation experiments using drug combinations. Our results are in agreement with established biological knowledge, predict novel interactions, and predict efficacious drug targets that are specific to the experimental cell line and potentially to related tumors. The method has the potential, with sufficient systematic perturbation data, to model, de novo and quantitatively, the effects of hundreds of proteins on cellular responses, on a scale that is currently unreachable in diverse areas of cell biology. In a disease context, the method is applicable to the computational design of novel combination drug treatments.
Collapse
Affiliation(s)
- Evan J. Molinelli
- Computational Biology Program, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America
- Tri-Institutional Program for Computational Biology and Medicine, Weill Cornell Medical College, New York, New York, United States of America
| | - Anil Korkut
- Computational Biology Program, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America
| | - Weiqing Wang
- Computational Biology Program, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America
| | - Martin L. Miller
- Computational Biology Program, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America
| | - Nicholas P. Gauthier
- Computational Biology Program, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America
| | - Xiaohong Jing
- Computational Biology Program, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America
| | - Poorvi Kaushik
- Computational Biology Program, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America
- Tri-Institutional Program for Computational Biology and Medicine, Weill Cornell Medical College, New York, New York, United States of America
| | - Qin He
- Computational Biology Program, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America
| | - Gordon Mills
- Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - David B. Solit
- Program in Molecular Pharmacology, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America
- Human Oncology and Pathogenesis Program, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America
| | - Christine A. Pratilas
- Program in Molecular Pharmacology, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America
- Department of Pediatrics, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America
| | - Martin Weigt
- Laboratoire de Génomique des Microorganismes, Université Pierre et Marie Curie, Paris, France
| | - Alfredo Braunstein
- Politecnico di Torino and Human Genetics Foundation, HuGeF, Torino, Italy
| | - Andrea Pagnani
- Politecnico di Torino and Human Genetics Foundation, HuGeF, Torino, Italy
| | - Riccardo Zecchina
- Politecnico di Torino and Human Genetics Foundation, HuGeF, Torino, Italy
| | - Chris Sander
- Computational Biology Program, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America
- * E-mail:
| |
Collapse
|
10
|
Tjärnberg A, Nordling TE, Studham M, Sonnhammer EL. Optimal Sparsity Criteria for Network Inference. J Comput Biol 2013; 20:398-408. [DOI: 10.1089/cmb.2012.0268] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Affiliation(s)
- Andreas Tjärnberg
- Stockholm Bioinformatics Center, Science for Life Laboratory, Stockholm, Sweden
- Department of Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden
| | - Torbjörn E.M. Nordling
- Stockholm Bioinformatics Center, Science for Life Laboratory, Stockholm, Sweden
- Automatic Control Lab, School of Electrical Engineering, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Matthew Studham
- Stockholm Bioinformatics Center, Science for Life Laboratory, Stockholm, Sweden
- Department of Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden
| | - Erik L.L. Sonnhammer
- Stockholm Bioinformatics Center, Science for Life Laboratory, Stockholm, Sweden
- Department of Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden
- Swedish eScience Research Center, Stockholm, Sweden
| |
Collapse
|
11
|
Cheng L, Hou ZG, Lin Y, Tan M, Zhang WC, Wu FX. Recurrent Neural Network for Non-Smooth Convex Optimization Problems With Application to the Identification of Genetic Regulatory Networks. ACTA ACUST UNITED AC 2011; 22:714-26. [PMID: 21427022 DOI: 10.1109/tnn.2011.2109735] [Citation(s) in RCA: 116] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
|
12
|
Chang H, Richard G, Julius AA, Belta C, Amar S. An application of monotone functions decomposition to the reconstruction of gene regulatory networks. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2011; 2011:2430-2433. [PMID: 22254832 DOI: 10.1109/iembs.2011.6090676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
We describe the reconstruction of a gene regulatory network involved with the Toll-like Receptor signaling pathways. By applying our recent identification algorithm to a time series gene expression dataset, we identify regulatory interactions between genes and construct discrete-time piece-wise affine regulatory functions. Our validation shows that our model predicts the expression levels of the genes involved in the network with good accuracy.
Collapse
Affiliation(s)
- H Chang
- Dept of Mechanical Engineering, Boston University, Boston, MA 02215, USA.
| | | | | | | | | |
Collapse
|
13
|
Lai D, Yang X, Wu G, Liu Y, Nardini C. Inference of gene networks--application to Bifidobacterium. ACTA ACUST UNITED AC 2010; 27:232-7. [PMID: 21075742 DOI: 10.1093/bioinformatics/btq629] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
MOTIVATION The reliable and reproducible identification of gene interaction networks represents one of the grand challenges of both modern molecular biology and computational sciences. Approaches based on careful collection of literature data and network topological analysis, applied to unicellular organisms, have proven to offer results applicable to medical therapies. However, when little a priori knowledge is available, other approaches, not relying so strongly on previous literature, must be used. We propose here a novel algorithm (based on ordinary differential equations) able to infer the interactions occurring among genes, starting from gene expression steady state data. RESULTS The algorithm was first validated on synthetic and real benchmarks. It was then applied to the reconstruction of the core of the amino acids metabolism in Bifidobacterium longum, an essential, yet poorly known player in the human gut intestinal microbiome, known to be related to the onset of important diseases, such as metabolic syndromes. Our results show how computational approaches can offer effective tools for applications with the identification of potential new biological information. AVAILABILITY The software is available at www.bioconductor.org and at www.picb.ac.cn/ClinicalGenomicNTW/temp2.html.
Collapse
Affiliation(s)
- Darong Lai
- Key Laboratory of Computational Biology, Chinese Academy of Sciences Max Planck Institute Partner Institute for Computational Biology (CAS-MPG), Shanghai, People's Republic of China
| | | | | | | | | |
Collapse
|