Reconstructing static and dynamic models of signaling pathways using Modular Response Analysis

Abstract In this review we discuss the origination and evolution of Modular Response Analysis (MRA), which is a physics-based method for reconstructing quantitative topological models of biochemical pathways. We first focus on the core theory of MRA, demonstrating how both the direction and the strength of local, causal connections between network modules can be precisely inferred from the global responses of the entire network to a sufficient number of perturbations, under certain conditions. Subsequently, we analyze statistical reformulations of MRA and show how MRA is used to build and calibrate mechanistic models of biological networks. We further discuss what sets MRA apart from other network reconstruction methods and outline future directions for MRA-based methods of network reconstruction.

[1]  Tapesh Santra,et al.  Integrating Bayesian variable selection with Modular Response Analysis to infer biochemical network topology , 2013, BMC Systems Biology.

[2]  Boris N. Kholodenko,et al.  Untangling the signalling wires , 2007, Nature Cell Biology.

[3]  Bertram Klinger,et al.  Reverse engineering a hierarchical regulatory network downstream of oncogenic KRAS , 2012, Molecular systems biology.

[4]  Sach Mukherjee,et al.  Network inference using informative priors , 2008, Proceedings of the National Academy of Sciences.

[5]  H. Resat,et al.  EGFR signaling pathways are wired differently in normal 184A1L5 human mammary epithelial and MDA-MB-231 breast cancer cells , 2017, Journal of cell communication and signaling.

[6]  A Kriete,et al.  Quantifying gene network connectivity in silico: scalability and accuracy of a modular approach. , 2006, Systems biology.

[7]  Neda Bagheri,et al.  The DIONESUS algorithm provides scalable and accurate reconstruction of dynamic phosphoproteomic networks to reveal new drug targets. , 2015, Integrative biology : quantitative biosciences from nano to macro.

[8]  Eduardo Sontag,et al.  Untangling the wires: A strategy to trace functional interactions in signaling and gene networks , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[9]  H. Shankaran,et al.  Integrated analysis reveals that STAT3 is central to the crosstalk between HER/ErbB receptor signaling pathways in human mammary epithelial cells. , 2015, Molecular bioSystems.

[10]  D. Botstein,et al.  Cluster analysis and display of genome-wide expression patterns. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[11]  Muriel Médard,et al.  Network deconvolution as a general method to distinguish direct dependencies in networks , 2013, Nature Biotechnology.

[12]  C. Eckart,et al.  The approximation of one matrix by another of lower rank , 1936 .

[13]  Steffen Klamt,et al.  Silence on the relevant literature and errors in implementation , 2015, Nature Biotechnology.

[14]  Julio R. Banga,et al.  Reverse engineering and identification in systems biology: strategies, perspectives and challenges , 2014, Journal of The Royal Society Interface.

[15]  Bertram Klinger,et al.  Supplementary Materials for : Network quantification of EGFR signaling unveils potential for targeted combination therapy , 2022 .

[16]  Gene H. Golub,et al.  Matrix computations , 1983 .

[17]  Alberto de la Fuente,et al.  Discovery of meaningful associations in genomic data using partial correlation coefficients , 2004, Bioinform..

[18]  B. Kholodenko,et al.  Quantification of information transfer via cellular signal transduction pathways , 1997, FEBS letters.

[19]  Pei Wang,et al.  Integrative random forest for gene regulatory network inference , 2015, Bioinform..

[20]  J. Collins,et al.  Inferring Genetic Networks and Identifying Compound Mode of Action via Expression Profiling , 2003, Science.

[21]  Luonan Chen,et al.  Data-based prediction and causality inference of nonlinear dynamics , 2017, Science China Mathematics.

[22]  Tapesh Santra,et al.  Integrating network reconstruction with mechanistic modeling to predict cancer therapies , 2016, Science Signaling.

[23]  D M Tartakovsky,et al.  Comparison of statistical and optimisation-based methods for data-driven network reconstruction of biochemical systems. , 2012, IET systems biology.

[24]  Chris Wiggins,et al.  ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks in a Mammalian Cellular Context , 2004, BMC Bioinformatics.

[25]  William H. Press,et al.  The Art of Scientific Computing Second Edition , 1998 .

[26]  Boris N Kholodenko,et al.  Toggle switches, pulses and oscillations are intrinsic properties of the Src activation/deactivation cycle , 2009, The FEBS journal.

[27]  P. Geurts,et al.  Inferring Regulatory Networks from Expression Data Using Tree-Based Methods , 2010, PloS one.

[28]  B N Kholodenko,et al.  Why do protein kinase cascades have more than one level? , 1997, Trends in biochemical sciences.

[29]  Eduardo D. Sontag,et al.  Inferring dynamic architecture of cellular networks using time series of gene expression, protein and metabolite data , 2004, Bioinform..

[30]  Satoru Miyano,et al.  WEIGHTED LASSO IN GRAPHICAL GAUSSIAN MODELING FOR LARGE GENE NETWORK ESTIMATION BASED ON MICROARRAY DATA , 2007 .

[31]  Eduardo Sontag,et al.  Inference of signaling and gene regulatory networks by steady-state perturbation experiments: structure and accuracy. , 2005, Journal of theoretical biology.

[32]  B. Kholodenko,et al.  Rate limitation within a single enzyme is directly related to enzyme intermediate levels , 1994, FEBS letters.

[33]  A. T. Vasconcelos,et al.  Genome-wide partial correlation analysis of Escherichia coli microarray data. , 2007, Genetics and molecular research : GMR.

[34]  Chris Sander,et al.  A Multi-Method Approach for Proteomic Network Inference in 11 Human Cancers , 2015, bioRxiv.

[35]  Tapesh Santra Fitting mathematical models of biochemical pathways to steady state perturbation response data without simulating perturbation experiments , 2017 .

[36]  R. Tibshirani Regression Shrinkage and Selection via the Lasso , 1996 .

[37]  Sabine Van Huffel,et al.  The total least squares problem , 1993 .

[38]  D. Koshland,et al.  An amplified sensitivity arising from covalent modification in biological systems. , 1981, Proceedings of the National Academy of Sciences of the United States of America.

[39]  K. Sachs,et al.  Causal Protein-Signaling Networks Derived from Multiparameter Single-Cell Data , 2005, Science.

[40]  Joshua M. Stuart,et al.  A Gene-Coexpression Network for Global Discovery of Conserved Genetic Modules , 2003, Science.

[41]  P. Bastiaens,et al.  Growth factor-induced MAPK network topology shapes Erk response determining PC-12 cell fate , 2007, Nature Cell Biology.

[42]  P. Brazhnik,et al.  Linking the genes: inferring quantitative gene networks from microarray data. , 2002, Trends in genetics : TIG.