As a bioengineer, I believe that building quantitative models of systems is one of the most important components of systems biology. Unlike developing mathematical models of macroscopic systems like some organs and tissues, building models of systems from molecular components is a very hard task. Challenges in building biochemical models include (1) the complexity of proteomic states and interactions, (2) integration of diverse data to infer biochemical interactions, and (3) temporal state of biochemical models. Challenges in building mathematical models include (1) incorporating statistical\/probabilistic information into analytical models, (2) utilizing qualitative constraints into mathematical models, and (3) incomplete knowledge and coarse-graining. Challenges in computational modeling are: (1) the absence of knowledge about model parameters such as rate constants, (2) local versus global concentrations of species and multiple length and time-scales, and (3) variation among different cell-types and sub-populational variability or variability among biological repeats. We have developed strategies addressing each of these challenges. In dealing with the problem of parameter estimation and optimization for biochemical network modeling, we have developed new methods involving multi-parametric sensitivity analysis and optimization with particle-swarm genetic algorithms. In order to reduce the complexity of the networks, we have developed a method that uses mixed-integer nonlinear programming for reduced order models. In building temporal networks, we have built probabilistic methods that will allow construction of time-series networks. We are also actively involved in developing stochastic kinetic modeling strategies for dealing with concentrations involving small number of molecules varying spatially in cellular locations. For all these developments, excellent data-driven networks of the GPCR and Calcium Signaling Pathway served as our models. Our kinetic modeling of the GPCR network established the ternary complex involving the receptor, G-protein and the RGS protein. This was subsequently verified with FRET experiments. Our calcium modeling led to prediction of calcium response in macrophages where components were knocked down and subsequent experiments verified our results. We also showed that single cell measurements of calcium in macrophage cells (thousands) in the Meyer laboratory in Stanford collectively fell into one of four types of curves that were reflected collectively in the measurements of whole ensemble of cells. This clearly showed that while microscopically cells differ in their behavior (concentrations of components), the collective phenotype reflects the \u201cstates\u201d of cells which are finite, at least in a coarse-grained description. We then asked the question, how can we investigate local concentration of components in cells especially if these are small numbers of molecules? We developed a novel hybrid method that uses classical kinetics for components with large concentrations and a stochastic method for cases where the concentrations are low. We then examined the calcium measurements in the light of small concentrations.<\/p>\n
Much of the above falls under the category of \u201cequation (physics)-driven\u201d methods and appeared to be divorced from data-driven \u201comics\u201d measurements and methods. Very early on, I realized that it is essential to find ways to integrate data-driven and physics-driven modeling to produce models at multi-scale and granularity. And most importantly while \u201cphysical modeling\u201d is intrinsically dynamic in nature, the data-driven models warrant time series measurements. The great merit of time series measurements is that they have the potential to provide some notion of \u201ccausal mechanisms\u201d. The macrophage once again served as an excellent system since we have systematic time series of measurements of all components be they transcripts, proteins or lipids. Our goal then became reconstruction of networks from data, first canonical and static and subsequently dynamic network topologies.<\/p>\n
A few years ago, I joined forces with mathematicians and computer scientists and we developed a Center for the Science of Information (funded by a NSF Science and Technology Center Initiative) involving Purdue, Stanford, MIT, Princeton, UIUC and UCSD. My main focus in this project was the development of networks from data both static and dynamic, establish notions of causality and modularity. To facilitate research in my laboratory, I divided the network reconstruction methods into algebraic, Bayesian and information-theoretic. Our first work in this area analyzed existing methods such as PLS, PCR, LASSO and LMI (established methods) for introduction of noise and missing data \u2013 things commonplace in biology. We established the limits of applicability of these methods. We then combined these methods to produce a superior method called DP-LASSO. In parallel, we extended an information-theoretic method with a novel cut-off criterion to build macrophage networks. I was, however, keen on exploring if we can a) decipher causal mechanisms from network reconstruction from dynamic data and b) ask if network topologies themselves are dynamic. We developed methods using \u201cGranger causality\u201d arguments to accomplish both and these are early days in these developments.<\/p>\n
In parallel, we began exploring developing new methods for identifying modularity in large networks. Starting with protein-protein interaction networks and subsequently correlation networks from transcriptional data, we developed several exciting strategies. First, we developed a novel stopping criteria for a well-established Newman-Girvan betweenness algorithm. We then developed a variational Bayes method followed by a simple but elegant Newton method. All of these are now published in peer-reviewed journals.<\/p>\n
In recent work, we have developed exciting methods for inferring time-varying causal networks from time-series data. We have applied this to a mammalian cell cycle and predicted the phases of the cell cycle without a priori<\/em> assumptions. This method promises to be extremely useful in building causal networks.<\/p>\n\n\n <\/p>\n\n\n\n Dysregulated mechanisms underlying Duchenne muscular dystrophy from co-expression network preservation analysis.<\/strong> Mukund K, Subramaniam S.<\/p>\n\n\n\n Information theoretic approach to complex biological network reconstruction: application to cytokine release in RAW 264.7 macrophages.<\/strong> Farhangmehr F, Maurya MR, Tartakovsky DM, Subramaniam S.<\/p>\n\n\n\n Time-varying causal inference from phosphoproteomic measurements in macrophage cells.<\/strong> Masnadi-Shirazi M, Maurya MR, Subramaniam S.<\/p>\n\n\n\n A Newtonian framework for community detection in undirected biological networks.<\/strong> Narayanan T, Subramaniam S.<\/p>\n\n\n\n Community Structure Analysis of Gene Interaction Networks in Duchenne Muscular Dystrophy.<\/strong> Narayanan T, Subramaniam S.<\/p>\n\n\n\n Inferring the effective TOR-dependent network: a computational study in yeast.<\/strong> Mohammadi S, Subramaniam S, Grama A.<\/p>\n\n\n\n The poor performance of TMM on microRNA-Seq.<\/strong> Garmire LX, Subramaniam S.<\/p>\n\n\n\n Template-based combinatorial enumeration of virtual compound libraries for lipids.<\/strong> Sud M, Fahy E, Subramaniam S.<\/p>\n\n\n\n Evaluation of normalization methods in mammalian microRNA-Seq data.<\/strong> Garmire LX, Subramaniam S.<\/p>\n\n\n\n Gene-expression measurement: variance-modeling considerations for robust data analysis.<\/strong> Subramaniam S, Hsiao G.<\/p>\n\n\n\n Comparison of statistical and optimisation-based methods for data-driven network reconstruction of biochemical systems.<\/strong> Asadi, B, Maurya MR, Tartakovsky DM, Subramaniam S.<\/p>\n\n\n\n Introduction to Network Biology in \u201cFunctional Coherence of Molecular Networks to Bioinformatics.\u201d<\/strong> Doubly Penalized LASSO Reconstruction of Biological Networks.<\/strong> Asadi B, Tartakovsky DM, Maurya MR, Subramaniam S.<\/p>\n\n\n\n Stochastic operator-splitting method for reaction-diffusion systems.<\/strong> Choi T, Maurya MR, Tartakovsky DM, Subramaniam S.<\/p>\n\n\n\n Modularity detection in protein-protein interaction networks.<\/strong> Narayanan T, Gersten M, Subramaniam S, Grama A. (2011)<\/p>\n\n\n\n A global clustering algorithm to identify long intergenic non-coding RNA\u2013with applications in mouse macrophages.<\/strong> Garmire LX, Garmire DG, Huang W, Yao J, Glass CK, Subramaniam S.<\/p>\n\n\n\n Community detection in biological networks using a variational bayes approach.<\/strong> Narayanan T, Subramaniam S.<\/p>\n\n\n\n Comparison of statistical and optimization-based methods for data-drive network reconstruction of biochemical systems.<\/strong><\/p>\n\n\n\n Asadi B, Tartakovsky DM, Maurya MR, Subramaniam S. (2010)<\/p>\n\n\n\n Regulatory network of microRNAs in RAW 264.7 macrophage cells.<\/strong> Garmire LX, Shen Z, Briggs S, Yeo G, Subramaniam S, Glass C. (2010)<\/p>\n\n\n\n Stochastic hybrid modeling of intracellular calcium dynamics.<\/strong> Choi T, Maurya MR, Tartakovsky DM, Subramaniam S.<\/p>\n\n\n\n Classification studies based on a spectral representation of DNA.<\/strong> Bieli\u0144ska-Waz D, Subramaniam S.<\/p>\n\n\n\n Mixed-integer nonlinear optimisation approach to coarse-graining biochemical networks.<\/strong> Maurya MR, Bornheimer SJ, Venkatasubramanian V, Subramaniam S. (2009)<\/p>\n\n\n\n Functional coherence in domain interaction networks.<\/strong> Pandey J, Koyut\u00fcrk M, Subramaniam S, Grama A. (2008)<\/p>\n\n\n\n Peak-Finding Refinement in the Chip-SEQ Experiment.<\/strong><\/p>\n\n\n\n Garmire LX, Garmire DG, Benner C, Ko P, Glass CK, Subramaniam S. (2008)<\/p>\n\n\n\n A kinetic model for calcium dynamics in RAW 264.7 cells: 2. Knockdown response and long-term response.<\/strong> Maurya MR, Subramaniam S. (2007)<\/p>\n\n\n\n A kinetic model for calcium dynamics in RAW 264.7 cells: 1. Mechanisms, parameters, and subpopulational variability.<\/strong> Maurya MR, Subramaniam S. (2007)<\/p>\n\n\n\n Detecting conserved interaction patterns in biological networks.<\/strong> Koyut\u00fcrk M, Kim Y, Subramaniam S, Szpankowski W, Grama A. (2006)<\/p>\n\n\n\n Reduced-ordered modeling in biochemical networks: Application to the GTPase-cycle signaling module.<\/strong> Maurya MR, Bornheimer SJ, Venkatasubramanian V, Subramaniam S. (2005)<\/p>\n\n\n\nRELATED PUBLICATIONS<\/summary>\n
BMC Research Notes. 2015 May 3: 8: 182 PubMed<\/a><\/strong><\/p>\n\n\n\n
BMC Systems Biology. 2014 Jun 25; 8: 77 PubMed<\/a><\/strong><\/p>\n\n\n\n
IEEE Transaction on Biomedical Circuits Systems. 2014 Feb; 8 (1):74-86 PubMed<\/a><\/strong><\/p>\n\n\n\n
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PLoS One. 2013 Jun 19; 8 (6): e67237 PubMed<\/a><\/strong><\/p>\n\n\n\n
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RNA. 2012 Jun; 18 (6): 1279-1288 PubMed<\/a><\/p>\n\n\n\n
Nature Immunology. 2012 Feb 16; 13 (3): 199-203 PubMed<\/a><\/p>\n\n\n\n
IET Systems Biology. 2012 Oct; 6(5):155-63 PubMed<\/a><\/p>\n\n\n\n
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IEEExplore. 2012 Dec 3; 13154844 IEEEXplore Digital Library<\/a><\/p>\n\n\n\n
The Journal of Chemical Physics. 2012 Nov 14; 137 (18): 184102 PubMed<\/a><\/p>\n\n\n\n
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PLoS One. 2011; 6 (9): e24051 PubMed<\/a><\/strong><\/p>\n\n\n\n
Conference Proceddings of the ISCA 3rd International Conference on Bioinformatics and Computational Biology, BICoB-2011. 2011 Mar 23<\/p>\n\n\n\n
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The Journal of Chemical Physics. 2010 Oct 28; 133 (16): 165101 PubMed<\/a><\/strong><\/p>\n\n\n\n
Journal of Theoretical Biology. 2010 Oct 21; 266 (4): 667-674 PubMed<\/a><\/strong><\/p>\n\n\n\n
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