Welcome to Mano Ram Maurya's Home Page

We are interested in mechanistic quantitative modeling of biological systems so that their quantitative behavior can be predicted and new hypotheses can be generated. Before conducting costly experiments, the cellular system can be simulated on a computer so that potentially less successful scenarios can be discarded. I have studied several signaling pathways through mechanistic (kinetic) modeling. Examples are: (1) the Mitogen Activated Protein Kinase (MAPK) signaling pathway which plays a role in the pathology of cancer, (2) the GTPase cycle module of G-protein signaling which affects many biological functions, (3) calcium signaling in RAW 264.7 macrophage cells (a cell type in the immune system) and (4) lipid metabolism in RAW 264.7 and bone-marrow derived macrophages.

The diagram on the left shows a schematic of regulation of cytosolic calcium (Ca2+). Please see my publication (Maurya and Subramaniam, Biophysical Journal, 2007) for more details.

We have used existing bioinformatics tools to analyze quantitative data on RAW 264.7 cell activation upon various single and double ligands (courtesy Alliance for Cellular Signaling (AfCS)). The heatmap on the left shows the bi-clustering of the phosphoprotein data (signaling pathway activations) upon stimulation by various single ligands. Given the high correlation in the phosphoprotein data, we used a principal component regression and partial least squares approaches to analyze these datasets further towards network reconstruction. Please see my publications (Pradervand et al., Genome Biology, 2006; Gupta et al., PLoS Computational Biology, 2010) for more details. I have also developed a time-interval weighted correlation metric approach for hierarchical clustering of time-course data and used it to analyze lipidomic and genomic temporal data jointly (Dennis et al., Journal of Biological Chemistry, 2010).

We have developed an approach for multiscale stochastic simulation of biochemical reactions and related processes. Our approach handles different types of flux expressions such as simple rate laws including law of mass action (LOMA) kinetics, Michaelis-Menten (M-M) kinetics and Hill-dynamics as well as more complex forms. WE have applied this technique to a model of the regulation of the dynamics of cytosolic calcium ions (Ca2+) in mouse macrophage RAW 264.7 cells that we have developed (Maurya and Subramaniam, Biophysical Journal, 2007). The panel on the left shows a comparison of actual single cell calcium data and the stochastic simulation results accounting for the experimental noise). Please see our publication (Choi et al., Journal of Chemical Physics, 2010) for additional details.