Mycobacterium tuberculosis: One of the major aims has been to use the tools of systems biology to understand the biology of Mycobacterium tuberculosis; and the dynamics of its interaction with the host. We are using unbiased chemical biology approaches to target critical components of the Mtb signalling network which sense environmental cues and likely regulate genetic programs linked to survival of the bacterium under conditions of stress imposed by the host. We have screened a library of small molecules to identify the top hits which dock to the conserved DNA binding pocket of a family of response regulators (RRs) of Mtb. MtrA is an essential RR of Mtb. Genome-wide ChIP-sequencing has uncovered a likely relationship between MtrA and the genes for the resuscitation promoting factors (Rpfs) A, B and C. This has important implications suggesting that MtrA could be a regulator of entry into and/or exit out of dormancy. A modified genomic SELEX approach has been developed as a high throughput approach to identify the cognate promoters for each sigma factor of Mtb and to build a transcriptional network of Mtb. A systems biology approach has been adopted to validate the input-output robustness of the MprAB two component system signalling pathway. A heterologous assay system has been developed in E. coli for this purpose.
Mycobacterium-macrophage interactions: The central goal of our efforts to understand Mtb infection is to understand the interplay between the host and pathogen signalling networks and genetic programs. We are using genome-wide approaches to understand how the macrophage transcriptome is remodelled in response to challenge by Mtb and how this influences the fate of the pathogen within its intracellular niche. Mapping of the TNFAIP3 (A20) interacting network (which is regulated by let-7f) uncovered how the let-7/A20 axis offers Mtb a survival advantage in macrophages. Genome-wide transcriptomics and in silico analyses have shown that mycobacterial LAM uses PPAR gamma signalling to influence pathways which are critical to infection.
Helicobacter pylori: We have performed genome-wide transcriptional profiling of H. pylori-infected gastric epithelial cells to generate an miRNA-mRNA network that has been used as a starting point for analyzing how miRNAs could modulate the response to H. pylori infection. The miRNA-mRNA network enabled identification of the some of the important nodes which could regulate the outcome of infection.
Theoretical studies: Theoretical and computational approaches have been undertaken to understand signal transduction in the prokaryotic system. The origins and consequences of phenotypic heterogeneity in microbial cell populations have been analyzed using the concepts and techniques of nonlinear dynamics, statistical physics and information theory. The generation of phenotypic heterogeneity in microbial populations as a strategy for coping with stress and the role of heterogeneity in cellular decision making, e.g., cell differentiation, have been studied.
A theoretical model has been proposed to understand DevR regulated gene expression in M. tuberculosis.
Efforts have been made to identify and compute early signatures of regime shifts in cell biological phenomena. It has been demonstrated that the early signatures of approaching regime shifts, which include critical slowing down, rising variance and lag-1 autocorrelation function, provide the means to distinguish between two proposed mechanisms, bistability and excitability, underlying competence development in B. subtilis.
The role of relaxation time scale in signal transmission has been modelled using a stochastic framework. The proposed model quantifies several testable statistical quantities related to class of signaling motifs.
A mathematical model has been developed to provide physical understanding of the observed broad heterogeneity in the distribution of the stem cell marker protein Sca-1 in a clonal population of hematopoietic stem cells. The model suggests that the experimental features provide signatures of criticality. Investigation of the dynamics of a four gene motif has suggested that post-transcriptional regulation has a greater capacity for information in comparison with that of transcriptional regulation.
A high resolution network model (incorporating the concept of Shortest Path) has been developed that elucidates key gene drivers that dictate the spatio-temporal progression of a cellular system (viz. ovarian cancer).