Research Overview
In the last few decades, biological research has become more and more interdisciplinary and diverse. The wealth of information gathered at the molecular level due to novel molecular biology techniques, encouraged biologists as well as many chemists and physicists to rethink about the fundamental processes that are regulated by different complex mechanisms in the cells of a human being. How different proteins dynamically regulate different events within the cell? How the cells sense extracellular signals? How cells react to any external perturbation? Are there certain network motifs that are repetitively involved in such regulatory mechanisms? How intrinsic and extrinsic noises influence the cell fate decisions? In our research, we would like to ask questions of these kinds by combining both experimental and theoretical approaches to understand the behavior of the different cellular signaling pathways and how they alter the cellular decision making processes under different situations. Obviously to explain such intriguing questions, one would require expertise from many disciplines to understand the regulatory mechanism behind these biological processes both qualitatively and quantitatively. We wish to tackle these kinds of problems theoretically by making suitable experimental collaborations to answer some of the relevant questions in biology in a systems biology framework. We think theoretical understanding of such complex biological events by keeping close relationship with the experimental findings, is the key to develop proper diagnostic measure for different types of life threatening diseases.
Cell cycle commitment and progression in mammalian cells is highly heterogenous and significantly influence cell-fate choices in decision-making processes. Identifying the factors controlling these heterogeneities will help to design approaches that can reduce the cellular heterogeneity observed in disease conditions such as cancer and thereby help to achieve increased therapeutic efficiency. In this regard, we take a system biology approach to answer the following questions about mammalian cell cycle heterogeneity such as how the change in serum level simultaneously modifies the cell cycle duration related heterogeneities in cell lineage pairs and overall cellular population under culture conditions? Which noise source (Intrinsic or Extrinsic) majorly controls the heterogeneity in cell cycle and phase durations? Can we fine-tune these cell cycle period and phase duration heterogeneities for the overall cellular population to attain therapeutically advantageous situation?
Identifying sources of mammalian cell cycle heterogeneity
Modelling mammalian cell colony growth dynamics
Cell migration plays an important role during several biological processes such as wound healing, development, immune response and in disease conditions such as cancer metastasis. Cell motility along with cell proliferation and cell-cell interactions control the collective migratory dynamics and spreading of cells from a confined cell colony. Agent-based models can be employed to identify the migratory rules followed by single cells that can cause emerging migratory patterns observed in cell colonies. Some of the major questions we ask are how cell-cell interactions under varied growth condition alter properties of individual cell motion and how do they in turn impact the cell colony growth dynamics using agent-based modelling approaches.
Investigating Spatiotemporal Pattern Formation in Chemical and Biological systems
Spatiotemporal pattern formation in chemical and biological systems is an intriguing and exciting phenomenon observed in nature. According to Turing’s hypothesis, the spatially heterogeneous pattern emerges from a reaction-diffusion system due to diffusion-driven instability caused by the difference in diffusivities of the activator and inhibitor-like reactant species. Nonetheless, creating a condition of a wider difference in diffusivity between the inhibitor and activator, still remained a big challenge to overcome experimentally. We are trying to find the way out through analytical and numerical studies how to circumvent such a challenging experimental scenario in chemical systems by imposing different kind of external perturbations? What is the effect of external perturbations on the spatiotemporal patterns formed due to diffusion-driven instability?
Can we put forward the idea of spatiotemporal pattern formation in reaction-diffusion systems to study limb formation in chick embryos?
Stem cells are special in a sense that they can self-renew themselves, and if required can differentiate into various cell types under appropriate physiological conditions. These unique features of stem cells can in principle be utilized to develop potent therapeutic strategies to treat different kind of diseases (e.g. spinal cord injury, stroke, Parkinson’s or Alzheimer’s diseases etc.) that require repair and regeneration of damaged tissues by altering the balance between proliferation and differentiation propensities of stem cells in a specific manner. Unfortunately, how this balance is precisely maintained by these stem cells under different conditions, still remains as an open ended challenging question. At this moment, it is important to ask how this sort of precision is achieved in stem cell development while remaining under a highly fluctuating environment? Does the fluctuating cellular environment help to transit from one state to another state? If so, then what is the contribution of the intrinsic (due to low mRNA copy numbers) and extrinsic (other than intrinsic) source of fluctuations? How does the intra and extracellular signalling influence the transition between the different fate commitment? And ultimately is it possible to fine tune the fate commitment events by refining the fluctuations? We are trying to find the answer of these questions through mathematical and computational modelling.
Modelling stem cell self-renewal and differentiation
Robust Decisions Making in Biological Systems Organized by Network Motifs
Biological systems are extremely robust in making a decision under the influence of a specific external signal. Recent studies have revealed that in the context of epithelial to mesenchymal transition events and in stem cell differentiation regulation the robust decision-making has been shown to be dynamically organized by complex tri-stable, Mushroom or Isola kinds of bifurcations related to a specific regulatory gene. In this regard, a few important questions still remained unanswered. What are the minimal biological network motif structures that allow to reconcile such bifurcation features? Herein, in our lab, we are trying to identify and theoretically investigate a number of biological network motifs, which have the potential to produce such bifurcation features. Even if a network motif structure is suitable for producing such complex bifurcation features, under what circumstances it will or will not produce such kinds of bifurcations? And which network motif will give rise to the most robust bifurcation feature among the potential network motifs that have the capabilities of showing that particular steady-state features?
Investigating cellular proliferation commitment organized by miR-17-92 cluster
miRNAs are one of the important regulatory molecules in almost all biological processes. Dysregulation in miRNA expression and function has been linked to various diseases, especially in various cancers. In many solid cancers and in some hematopoietic cancers, the members of mir-17-92 cluster are highly over expressed, while in some other cancer cell types, over expressing the members of mir-17-92 cluster reduces proliferation significantly. By using single cell reporter studies coupled with mathematical and computational modelling, we are investigating how miRNAs, like miR-17-92 cluster are involved in G1-S regulation in different cell-types and whether we can provide a quantitative route to fine-tune the cellular proliferation responses to our advantage by only altering the mir-17-92 dynamics in a cell-type specific manner or not?
