Our laboratory currently focuses on following research themes:
Dynamics of Epithelial-Mesenchymal Plasticity
Epithelial-mesenchymal plasticity (EMP) involves reversible and dynamic cell-state transitions among epithelial, mesenchymal and hybrid epithelial/mesenchymal (E/M) phenotypes. We focus on identifying the molecular factors stabilizing hybrid E/M cell-states, and characterizing the association of EMP with other axes of plasticity, such as tumor-initiation potential, immune evasion etc.
![](https://be.iisc.ac.in/~mkjolly/wp-content/uploads/2022/03/Teams-landscape-schematic-1024x576.png)
Mechanisms underlying phenotypic heterogeneity in cell populations
Stochastic cell-state transitions driven by various factors such as epigenetic modifications and asymmetric cell division can enable phenotypic heterogeneity in a genetically identical population. Such heterogeneity can enable bet-hedging, thus facilitating survival of a population under stress and the relapse of population upon stress withdrawal. We aim to identify different mechanisms and underlying principles enabling such phenotypic heterogeneity in diverse biological contexts.
![](https://be.iisc.ac.in/~mkjolly/wp-content/uploads/2022/10/2022-10-29_09-53-1.png)
Spatiotemporal pattern formation and collective behavior in cell populations
Spatiotemporal dynamics of cellular phenotypes are often driven by their crosstalk with their microenvironment, involving cell-cell communication based feedback loops. We develop multi-scale mechanism-based models as predictive ‘in silico co-culture’ systems to elucidate the collective emergent dynamics in multiple scenarios: tissue-level patterning, collective cell migration, and tumor-stroma crosstalk.
![](https://be.iisc.ac.in/~mkjolly/wp-content/uploads/2022/10/2022-10-29_09-56.png)
Single-cell analysis of cell-fate decisions in development and cancer
Recent single-cell high-throughput data collection has enabled tracking the trajectories of cell-fate decisions and mapping phenotypic heterogeneity in diverse biological contexts. We examine such single-cell transcriptomic data to identify branching points in decision-making, to characterize subpopulations with specific molecular and functional attributes, and to probe the dynamic evolution of different modules of co-expressed gene programs.
![](https://be.iisc.ac.in/~mkjolly/wp-content/uploads/2022/10/2022-10-29_10-09.png)
Non-genetic mechanisms of adaptive therapy resistance
Besides genetic mutations, non-genetic mechanisms such as phenotypic switching and stochastic cell-to-cell heterogeneity can enable adaptation to various stresses. For instance, drug treatment can induce a phenotypic switch in a fraction of cells to another state (Lamarckian induction). We aim to elucidate how cells adapt reversibly to various stresses, and how we can design strategies to outcompete these adaptation strategies.
![](https://be.iisc.ac.in/~mkjolly/wp-content/uploads/2022/10/2022-10-29_10-06.png)
Design principles of cell-fate decision-making regulatory networks
Cell-fate decision-making regulatory networks often have unique features in terms of their emergent dynamics, for instance, a limited number of phenotypes despite their complex and large architecture, and balancing robustness against various perturbations with enabling cell-state switching. We are interested in decoding the topological hallmarks of biological networks, and the functional implications of those hallmarks.
![](https://be.iisc.ac.in/~mkjolly/wp-content/uploads/2022/10/2022-10-29_10-08.png)