Physical Biology Group
Institute of Mathematical Sciences
Physics of Biomolecular Condensation and Their Physiological Implications
Banani et al. (2017) Nature Reviews Molecular Cell Biology
A cell is akin to a densely packed bag of chemicals. A central challenge in cell biology is to decipher the principles underlying the diverse biophysical processes within such a crowded milieu. Experimental evidence suggests that proteins and nucleic acids can undergo phase separation to form membrane-less compartments known as biomolecular condensates. These condensates play a crucial role in orchestrating the spatiotemporal biochemistry of cells, thereby regulating various physiological functions. Two distinctive features differentiate cellular phase-separated bodies from their inanimate counterparts: firstly, they consist of multiple components, and secondly, they involve energy consumption. We conduct theoretical work to unearth the biophysical principles that govern the formation and physiological function of biomolecular condensates by employing tools from statistical mechanics and polymer physics.
Gene regulation out of equilibrium
Individual cells constantly make decisions about what to eat, where to go, and when to divide in response to environmental cues. These decisions are made by regulating the expression of various genes. Recent experiments suggest that different steps involving gene expression involve energy expenditure, resulting in the system being pushed out of equilibrium. We explore how gene regulation out of equilibrium impacts cellular decision-making using ideas from statistical mechanics. Furthermore, we study how such nonequilibrium mechanisms evolve.
Biophysics of Actin Cytoskeleton Dynamics and Organelle Biogenesis
Cells contain distinct sub-cellular structures, including the actin cytoskeleton and numerous organelles such as lysosomes, mitochondria, peroxisomes etc. Each of these structures is tailored to achieve specific functions, analogous to how organs in the human body serve diverse biological roles. The size, number, shape, and position of these structures are regulated in a coordinated manner, often exhibiting simple scaling laws. How such simple laws emerge from a complex interplay of the underlying molecular processes remains a question. Advancements in fluorescence microscopy have enabled the quantification of number, size, and position of these structures at a single-cell level. By using tools from machine learning, we analyse these microscopy images to extract quantitative information. Subsequently, we construct theoretical models to interpret the extracted data and provide guidance for new experiments.
