Research

November10

Cellular Biophysics of Cytoskeleton and Cell Shape


We are interested in spatial pattern formation driven by physical mechanisms (as opposed to template-based genetic programs). To this end we examine some of the processes we have found to be important in cell shape determination using a combination of computer simulations of physical interactions (Langevin dynamics, Reaction & Diffusion), in vitro reconstitution of proteins and quantitative microscopy:

Cytoskeleton-motor Mechanics and Regulators: in silico and in vitro
DIC image of GTP-tubulin polymers

GTP-tubulin polymers from goat brain tubulin with methlycellulose in DIC microscopy (40x). Kunalika Jain.

Work in the past few years had focussed on centrosome nucleated MT asters moving centripetally towards the chromatin in an in vitro reconstitution system (Athale et al. 2014 Phys. Biol. 11: 016008). To examine the in vivo role of such movement we analysed previously published experimental data on the role of self-organisation and gradients in mouse oocytes undergoing meiosis I (Khetan & Athale, 2016 PLoS Comp Biol).

Reaction-diffusion patterns of signalling proteins: Transport of linear MTs in axonal growth cone turning was explored in a theoretical model to examine the limits of sensing of the MT system (Mahajan and Athale 2012). This interplay between forces and biochemistry continues to interest us in multiple systems.

Computational Imaging for Microscopy of Cells and Molecules
Image from the report by Chaphalkar et al.

Amtrak used to quantify E. coli nucleoids

Mathematical modeling: From ‘hopping’ to ‘corraled’ diffusion of membrane receptors

Schematic of models of receptor mobility diffusion and dimerization, REF: Deshpande et al. (2017) Phys. Biol.

Receptor aggregation dynamics by dimerization has been shown in multiple studies to be important for receptor signalling, as exemplified by epidermal growth factor receptpr (EGFR) dynamics. The state of the receptor is thought to be critical for signalling in cancer cells, as summarized in a theoretical model by us in 2005 (Athale, Mansury & Deisboeck J. Theor. Biol.). The microscopic details of such receptor dynamics have been more recently modeled using coarse-grained simulations in collaboration with the Biophysical Chemistry lab in NCL Pune (Pawar et al. 2014, Sengupta et al. 2016). We have more recently developed a Monte-Carlo simulation in MATLAB to simulate the effect of spatial obstructions to receptor dimerization kinetics based on a diffusion and aggregation model in a heterogenous environment (Deshpande et al. (2017) Phys. Biol).

Biophysics of Bacterial Cell Size
Image from Gangan & Athale (2017) of microfluidics of E. coli cells grown in LB at 37 deg C

Constitutive GFP expressing E. coli in a "mother machine" microfluidics chip.

In an earlier study, we used an imaging approach to estimating the statistical variability in cell shape in a population (Athale &Chaudhari Bioinformatics). In more recent work we pursued this further to examine how cells grown in continuous culture could be used to inhibit the replication processivity, to examine the link between division and replication. Indeed, we appear to have found one of the (possibly multiple) causes that can link the rate of growth of single cells to their stochastic elongation (Gangan & Athale 2017 Roy. Soc. open science). We are currently working on formalizing our insights in the form of a theoretical model using an Agent-Based Modeling (ABM) approach.

ABMs are a class of discrete models more often found in Econophysics and SocialDynamics. They are related to cellular automata, but allow more flexibility of implementing rule-based behaviour. In previous work, we had examined colony growth dynamics of simulated cancer cells. Specifically we aimed to mimic the avascular growth of glioblastoma multiform (GBM) and by integrating a singalling network, find possible linkages to stop proliferation or migration (work done in CBML lab, MIT-MGH 2003-5).

ABM for tumour growth: network model from Zhang, Athale & Deisboeck (2007) J. Theor. Biol.

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