Cytoskeleton and Cell Shape (CyCelS) Lab

Cytoskeleton and Cell Shape Lab
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. Combined with a dynamic and talented team of PhD students trained in microbiology, biotechnology and engineering, we are making inroads into questions of the role of self-organization in cell morphogenesis or how physical principles drive cell shape.

Open PhD & iPhD Positions in 2019

Cytoskeleton-Motor Mechanics and Regulation: In silico, in vitro & in vivo
Setup of the dynein gliding assay in simulation and experiment

Dynein Collective Transport: Simulation & Experiment (Jain et al. 2019 Soft Matter)

Purified yeast dynein motors drive the transport of Rh-Tubulin labelled MT filaments (Jain et al. 2019)

In vitro reconstitution of purified molecular motors and microtubules (MTs)

Recently, taking a simpler view of “gliding assays” in terms of linear MT transport we investigated the “search” and “collective mechanics” of a well studied minus-ended motor protein (Dynein) from S. cerevisiae. This ‘minimal’ dynein in experiments showed a 2D directionality that increased with MT length.

Stochastic simulations of multi-protein transport

A mathematical model that is based on detailed single-molecule mechanics suggests indeed teams of 5-10 motors are responsible for such a  “coordinated transport”. This is now a paper in RSC Soft Matter (Jain, Khetan and Athale 2019).

Integrating experiments and theory

Our time-lapse movies from experiments allow only the interpretation of the experiment, but in simulations using Cytosim, we can distinguish between free and bound motors.

Simulating MT asters

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).

Simulation movie of dynein based gliding assay
Gliding assay in simulation: Bound dynein (bright green) and “searching” microtubules (grey) in 2D

Mathematical modeling of cytoskeletal basis of neuronal growth cone turning

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 in theory and experiment continues to interest us in multiple systems.

In collaboration with the lab of Prof. Marie Delattre who heads the Plasticity and Evolution of Cell Division Lab at ENS Lyon, we are working actively on quantifying and modeling the mechanics of spindle segregation during embryonic division of the nematode Caenorhabditis elegans.

Kinetics, Biochemistry and Mathematical Modeling of Tubulin Polymerization  and Drug Interactions
Schematic depicting the scale of MTs and kinetics of polymerization and depolymerization

MT size and kinetics of polymerization (Athale, C.A. 2011, Modelling the Spatial Pattern Forming Modules in Mitotic Spindle Assembly)

The diversity of microtubule (MT) inhibitors that originates from plants and have medicinal uses has fascinated us. We have together with our collaborators Prof. Uma Shaanker’s group in UAS Bangalore, begun to investigate the role of MT polymerization kinetics in presence of colchicine – both produced from the plant and present in the insect that feeds on it.
An exciting combination of ECOLOGY, BIOCHEMISTRY, BIOPHYSICS, MICROSCOPY, DRUG-DISCOVERY and EVOLUTION awaits to be discovered.

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.

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

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 gro

wth 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).

Self-Organized Collective Behaviour

Self-organization of aster patterns by mechanics (Khetan & Athale, in press)

As with flocks of birds, schooling of fish and people milling at a railway station, collective patterns that emerge have fascinated humankind since time immemorial. We hope to work on biomolecules and find out the laws by which they organize. Finding the similarities and differences to more complex pattern, is our way of working our way upwards in complexity, in the hope of finding out the universal laws and the exceptions that guide such patterns. The 1974 parable from Films Division India “एक, अनेक और एकता”, a must watch.

The Films Division Documentary on unity of purpose

Collective effects self-organized: in a social context (Films Division India): This social parable from Films Division India (1974) has amazing parallels in collective behaviour as we think of it- birds swarming, people at a railway station, fish schooling.


  1. Department of Biotechnology, Govt. of India Dept. of Biotechnology, Govt. of India
  2. Department of Science and Technology, Govt. of India
  3. Private companies for some short term projects with BS-MS undergraduate students (iGEM)
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