BioMechanics and Dynamics of Spatial Patterns

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
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).

Simulated MTs (cyan) inside a neuronal growth cone geometry with an initially uniform stathmin concentration (bright blue).

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.

Mathematical modeling of  clustering: cytoskeletal interactions of receptors

Image from Gangan & Athale (2017) (Roy. Soc. open science) of microfluidics of E. coli cells grown in LB at 37 deg C

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.

Mathematical Biology of Bacterial Spatial Spread: Single Cells to Biofilms
Image from Gangan & Athale (2017) of microfluidics of E. coli cells grown in LB at 37 deg C

HupA (red) expressing E. coli with DIC (green) overlaid in confocal microscopy

A population approach to estimating the statistical variability in cell shape (Athale & Chaudhari 2011 Bioinformatics) led us to discover the role of RecA in such variability that increases with increasing growth rates (Gangan & Athale 2017, Royal Soc. open science). In the process, we have built a mother machine applying micro-fluidics to bacterial cell biology.

Currently we are exploring the collective patterns in spatial spread of bacteria, using E. coli as a model system.

Populations of E. coli in microscopy

Amtrak used to quantify E. coli nucleoids

Image-Quantification of Kinetics and Morphology: Image analysis tool development
Kymography analysis of time-series data in cell biological time-series is typically done manually (e.g.: Multi-Kymography plugin for ImageJ, EMBL), potentially due to the subjective nature of selecting the line of interest. However based on some ideas on how to make the process objective, we have developed an automated multi-track kymography (AMTraK) program with a GUI front-end to make the process of quantitative kymograph analysis easier (Chaphalkar AR, Jain K, Gangan MS, Athale CA (2016) PLoS ONE).

Image from the report by Chaphalkar et al.The program described in the paper by Chaphalkar et al. can be downloaded from here.

Philosophy of the lab

Work in the lab is guided by the vision of developing a system level understanding of shape (pattern-formation/morphogenesis). Our choice of scale (cells) is driven by ease of manipulation and tractability. Self organizing systems studied in physics as well as social systems tend to show feedbacks and a high degree of connectivity- something that has increasingly become accepted as a signature of biological systems.

A lot of what we do can also be classified as Physical Biology of the Cell putting us nicely (for some) at the interface of physics and biology.

We have hosted IISER Pune team for the international genetically engineered machines (iGEM) contest in 2015 and 2017. You can read more about iGEM@iiserpune here.

posted under | Comments Off