# Elucidating biophysical mechanisms for force sensing and control using non-equilibrium statistical mechanics and AI

> **NIH NIH R35** · UNIVERSITY OF CHICAGO · 2022 · $382,751

## Abstract

Non-equilibrium activity is crucial for maintain and modulating tissue shape, development and
morphogenesis, lysosome dynamics, cell membrane remodeling during cell division or membrane fusion
and fission events. Importantly many of these processes play a significant role in human health helping
regulate for instance immune system function and ensuring accurate developmental morphogenesis.
However, there is a major gap in our understanding of how microscopic non-equilibrium biophysical
driving forces give rise to a desired molecular response, function, or control. Indeed, while the theoretical
and computational frameworks for the study of equilibrium biological processes are very well developed,
there are very limited analogous tools for the study of complex far-from-equilibrium biological systems.
Further, the large length and time scales of biological systems and processes make explicit computational
simulations impractical.
Addressing this problem requires the development of a range of multiscale non-equilibrium statistical
mechanics techniques in combination with tools from machine learning and artificial intelligence so that
the large length and time scales associated with the above-mentioned biological processes can be
appropriately captured. The work outlined in this proposal builds towards these long-term goals by focusing
on three paradigmatic example systems 1) Understanding and predicting non-equilibrium lysosomal
dynamics and morphologies 2) Understanding and modelling cytoskeletal processes responsible for
developmental patterning, cell-cell communication, and force generation 3) Developing frameworks for
determining drivers of cell fate and differentiation from single cell RNA sequencing data. Each of these
paradigmatic examples has implications for diseases. These paradigmatic examples build on the recent
foundational non-equilibrium statistical mechanics frameworks developed by my group and expand them
so that they can be utilized in biological contexts.

## Key facts

- **NIH application ID:** 10501942
- **Project number:** 1R35GM147400-01
- **Recipient organization:** UNIVERSITY OF CHICAGO
- **Principal Investigator:** Suriyanarayanan Vaikuntanathan
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $382,751
- **Award type:** 1
- **Project period:** 2022-08-01 → 2027-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10501942

## Citation

> US National Institutes of Health, RePORTER application 10501942, Elucidating biophysical mechanisms for force sensing and control using non-equilibrium statistical mechanics and AI (1R35GM147400-01). Retrieved via AI Analytics 2026-05-31 from https://api.ai-analytics.org/grant/nih/10501942. Licensed CC0.

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