# MANET: Maximum Entropy Neural Networks for Mechanistic Modeling of Single Cell Behavior

> **NIH NIH R35** · UNIVERSITY OF FLORIDA · 2021 · $369,960

## Abstract

Project Summary/Abstract
Despite recent experimental advances in single cell techniques and a concurrent development in
statistical methods, our ability to predict single cell dynamics and identify the biochemical
processes that dictate cell-to-cell variability remains rudimentary. We have identified the key
roadblock in achieving mechanistic understanding of single cell behavior: we do not have
computational methods to integrate single cell data with mechanistic signaling network models.
Building upon our previous work and leveraging cutting-edge developments in neural networks,
we propose a comprehensive research program to bridge this gap.
The central problem in integration of single cell data with mechanistic models is that even large-
scale data only partially constrain the models, leading to a family of models that fit the data equally
well. How do we then choose from the models? Our strategy is to use the Maximum Entropy (Max
Ent) approach which infers the least complex model: one that does not disfavor any outcome
unless warranted by the data and the mechanistic constraints. Over the past decade, we have
pioneered the novel use of Max Ent to model dynamics of biological networks. In the next five
years, we plan to have two main research goals; (1) to build and validate the computational
architecture required to integrate single cell data with models and (2) in close collaboration with
experimentalists, use the developed framework to study the variability in two important
pathways; the mitogen activated protein kinase (MAPK) pathway and mechanotransduction. We
envision that this framework will be indispensable in exploring the mechanistic origins of cell-to-
cell variability across a broad range of signaling networks. Notably, under-constrained models are
ubiquitous in many areas of quantitative biology, including two of the laboratory’s other research
foci: metabolism and microbiome dynamics. The program proposed here will directly benefit
integration of large-scale data with mechanistic models and a principled exploration of otherwise
hidden hypotheses.

## Key facts

- **NIH application ID:** 10273855
- **Project number:** 1R35GM142547-01
- **Recipient organization:** UNIVERSITY OF FLORIDA
- **Principal Investigator:** Purushottam Dixit
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $369,960
- **Award type:** 1
- **Project period:** 2021-09-01 → 2026-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10273855

## Citation

> US National Institutes of Health, RePORTER application 10273855, MANET: Maximum Entropy Neural Networks for Mechanistic Modeling of Single Cell Behavior (1R35GM142547-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10273855. Licensed CC0.

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