# MODELING EMERGENT BEHAVIORS IN SYSTEMS BIOLOGY: A BIOLOGICAL PHYSICS APPROACH

> **NIH NIH R35** · BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) · 2024 · $396,000

## Abstract

Project Summary
Biology is full of stunning examples of emergent behaviors – behaviors that arise from, but cannot be reduced
to, the interactions of the constituent parts that make up the system under consideration. These behaviors
span the full spectrum of length scales, from the emergence of distinct cell fates (e.g. neurons, muscle, etc.)
due to the interactions of genes within cells, to the formation of complex ecological communities arising from
the interactions of thousands of species. The overarching goal of my research is to develop new
conceptual, theoretical, and computational tools to model such emergent, system-level behaviors in
biology. To do so, we utilize an interdisciplinary approach that is grounded in Biological and Statistical
Physics, but that draws heavily from Machine Learning, Information Theory, and Theoretical Ecology. Our work
is unified and distinguished by our deep commitment to integrating theory with the vast amount of
biological data now being generated by experiment. An important goal of the proposed research is to find
common concepts and tools that transcend traditional biological sub-disciplines and model systems.
The proposed research pursues three distinct but conceptually interrelated research directions: (1)
identifying the ecological principles governing community assembly in microbial communities and developing
techniques for understanding function, diversity, and stability in microbiomes; (2) developing new mathematical
and computational tools for modeling information processing in biochemical networks, especially the gene
networks underlying cellular identity and the signaling networks that control collective behavior in the NIH
model organism Dictyostelium discoideum; and (3) understanding and developing new interpretable machine
learning techniques for systems and synthetic biology, with special attention paid to the unique challenges
posed by living systems with regards to data heterogeneity, biological interpretability, and potential sources of
bias. In addition to developing physics-based and machine learning-inspired models for diverse biological
phenomena, the proposed research will yield a series of practical and important computational tools and
algorithms which we will make publically available including: (1) our “Community Simulator” Python package
for simulating microbial communities based on the novel microbial consumer resource model framework we
have developed; (2) new machine learning and statistical algorithms for analyzing microbial communities and
gene networks; and (3) new computational tools for predicting the behavior of synthetic biological parts and
circuits in diverse contexts. These computational tools will allow scientists to leverage the power of modern
theory, computation, and advances in Deep Learning to tackle fundamental problems relevant to human
disease.

## Key facts

- **NIH application ID:** 10795856
- **Project number:** 5R35GM119461-08
- **Recipient organization:** BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
- **Principal Investigator:** Pankaj Mehta
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $396,000
- **Award type:** 5
- **Project period:** 2016-07-18 → 2027-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10795856

## Citation

> US National Institutes of Health, RePORTER application 10795856, MODELING EMERGENT BEHAVIORS IN SYSTEMS BIOLOGY: A BIOLOGICAL PHYSICS APPROACH (5R35GM119461-08). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10795856. Licensed CC0.

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