# Modeling Emergent Behaviors in Systems Biology: A Biological Physics Approach

> **NIH NIH R35** · BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) · 2020 · $330,294

## 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 Physics, but 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 modern DNA sequencing-based techniques and quantitative microscopy. An important
goal of the proposed research is to find common concepts and tools that transcend traditional biological
sub-disciplines and models systems. The proposed research pursues four distinct but conceptually
interrelated research directions: (1) understanding how distinct cell fates emerge from bimolecular
interactions within mammalian cells (2) investigating how bimolecular networks within cells exploit energy
consumption to improve computations, with applications to Synthetic Biology; (3) identifying the ecological
principles governing community assembly in microbial communities and developing techniques for synthetically
engineering ecological communities; and (4) developing new machine learning algorithms and techniques for
biological data analysis. In addition to developing physics-based models for diverse biological phenomena, the
proposed research will yield a series of practical important tools and algorithms which we will make
publically available including: (1) a new linear-algebra based algorithm for assessing the fidelity of directed
differentiation and cellular reprogramming protocols and visualizing reprogramming/differentiation dynamics
and (2) improved algorithms for inferring microbial interactions in the human microbiome from high-throughput
sequence data. These computational tools will allow scientists to realize the immense therapeutic potential of
cellular reprogramming and microbial ecology-based techniques for studying and treating human disease.

## Key facts

- **NIH application ID:** 9963304
- **Project number:** 5R35GM119461-05
- **Recipient organization:** BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
- **Principal Investigator:** Pankaj Mehta
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $330,294
- **Award type:** 5
- **Project period:** 2016-07-18 → 2022-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9963304

## Citation

> US National Institutes of Health, RePORTER application 9963304, Modeling Emergent Behaviors in Systems Biology: A Biological Physics Approach (5R35GM119461-05). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9963304. Licensed CC0.

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