# Machine Learning and Control Principles for Computational Biology

> **NIH NIH R35** · BRIGHAM AND WOMEN'S HOSPITAL · 2021 · $447,500

## Abstract

Summary/Abstract
With our increasing ability to measure biological data at scale and the digitalization of health records,
computational thinking is becoming ever more important in the biological science and healthcare. The research
directions proposed in this grant look to build robust machine learning models and tool for computational biology
by including principles and analysis from other engineering fields, like control, that have a proven record of
incorporating robustness into the systems they have automated. This increased robustness will save resources
during the development of these machine learning models. It will also lead to more reliable diagnostics, clinical
tools, and machine learning based biological discoveries. We have proposed three future research directions at
the intersection of machine learning, control, and computational biology (a) modeling dynamical systems, (b)
robust optimization schemes (c) control principles for in vivo modeling of microbial communities. The first
proposed research area involves the development of flexible models for performing inference on dynamical
systems models with time-series data. Dynamical systems models are able to learn mathematically causal
relationships between variables, compared to other models whose parameters may only have correlative
relationships. Our flexible models will be differentiable allowing them to be trained using the same efficient
algorithms and hardware that have propelled deep learning models into the spotlight. These differentiable
methods will allow for us to more easily integrate the uncertainty associated with biological measurements into
our models. The second research area looks to develop more robust gradient optimization algorithms, the work
horse for training deep neural networks. Many of the popular algorithms used to train deep neural networks were
not explicitly designed to be robust. By developing more robust optimization techniques machine learning models
trained on disparate data sets at different hospital or labs will be more reproducible and will require less time for
tuning parameters, ultimately saving resources as well. These robust optimization techniques will also aid in the
certification of machine learning based tools that will ultimately be deployed in the clinic. The third research area
we propose is an approach for the discovery and design of robust microbial communities. Communities of
commensal, or engineered, bacteria have long been proposed as alternative therapies for the treatment of gut
related illness (“bugs as drugs”). We propose a top down approach to identifying putative microbial consortia
members from time-series experiments with germ free mice colonized by complex flora. By beginning the
consortia design process in vivo we hope to overcome the challenge that many other attempts at consortia
construction have encountered where in vitro designed communities do not reproduce their intended properties
once transferred into living hos...

## Key facts

- **NIH application ID:** 10276879
- **Project number:** 1R35GM143056-01
- **Recipient organization:** BRIGHAM AND WOMEN'S HOSPITAL
- **Principal Investigator:** Travis Eli Gibson
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $447,500
- **Award type:** 1
- **Project period:** 2021-09-01 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10276879

## Citation

> US National Institutes of Health, RePORTER application 10276879, Machine Learning and Control Principles for Computational Biology (1R35GM143056-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10276879. Licensed CC0.

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