Hi All,

The BWH Precision Medicine Program and the BWH Department of Pathology (Division of Computational Pathology) are excited to announce the Junior Investigators Advanced Biomedical Computation (ABC) Series.

This is an opportunity for junior computational scientists with applications in all areas of biomedical science to give informal presentations of their work using chalk talks or PPT. The goal is to educate the community on advanced computational approaches in biomedical sciences, and to engage the audience in a conversation about the challenges faced in real-world projects and exchange ideas on possible solutions with a friendly audience.

Please see below the ABC Series line up for the next few months and do not hesitate to let us know if you have any question.

Kind regards,
Georg and Claudia

Georg K. Gerber, MD, PhD, MPH
Chief, Division of Computational Pathology
ggerber@bwh.harvard.edu

Claudia Rizzini, PhD
Managing Director, Precision Medicine
crizzini@bwh.harvard.edu

JUNIOR INVESTIGATORS
Advanced Biomedical Computation (ABC) SERIES

4:00 pm-5:00 pm

Cotran Conference Center
Cotran 3 (formerly Amory Building), 75 Francis Street, BWH

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January 27, 2020

 

Travis Gibson, PhD

Instructor, Computational Pathology, Brigham and Women’s Hospital

 

Learning Microbial Dynamics for Therapeutic Applications: Scalable Inference, Robustness, and Control.

Abstract

Microbes are everywhere, including in and on our bodies, and have been shown to play key roles in a variety of prevalent human diseases. Consequently, there has been intense interest in the design of bacteriotherapies or “bugs as drugs,” which are communities of bacteria administered to patients for specific therapeutic applications. Central to the design of such therapeutics is an understanding of the causal microbial interaction network and the population dynamics of the organisms. Toward that direction I will present recent work on a Bayesian nonparametric model and associated efficient inference algorithm that addresses the key conceptual and practical challenges of learning microbial dynamics from time series microbe abundance data. These challenges include highdimensional (300+ strains of bacteria in the gut) but temporally sparse and non-uniformly sampled data; high measurement noise; and, nonlinear and physically non-negative dynamics. In a related work I will discuss a simpler inference problem surrounding the engineering of an interdependent consortia of bacteria. Here we will focus on experimental design for inference and discuss best practices for designing synthetic bacterial consortia for clinical/pharmaceutical applications.