Determining the molecular intermediates between genotype and phenotype

Speaker: David Knowles , Columbia University

Date: Wednesday, October 04, 2023

Time: 11:30 AM to 1:00 PM Note: all times are in the Eastern Time Zone

Public: Yes

Location:

Event Type: Seminar

Room Description:

Host: Bonnie Berger, CSAIL MIT

Contact: Shuvom Sadhuka, ssadhuka@mit.edu

Relevant URL: https://mit.zoom.us/j/93513735220

Speaker URL: None

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Reminders to: seminars@csail.mit.edu, bioinfo-seminar@lists.csail.mit.edu

Reminder Subject: TALK: Bioinformatics Seminar: David Knowles "TBA", October 4, 2023

Abstract: I will describe two projects that aim to better dissect the causal chain from functional genetic variant through molecular intermediates and finally to organismal trait or disease risk. In the first, we use pooled profiling of splicing factor binding across individuals to measure and then computationally model genetic effects on both binding and RNA splicing. In the second, we developed an instrumental variable-based causal network inference method that scales to hundreds of nodes by leveraging convex optimization. We apply this approach to learning phenome-wide trait networks from the UK biobank and directed gene regulatory networks from Perturb-seq.

Bio: Dr Knowles uses statistical machine learning—probabilistic graphical models, deep learning and convex optimization—to address challenges in understanding large genomic datasets. His lab develops methods to map the causes and consequences of transcriptomic dysregulation, especially RNA splicing, across the spectrum from rare to common genetic disease. They collaborate with labs at NYGC, Columbia and MSSM, focusing on understanding the genetic basis of neurological diseases, both degenerative and psychiatric.

Dr Knowles studied Natural Sciences and Information Engineering at (old) Cambridge before obtaining an MSc in Bioinformatics and Systems Biology at Imperial College London. During his PhD in the Cambridge University Machine Learning Group under Zoubin Ghahramani he worked on variational inference and Bayesian nonparametric models for factor analysis, hierarchical clustering and network analysis. He was a postdoc at Stanford developing methods for functional genomics with Daphne Koller (CS), Sylvia Plevritis (Computational Systems Biology/Radiology) and Jonathan Pritchard (Genetics/Biology). At Columbia, he is an Assistant Professor of Computer Science, an Interdisciplinary Appointee in Systems Biology and an Affiliate Member of the Data Science Institute. He is also a Core Faculty Member at the New York Genome Center.

Zoom link: https://mit.zoom.us/j/93513735220

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Created by Jose Abola Email at Wednesday, September 20, 2023 at 2:28 PM.