“Systematic Multi-scale Modeling and Analysis for Gene Regulation”

Speaker: Daifeng Wang
MBB, Mark Gerstein Lab

Abstract: The rapidly increasing quantity of biological data offers novel and diverse resources to study biological functions at the system level. Integrating and mining these various large-scale datasets is both a central priority and a great challenge for the field of systems biology and necessitates the development of specialized computational approaches. In this talk, I will present several novel computational systems approaches in a multi-scale modeling framework to study gene expression and regulation with applications to cancer and developmental biology: 1) a logic-circuit based method to identify the genome-wide cooperative logics among gene regulatory factors and pathways for the first time in cancers such as acute myeloid leukemia, which provided unprecedented insights into the gene regulatory logics in complex biological systems; 2) an integrated method using the state-space model and dimensionality reduction to identify principal temporal expression patterns driven by internal and external gene regulatory networks, which established an entirely new analytical platform to identify systematic and robust dynamic patterns from high dimensional, complex and noisy biomedical data. 

“Walking motor coordination in Drosophila”

Speaker: Damon Clark
MCDB 

Talk Summary: As flies walk and turn, they use a single neural architecture to coordinate a host of different leg movements. That architecture must generate relatively precise, repeatable patterns, but also be flexible enough to allow for all sorts of leg movement patterns. We are investigating how this is accomplished. A first step is to characterize the leg coordination patterns themselves, and we are using some blind and some not-so-blind machine learning analyses to do this.

“Machine Psychiatry: Predicting treatment outcomes in Major Depression”

Speaker: Adam Chekroud
Psychology, Greg McCarthy Lab

Talk Summary: Most depressed patients fail to reach remission with their first line of treatment, thus beginning a trial and error treatment-selection process. Matching patients to interventions that are likely to succeed might minimize - and perhaps eliminate - this trial and error process. Here, I present a model that is optimized to detect future responders to a specific antidepressant, using a simple 10-minute questionnaire. The model’s accuracy is above chance in two large clinical trial cohorts, and outperforms many practicing psychiatrists.

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