CHME Seminar Abstracts

Fall 2024 CHME Seminar Abstracts

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Friday, September 6th, 2024

"Drug Discovery using Systems Biology and Mechanistic AI"
Dr. Sriram Chandrasekaran, Associate Professor, Associate Chair of Research - Biomedical Engineering, Unviersity of Michigan

Drug combinations are increasingly used to tackle drug resistance in cancer and infectious diseases.  Yet we lack a rational basis to design such multimodal treatments. Current drug-discovery approaches are unable to screen an astronomical number of drug combinations and do not account for metabolic heterogeneity or the complex in vivo environment. We have developed AI tools - INDIGO, MAGENTA, and CARAMeL,  which predict the efficacy of millions of drug regimens based on the properties of the drugs, the pathogen or tumor, and the immune environment.  Our hybrid AI methods utilize chemogenomics and metabolomics data and combine chemical engineering models with machine learning, which provides both predictive power and mechanistic insights. Using these methods, we have identified highly synergistic drugs to treat drug resistant infections including Tuberculosis, the world's deadliest bacterial infection.  Our approach also accurately predicts the outcome of past clinical trials of multi-drug regimens. Our ultimate goal is to create a personalized approach to treat infections and cancers using AI.

Sriram Chandrasekaran is an Associate Professor and Associate Chair of Research in Biomedical Engineering at the University of Michigan-Ann Arbor. He leads the Systems Biology & Drug Discovery lab. He received his PhD in Biophysics from the University of Illinois at Urbana-Champaign and worked as a Harvard Junior Fellow at Harvard University and MIT. He has developed over 15 systems biology methods for drug discovery and bioengineering. A key focus of his lab is the development of mechanistic AI methods that use both engineering models and machine learning. He teaches a course called AI in BME that introduces students to AI algorithms and their applications in biomedical engineering. Sriram is the recipient of several awards including MIT Technology Review’s Top Innovators Under 35 (TR35) award,  Distinguished Young Investigator Award from the AICHE COBRA society, EBS Outstanding Teaching Award, NIH R35 MIRA award, the Machine Learning in the Chemical Sciences Award from the Camille & Henry Dreyfus Foundation.

 

Friday, September 20th, 2024

"Nano-enabled interfaces for the detection of charged environmental pollutants"
Dr. Geoff Bothun, Professor, Department Chair - Chemical Engineering, University of Rhode Island

Over the last five years our group has been pursuing an exciting yet challenging goal – to develop Surface-Enhanced Raman Scattering (SERS) sensors for the ultrasensitive detection of environmental pollutants including fertilizer runoff, textile dyes, and micro/nano-plastics. SERS is performed using nanostructured metallic surfaces such as gold or silver, which generate intense localized electromagnetic fields when exposed to incident light in the visible to near-infrared range. Molecules on or near the surface experience this field and vibrate and stretch, resulting in inelastic light or Raman scattering. There are many fundamental and translational challenges to developing SERS for field deployment including substrate (sensor) reproducibility and degradation, fouling, interfering analytes, and cost. Surface functionalization can address some of these challenges, but any surface coating will create an adsorption barrier that increases the separation distance between a target analyte and the metallic nanostructures. I will describe our efforts to address these challenges, including (1) identifying robust SERS substrates capable of detecting charged analytes in situ with the help of simple statistical models, (2) the design of millifluidic flow devices that allow for the application of an electrical potential gradient capable of drawing charged analytes to a substrate surface, and (3) how this same potential gradient can be used to inhibit marine biofouling.

Friday, October 11th, 2024

 

Friday, November 8th, 2024

 

Friday, November 22nd, 2024