Abstract: Biological networks regulate cellular development, physiology, and functionality. Interventions targeting biological networks are promising strategies for treating a wide range of chronic and infectious diseases. However, network mechanisms regulating cellular responses to external stimuli are difficult to study due to complexities in scale, connectivity, and uncertainty. To address these challenges, we have developed an interpretable machine learning approach that integrates high-throughput experimentation, predictive network modeling, and machine learning. Our approach directly delivers pathway mechanisms underlying cell phenotypes that can be experimentally validated. Here we will discuss our efforts in using this approach to study mechanisms involved in antibiotic lethality and bacterial stress responsiveness and our ongoing efforts in extending this approach to engineer therapeutic immune cells.

Jason Yang is Assistant Professor & Chancellor Scholar, Microbiology, Biochemistry and Re-Emerging Pathogens, at
Rutgers University.

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