Computational Biology

The Computational Biology research area integrates cutting-edge computational methods, machine learning, and mathematical modeling to study complex biological systems. Investigators utilize hierarchical modeling, Bayesian inference, AI-driven simulations, and large-scale data analytics to explore inter-individual heterogeneity, systems biology, pharmacometrics, and biomedical informatics. Our researchers have made significant contributions to understanding gene regulation, protein interactions, circadian rhythms, stress responses, as well as disease progression, as well as optimizing drug development and precision medicine. Our work has uncovered novel biological insights by leveraging multi-omics data, developing predictive models for therapeutic interventions, and advancing computational tools that bridge theoretical and experimental biology.

PPN Computational Biology Labs:

Chella Krishnan Laboratory

The major research focus of the K Lab is to understand how host genetic background and sex differences influence mitochondrial (mal)adaptation and increase the susceptibility to several cardiometabolic diseases.

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Herman Laboratory

We explore structural, functional and molecular biological principles underlying stress integration, with an emphasis on delineating mechanisms linking stress with mental illness and cognitive disorders.

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Hong Laboratory

Our long-term goal is to utilize temporal information from the circadian clock and its connections with other cellular processes (e.g. cell cycle, metabolism, etc.) to improve human health.

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Timme Laboratory

Research in our laboratory is focused on understanding the neural computations underlying decision-making and how they malfunction during addiction.

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Zhang Laboratory

In my research, I combine biological intuition with mathematical modeling to make clear the seemingly confusing networks.

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