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RESEARCH PROGRAM OVERVIEW

 

Biological systems are beautifully complex and their nonlinear dynamics mediate the difference between health or disease, treatment success or treatment failure. The Yang Lab seeks fundamental, first-principles understanding into the molecular mechanisms underlying disease progression and cure for chronic and infectious diseases.

 

Projects in our lab leverage advances in systems biology, synthetic biology, machine learning, and artificial intelligence to drive mechanistic discovery. We employ quantitative, live-cell, dynamic, high-throughput, and multi-OMIC experimental approaches. We couple these to network modeling, machine learning, and bioinformatic analyses. Our research focuses on the greatest global health challenges of our time, including antimicrobial resistance, tuberculosis, and heart failure.

MACHINE LEARNING-GUIDED DISCOVERY

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Yang JH, Cell 2019

Recent advances in high-throughput experimental technologies and data analyses have enabled the unprecedented observation, quantification and association of biological signals with cellular and clinical phenotypes. We are developing integrated network modeling and interpretable machine learning-based approaches to rapidly reveal causal mechanisms underlying therapeutic efficacy and disease pathogenesis.

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Relevant Publications:​

ANTIMICROBIAL RESISTANCE AND PERSISTENCE

Antimicrobial resistance poses an urgent and growing threat to global health. Despite knowledge on the primary targets for conventional antibiotics, it remains unclear why antibiotic treatment can sometimes fail. We are combining high-throughput assays, OMICS-characterization, fluorescence microscopy and animal experiments with network modeling, machine learning and bioinformatic analyses to explore how bacterial metabolism and bacterial stress responses regulate antimicrobial efficacy.

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Relevant Publications:

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Lopatkin AJ, Science 2021

TUBERCULOSIS

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Tuberculosis (TB) is the leading cause of death from a single infectious agent from across the world. Standard treatment for TB involves combination therapy with at least 4 antibiotics for a minimum of 6 months. Even so, TB infection relapse rates are ~5% and patients may harbor undetectable latent infections lasting years before relapse. We are applying our network modeling and machine learning methods towards understanding mechanisms underlying the mechanisms underlying TB relapse and latency from human-derived biospecimens. We experimentally validate these mechanisms in a shared BSL-3 facility in the Center for Emerging and Re-Emerging Pathogens and Public Health Research Institute.

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Relevant Publications:

IMMUNOENGINEERING

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Lim WA, Cell 2017

Advances in genetics and other experimental platforms are for the first time providing unprecedented tools for engineering biological organisms that can perform useful functions. Based on our discoveries on how innate immune cells make context-dependent decisions, we are applying approaches from synthetic biology to engineer next generation cell-based immunotherapies for chronic and infectious diseases. We are inspired by the natural complexity of living systems and seek to engineer high-order gene circuits for controlling immune cell behaviors. We combine dynamic live-cell imaging experiments, phenotypic assays with network modeling and machine learning to reverse engineer the biological circuits underlying how immune cells process extracellular cues. We use this information to forward engineer immune cell therapies for complex and chronic diseases.

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Relevant Publications:

HEART FAILURE

Cardiovascular diseases are the leading causes of death around the world. First-line therapeutics target the same cell signaling pathways as those used by the body to adapt to changes in oxygen demand in healthy individuals. We previously demonstrated how network topology and subcellular compartmentation both regulate β-adrenergic signaling responses in cardiac myocytes, and how these are able to select between healthy physiologic responses vs. pathophysiologic responses to receptor activation. We are combining dynamic live-cell imaging experiments, phenotypic assays with network modeling and machine learning to understand cell signaling dynamics in cardiac cells in the context of heart failure.

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Relevant Publications:

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COLLABORATORS

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David Alland, Rutgers New Jersey Medical School

Caleb Bashor, Rice University

Robert Brewster, University of Massachusetts Chan Medical School

James Collins, Massachusetts Institute of Technology

Christoph Grundner, Seattle Children's Research Institute

Bear Huang, Johns Hopkins Medical Institute

Allison Lopatkin, University of Rochester

Peijun Ma, St. Jude Children's Hospital

Shuyi Ma, Seattle Children's Research Institute

Rohan Maddamsetti, Rutgers University

Michael Manhart, Rutgers University

Matthew McNeil, University of Otago

Bernhard Palsson, University of California, San Diego

Adam Rosenthal, University of North Carolina Medical School

Padmini Salgame, Rutgers New Jersey Medical School

David Sherman, University of Washington

Christina Stallings, Washington University of St. Louis

RESEARCH SUPPORT

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Our work is generously supported by the following sponsors:

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