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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.

 

We leverage advances in systems biology and biomedical data science to drive our discovery of mechanistic insights. 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:​

ENGINEERED IMMUNE CELL THERAPIES

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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:

ANTIMICROBIAL RESISTANCE

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 contributes to 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.

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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

James Collins, Massachusetts Institute of Technology

Veronique Dartois, Hackensack Meridian Health

Cheryl Day, Emory University

Allison Lopatkin, Barnard College

Shuyi Ma, Seattle Children's Hospital

Bernhard Palssön, University of California, San Diego

Dane Parker, Rutgers New Jersey Medical School

Jyothi Rengarajan, Emory University

Jeanne Salje, Rutgers New Jersey Medical School

Jeffrey Saucerman, University of Virginia

Padmini Salgame, Rutgers New Jersey Medical School

David Sherman, University of Washington

Graham Walker, Massachusetts Institute of Technology

Jin Zhang, University of California, San Diego

RESEARCH SUPPORT

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Our work is generously supported by the following sponsors:

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