RESEARCH PROGRAM OVERVIEW

 

Biological systems are beautifully complex and their nonlinear organization mediate the bifurcation between health and disease, treatment success and treatment failure. The Yang Lab seeks first-principles understanding into how cells process and respond to signals and stresses in their local environment. Such insights are important for the rational design of next-generation therapeutics that can address our most pressing public health needs.

 

Our overall goal is to tackle the most important challenges in global health with systems approaches that can enable precision medicine.

WHITE-BOX MACHINE LEARNING

 

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. However, current methods for inferring biological mechanisms from large datasets frequently encode such information in experimentally inaccessible "black-boxes". We are developing integrated network modeling and machine learning "white-box" approaches for rapidly revealing causal mechanisms underlying drug efficacy and disease pathogenesis.

Relevant Publications:

Yang JH, Wright SN*, Hamblin M*, McCloskey D, Alcantar MA, Schrübbers L, Lopatkin AJ, Satish S, Nili A, Palsson BO, Walker GC, Collins JJ. A white-box machine learning approach for revealing antibiotic mechanisms of action. Cell. 2019;177(6):1649-1661.

ANTIBIOTIC EFFICACY

Antimicrobial resistance is a growing threat to global health. Despite our knowledge of the primary targets for conventional antibiotics, it remains unclear why antibiotic treatment can sometimes fail. We are developing integrated experimental and computational systems biology approaches for studying mechanisms underlying the context-dependence of antibiotic efficacy.

Relevant Publications:

Yang JH*, Bening SC*, Collins JJ. Antibiotic efficacy – context matters. Curr Opin Microbiol. 2017; 39:73-80.

 

Yang JH*, Bhargava P*, McCloskey D, Mao N, Palsson BO, Collins JJ. Antibiotic-induced changes to the host metabolic environment inhibit drug efficacy and alter immune function. Cell Host Microbe. 2017; 22(6):757-765.

 

Lopatkin AJ, Stokes JM, Zheng EJ, Yang JH, Takahashi MK, You L, Collins JJ. Bacterial metabolic state more accurately predicts antibiotic lethality than growth rateNat Microbiol. In press.

TUBERCULOSIS RELAPSE AND LATENCY

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 white-box machine learning methods towards understanding mechanisms underlying the efficacy of TB antibiotics. We are also developing data-driven approaches for inferring 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.

Relevant Publications:

Yang JH, Wright SN*, Hamblin M*, McCloskey D, Alcantar MA, Schrübbers L, Lopatkin AJ, Satish S, Nili A, Palsson BO, Walker GC, Collins JJ. A white-box machine learning approach for revealing antibiotic mechanisms of action. Cell. 2019;177(6):1649-1661.

IMMUNOLOGICAL DECISION MAKING

Human immune cells possess an incredible ability to integrate many simultaneous (and sometimes contradictory) signals to make complex decisions in selecting between different cellular behaviors. Additionally, antibiotics are able to non-specifically act on immune cells and augment their function. We are integrating the use of network models, machine learning and live cell imaging to better understand how innate immune cells process and respond to extracellular cues and the impact of these responses on the pathogenesis and resolution of infectious diseases.

Relevant Publications:

Yang JH*, Bhargava P*, McCloskey D, Mao N, Palsson BO, Collins JJ. Antibiotic-induced changes to the host metabolic environment inhibit drug efficacy and alter immune function. Cell Host Microbe. 2017; 22(6):757-765.

Sample V*, DiPilato LM*, Yang JH*, Ni Q, Saucerman JJ, Zhang J. Regulation of nuclear PKA revealed by spatiotemporal manipulation of cAMP. Nat Chem Biol. 2012; 8(4):375-82.

Yang JH, Polanowska-Grabowska RK, Smith JS, Shields CW, Saucerman JJ. PKA catalytic subunit compartmentation regulates contractile and hypertrophic responses to β-adrenergic signaling. J Mol Cell Cardiol. 2014; 66:83-93.

COLLABORATORS

We collaborate with the following labs:

David Alland, Rutgers New Jersey Medical School

James Collins, Massachusetts Institute of Technology

Veronique Dartois, Hackensack Meridian Health

Cheryl Day, Emory University

Bernhard Palssön, University of California, San Diego

Jyothi Rengarajan, Emory University

Jeffrey Saucerman, University of Virginia

David Sherman, University of Washington

Graham Walker, Massachusetts Institute of Technology

Jin Zhang, University of California, San Diego

FUNDING

Our work is generously supported by:

National Institutes of Health

Rutgers New Jersey Medical School