Systems Biology Consortium for Infectious Diseases

The Systems Biology Consortium for Infectious Diseases is a group of interdisciplinary scientists that bridge disparate scientific disciplines including microbiology, immunology, infectious diseases, microbiome, mathematics, physics, bioinformatics, computational biology, machine learning, statistical methods, and mathematical modeling. These teams integrate large-scale experimental biological and clinical data across temporal and spatial scales. Scientists iteratively test and validate hypotheses to gain insight into the overall complexity of the biological, biochemical, and biophysical molecular processes within microbial organisms as well as their interaction with the host. The research findings drive innovation and discovery, with the goal of developing novel therapeutic and diagnostic strategies, and predictive signatures of disease to alleviate infectious disease burden and provide solutions to complex public health challenges and disease outbreaks.

Currently funded centers:
SARS-CoV adaptations through a Systems Biology Lens (SYBIL)
PI: Adolfo Garcia-Sastre, Icahn School of Medicine at Mount Sinai (RePORTER)

Successful Clinical Response in Pneumonia Therapy (SCRIPT) Systems Biology Center
PI: Richard Wunderink, Northwestern University at Chicago (RePORTER)

Center for Viral Systems Biology (CViSB)
PI: Kristian Andersen, Scripps Research (RePORTER)

Systems Epigenomics of Persistent Bloodstream Infection
PI: Michael Yeaman, University of California, Los Angeles (RePORTER)

Host Pathogen Mapping Initiative (HPMI)
PI: Nevan Krogan, University of California, San Francisco (RePORTER)

The SARS-CoV Adaptations Through a Systems Biology Lens (SYBIL) Center uses an integrated systems biology approach to understand the molecular mechanisms that govern SARS-CoV replication, pathogenesis, disease severity, and adaptation to human hosts. SYBIL combines multi-omics technologies, including transcriptomics, epigenomics, proteomics, and functional genomics, with computational modeling and machine learning to identify and validate critical virus-host networks. By integrating data from human clinical samples, animal models, and human and bat cellular systems, the Center seeks to identify biomarkers associated with disease outcomes, uncover mechanisms that enable zoonotic viruses to adapt to humans, and discover host and viral factors that can be targeted for therapeutic intervention. SYBIL aims to generate predictive models of coronavirus infection that can inform treatments for COVID-19 and strengthen preparedness for future emerging viral diseases.

The Super-SCRIPT (Successful Clinical Response In Pneumonia Therapy, SCRIPT²) Systems Biology Center aims to advance our understanding of severe pneumonia through an innovative, integrated systems biology approach. By combining cutting-edge multi-omics technologies, clinical phenotyping, and machine learning, SCRIPT² explores the complex host/pathogen interactions that influence the clinical course of pneumonia. With a focus on both community-acquired pneumonia (CAP) and hospital-acquired/ventilator-associated pneumonia (HAP/VAP), the center seeks to identify biomarkers and therapeutic targets that can improve patient outcomes and inform clinical decision-making.

What are the immunological, genetic, microbial and physiological attributes that play essential roles in determining outcomes from viral infections? The mission of the Center for Viral Systems Biology (CViSB; pronounced “SEE-VIZ-bee”) is to identify such factors and elucidate the molecular and immunological networks that determine outcomes of human disease. We hope that via this research we will be able to provide a deep system-level understanding of the virus and human determinants of clinical outcome to discover predictive markers of disease, and guide future therapies.

Persistent bloodstream infections are life-threatening emergencies that pose significant challenges to effective treatment. A disease mystery is central to these infections: the causative pathogen is susceptible to antimicrobials in conventional laboratory testing—but not in the human being. Persistent bloodstream infections caused by Staphylococcus aureus and Candida albicans are increasingly common, but there are few therapeutic options and patients frequently succumb. In this U19 Center, we will use state-of-the-art technologies in synergistic research to understand the host-pathogen interactions driving persistence. In turn, these data will be analyzed using powerful computational methods to detect hidden patterns within large complex datasets. As a result, new approaches to identify and treat high risk patients can be developed and applied to improve and save lives. These goals are ideally aligned with priorities of the National Institutes of Health and Centers for Disease Control & Prevention.

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The Host Pathogen Map Initiative (HPMI) is an NIAID-funded Systems Biology Center led by investigators at the University of California San Francisco, the Gladstone Institutes, the University of California Berkeley, Memorial Sloan Kettering Cancer Center, and the University of Washington. HPMI applies multidisciplinary systems biology approaches to define the molecular interactions between human host cells and respiratory bacterial and viral pathogens. By integrating protein interaction mapping, functional genomics, computational modeling, and advanced primary cell models, HPMI seeks to identify host pathways that regulate infection, uncover mechanisms of disease progression, and accelerate the development of host-directed therapeutic strategies. The Center fosters collaboration across experimental and computational disciplines while making data, interaction networks, and analytical resources broadly available to the scientific community.