# Systems Biology Core

> **NIH NIH U19** · RBHS-NEW JERSEY MEDICAL SCHOOL · 2021 · $384,717

## Abstract

SYSTEMS BIOLOGY CORE ABSTRACT
 This overall TBRU Program seeks to understand how both host and bacterial heterogeneity act together to
promote TB clinical phenotypes such as transmission, disease progression, and drug tolerance. These two
subjects (biological heterogeneity and clinical phenotypes) are emergent properties of intracellular networks and
of multicellular interactions, respectively, rendering this overall topic challenging to study by conventional
hypothesis-driven research approaches. Systems-level analyses are required in order to account for the
biological complexity imposed by cellular network and multicellular interactions and advanced computational
techniques are required to elucidate biological understanding from the increasingly large quantitative datasets
generated by modern advanced experimental platforms. The Systems Biology Core is designed to meet both of
these needs, providing advanced bioinformatics, biomedical data science, and network modeling analysis
services to support each Project in this Proposal. This Core is led by Drs. Evan Johnson, Shuyi Ma, and Jason
Yang, each with extensive subject-matter expertise in diverse computational and systems biology analytical
approaches, and each of whom actively collaborates with other investigators from this TBRU on diverse
tuberculosis research projects. The Systems Biology Core will aid in the standardized processing and analysis
of data from each Project, generation of experimentally testable hypotheses for each Project, and integrative
analyses of mechanisms connecting clinical phenotypes across Projects. Two key strengths of this Core that
differentiate it from other computational cores and that enable this TBRU to uniquely study clinical biospecimens
are: (i) the extensive expertise in using biomarker signatures such as PREDICT29 to detect incipient and
subclinical TB disease, expanding the range of clinical Mtb strains and host cells that can be studied; and (ii) the
extensive expertise in multiscale cellular network modeling and interpretable machine learning, expanding the
breadth and precision of biological hypotheses that can be generated from each set of experimental data.
Investigators in this TBRU have uniquely developed Mtb gene regulatory and metabolic network models, which
will be used by this Systems Biology Core to form condition-specific models of host and Mtb cell physiology
corresponding to experimental samples for each Project. These models will not only enable the Core to
deconvolve the large experimental datasets generated in this Program, but will also enable the Core to directly
predict causal gene regulatory and metabolic gene and pathway mechanisms that underlie each of the key
clinical phenotypes studied from these clinical samples: TB transmission, disease progression and drug
tolerance. These models and analyses will enable direct integration between Projects, allowing this TBRU to
determine how these clinical phenotypes may be mechanistically l...

## Key facts

- **NIH application ID:** 10271647
- **Project number:** 1U19AI162598-01
- **Recipient organization:** RBHS-NEW JERSEY MEDICAL SCHOOL
- **Principal Investigator:** William Evan Johnson
- **Activity code:** U19 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $384,717
- **Award type:** 1
- **Project period:** 2021-09-23 → 2026-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10271647

## Citation

> US National Institutes of Health, RePORTER application 10271647, Systems Biology Core (1U19AI162598-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10271647. Licensed CC0.

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