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 linked. Together, this Core will synergize with each and with all Projects to mechanistically bridge host and pathogen heterogeneity with clinical outcomes.