Metabolic Objectives and Trade-Offs: Inference and Applications.

Determining appropriate cellular objectives is crucial for the system-scale modeling of biological networks for metabolic engineering, cellular reprogramming, and drug discovery applications. The mathematical representation of metabolic objectives can describe how cells manage limited resources to achieve biological goals within mechanistic and environmental constraints. While rapidly proliferating cells like tumors are often assumed to prioritize biomass production, mammalian cell types can exhibit objectives beyond growth, such as supporting tissue functions, developmental processes, and redox homeostasis.

Inferring metabolic objectives and trade-offs in single cells during embryogenesis.

While proliferating cells optimize their metabolism to produce biomass, the metabolic objectives of cells that perform non-proliferative tasks are unclear. The opposing requirements for optimizing each objective result in a trade-off that forces single cells to prioritize their metabolic needs and optimally allocate limited resources. Here, we present single-cell optimization objective and trade-off inference (SCOOTI), which infers metabolic objectives and trade-offs in biological systems by integrating bulk and single-cell omics data, using metabolic modeling and machine learning.

Genome-scale modeling identifies dynamic metabolic vulnerabilities during the epithelial to mesenchymal transition.

Epithelial-to-mesenchymal transition (EMT) is a conserved cellular process critical for embryogenesis, wound healing, and cancer metastasis. During EMT, cells undergo large-scale metabolic reprogramming that supports multiple functional phenotypes including migration, invasion, survival, chemo-resistance and stemness. However, the extent of metabolic network rewiring during EMT is unclear.

Transfer learning predicts species-specific drug interactions in emerging pathogens.

Machine learning (ML) algorithms are necessary to efficiently identify potent drug combinations within a large candidate space to combat drug resistance. However, existing ML approaches cannot be applied to emerging and under-studied pathogens with limited training data. To address this, we developed a transfer learning and crowdsourcing framework (TACTIC) to train ML models on data from multiple bacteria.

Integrative analysis of multimodal patient data identifies personalized predictors of tuberculosis treatment prognosis.

Tuberculosis (TB) afflicted 10.6 million people in 2021, and its global burden is increasing due to multidrug-resistant TB (MDR-TB) and extensively resistant TB (XDR-TB). Here, we analyze multi-domain information from 5,060 TB patients spanning 10 countries with high burden of MDR-TB from the NIAID TB Portals database to determine predictors of TB treatment outcome.

Leveraging metabolic modeling and machine learning to uncover modulators of quiescence depth.

Quiescence, a temporary withdrawal from the cell cycle, plays a key role in tissue homeostasis and regeneration. Quiescence is increasingly viewed as a continuum between shallow and deep quiescence, reflecting different potentials to proliferate. The depth of quiescence is altered in a range of diseases and during aging.

Acetyl-CoA metabolism drives epigenome change and contributes to carcinogenesis risk in fatty liver disease.

Background: The incidence of non-alcoholic fatty liver disease (NAFLD)-associated hepatocellular carcinoma (HCC) is increasing worldwide, but the steps in precancerous hepatocytes which lead to HCC driver mutations are not well understood. Here we provide evidence that metabolically driven histone hyperacetylation in steatotic hepatocytes can increase DNA damage to initiate carcinogenesis.

Methods: Global epigenetic state was assessed in liver samples from high-fat diet or high-fructose diet rodent models, as well as in cultured immortalized human hepatocytes (IHH cells).

Machine learning to design antimicrobial combination therapies: Promises and pitfalls.

Combination therapies can overcome antimicrobial resistance (AMR) and repurpose existing drugs. However, the large combinatorial space to explore presents a daunting challenge. In response, machine learning (ML) algorithms are being applied to identify novel synergistic drug interactions from millions of potential combinations.

Metabolism, HDACs, and HDAC Inhibitors: A Systems Biology Perspective.

Histone deacetylases (HDACs) are epigenetic enzymes that play a central role in gene regulation and are sensitive to the metabolic state of the cell. The cross talk between metabolism and histone acetylation impacts numerous biological processes including development and immune function. HDAC inhibitors are being explored for treating cancers, viral infections, inflammation, neurodegenerative diseases, and metabolic disorders.

Next-Generation Genome-Scale Metabolic Modeling through Integration of Regulatory Mechanisms.

Genome-scale metabolic models (GEMs) are powerful tools for understanding metabolism from a systems-level perspective. However, GEMs in their most basic form fail to account for cellular regulation. A diverse set of mechanisms regulate cellular metabolism, enabling organisms to respond to a wide range of conditions.

Nutrient Sensing by Histone Marks: Reading the Metabolic Histone Code Using Tracing, Omics, and Modeling.

Several metabolites serve as substrates for histone modifications and communicate changes in the metabolic environment to the epigenome. Technologies such as metabolomics and proteomics have allowed us to reconstruct the interactions between metabolic pathways and histones. These technologies have shed light on how nutrient availability can have a dramatic effect on various histone modifications.