Thursday, November 21, 2024
11/21/2024
Project Summary/Abstract Title: New Computational Systems Biology Methods for Modeling Gene Regulatory Circuits One of the biggest challenges in biology is to elucidate complex gene interactions and networks for the purpose of developing interventions in human disease. Particularly important are those gene networks that control cellular state transitions (e.g., replicative to quiescent, epithelial to mesenchymal, etc.). Thanks to the emergence of next-generation sequencing technology, rich data resources are available for mapping gene regulatory interactions. However, the field still lacks a systems-level understanding of how genes in a network collectively perform their functions and control cellular state transitions, information that is critical for informed clinical intervention. The PI’s long-term goal is to design effective computer-aided strategies for predicting therapeutic interventions by integrating knowledge of gene regulatory networks, genomics data from patients, and systems- biology model simulations. So far, numerous computational methods have been developed to infer and model gene regulatory networks. However, they typically suffer from the following issues. First, current approaches are still ineffective to choose an appropriate set of genes and regulatory interactions in a network to model. Current approaches infer regulatory relationships based on association of gene expression signals, but generally don't also consider whether an inferred gene regulatory network can operate as a functional dynamical system driving expected transitions between the network states. Second, traditional mathematical modeling is hard to be applied systematically to large systems, because many kinetic parameters are unmeasurable directly from experiments, especially in vivo. The parameter uncertainty and the potential risk of overfitting in large systems have limited the predictive power of systems biology. To address these issues, the PI’s research program will develop a suite of computational systems biology algorithms to construct and model high-quality core regulatory circuits driving cellular state transitions. We have recently developed enhanced ensemble-based mathematical modeling algorithms for simulating network behaviors without the need of detailed kinetic parameters. This advance has allowed an integrated top-down and bottom-up systems-biology modeling, as evident from the PI’s recently developed network reconstruction and modeling method NetAct and network coarse-graining algorithm SacoGraci. The PI’s research program will further advance novel technologies of ensemble-based modeling and their applications to optimize high-quality systems-biology models that capture cellular state transitions. The algorithms will be benchmarked and refined using in-silico simulated data, publicly available omics data sets, and data from collaborations, with a focus on cell differentiation in developmental processes and state transitions in oncogenesis. Success of the research program will result in a comprehensive toolkit that will unveil the gene regulatory mechanism of cellular state transitions. The algorithmic development is expected to have a broad impact on not only basic research in systems biology but also shed light on therapeutic intervention in genomic medicine.
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