A major limitation of most network mapping and analysis efforts is that they implicitly consider the system under static conditions, while real biological systems are under constant change. The dynamics of these biological systems are a reflection of context specificity (e.g., cell type), responses to environmental perturbations (e.g., chemical perturbations or viral infections), and genetic alterations (e.g., somatic mutations). Ultimately, we must understand how these dynamics affect ? or are affected by ? the underlying physical and genetic networks active at a particular time. Differential analysis of biological systems under multiple conditions (or in multiple systems) allows us to gain fundamental understanding of these biological responses and how biological networks are re-wired in response to perturbations and alterations. In this project, we will develop a series of tools and methodologies for conducting differential analyses of biological networks altered under multiple conditions. We will pursue novel algorithmic methods that allow us to make use of high-throughput, proteomic-level data to recover biological networks under specific biological perturbations. The software tools developed in this project allow researchers to further predict, analyze, and visualize the effects of these perturbations and alterations, while aggregating additional information regarding the known roles of the dynamic interactions and their participants.
TRD 1: DIFFERENTIAL NETWORKS ? PROJECT NARRATIVE Network models are frequently used to integrate molecular data with prior biological knowledge, with the goal of elucidating disease pathways and identifying potential drug targets. We will develop novel bioinformatic tools that allow researchers to use high-throughput proteomic and genomic data to model the effects of dynamic network perturbations and gain an understanding of how these perturbations re-wire biological networks. These tools will help make clinically relevant diagnoses and predictions about an individual and their potential response to therapeutic interventions.
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| Wang, Sheng; Ma, Jianzhu; Zhang, Wei et al. (2018) Typing tumors using pathways selected by somatic evolution. Nat Commun 9:4159 |
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| Ebhardt, H Alexander; Root, Alex; Liu, Yansheng et al. (2018) Systems pharmacology using mass spectrometry identifies critical response nodes in prostate cancer. NPJ Syst Biol Appl 4:26 |
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