Quantifying phenotypic adaptation of biological systems in dynamic environments
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Principal Investigator: Jason George Organization: TEXAS ENGINEERING EXPERIMENT STATION Fiscal Year: 2024 Award: $358,063 Funding agency: National Institute of General Medical Sciences PROJECT SUMMARY/ABSTRACT Despite recent experimental advances in high-dimensional time-course data generation and accompanying inferential statistical approaches, we still lack the ability to reliably estimate the time-dynamical adaptation mechanisms that biological systems utilize to navigate varying environments. The major obstacle preventing a mechanistic understanding of dynamic adaptation is an absence of hybrid theory- and data-driven models that integrate biological mechanisms of adaptation with their effects on state transitions and associated fitness in a variable environment. By leveraging my prior expertise in modeling stochastic biological processes and building upon our recent mathematical characterization of optimized adaptation strategies, this research project will develop a comprehensive computational framework to address this need. In the next five to ten years, we will address three main research goals: 1) design a mathematical framework for tracking the effects of variable, time- dependent adaptation, 2) validate computational models accounting for dynamic adaptability using time-course gene expression and sequencing data, and; 3) apply, in close experimental and clinical collaboration, the modeling framework to understand disease-specific adaptability through antigen signatures that evolve in the presence of an adaptive T cell immune repertoire. Since dynamic adaptation is fundamental to many biological processes with therapeutic implications for treatment resistance, this modeling framework will be useful for predicting the time-dependent effects of prior environmental histories on the phenotypic outcome of adaptive systems. Such predictions will also enable additional in silico evaluation of the effects of intervention on disease outcome. The proposed research will improve our understanding of the general principals governing dynamic adaptation in biological systems and provide a more comprehensive characterization of the role of antigenic adaptability in the setting of a time-varying immune environment. Terms: <2019 novel corona virus><2019 novel coronavirus><2019-nCoV><Accounting><Address><Antigens><Biological><Biological Adaptation><Biological Function><Biological Process><COVID-19 virus><COVID19 virus><Cancers><Clinical><CoV-2><CoV2><Collaborations><Communicable Diseases><Computer Models><Computerized Models><Data><Disease><Disease Outcome><Disorder><Doctor of Medicine><Doctor of Philosophy><Environment><Evaluation><Gene Expression><Generations><Goals><History><Hybrids><Immune><Immune Targeting><Immune system><Immunes><Infectious Disease Pathway><Infectious Diseases><Infectious Disorder><Instruction><Knowledge><M.D.><Malignant Neoplasms><Malignant Tumor><Math><Mathematics><Medical><Modeling><NIH RFA><Outcome><Ph.D.><PhD><Phenotype><R-Series Research Projects><R01 Mechanism><R01 Program><Recording of previous events><Request for Applications><Research><Research Grants><Research Project Grants><Research Projects><Role><SARS corona virus 2><SARS-CO-V2><SARS-COVID-2><SARS-CoV-2><SARS-CoV2><SARS-associated corona virus 2><SARS-associated coronavirus 2><SARS-coronavirus-2><SARS-related corona virus 2><SARS-related coronavirus 2><SARSCoV2><Sampling><Severe Acute Respiratory Coronavirus 2><Severe Acute Respiratory Distress Syndrome CoV 2><Severe Acute Respiratory Distress Syndrome Corona Virus 2><Severe Acute Respiratory Distress Syndrome Coronavirus 2><Severe Acute Respiratory Syndrome CoV 2><Severe Acute Respiratory Syndrome-associated coronavirus 2><Severe Acute Respiratory Syndrome-related coronavirus 2><Severe acute respiratory syndrome associated corona virus 2><Severe acute respiratory syndrome coronavirus 2><Severe acute respiratory syndrome related corona virus 2><System><T-Cells><T-Lymphocyte><Therapeutic><Time><Treatment Failure><Wuhan coronavirus><biologic><biological systems><computational framework><computational modeling><computational models><computer based models><computer framework><computerized modeling><coronavirus disease 2019 virus><coronavirus disease-19 virus><data-driven model><design><designing><fitness><hCoV19><high dimensionality><histories><immunogen><improved><in silico><intervention effect><leukemia><malignancy><nCoV2><neoplasm/cancer><prevent><preventing><resistance to therapy><resistant to therapy><social role><theories><therapeutic resistance><therapy failure><therapy resistant><thymus derived lymphocyte><time use><treatment resistance>