2023-2024 Course Catalog
Welcome to Virginia Tech! We are excited that you are here planning your time as a Hokie.
Welcome to Virginia Tech! We are excited that you are here planning your time as a Hokie.
Introduction to fundamental concepts of systems biology: biological systems, molecular regulatory networks, modeling approaches in systems biology with case studies, high-throughput data generation and bioinformatics data processing.
Fundamental mathematical methods in systems biology, including differential equations, graph theory, Boolean mathematics, and concepts of probability. Applications of these methods to developing models of biological regulatory networks and dynamical systems. Software tools for Systems Biology.
Bioinformatic approaches in omics, namely genomics and transcriptomics. 3035: Genomic architecture and evolution. Gene expression and post-translational regulation. Structure and function of genes and other genetic elements. Experimental techniques for generating genomic and transcriptomic data. 3036: Statistical, evolutionary, and computational models and methods to analyze omics data. Techniques for visualization and biological interpretation of omics data derived from experiments. Application of Python and R to bioinformatics. Case studies and specific applications in molecular biology, including comparative genomics, cancer, and infectious diseases.
Bioinformatic approaches in omics, namely genomics and transcriptomics. 3035: Genomic architecture and evolution. Gene expression and post-translational regulation. Structure and function of genes and other genetic elements. Experimental techniques for generating genomic and transcriptomic data. 3036: Statistical, evolutionary, and computational models and methods to analyze omics data. Techniques for visualization and biological interpretation of omics data derived from experiments. Application of Python and R to bioinformatics. Case studies and specific applications in molecular biology, including comparative genomics, cancer, and infectious diseases.
In-depth study of how molecular regulatory networks determine the physiological properties of prokaryotic and eukaryotic cells. 3115: Biochemical reaction networks, nonlinear dynamical systems, parameter estimation, bifurcation theory, switches and oscillators, gene regulatory networks, signaling pathways, metabolic networks, neural networks, applications. 3116: Stochastic effects, cell cycle and cancer, spatial effects, motility, development, tissue dynamics, applications.
In-depth study of how molecular regulatory networks determine the physiological properties of prokaryotic and eukaryotic cells. 3115: Biochemical reaction networks, nonlinear dynamical systems, parameter estimation, bifurcation theory, switches and oscillators, gene regulatory networks, signaling pathways, metabolic networks, neural networks, applications. 3116: Stochastic effects, cell cycle and cancer, spatial effects, motility, development, tissue dynamics, applications.
Career planning, interviewing skills, and training in written and oral communication in systems biology. Critical evaluation of research, effective communication of scientific results, ethical standards in science, societal trends.
Training and practical experience in the conduct of systems biology research. 4065: Plan a research project, develop a research hypothesis, and perform preliminary testing and analysis. 4066: Execute, refine, complete, and document the projects results.
Training and practical experience in the conduct of systems biology research. 4065: Plan a research project, develop a research hypothesis, and perform preliminary testing and analysis. 4066: Execute, refine, complete, and document the projects results.
Dynamic modeling of gene regulatory networks. Gene regulatory networks with oscillatory and switch-like dynamic behavior. Design of synthetic genetic switches and oscillators. Modeling gene regulation controlling cell fate, cell differentiation, cell-to-cell communication, synchronization and developmental processes. Real-world research problems and applications.
Big data analysis in systems biology. Emphasis on data storage/retrieval and curation, statistical modeling of gene expression, enrichment analysis, clustering, parameter optimization and estimation in systems biology models, linear and nonlinear classification methods.
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