Module #1 Introduction to Computational Biophysics Overview of the field, importance, and applications of computational biophysics
Module #2 Physical Principles of Biological Systems Review of fundamental physical principles underlying biological systems, including thermodynamics, statistical mechanics, and electromagnetism
Module #3 Computational Tools for Biophysics Introduction to programming languages and software used in computational biophysics, including Python, MATLAB, and molecular dynamics simulation packages
Module #4 Mathematical Modeling of Biological Systems Introduction to mathematical modeling techniques, including differential equations, stochastic processes, and network analysis
Module #5 Structural Biology and Molecular Modeling Introduction to structural biology, protein structure prediction, and molecular modeling techniques
Module #6 Molecular Dynamics Simulation Introduction to molecular dynamics simulation, including force fields, integration algorithms, and simulation protocols
Module #7 Monte Carlo Simulation Introduction to Monte Carlo simulation, including random number generation, Markov chains, and Monte Carlo integration
Module #8 Coarse-Grained Modeling Introduction to coarse-grained modeling, including simplified representations of biological systems and coarse-grained force fields
Module #9 Free Energy Calculations Introduction to free energy calculations, including thermodynamic integration, free energy perturbation, and umbrella sampling
Module #10 Bioinformatics and Sequence Analysis Introduction to bioinformatics, including sequence alignment, phylogenetics, and genomics
Module #11 Computational Analysis of Biological Networks Introduction to computational analysis of biological networks, including network topology, network dynamics, and network inference
Module #12 Computational Systems Biology Introduction to computational systems biology, including systems-level modeling of biological systems and whole-cell modeling
Module #13 Cellular Biophysics Introduction to cellular biophysics, including cell mechanics, cell adhesion, and cell signaling
Module #14 Computational Neuroscience Introduction to computational neuroscience, including modeling of neural systems and neural networks
Module #15 Machine Learning in Biophysics Introduction to machine learning, including supervised and unsupervised learning, and applications to biophysics
Module #16 Big Data and High-Performance Computing in Biophysics Introduction to big data and high-performance computing, including parallel computing, distributed computing, and cloud computing
Module #17 Case Studies in Computational Biophysics Real-world examples of computational biophysics applied to biological systems and diseases
Module #18 Modeling of Biological Processes Modeling of specific biological processes, including protein-ligand binding, protein folding, and membrane transport
Module #19 Modeling of Biological Systems Modeling of specific biological systems, including gene regulatory networks, metabolic networks, and signaling networks
Module #20 Computational Biophysics of Cellular Processes Computational biophysics of cellular processes, including cell division, cell migration, and cell death
Module #21 Computational Biophysics of Developmental Biology Computational biophysics of developmental biology, including pattern formation, morphogenesis, and tissue engineering
Module #22 Computational Biophysics of Disease Computational biophysics of disease, including modeling of disease mechanisms and development of therapeutic strategies
Module #23 Computational Biophysics and Drug Discovery Computational biophysics and drug discovery, including virtual screening, lead optimization, and pharmacokinetics
Module #24 Computational Biophysics and Systems Medicine Computational biophysics and systems medicine, including systems-level understanding of disease and personalized medicine
Module #25 Course Wrap-Up & Conclusion Planning next steps in Computational Biophysics and Modeling career
