Modeling in Biology

  1. Advanced and Emerging Topics
    1. Multiscale Modeling
      1. Linking Molecular and Cellular Levels
        1. Cellular to Tissue Scale
          1. Tissue to Organ Scale
            1. Organism to Population Scale
              1. Bridging Temporal Scales
                1. Bridging Spatial Scales
                  1. Homogenization Techniques
                  2. Hybrid Models
                    1. Combining Deterministic and Stochastic Elements
                      1. Integrating Different Modeling Paradigms
                        1. Multi-Method Approaches
                          1. Switching Between Model Types
                          2. Data-Driven and Machine Learning-Assisted Modeling
                            1. Automated Model Discovery
                              1. Symbolic Regression
                                1. Neural Ordinary Differential Equations
                                  1. Integrating Big Data
                                    1. Genomics Data
                                      1. Proteomics Data
                                        1. Imaging Data
                                          1. Time-Series Data
                                          2. Physics-Informed Neural Networks
                                          3. Model Selection and Comparison
                                            1. Information Criteria
                                              1. Akaike Information Criterion
                                                1. Bayesian Information Criterion
                                                  1. Deviance Information Criterion
                                                  2. Model Complexity vs Predictive Power
                                                    1. Occam's Razor in Modeling
                                                      1. Ensemble Methods
                                                      2. Uncertainty Quantification
                                                        1. Sources of Uncertainty
                                                          1. Parameter Uncertainty
                                                            1. Model Structure Uncertainty
                                                              1. Data Uncertainty
                                                              2. Propagation of Uncertainty
                                                                1. Monte Carlo Methods
                                                                  1. Polynomial Chaos Expansion
                                                                  2. Quantitative Methods for Uncertainty Analysis
                                                                    1. Confidence Intervals
                                                                      1. Prediction Intervals
                                                                        1. Sensitivity to Uncertainty
                                                                      2. High-Performance Computing
                                                                        1. Parallel Computing
                                                                          1. GPU Acceleration
                                                                            1. Cloud Computing
                                                                              1. Distributed Simulations
                                                                              2. Model Reduction Techniques
                                                                                1. Dimensional Reduction
                                                                                  1. Proper Orthogonal Decomposition
                                                                                    1. Reduced Order Models
                                                                                      1. Quasi-Steady State Approximation
                                                                                      2. Ethical Considerations in Modeling
                                                                                        1. Model Transparency
                                                                                          1. Reproducibility
                                                                                            1. Open Science Practices
                                                                                              1. Use in Public Policy
                                                                                                1. Clinical Decision-Making
                                                                                                  1. Data Privacy and Security
                                                                                                    1. Bias in Models
                                                                                                      1. Responsible AI in Biology

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                                                                                                    6. Model Implementation, Analysis, and Validation

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                                                                                                    1. Introduction to Biological Modeling