model fitting
Definition
Model fitting is the process of adjusting mathematical or statistical model parameters to best represent observed biological data. In life sciences, this involves selecting appropriate models (linear, nonlinear, mechanistic, or machine learning-based) and optimizing their parameters to minimize the difference between predicted and experimental values. Model fitting is crucial for understanding biological mechanisms, predicting system behavior, and quantifying relationships between variables such as dose-response curves, enzyme kinetics, or gene expression dynamics. Quality of fit is assessed using metrics like R-squared, AIC, or cross-validation error. Proper model fitting enables researchers to make reliable predictions, test hypotheses, and extract meaningful biological insights from complex datasets.
Visualize model fitting in Nodes Bio
Researchers can visualize model fitting results across biological networks by mapping fitted parameters as node attributes and prediction accuracy as edge weights. Network visualization reveals which pathways or protein interactions are best explained by specific models, identifying well-characterized versus poorly understood regions. Nodes Bio enables comparison of multiple competing models across the same network structure, highlighting areas requiring additional experimental validation or alternative modeling approaches.
Visualization Ideas:
- Metabolic networks with enzyme kinetic parameters as node sizes and fit quality as color gradients
- Signaling pathways showing dose-response curve parameters with confidence intervals as edge thickness
- Gene regulatory networks displaying temporal model predictions versus experimental data across different cell states
Example Use Case
A systems biology team studying cancer metabolism fits kinetic models to enzyme activity data across glycolysis and oxidative phosphorylation pathways. They discover that while most reactions follow Michaelis-Menten kinetics with high confidence, three key regulatory enzymes show poor fit quality. Network visualization reveals these poorly-fitted nodes are hub proteins with multiple post-translational modifications not captured in the initial model. This insight prompts inclusion of allosteric regulation terms, significantly improving model accuracy and revealing previously unknown metabolic control mechanisms in tumor cells.