Parameter Estimation, Optimization & Bayesian Calibration — Hands-on

Learn how to turn mechanistic and constraint-based models into quantitatively calibrated decision tools. This module covers cost functions and likelihoods, local and global optimization, Bayesian calibration with MCMC, and uncertainty-aware prediction. You will work with ODE/SDE models, signaling and metabolic networks, and implement end-to-end estimation workflows using Python (SciPy/lmfit/PyMC) , R (FME/bbmle) , COPASI, and Stan, with publication-ready figures and reports.

Parameter Estimation, Optimization & Bayesian Calibration

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Session Index

Session 1 — Fundamentals & Cost Functions Session 2 — Optimization Strategies & Workflows Session 3 — Bayesian Calibration & MCMC Session 4 — Mini Capstone: Calibrated Model with Uncertainty

Session 1

Fee: Rs 8800

Fundamentals & Cost Functions

Framing parameter estimation problems

ODE/SDE models constraint-based models (overview) observables & experimental design

Error models, cost & likelihood

least squares / weighted least squares Gaussian likelihoods log-likelihood & information criteria (AIC/BIC)

Toolchain & basic fitting

Python: SciPy optimize, lmfit R: FME, bbmle COPASI fits

Session 2

Fee: Rs 11800

Optimization Strategies & Workflows

Local optimization methods

gradient-based (BFGS, Levenberg–Marquardt) Nelder–Mead & trust-region multi-start strategies

Global & hybrid optimization

genetic algorithms / DE particle swarm / simulated annealing global + local refinement workflows

Identifiability & practical issues

structural vs practical identifiability (overview) parameter bounds & scaling overfitting & regularization

Session 3

Fee: Rs 14800

Bayesian Calibration & MCMC

Priors, posteriors & Bayes workflow

prior elicitation posterior interpretation credible intervals vs confidence intervals

MCMC algorithms & diagnostics

Metropolis–Hastings / Gibbs HMC / NUTS (Stan, PyMC overview) convergence checks (R-hat, ESS, trace plots)

Toolchain for Bayesian calibration

Stan / CmdStanR / CmdStanPy PyMC / emcee COPASI parameter estimation (Bayesian mode overview)

Session 4

Fee: Rs 18800

Mini Capstone: Calibrated Model with Uncertainty

End-to-end case study: from time-course data to calibrated model

Theory + Practical

Uncertainty, prediction & scenarios

posterior predictive checks prediction intervals & bands scenario simulations with parameter samples

Deliverables

PDF/HTML calibration report Python/R notebook + model files environment.yml/requirements.txt

PDF

Parameter Estimation, Optimization & Bayesian Calibration Training | Frequentist & Bayesian Workflows

Parameter Estimation, Optimization & Bayesian Calibration — Hands-on