URL study guide
https://tue.osiris-student.nl/onderwijscatalogus/extern/cursus?cursuscode=8CM00&collegejaar=2025&taal=enDescription
Sequence alignment, pre-processing en analyse van RNAseq data, graph theory.Genome-scale metabolic models, constraint-based simulation, including Flux Balance Analysis with linear programming.
Nonlinear state space models (system of coupled differential equations) of biochemical networks: modeling with enzyme kinetics, implementation and simulation in Matlab or Python.
Parameter sensitivity analysis, including global sensitivity analysis, latin hypercube sampling (LHS), bootstrap..
Parameter estimation for nonlinear models, numerical optimization and regularization, parameter identifiability (Profile Likelihood Analysis) and uncertainty analysis.
Objectives
After passing the course, the student:- Has knowledge about concepts and algorithms for sequence alignment to arrange the primary sequences of DNA, RNA, or protein. Understands and can apply bioinformatics' tools and algorithms to clean, process, and perform standard analysis on transcriptomic data derived from experiments.
- Has knowledge about concepts of networks in systems biology. Understands and can apply mathematical and computational procedures for static representation in graphs and topological analysis of biochemical pathways (degree distribution, hubs, distance metrics, cluster coefficient).
- Has knowledge about about the concept of Genome-scale metabolic models, has insight in the procedures of constraint-based simulation and can apply Flux Balance Analysis. Can generate context specific metabolic reconstructions using experimental data and can derive biological insights by comparing multiple properties of the models.
- Can integrate insights about dynamic biochemical networks modelled as state-space models using enzyme kinetics with application of numerical algorithms for simulation in Matlab. Can analyse and reflect about the influence of network structure (topology, modularity, feedback) and kinetic parameters (different methods for parameter sensitivity analysis) on system dynamics. Has insight in typical properties of complex, nonlinear biochemical networks, like robustness.
- Has knowledge about concepts and theory on system identification / data assimilation for biomedical and systems biology applications. Understands how experiments can be designed to yield (optimally) informative data. Can apply procedures to estimate model parameters from experimental time-series data combined with (sampling based) methods for uncertainty quantification and identifiability analysis.
- Has knowledge about machine learning methods for biomolecular time-series data in combination with differential equation models, Has knowledge about different optimization problems and solution methods (linear programming, local, global and hybride search algorithms, convexity, regularization) and can apply these methods.
- Is familiar with concepts like ‘virtual patients’ and ‘digital twinning’ in biomedical research.
- Is familiar with principles of reproducible computational research.
The assignments contain strong practical and research component. The description of the assignments leaves room for interpretation by the student. Students are encouraged to come up with creative and innovative ideas and solutions, implement the necessary code and function, and interpret their findings in a research context.