Computational Biology

Subject description

  • Biological networks: gene-regulatory networks, signalling networks, metabolic networks.
  • Biological information processing.
  • Computational approaches for modelling of biological systems: deterministic and stochastic modelling, agent-based modelling, logic modelling, constraint-based modelling, genome-scale modelling.
  • Dynamical simulations of biological systems.
  • Computational analysis of biological systems: qualitative and quantitative analysis, investigation of feasible values of kinetic parameters.
  • Heuristics for analysis and design of biological systems.
  • Context-specific modelling and adaptation of computational models to a specific context.
  • Population-based modelling of biological entities using agent-based models.
  • Complementing experimental work with computational modelling. Application of experimental data to the establishment, enhancement, and adaptation of computational models.
  • Interpretation of experimental data using computational models.
  • Application of computational models to the generation of new data and hypothesis testing.

The subject is taught in programs

Objectives and competences

To get an overview on the computational modelling, simulation, analysis, and design approaches in the biological systems domain. To be able to construct and adapt a computational model in the context of the student's research work. To be able to use computational models in a combination with experimental work for data interpretation, generation of new data, and testing of hypotheses.

Teaching and learning methods

Seminars, hands-on tutorials, individual consultations

Expected study results

Knowledge and understanding of computational modelling, analysis, and design of biological systems.

Basic sources and literature

  • Alon, Uri. (2006) An introduction to systems biology: design principles of biological circuits. Chapman and Hall/CRC.
  • BØ, P. (2015) Systems Biology: Constraint-Based Reconstruction and Analysis. Cambridge University Press; 1st edition.
  • Ingalls, B. P. (2013). Mathematical modeling in systems biology: an introduction. MIT press.
  • Sneppen, K. (2014). Models of life. Cambridge University Press.
  • Klipp, E., Liebermeister, W., Wierling, C., & Kowald, A. (2016). Systems biology: a textbook. John Wiley & Sons.

Ostalo: revijski članki s področja, tekoča periodika in druga učna gradiva.

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