After completing the module the student is able to:
- knows how computational models of dynamical systems can be used to investigate biological processes. (e.g.topics mentioned in 3).
In particular;• the need of computational models• how to formulate computational models• how to analyze computational models• how to interpret results of computational models
- knows implicit assumptions of various model formalisms.
In particular:• ODE and PDE.• FSM and CA• event based models (e.g. Gillespie)• individual (particle) based models• evolutionary models
- knows basic theory derived from computational modeling of• network dynamics (e.g. cell cycle, cell differentiation).
In particular:• spatial pattern formation (e.g. spiral and chaotic waves)• multilevel evolution (genome evolution, eco-evolutionary dynamics)• multilevel morphogenesis (from genes, to cells to tissues to organism)
- able to understand current literature using modeling. In particular• extracting the bottom line• evaluating the explicit and implicit assumptions of the models• relating the discussion to the theoretical knowledge gained in 3
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During the course, the emphasis will be on composing and analysing exact models based on specific hypotheses. The results of the analyses offer an understanding of the original biological system. The models studied address fundamental questions from a variety of biological fields, including:
* Multi-level evolution:
- pre-biotic evolution
- eco-evolutionary dynamics and spatial pattern formation
- genome evolution (e.g. interaction between gene regulation and evolution)
* Developmental dynamics:
- pattern formations
- morphogenesis and mechanical interactions between cells
- evolution and morphogenesis
* Immune system dynamics:
- self/non-self discrimination
- host-pathogen co-evolution
* Behaviour:
- self-structuring through local interactions
- interface between learning and evolution
A number of different model formalisms are used, namely:
* (Non-linear) differential/difference equations (ODE and PDE)
* Cellular automata machines
* Individually oriented models
* Evolutionary computation
After completion the course, the student:
- knows how computational models of dynamical systems can be used to investigate biological processes. (e.g. topics mentioned in 3). In particular;
• the need of computational models
• how to formulate computational models
• how to analyze computational models
• how to interpret results of computational models
- knows implicit assumptions of various model formalisms. In particular:
• ODE and PDE.
• FSM and CA
• event based models (e.g. Gillespie)
• individual (particle) based models
• evolutionary models
- knows basic theory derived from computational modeling of
• network dynamics (e.g. cell cycle, cell differentiation). In particular:
• spatial pattern formation (e.g. spiral and chaotic waves)
• multilevel evolution (genome evolution, eco-evolutionary dynamics)
• multilevel morphogenesis (from genes, to cells to tissues to organism)
- able to understand current literature using modeling. In particular
• extracting the bottom line
• evaluating the explicit and implicit assumptions of the models
• relating the discussion to the theoretical knowledge gained in 3.
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