Engineering approaches to biologic systems often ignore the fact that they are the by-product of evolutionary forces, thus incorporating historical accidents, tinkering and constraints that have shaped their organisation. That means that, whereas some structures reveal optimal solutions, others very well illustrate completely suboptimal designs indicating the historical role played by accidents.

What determines the structure and functional traits of biological systems? Why are biological systems modular? Is biological complexity the result of a pure optimization process? What is the role of natural selection? What is the evolutionary origin of aging and cancer? What is the origin of asymmetry in the brain? How can a system store memories as it occurs in the brain? These are questions that we address within this course using relevant biological examples along with mathematical and computational models.


1. Complex networks.
2. Cancer dynamical systems: 
         2.1. Cancer as a complex adaptive system. Biological and clinical views of cancer.
         2.2. The hallmarks of cancer. The steps behind tumor progression.
         2.3. The evolutionary ecology of cancer. Cancer as an ecological dynamical system. Growth laws for tumors.
         2.4. Genetic heterogeneity, instability, and thresholds in cancer. Cancer-free attractors. Bifurcations in cancer models.
         2.5. Standard and non-standard cancer therapies. Immuno-therapy. Differentiation therapy.
         2.6. Non-genetic heterogeneity. Plasticity of cancer cells. Adaptation of cancer cells to therapy. 
         2.7. Cancer spatial dynamics. Cellular automata models.
3. Microbiome dynamics.
4 Neural diseases.