Biophysics

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- Introduction to machine learning. Basic concepts. Perceptron algorithm. Main algorithms in Machine Learning (linear regression, logistic, decision trees, random forest, SVM, KNN). Artificial neural networks (mathematical/biological foundation, learning paradigms, main architectures).
- Main molecular constituents. Structures and conformations. Levels of organization. Inter- and intramolecular forces
- Thermodynamics of living systems and related concepts.
- Quantitative biology. Markov chains. Bioinformatics methods. Normal modes, QSAR, QSPR, Docking.
- Mathematical modeling: Lokta-Volterra, van der Pol oscillator, Fitzhugh-Nagumo, Duffing oscillator, nerve impulse (Hodgkin-Huxley model, soliton model), statistical models in genomics, population and epidemiological models. Stochastic processes, random walks.
- Spatio-temporal ordering. Reaction-diffusion equations. Belousov-Zhabotinski, Bruselator, Oregonator, Turing patterns (animal skin patterns).
- Chaos and fractals in Nature.
- Introduction to the main experimental methods in biophysics.
- Introduction to biotechnology: biomimicry, biofunctionalization, LOC (Lab on a Chip) integrated devices.

“Physical Biology of the cell” Rob Phillips et al. Garland Science, 2013
“Física Biológica. Energía, información, vida” Philip Nelson. Reverté, 2004
“Biofísica” M. V. Volkenshtein. Mir, 1985
“Mathematical Biology: I & II”. J. D. Murray. Springer 2001
“Computational Physics” Mark Newman, 2012.
“Bioinformatics Algorithms: Design and implementation in Python” Miguel Rocha. AP, 2018.
“Biofisica. Procesos de autoorganizacion e Biología” Francisco Montero. Federico Morán. Eudema Universidad 1992
“Entropy, order parameters and complexity”. J.P. Sethna. Oxford University Press, 2006.
“An introduction to Stochastic Processes with Applications to Biology” Linda J. S. Allen. CRC Press 2011
“Statistical Modelling of Mollecular Descriptors in QSAR/QSPR” Matthias Dehmer, Kurt Varmuza and Danail Bonchev. Wiley 2012.

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