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Derived from
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20410568 IN470- COMPUTATIONAL METHODS IN SYSTEMS BIOLOGY in Computational Sciences LM-40 CASTIGLIONE Filippo
(syllabus)
Introduction to Systems Biology: What Computational Biology is about; the role of mathematical modelling and bioinformatics; what are the aims and the main problems; what are the theoretical tools used.
Introduction to Molecular and Cell Biology; basic knowledge of genetics, proteomics and cellular processes; ecology and evolution; outline of the availability of open access biological data.
Recall of probability theory: discrete and continuous random variables; distributions; entropy; inference; sampling; estimation of probability; the EM algorithm (Expectation Maximisation).
Recall of information theory: Shannon entropy and other measures of entropy; measures of diversity (Shannon-Wiener index, Simpson index).
Introduction to Stochastic Processes: random walks, Markov chains.
Introduction to Machine Learning and Pattern Recognition: supervised and unsupervised learning; Bayesian methods; non-parametric techniques; Neural Networks; stochastic methods; clustering; k-means.
Sequence analysis: alignment algorithms (e.g., BLAST); Hidden Markov Models (HMM); internet services available.
Recall of Graph Theory: notation; types of graphs; representation; algorithms on graphs; statistical properties of the networks; centrality measures; motifs; network clustering.
Biological Networks: networks of signal transduction; gene regulatory networks; protein-protein interaction networks; metabolic networks; internet services available.
Bio-mathematical models; predictions by theoretical models; continuous models; recall of numerical methods for solving systems of ordinary differential equations; discrete models; spin models (Ising models); Cellular Automata; Boolean networks; Agent-based models; data fitting and parameter estimation; available software.
(reference books)
• E.S. Allman, J.A. Rhodes. Mathematical Models in Biology: An Introduction (2004) Cambridge University Press.
• W.J. Ewens, G.R. Grant. Statistical Methods in Bioinformatics, An Introduction (2005) Springer Verlag.
• R. Durbin, S. Eddy, A. Krogh, G. Mitchison. Biological sequence analysis - Probabilistic models of proteins and nucleic acids (1998) Cambridge University Press.
• B.H. Junker, F. Schreiber (eds). Analysis of biological networks (2008) John Wiley & Sons.
• R.O. Duda, P.E. Hart, D.G. Stork. Pattern Classification (2001) John Wiley & Sons.
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Università Roma Tre