Acquire the basic knowledge of biological systems and problems related to their understanding, also in relation to deviations from normal functioning and thus to the insurgence of pathologies. Take care of the modeling aspect as well as of numerical simulation, especially for problems formulated by means of equations and discrete systems. Acquire the knowledge of the major bio-informatics algorithms useful to analyze biological data
Curriculum
teacher profile teaching materials
- Reminders of probability theory (probability distributions, Bayes' theorem, random walks)
- Sequence alignment: scoring matrices, global and local alignment methods (dynamic programming, BLAST)
- Phylogenetic trees: distance methods (ultrametric and additive trees), probabilistic methods (maximum likelihood, Bayesian methods)
- Population genetics: Hardy-Weinberg law, genetic drift, natural selection
- Boolean networks: gene regulatory networks, asyncronous Boolean networks, attractors and attractive cycles, positive and negative cycles.
- Allman E. S., Rhodes J. A., Mathematical Models in Biology. An Introduction, Cambridge University Press, 2004.
- Citterich M. H., Ferr ́e F., Pavesi G., Romualdi C., Pesole G., Fon- damenti di bioinformatica, Biologia Zanichelli, 2018.
- Gillespie, J., Population Genetics: a Coincise Guide, Johns Hopkins University Press, 2004.
-R uet P., Local cycles and dynamical properties of Boolean networks, Mathematical Structures in Computer Science, Volume 26, Issue 4, May 2016, pp. 702 - 718.
Programme
- Fundamental notions of molecular biology (nucleic acids, protein structures, central dogma)- Reminders of probability theory (probability distributions, Bayes' theorem, random walks)
- Sequence alignment: scoring matrices, global and local alignment methods (dynamic programming, BLAST)
- Phylogenetic trees: distance methods (ultrametric and additive trees), probabilistic methods (maximum likelihood, Bayesian methods)
- Population genetics: Hardy-Weinberg law, genetic drift, natural selection
- Boolean networks: gene regulatory networks, asyncronous Boolean networks, attractors and attractive cycles, positive and negative cycles.
Core Documentation
- Clote P., Backofen, R., Computational Molecular Biology. An Introduction, Wiley Series in Mathematical and Computational Biology, 2000.- Allman E. S., Rhodes J. A., Mathematical Models in Biology. An Introduction, Cambridge University Press, 2004.
- Citterich M. H., Ferr ́e F., Pavesi G., Romualdi C., Pesole G., Fon- damenti di bioinformatica, Biologia Zanichelli, 2018.
- Gillespie, J., Population Genetics: a Coincise Guide, Johns Hopkins University Press, 2004.
-R uet P., Local cycles and dynamical properties of Boolean networks, Mathematical Structures in Computer Science, Volume 26, Issue 4, May 2016, pp. 702 - 718.
Type of evaluation
Beyond a written exam, students are asked to give a group seminar on a topic relevant to the course and previously discussed with the teacher. teacher profile teaching materials
- Reminders of probability theory (probability distributions, Bayes' theorem, random walks)
- Sequence alignment: scoring matrices, global and local alignment methods (dynamic programming, BLAST)
- Phylogenetic trees: distance methods (ultrametric and additive trees), probabilistic methods (maximum likelihood, Bayesian methods)
- Population genetics: Hardy-Weinberg law, genetic drift, natural selection
- Boolean networks: gene regulatory networks, asyncronous Boolean networks, attractors and attractive cycles, positive and negative cycles.
- Allman E. S., Rhodes J. A., Mathematical Models in Biology. An Introduction, Cambridge University Press, 2004.
- Citterich M. H., Ferr ́e F., Pavesi G., Romualdi C., Pesole G., Fon- damenti di bioinformatica, Biologia Zanichelli, 2018.
- Gillespie, J., Population Genetics: a Coincise Guide, Johns Hopkins University Press, 2004.
-R uet P., Local cycles and dynamical properties of Boolean networks, Mathematical Structures in Computer Science, Volume 26, Issue 4, May 2016, pp. 702 - 718.
Programme
- Fundamental notions of molecular biology (nucleic acids, protein structures, central dogma)- Reminders of probability theory (probability distributions, Bayes' theorem, random walks)
- Sequence alignment: scoring matrices, global and local alignment methods (dynamic programming, BLAST)
- Phylogenetic trees: distance methods (ultrametric and additive trees), probabilistic methods (maximum likelihood, Bayesian methods)
- Population genetics: Hardy-Weinberg law, genetic drift, natural selection
- Boolean networks: gene regulatory networks, asyncronous Boolean networks, attractors and attractive cycles, positive and negative cycles.
Core Documentation
- Clote P., Backofen, R., Computational Molecular Biology. An Introduction, Wiley Series in Mathematical and Computational Biology, 2000.- Allman E. S., Rhodes J. A., Mathematical Models in Biology. An Introduction, Cambridge University Press, 2004.
- Citterich M. H., Ferr ́e F., Pavesi G., Romualdi C., Pesole G., Fon- damenti di bioinformatica, Biologia Zanichelli, 2018.
- Gillespie, J., Population Genetics: a Coincise Guide, Johns Hopkins University Press, 2004.
-R uet P., Local cycles and dynamical properties of Boolean networks, Mathematical Structures in Computer Science, Volume 26, Issue 4, May 2016, pp. 702 - 718.
Type of evaluation
Beyond a written exam, students are asked to give a group seminar on a topic relevant to the course and previously discussed with the teacher. teacher profile teaching materials
- Reminders of probability theory (probability distributions, Bayes' theorem, random walks)
- Sequence alignment: scoring matrices, global and local alignment methods (dynamic programming, BLAST)
- Phylogenetic trees: distance methods (ultrametric and additive trees), probabilistic methods (maximum likelihood, Bayesian methods)
- Population genetics: Hardy-Weinberg law, genetic drift, natural selection
- Boolean networks: gene regulatory networks, asyncronous Boolean networks, attractors and attractive cycles, positive and negative cycles.
- Allman E. S., Rhodes J. A., Mathematical Models in Biology. An Introduction, Cambridge University Press, 2004.
- Citterich M. H., Ferr ́e F., Pavesi G., Romualdi C., Pesole G., Fon- damenti di bioinformatica, Biologia Zanichelli, 2018.
- Gillespie, J., Population Genetics: a Coincise Guide, Johns Hopkins University Press, 2004.
-R uet P., Local cycles and dynamical properties of Boolean networks, Mathematical Structures in Computer Science, Volume 26, Issue 4, May 2016, pp. 702 - 718.
Programme
- Fundamental notions of molecular biology (nucleic acids, protein structures, central dogma)- Reminders of probability theory (probability distributions, Bayes' theorem, random walks)
- Sequence alignment: scoring matrices, global and local alignment methods (dynamic programming, BLAST)
- Phylogenetic trees: distance methods (ultrametric and additive trees), probabilistic methods (maximum likelihood, Bayesian methods)
- Population genetics: Hardy-Weinberg law, genetic drift, natural selection
- Boolean networks: gene regulatory networks, asyncronous Boolean networks, attractors and attractive cycles, positive and negative cycles.
Core Documentation
- Clote P., Backofen, R., Computational Molecular Biology. An Introduction, Wiley Series in Mathematical and Computational Biology, 2000.- Allman E. S., Rhodes J. A., Mathematical Models in Biology. An Introduction, Cambridge University Press, 2004.
- Citterich M. H., Ferr ́e F., Pavesi G., Romualdi C., Pesole G., Fon- damenti di bioinformatica, Biologia Zanichelli, 2018.
- Gillespie, J., Population Genetics: a Coincise Guide, Johns Hopkins University Press, 2004.
-R uet P., Local cycles and dynamical properties of Boolean networks, Mathematical Structures in Computer Science, Volume 26, Issue 4, May 2016, pp. 702 - 718.
Type of evaluation
Beyond a written exam, students are asked to give a group seminar on a topic relevant to the course and previously discussed with the teacher.