Digitale Lehre
moodle
Lehrinhalte
The module provides an introduction to modeling and analysis approaches relevant to synthetic biology. It builds on the mathematical basis provided in the module “mathematical foundations of modeling and analysis”. Apart from short introductory lectures, practical programming of respective algorithms will be the main modality to learn the subject. The course covers purely data-driven methods from biostatistics and machine learning but also first-principle modeling approaches from biophysics and biochemistry. Concrete scientific problem statements will used to learn about the modeling and analysis algorithms.
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[*]Introduction to scientific programming using Julia
[*]Introduction to biostatistics, bioinformatics and machine learning
[*]Deterministic and stochastic approaches for modeling reaction networks
[*]Thermodynamic analysis of reactions networks
[*]Principles of molecular dynamics, structure prediction
[*]Statistical methods for structure prediction
[*]Numerical solution and simulation methods
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Literatur
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[*]Neil Jones & Pavel Pevzner. An Introduction to bioinformatics algorithms, MIT Press, 2004
[*]Daniel Beard & Hing Qian. Chemical Biophysics, Cambridge University Press, 2010
[*]Darren Wilkinson. Stochastic modeling for systems biology, CRC Press, 2006
[*]Kevin P. Murphy. Machine Learning – A probabilistic perspective, MIT Press, 2012
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Voraussetzungen
Passing of module “Basics in Synthetic Biology”
Online-Angebote
moodle
- Lehrende: Heinz Köppl