- Code: S53MA2Z2B
- Audience: M1 Computational and Mathematical Biology
- AMU site: https://formations.univ-amu.fr/ME5SBI-S53MA2Z2B-en.html
- Teacher: Jacques van Helden
This course aims at providing students with a practical approach of the analysis of biological data with R, based on the concepts acquired in the course “Probabilities and statistics for modelling 1”. The associated mathematical foundations will be developed in the course “Advanced statistics”. The following notions will be investigated :
- Sampling and estimation (moments, robust estimators, confidence intervals)
- Additional distributions
- Hypothesis testing (mean comparison, goodness of fit,…)
- Teacher: Jacques VAN HELDEN
- Code: S40BI1Z1
- Language : English
- Hourly volume : 18 hours
This course Fundamentals in biology is divided in 2 parts taught during the 1st and 2nd semester of CMB. In the first semester, we will give a presentation of the evolutionary theories that have founded modern biology (from Lamarck to Darwin), and a synthesis of the discoveries that have led to current concepts of molecular and cellular biology : the role of macromolecules in cell function (information transfer between DNA, RNA, proteins, regulation, etc.), heredity and cellular adaptation. Below are some topics covered during the lectures : Information, evolution causes for living organisms Cellular information Epigenetics – phenomenons, information, adaptation, mechanisms
This course is a quick revision of the basics of probability and statistics. The concepts will be taught in relation to concrete biology exemples (genome analysis, complex systems).
The following topics will be covered.
- Combinatorial analysis
- Concept of probability
- Discrete distributions (Bernoulli, binomial, geometric, hypergeometric, Poisson)
- Quick review of the basic continuous laws (normal and Student’s distribution)
- Estimation and sampling
- Statistical hypothesis tests
Some biological examples of applications could concern the probability of patterns in genomic sequences, the detection of differentially expressed genes.