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Statistics (applied) - BSc Biotechnology
Aim and Program
The aim of the course is to make the students acquainted with basic statistical ideas and methods and their applications in the correct planning of experiments, data sampling and analysis, and data presentation. The course intends to conjugate concepts of basic statistics and probability theory with real situations as they emerge in a generic biotechnology laboratory. The lessons will integrate the topics presented and discussed in the reference textbook.
The program will essentially follow the topics listed in the textbook up to chapter 17 with the following extras: key aspects in probability theory, probability distributions in the biotechnology lab (practical examples), error propagation theory
Textbook
Reference textbook: Michael C. Whitlock, Dolph Schluter. Analisi Statistica dei dati biologici. Zanichelli, 2010. ISBN: 978-88-08-06297-0
Mathematical and Statistical Methods for Biology - BSc Applied Mathematics
Aim and Program
This course is an introduction to the most common mathematical models in biology and biomedicine. At the end of the course the students should be able to:
- i) understand and critically discuss basic models of biological systems, with particular emphasis to the validity of assumptions and of model parameters;
- ii) model simple phenomena, analyze them (numerically and/or analytically), and understand the effect of parameters;
- iii) compare the predictions given by the models with experimental data;
- iv) communicate results in interdisciplinary teams
The course will be divided in two parts. To see the full program follow this link.
Textbook (part B only)
Interested students are invited to search the web to find free books and lecture notes. A couple of interesting links are given below:
Lecture slides (part B only)
Mathematical Methods for Biotechnology - MD Industrial Biotechnology
Aim
The aim of the course is to provide students with basic conceptual and methodological tools to approach biological systems on a quantitative basis, and hence the cultural elements that are needed to analyze and understand the underlying mechanisms of action. At the same time, these tools will allow the students to acquire the skills to professionally operate in multidisciplinary contexts in biotechnology and related applied fields.
Program
Each class introduces basic concepts of quantitative biotechnology through combination of lectures and exercises. Exercises will be solved using RStudio, a free and open-source integrated development environment for R, a programming language for statistical computing and graphics. For each argument, a number of problems derived from the scientific literature and from the research activity of the teacher will be proposed and solved. The first part of the course will revise the basic facts about probability theory and univariate statistics and then concepts of multivariate statistics and modeling of biotechnology processes will be introduced. Lectures:
- Introduction to RStudio and R: presentation, installation and basic commands
- Discrete and continuous probability distributions
- Univariate statistics: main tests used for the analysis of biotechnology experiments
- Two-ways ANOVA and its application two study the synergism between two treatments
- Correlation and linear regression in one dimension and multiple dimensions
- Principles of linear algebra
- The Mahalanobis distance
- Principal Component Analysis
- Survival analysis: Kaplan-Meier method and the Cox proportional hazard model
- Error propagation theory
- Principles of non-linear regression
- Numerical solution of differential equations and fit of differential equations to experimental data
- Mathematical models in biology and medicine
Lecture slides
- MetQuBio N.1
- MetQuBio N.2 → exercises with R
- MetQuBio N.3 → exercises with R
- MetQuBio N.4 → exercises with R → dataset 1 (chi-squared) → dataset 2 (ANOVA)
- MetQuBio N.5 → exercises with R → dataset 1 → dataset 2
- MetQuBio N.6 → exercises with R → dataset → addendum 1 → addendum 2
- MetQuBio N.7 → exercises with R → addendum → addendum n.2
- MetQuBio N.8 → exercises with R
- MetQuBio N.9 → exercises with R
- MetQuBio N.10A
- MetQuBio N.10B → exercises with R
- MetQuBio N.11
- MetQuBio N.12