Systems Medicine

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Introduce biomedical researchers to concepts and approaches used in Systems Medicine and train them in using currently available tools.

This course is part of the 'Systems Medicine' training program.

The course runs once a year on five course days. It consists of plenary presentations and computer exercises, and discusses various modelling approaches.

Scheduled dates:
November 27 thru December 1, 2017

Systems biology is an approach applied in biomedical and biological scientific research aimed to increase our understanding of biological systems. Systems biology is an inter-disciplinary field of study that focuses on complex (dynamic) interactions between components within biological systems (e.g., pathways, cells, organs, organisms).Top-down systems biology comprises a holistic perspective often involving the (statistical) analysis of genome-wide datasets. In contrast, bottom-up systems biology generally starts with the mathematical modelling of smaller subsystems. Systems biology involves wet-lab experiments and computational approaches. It brings together experimental information about the interplay of the components of the systems in quantitative mathematical models to generate testable hypotheses, allow prediction of the behavior of such systems, and discover emergent properties.
Systems Medicine is the application of systems biology to medical research and practice. Understanding the functioning of biological systems is essential for understanding (the progression of) disease, and for the identification of biomarkers and the development of effective drugs and (personalized) therapies for disease.

The topics covered are:
- General introduction to systems biology and systems medicine
- Systems Biology for the clinic: Systems Medicine
- Omics-based Interaction networks
- Multi-omics statistics in Systems Medicine
- Constraint based models and Flux Balance Analysis
- Mechanistic models (differential equations, agent-based models)
- Kinetic models
- Examples of systems biology and systems medicine projects

Computer exercises include:
- Networks and statistical integration. Analyses of omics data from a mouse nutrigenomics study.
- Differential equations. We will use the SIR model as an example. SIR is an epidemiological model that computes the theoretical number of people infected with a contagious illness in a closed population over time. It involves susceptible people S(t), number of people infected I(t), and number of people who have recovered R(t).
- Flux Balance Analysis with E.coli metobolic network.
- Kinetic models. Through a number of cool exercises you will get hands-on experience with published kinetic models that were developed to understand diseases. This will show you how easy they are to use and insights you can obtain from them.

For the computer exercises you will need to bring your own laptop. You will receive instructions prior to the start of the course about software that you need to install.

Target audience:
AMC PhD candidates involved or interested in approaches to analyze, model, and understand systems such as pathways, cells, organs and organisms. Participants are expected to have a general familiarity with computers. Basic knowledge of omics technologies is assumed. Experience with R is recommended. If you do not have experience with R it is recommended to make the R tutorial 'Computing in R' (wiki.bioinformaticslaboratory) prior to the course.

The course is given in Dutch or English, depending on the participants.

A certificate is issued. Attendance is registered.

Study load:
40 hours, which is comparable to 1.4 ECTS points.

Number of participants:
Maximum 25 per course.

No charge for registered AMC PhD candidates. Employees of the AMC or AMC Medical Research BV can participate provided slots are available. All other participants are charged a fee of 1,250 euro.

Course Coordinator:
Prof. dr. A.H. van Kampen / Department of Clinical Epidemiology, Biostatistics and Bioinformatics (KEBB) / / tel. +31 (0)20 566 7096.

More information:
From the course coordinator or AMC Graduate School / / room J1A-112 / tel. +31 (0)20 566 4618


AMC Graduate School
Tel: +31 (0)20 - 5663108