Mathematics of life: modelling molecular mechanisms
Date: 12 - 16 September 2022
This course will provide participants with an introduction and hands-on training on modelling approaches, tools and resources used in systems biology as well as touch on network analysis.
Computer models are increasingly used to understand the essential processes of biology. Researchers in academic institutions as well as the pharmaceutical industry use mathematical models to generate hypotheses on how complex biomolecular systems work. Modelling of biochemical pathways deregulated in disease conditions can offer mechanistic insights into the pathology, help to elucidate mechanisms behind drug action, and predict the dose required for treatment thus facilitating fundamental research and drug discovery. This course will provide a helpful brief introduction to key modelling concepts and hands on training to use popular tools and resources used in this scientific field.
In-person course
We plan to deliver this course in an in-person manner onsite at our training suite at EMBL-EBI, Hinxton. Please be aware that we are continually evaluating the ongoing pandemic situation and, as such, may need to change the format of courses at short notice. Your safety is paramount to us; you can read our COVID guidance policy for more information. All information is correct at time of publishing.
Venue: European Bioinformatics Institute Hinxton
Region: Cambridge
Country: United Kingdom
Postcode: CB10 1SD
Target audience: This course is aimed at experimental biologists, bioinformaticians and mathematicians who have just started in systems biology, are familiar with the basic terminology in this field and who are now keen on gaining a better knowledge of systems biology modelling approaches to understand biological and biomedical problems. An experience of using a programming language (e.g Python, R, Matlab) would be a benefit but is not mandatory. An undergraduate knowledge of molecular and cellular biology or some background in mathematics is highly beneficial.
Capacity: 30
Activity log