Description of the Course
Computer models are increasingly used by researchers as well as the pharmaceutical industry to investigate disease initiation, progression and therapy. Modelling of biochemical pathways deregulated in disease condition offers mechanistic insights into the pathology; helps to elucidate mechanisms behind drug action and predict dose required for treatment and thereby greatly facilitates fundamental research and drug discovery. Such modelling requires a background in both molecular biology and mathematics.
This hands-on workshop will train researchers from life science and mathematics to communicate and work together to learn to build and analyse computer models using user-friendly open-source software. Various modelling approaches and tools will be introduced to the participants. They will explore a few of the disease-specific computer models from a recent publication, freely available in BioModels repository. Other EMBL- EBI resources including Reactome , IntAct , OmicsDI , PRIDE, ExpressionAtlas will be introduced to the participants. The BioModels team will lead the training on kinetic modelling of signalling pathways, curation and annotation of mathematical models as well as standards in modelling (SBML, SED-ML, etc). The IITM/IBSE team will partner in the training on kinetic modelling approaches, and also introduce other paradigms such as constraint-based modelling (e.g. Flux Balance Analysis (FBA) for the analysis of metabolic pathways.
An essential agenda in the meeting will be group projects where participants from both experimental and theoretical background would work together to curate mathematical models and submit them to the BioModels repository. This workshop will allow EMBL- EBI to partner with IITM to share expertise on kinetic modelling and gain expertise on the curation of non-kinetic models (specifically, constraint-based models, using the newly developed FBC curation packages) in BioModels repository.
During this course you will learn about:
- Introduction to kinetic modelling
- Model curation and annotation with COPASI
- Data resources for modelling
- Constraint-based modelling
- Reproducibility in Systems Biology Modelling
Profile of the Instructor
Dr. Rahuman S. Malik Sheriff is the BioModels Senior Project Leader working with The Molecular Networks Team at EMBL’s European Bioinformatics Institute (EBI). He does research in Cell Biology, Cancer Research and Developmental Biology. His team develops tools and resources for the representation, deposition, discovery and analysis of pathway and systems biology data. The team follows an open-source, open-data approach and is a major contributor to community standards, in particular the Proteomics Standards Initiative (PSI) of the international Human Proteome Organization (HUPO) and COMBINE systems biology standards.
Karthik Raman is a Professor at the Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, IIT Madras. Karthik’s research group works on the development of algorithms and computational tools to understand, predict and manipulate complex biological networks. Broadly spanning computational aspects of synthetic and systems biology, key areas of research in his group encompass microbiome analysis, in silico metabolic engineering, biological network design and biological data analysis. Karthik also co-ordinates the Centre for Integrative Biology and Systems medicine (IBSE) at IIT Madras and is a core member of the Robert Bosch Centre for Data Science and Artificial Intelligence (RBC-DSAI). Karthik teaches courses on computational biology and systems biology at IIT Madras, and has also authored a textbook on Computational Systems Biology.
Eligibility
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.
Learning Outcomes
After the course, participants will be able to:
- Identify the strength and weakness of systems biology modelling approaches
- Access, query and retrieve data/ models from public repositories for systems biology modelling
- Use modelling software to develop reproducible systems biology models
- Discuss the real-life application of models in fundamental and industrial research
Certification
Attendance & Assessment is the criteria to obtain the certificate
Reviews
There are no reviews yet.