Computational and Systems Biology – CaSB

Computational and Systems Biology - CaSB is a major that trains students to solve basic and applied biological problems by combining math, computing, and a strong base of biological knowledge and concepts. Students learn to approach problems and formulate questions that span the full range of biological systems, from genes to cells to medicine to ecology to evolution. A major goal is to understand whole systems, from cells to organs to individuals to ecosystems, both in terms of their component parts and their emergent behaviors. Such diverse systems can all be studied and understood using cutting-edge modeling, analytical and numerical techniques, computer simulations, statistics, informatics, and data analysis of biological systems. This is accomplished through a highly integrated and inter-disciplinary training of students in the natural sciences, mathematics, and computer science. The major is designed for students with a strong interest in applying math and computational approaches to study questions in the life sciences that range from how cells process information, to which genes influence disease risk or response to medication, to what determines rates of tumor growth, to which factors drive biodiversity. There are currently five designated concentrations: Systems Biology, Bioinformatics, Neurosystems, Biomedical Systems or Computers, and Biosystems.

Systems Biology is concerned with the quantitative study of complex biosystems at the molecular, cellular, tissue, and systems scales. Its focus is on the function of the system as a whole, rather than on individual parts.

This exciting new arena applies mathematical modeling and engineering methods to the study of biological systems. This book is the first of its kind to focus on the newly emerging field of systems biology with an emphasis on computational approaches. The work covers new concepts, methods for information storage, mining and knowledge extraction, reverse engineering of gene and metabolic networks, as well as modelling and simulation of multi-cellular systems. Central themes include strategies for predicting biological properties and methods for elucidating structure-function relationships.