Biological processes can be characterized by different degrees of complexity at microscopic (genes, molecules), mesoscopic (protein-DNA complexes) and macroscopic (cells, organisms) levels. Historically, all biological systems have been studied at different levels. However, an increasing amount of experimental results and theoretical studies suggest that a more comprehensive system approach would tackle better biological problems. It would require a collaboration and intensive exchange between experimental and theoretical researchers from physics, chemistry, biology, mathematics, computer science, and engineering.
The proposed activity will answer the following fundamental questions: What are the properties of biological networks? How do they function? How do genes come together to form networks, and how can we use bioinformatics to discover such networks? Can our understanding of the fundamental mathematics inform the design of those bioinformatics methods? How is information transferred in cells? What role can synthetic biology perform in aiding our understanding of real life processes? How can different subjects of biological systems interact together to create effective dynamic systems?
Specific sub-areas of molecular and cellular biology generate their own sets of problems and mathematical challenges, to be addressed by individual workshops throughout the year. For example, how do cells develop, control, and regulate highly-efficient, highly-selective and robust biological transport? What are the algorithms and models that can help elucidate RNA structure and function? What are the basic pathways of cell-to-cell signaling? How can we design genetic regulatory networks with targeted function for synthetic biology? What are the mathematical principles behind DNA-protein interactions and the co-ordinated regulation of gene expression? The over-arching theme of the workshops bridges multiple scales, from the molecular to the cellular, in pursuit of the fundamental biological principles guiding the structure, evolution, and maintenance of these networks.
A unifying long-term goal of the proposed activities is to develop a unified approach to study the complexity of biological systems within cells. Such a comprehensive view of biology will require an application and development of new mathematical methods. Current approaches include hidden Markov processes, stochastic dynamics, graph theory, partial differential equations, discrete mathematics and other tools of probabilistic modeling, machine learning and computational analysis. As in the past, it is expected that new frontiers in biology will both benefit from and stimulate the development of novel mathematical techniques.