Presently, there is no clear way to determine if the current body of biological facts is sufficient to explain phenomenology. In the biological community, it is not uncommon to assume certain biological problems to have achieved a cognitive finality without rigorous justification. In these particular cases, rigorous mathematical models with automated tools for reasoning, simulation, and computation can be of enormous help to uncover cognitive flaws, qualitative simplification or overly generalized assumptions. Some ideal candidates for such study would include: prion hypothesis, cell cycle machinery (DNA replication and repair, chromosome segregation, cell-cycle period control, spindle pole duplication, etc.), muscle contractility, processes involved in cancer (cell cycle regulation, angiogenesis, DNA repair, apoptosis, cellular senescence, tissue space modeling enzymes, etc.), signal transduction pathways, circadian rhythms (especially the effect of small molecular concentration on its robustness), and many others. We believe that the difficulty of biological modeling will become acute as biologists prepare to understand even more complex systems.
Fortunately, in the past, similar issues had been faced by other disciplines: for instance, design of complex microprocessors involving many millions of transistors, building and controlling a configurable robots involving very high degree-of-freedom actuators, implementing hybrid controllers for high-way traffic or air-traffic, or even reasoning about data traffic on a computer network. The approaches developed by control theorists analyzing stability of a system with feedback, physicists studying asymptotic properties of dynamical systems, computer scientists reasoning about a discrete or hybrid (combining discrete events with continuous events) reactive systems---all have tried to address some aspects of the same problem in a very concrete manner. We believe that biological processes could be studied in a similar manner, once the appropriate tools are made available.
The goal of this course is to understand, design and create a large-scale computational system centered on the biology of individual cells, population of cells, intra-cellular processes, and realistic simulation and visualization of these processes at multiple spatio-temporal scales. Such a reasoning system, in the hands of a working biologist, can then be used to gain insight into the underlying biology, design refutable biological experiments, and ultimately, discover intervention schemes to suitably modify the biological processes for therapeutic purposes. The course will focus primarily on two biological processes: genome-evolution and cell-to-cell communication.