Game theory, biology, and the binding game
processes span across vastly different scales and necessarily have to be
understood at multiple levels of abstraction.
Towards clarifying the role that computation plays in such
understanding, we have recently developed a class of game theoretic models for
capturing coordinate operation of DNA binding regulators. Our work builds in part on the argument that
the roles of various molecular interactions cannot be understood in isolation
but that it is necessary to also capture the context provided by other mutually
constraining processes. Our game theoretic model allocates proteins to
neighborhoods of sites, and to sites themselves, in a resource constrained
manner, while explicitly capturing coordinate and competitive relations among
proteins with affinity to the site or region. We provide examples of known
biological subsystems that are naturally translated into our framework, and
illustrate predictions that can be derived from the model. The focus of the
talk will be on mathematical foundations of the modeling approach and requires
little or no biological background.
is joint work with Luis Perez-Breva, Luis Ortiz, and
Tommi S. Jaakkola received the M.Sc.
degree in theoretical physics from Helsinki University of Technology, Finland,
and Ph.D. from MIT in computational neuroscience. Following a postdoctoral
position in computational molecular biology (DOE/Sloan fellow, UCSC) he joined
the MIT EECS faculty 1998. His research interests include many aspects of
machine learning, statistical inference and estimation in the context of
graphical models, and analysis and development of algorithms for various modern
estimation problems such as those involving multiple predominantly incomplete
data sources. His applied research focuses on problems in computational biology
such as transcriptional regulation.