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\begin{document}
\title{Rule-Based Modeling for Systems Biology}
\author{Dr. James R. Faeder\\ Department of Computational and Systems Biology\\
University of Pittsburgh School of Medicine}
\date{\ }
\maketitle
\noindent {\bf Date: 10/20/2011}
\\
{\bf Time: 3:45-4:45pm}
\\
{\bf Place: 315 Armstorng Hall}
\\
\begin{abstract}
Cells possess complex sensory mechanisms that are governed by the
biochemical interactions of proteins. A typical signaling protein possesses
multiple interaction sites, whose activity can be modified both by
direct chemical modification and by the effects of modification or
interaction at other sites (allostery). This complexity at the protein level
leads to combinatorial complexity at the level of signaling networks - an
individual protein has many potential states of modification and
interaction, which gives rise to an ever-multiplying set of possible
complexes and poses a major barrier to traditional methods of modeling and
simulation. The need to simplify the development of signal transduction
models and to expand their scope in face of combinatorial complexity has
motivated the development of rule-based modeling languages, such as
BioNetGen and Kappa, which provide a rich and yet concise description of
signaling proteins and their interactions. Their success is demonstrated by
the growing community of users and the substantial number of models that
have been developed and published. While greatly facilitating the
translation of knowledge about signaling biochemistry into models, however,
rule-based languages do not directly address the combinatorial challenges
involved in the simulation of such models, which arise from the size of the
reaction network implied by the rules. For these, new agent-based stochastic
simulation methods have been developed for rule- based models
with computational requirements that are independent of the number of
possible species (i.e., complexes) and proportional to the number of
molecules (e.g., proteins) being simulated. In addition, general and
efficient implementations are now available that enable the rapid simulation
of rule-based models of virtually any complexity. The use of stochastic
simulations, however, exacerbates the already difficult problems common to
all complex models of relating model parameters to model behavior and of
estimating parameter values based on experimental observations and data. For
these, new statistical model checking algorithms and tools have been
developed that allow model properties to be determined from a minimal number
of simulation runs. Taken together, rule-based modeling languages and their
associated tools address the issue of combinatorial complexity in cell
regulatory networks, allowing the development, simulation, and analysis of
models with unprecedented scope and detail and, we hope, predictive
capability.
\end{abstract}
\end{document}