Technological breakthroughs have enabled complete genomic sequencing and
proteomic study of many species, fueling exponential growth in the
available biological data relevant to important biological functions.
The computational analysis of these datasets has been hampered by many
structural and scientific barriers. The application of symbolic toolsets
borrowed from the term rewriting and formal methods communities may help
accelerate biologists understanding of network effects in complex
biochemical systems of interest.
Unlike traditional biology that has focussed on single genes or proteins
in isolation, systems biology is concerned with the study of complex
interactions of DNA, RNA, proteins, information pathways, to understand
how they work together to achieve some effect. Most systems biology
research has focussed on stochastic or differential equation models of
biological systems, but lack of knowledge of crucial rate constants
reduces the utility of these approaches.
Symbolic Systems Biology, the application of highly automated symbolic
tools such as multiset rewriting engines, model checkers, decision
procedures, and SAT solvers to systems biology, attempts to leverage what
is already known about biochemical systems to accelerate biological
understanding. We have developed a toolset called Pathway Logic based on
Maude and other symbolic tools, and applied it to signaling and metabolic
pathway analysis. Pathway Logic builds on efficient pathway interaction
data curation, and enables animation of complex pathway interactions, and
in-silico gene knockout experiments. We have also developed methods to
automatically analyze "inherently continuous" or hybrid continuous-
discrete systems, creating completely symbolic representations which
enable extremely efficient analysis of certain types of questions. By
constructing an algebra and logic of signaling pathways and creating
biologically plausible abstractions, the goal of Symbolic Systems Biology
is to provide the foundation for the application of high-powered tools
which can facilitate human understanding of complex biological signaling
networks, such as multi-cellular signaling, bacterial metabolism and spore
formation, mammalian cell cycle control, and synthetic biological
circuits.
These tools will be connected to publicly available datasources and
toolsets including the BioSPICE platform (available through biospice.org)
which provides an interoperable service architecture for integrating model
builders, simulators, experimental data repositories, and various
analyzers.
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