This special focus is jointly sponsored by the Center for Discrete Mathematics and Theoretical Computer Science (DIMACS), the Biological, Mathematical, and Physical Sciences Interfaces Institute for Quantitative Biology (BioMaPS), and the Rutgers Center for Molecular Biophysics and Biophysical Chemistry (MB Center).
Algebraic models (polynomial dynamical systems, Boolean networks, logical models, Petri nets, etc.) have been used increasingly in systems biology to model a variety of biochemical networks such as gene regulatory, metabolic and signal transduction networks. These models have proven to be particularly useful when quantity or quality of the data is not sufficient to build detailed differential equation models, which require many parameters that are frequently unknown. In addition, algebraic models allow for a more intuitive understanding of their underlying structure and hence more accessible for their application in life sciences.
In the last years several algorithms related to the modeling and simulation of bio-chemical networks within the algebraic models' paradigm have been proposed and in some cases, software has been developed (e.g. 1,2,3,4). The integration of the many algorithms into one flowing toolkit could then be of great advantage to broaden their understanding and application.
The aim of this working group will be the development of an integrated software package of the several algorithm involved in the different tasks within the algebraic modeling paradigm. Our focus will be towards the Boolean version of such integrated software.
 E.S. Dimitrova, A. Jarrah, R. Laubenbacher and B. Stigler. 2007. A Gröbner fan method for bio-chemical network modeling. In Proceedings of the 2007 international Symposium on Symbolic and Algebraic Computation (Waterloo, Ontario, Canada, July 29 - August 01, 2007). ISSAC '07. ACM, New York, NY.  R. Laubenbacher and B. Stigler. (2004) A computational algebra approach to the reverse-engineering of gene regulatory networks , J. Theor. Biol. 229, 523-537.  A. Jarrah, R. Laubenbacher, B. Stigler, M. Stillman. 2007. Reverse Engineering of Polynomial Dy-namical Systems. Advances in Applied Mathematics. 39(4): 477- -489.  P. Vera-Licona, A. Jarrah, LD. Garcia, J. McGee, R. Laubenbacher. An optimization algorithm for the inference of biological networks. Preprint.