Multi-Agent Systems Applied in the Modeling and Simulation of Biological Problems: A Case Study in Protein Folding
Multi-agent system approach has proven to be an effective and appropriate abstraction level to construct whole models of a diversity of biological problems, integrating aspects which can be found both in "micro" and "macro" approaches when modeling this type of phenomena. Taking into account these considerations, this paper presents the important computational characteristics to be gathered into a novel bioinformatics framework built upon a multiagent architecture. The version of the tool presented herein allows studying and exploring complex problems belonging principally to structural biology, such as protein folding. The bioinformatics framework is used as a virtual laboratory to explore a minimalist model of protein folding as a test case. In order to show the laboratory concept of the platform as well as its flexibility and adaptability, we studied the folding of two particular sequences, one of 45-mer and another of 64-mer, both described by an HP model (only hydrophobic and polar residues) and coarse grained 2D-square lattice. According to the discussion section of this piece of work, these two sequences were chosen as breaking points towards the platform, in order to determine the tools to be created or improved in such a way to overcome the needs of a particular computation and analysis of a given tough sequence. The backwards philosophy herein is that the continuous studying of sequences provides itself important points to be added into the platform, to any time improve its efficiency, as is demonstrated herein.
[1] Jennings, N.R., CONTROLLING COOPERATIVE PROBLEM-SOLVING
IN INDUSTRIAL MULTIAGENT SYSTEMS USING JOINT INTENTIONS.
Artificial Intelligence, 1995. 75(2): p. 195-240.
[2] Jennings, N.R., On agent-based software engineering. Artificial
Intelligence, 2000. 117(2): p. 277-296.
[3] Jennings, N.R., et al., Agent-based business process management.
International Journal of Cooperative Information Systems, 1996. 5(2-3):
p. 105-130.
[4] Jennings, N.R., et al., TRANSFORMING STANDALONE EXPERTSYSTEMS
INTO A COMMUNITY OF COOPERATING AGENTS.
Engineering Applications of Artificial Intelligence, 1993. 6(4): p. 317-
331.
[5] Wooldridge, M., The Gaia methodology for agent-oriented analysis and
design. Autonomous Agents and Multi-Agent Systems, 2000. 3(3): p.
285-312.
[6] Wooldridge, M.J., Software engineering with agents: Pitfalls and pratfalls.
IEEE Internet Computing, 1999. 3(3): p. 20-+.
[7] Mach, R. and F. Schweitzer, Multi-agent model of biological swarming.
Advances in Artificial Life, Proceedings, 2003. 2801: p. 810-820.
[8] Gershenson, C., Design and Control of Self-organizing Systems. 2007,
Vrije Universiteit Brussel: Brussel.
[9] Omicini, A., et al., Coordination artifacts: Environment-based
coordination for intelligent agents. Proceedings of 3rd International Joint
Conference on Autonomous Agents and Multi-Agent Systems, 2004: p.
286-293.
[10] Conte, R., et al., Sociology and Social Theory in Agent Based Social
Simulation: A Symposium. Comput. Math. Organ. Theory, 2001. 7(3): p.
183-205.
[11] Nigel, G. and C. Rosaria, Artificial Societies: The Computer Simulation
of Social Life. 1995: Taylor \& Francis, Inc.
[12] Davidsson, P., Agent based social simulation: A computer science view.
Jasss-the Journal of Artificial Societies and Social Simulation, 2002. 5(1).
[13] Van Dyke Parunak, H., R. Savit, and R.L. Riolo, Agent-Based Modeling
vs. Equation-Based Modeling: A Case Study and Users- Guide, in Multi-
Agent Systems and Agent-Based Simulation. 1998. p. 10-25.
[14] Gonzalez, P.P., et al., Cellulat: an agent-based intracellular signalling
model. Biosystems, 2003. 68(2-3): p. 171-185.
[15] Lagunez-Otero, J., et al., Cellulat, in Artificial Life VIII: Proceedings of
the Eight International Conference on Artificial Life, R.K. Standish, M.A.
Bedau, and H.A. Abbass, Editors. 2002: Sydney, Australia. p. 97-100.
[16] Corradini, F., E. Merelli, and M. Vita, A multi-agent system for modelling
carbohydrate oxidation in cell. Computational Science and Its
Applications - Iccsa 2005, Pt 2, 2005. 3481: p. 1264-1273.
[17] Merelli, E., et al., Agents in bioinformatics, computational and systems
biology. Briefings in Bioinformatics, 2007. 8(1): p. 45-59.
[18] Corkill, D.D. Collaborating Software: Blackboard and Multi-Agent
Systems & the Future. in Proceedings of the International Lisp
Conference. 2003. New York.
[19] Lesk, A.M., Introduction to bioinformatics. 2002, Oxford ; New York:
Oxford University Press. 283 p.
[20] Sadqi, M., Atom-by-atom analysis of global downhill protein folding.
Nature, 2006. 442(7100): p. 317-321.
[21] Creighton, T.E., EXPERIMENTAL STUDIES OF PROTEIN FOLDING
AND UNFOLDING. Progress in Biophysics & Molecular Biology, 1978.
33(3): p. 231-297.
[22] Snow, C.D., et al., Absolute comparison of simulated and experimental
protein-folding dynamics. Nature, 2002. 420(6911): p. 102-106.
[23] Mann, M., S. Will, and R. Backofen, CPSP-tools - Exact and complete
algorithms for high-throughput 3D lattice protein studies. Bmc
Bioinformatics, 2008. 9.
[24] Shmygelska, A. and H.H. Hoos, An ant colony optimisation algorithm for
the 2D and 3D hydrophobic polar protein folding problem. Bmc
Bioinformatics, 2005. 6.
[25] Thachuk, C., A. Shmygelska, and H.H. Hoos, A replica exchange Monte
Carlo algorithm for protein folding in the HP model. Bmc
Bioinformatics, 2007. 8.
[26] Clementi, C., Coarse-grained models of protein folding: toy models or
predictive tools? Current Opinion in Structural Biology, 2008. 18(1): p.
10-15.
[27] Jacob, E., A. Horovitz, and R. Unger, Different mechanistic requirements
for prokaryotic and eukaryotic chaperonins: a lattice study.
Bioinformatics, 2007. 23(13): p. I240-I248.
[28] Blackburne, B.P. and J.D. Hirst, Population dynamics simulations of
functional model proteins. Journal of Chemical Physics, 2005. 123(15).
[29] Dill, K.A., THEORY FOR THE FOLDING AND STABILITY OF
GLOBULAR-PROTEINS. Biochemistry, 1985. 24(6): p. 1501-1509.
[30] Dill, K.A., Polymer principles and protein folding. Protein Science, 1999.
8(6): p. 1166-1180.
[1] Jennings, N.R., CONTROLLING COOPERATIVE PROBLEM-SOLVING
IN INDUSTRIAL MULTIAGENT SYSTEMS USING JOINT INTENTIONS.
Artificial Intelligence, 1995. 75(2): p. 195-240.
[2] Jennings, N.R., On agent-based software engineering. Artificial
Intelligence, 2000. 117(2): p. 277-296.
[3] Jennings, N.R., et al., Agent-based business process management.
International Journal of Cooperative Information Systems, 1996. 5(2-3):
p. 105-130.
[4] Jennings, N.R., et al., TRANSFORMING STANDALONE EXPERTSYSTEMS
INTO A COMMUNITY OF COOPERATING AGENTS.
Engineering Applications of Artificial Intelligence, 1993. 6(4): p. 317-
331.
[5] Wooldridge, M., The Gaia methodology for agent-oriented analysis and
design. Autonomous Agents and Multi-Agent Systems, 2000. 3(3): p.
285-312.
[6] Wooldridge, M.J., Software engineering with agents: Pitfalls and pratfalls.
IEEE Internet Computing, 1999. 3(3): p. 20-+.
[7] Mach, R. and F. Schweitzer, Multi-agent model of biological swarming.
Advances in Artificial Life, Proceedings, 2003. 2801: p. 810-820.
[8] Gershenson, C., Design and Control of Self-organizing Systems. 2007,
Vrije Universiteit Brussel: Brussel.
[9] Omicini, A., et al., Coordination artifacts: Environment-based
coordination for intelligent agents. Proceedings of 3rd International Joint
Conference on Autonomous Agents and Multi-Agent Systems, 2004: p.
286-293.
[10] Conte, R., et al., Sociology and Social Theory in Agent Based Social
Simulation: A Symposium. Comput. Math. Organ. Theory, 2001. 7(3): p.
183-205.
[11] Nigel, G. and C. Rosaria, Artificial Societies: The Computer Simulation
of Social Life. 1995: Taylor \& Francis, Inc.
[12] Davidsson, P., Agent based social simulation: A computer science view.
Jasss-the Journal of Artificial Societies and Social Simulation, 2002. 5(1).
[13] Van Dyke Parunak, H., R. Savit, and R.L. Riolo, Agent-Based Modeling
vs. Equation-Based Modeling: A Case Study and Users- Guide, in Multi-
Agent Systems and Agent-Based Simulation. 1998. p. 10-25.
[14] Gonzalez, P.P., et al., Cellulat: an agent-based intracellular signalling
model. Biosystems, 2003. 68(2-3): p. 171-185.
[15] Lagunez-Otero, J., et al., Cellulat, in Artificial Life VIII: Proceedings of
the Eight International Conference on Artificial Life, R.K. Standish, M.A.
Bedau, and H.A. Abbass, Editors. 2002: Sydney, Australia. p. 97-100.
[16] Corradini, F., E. Merelli, and M. Vita, A multi-agent system for modelling
carbohydrate oxidation in cell. Computational Science and Its
Applications - Iccsa 2005, Pt 2, 2005. 3481: p. 1264-1273.
[17] Merelli, E., et al., Agents in bioinformatics, computational and systems
biology. Briefings in Bioinformatics, 2007. 8(1): p. 45-59.
[18] Corkill, D.D. Collaborating Software: Blackboard and Multi-Agent
Systems & the Future. in Proceedings of the International Lisp
Conference. 2003. New York.
[19] Lesk, A.M., Introduction to bioinformatics. 2002, Oxford ; New York:
Oxford University Press. 283 p.
[20] Sadqi, M., Atom-by-atom analysis of global downhill protein folding.
Nature, 2006. 442(7100): p. 317-321.
[21] Creighton, T.E., EXPERIMENTAL STUDIES OF PROTEIN FOLDING
AND UNFOLDING. Progress in Biophysics & Molecular Biology, 1978.
33(3): p. 231-297.
[22] Snow, C.D., et al., Absolute comparison of simulated and experimental
protein-folding dynamics. Nature, 2002. 420(6911): p. 102-106.
[23] Mann, M., S. Will, and R. Backofen, CPSP-tools - Exact and complete
algorithms for high-throughput 3D lattice protein studies. Bmc
Bioinformatics, 2008. 9.
[24] Shmygelska, A. and H.H. Hoos, An ant colony optimisation algorithm for
the 2D and 3D hydrophobic polar protein folding problem. Bmc
Bioinformatics, 2005. 6.
[25] Thachuk, C., A. Shmygelska, and H.H. Hoos, A replica exchange Monte
Carlo algorithm for protein folding in the HP model. Bmc
Bioinformatics, 2007. 8.
[26] Clementi, C., Coarse-grained models of protein folding: toy models or
predictive tools? Current Opinion in Structural Biology, 2008. 18(1): p.
10-15.
[27] Jacob, E., A. Horovitz, and R. Unger, Different mechanistic requirements
for prokaryotic and eukaryotic chaperonins: a lattice study.
Bioinformatics, 2007. 23(13): p. I240-I248.
[28] Blackburne, B.P. and J.D. Hirst, Population dynamics simulations of
functional model proteins. Journal of Chemical Physics, 2005. 123(15).
[29] Dill, K.A., THEORY FOR THE FOLDING AND STABILITY OF
GLOBULAR-PROTEINS. Biochemistry, 1985. 24(6): p. 1501-1509.
[30] Dill, K.A., Polymer principles and protein folding. Protein Science, 1999.
8(6): p. 1166-1180.
@article{"International Journal of Biological, Life and Agricultural Sciences:54070", author = "Pedro Pablo González Pérez and Hiram I. Beltrán and Arturo Rojo-Domínguez and Máximo EduardoSánchez Gutiérrez", title = "Multi-Agent Systems Applied in the Modeling and Simulation of Biological Problems: A Case Study in Protein Folding", abstract = "Multi-agent system approach has proven to be an effective and appropriate abstraction level to construct whole models of a diversity of biological problems, integrating aspects which can be found both in "micro" and "macro" approaches when modeling this type of phenomena. Taking into account these considerations, this paper presents the important computational characteristics to be gathered into a novel bioinformatics framework built upon a multiagent architecture. The version of the tool presented herein allows studying and exploring complex problems belonging principally to structural biology, such as protein folding. The bioinformatics framework is used as a virtual laboratory to explore a minimalist model of protein folding as a test case. In order to show the laboratory concept of the platform as well as its flexibility and adaptability, we studied the folding of two particular sequences, one of 45-mer and another of 64-mer, both described by an HP model (only hydrophobic and polar residues) and coarse grained 2D-square lattice. According to the discussion section of this piece of work, these two sequences were chosen as breaking points towards the platform, in order to determine the tools to be created or improved in such a way to overcome the needs of a particular computation and analysis of a given tough sequence. The backwards philosophy herein is that the continuous studying of sequences provides itself important points to be added into the platform, to any time improve its efficiency, as is demonstrated herein.
", keywords = "multi-agent systems, blackboard-based agent architecture, bioinformatics framework, virtual laboratory, protein folding.", volume = "3", number = "10", pages = "502-10", }
