Theorie chemischer Organisationen angewendet auf Infektionsmodelle Chemical Organization Theory Applied to Virus Dynamics

Chemical organization theory has been proposed to provide a new perspective to study complex dynamical reaction networks. It decomposes a reaction network into overlapping sub-networks called organizations. An organization is an algebraically closed and self-maintaining set of molecular species. The set of organizations form a hierarchical “organizational structure”, which is here a lattice. In order to evaluate the usefulness of this approach we apply the theory to five models of immune response to HIV infection. We found four different lattices of organizations, which can be used as a first classification of the models. Furthermore, each organization found can be assigned to a functional state of the system. And finally, the lattice of organizations can be used to explain a treatment strategy on a more abstract level, i.e. as a movement from one organization into another.

[1]  J L Sullivan,et al.  Inappropriate model-fitting methods may lead to significant underestimates of viral decay rates in HIV dynamic studies. , 1999, Journal of acquired immune deficiency syndromes.

[2]  P ? ? ? ? ? ? ? % ? ? ? ? , 1991 .

[3]  J. Stelling,et al.  Robustness of Cellular Functions , 2004, Cell.

[4]  Steffen Klamt,et al.  Minimal cut sets in biochemical reaction networks , 2004, Bioinform..

[5]  Peter Dittrich,et al.  Chemical Organisation Theory , 2007, Bulletin of mathematical biology.

[6]  B. L. Clarke.  Stability of Complex Reaction Networks , 2007 .

[7]  Michael L. Mavrovouniotis,et al.  Petri Net Representations in Metabolic Pathways , 1993, ISMB.

[8]  M A Nowak,et al.  Virus phenotype switching and disease progression in HIV‐1 infection , 1999, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[9]  A. Barabasi,et al.  Network biology: understanding the cell's functional organization , 2004, Nature Reviews Genetics.

[10]  Peter Dittrich,et al.  Artificial Chemistry ’ s Global Dynamic . Movements in the Lattice of Organisation , 2022 .

[11]  C. Petri.  Kommunikation mit Automaten , 1962 .

[12]  M. Nowak,et al.  Population Dynamics of Immune Responses to Persistent Viruses , 1996, Science.

[14]  R. Albert,et al.  The large-scale organization of metabolic networks , 2000, Nature.

[15]  D. Fell,et al.  A general definition of metabolic pathways useful for systematic organization and analysis of complex metabolic networks , 2000, Nature Biotechnology.

[16]  R. Heinrich,et al.  Metabolic Pathway Analysis: Basic Concepts and Scientific Applications in the Post‐genomic Era , 1999, Biotechnology progress.

[17]  Paul Helman,et al.  An immunological approach to change detection: algorithms, analysis and implications , 1996, Proceedings 1996 IEEE Symposium on Security and Privacy.

[18]  S. Leibler,et al.  Robustness in simple biochemical networks , 1997, Nature.

[19]  B. L. Clarke.  Theorems on chemical network stability , 1975 .

[20]  J. Bailey.  Complex biology with no parameters , 2001, Nature Biotechnology.

[21]  Stefan Bornholdt,et al.  Topology of biological networks and reliability of information processing , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[22]  M. Feinberg,et al.  Dynamics of open chemical systems and the algebraic structure of the underlying reaction network , 1974 .

[23]  Reinhart Heinrich,et al.  Structural analysis of expanding metabolic networks. , 2004, Genome informatics. International Conference on Genome Informatics.

[24]  Peter Dittrich.  The Bio-Chemical Information Processing Metaphor as a Programming Paradigm for Organic Computing , 2005, ARCS Workshops.

[25]  M. Nowak,et al.  Specific therapy regimes could lead to long-term immunological control of HIV. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[26]  Leo W. Buss,et al.  “The arrival of the fittest”: Toward a theory of biological organization , 1994 .

[27]  Klaus-Peter Zauner,et al.  Conformation-based computing: a rational and a recipe , 2003 .

[28]  B. Palsson,et al.  The Escherichia coli MG1655 in silico metabolic genotype: its definition, characteristics, and capabilities. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[29]  A. Perelson,et al.  HIV-1 Dynamics in Vivo: Virion Clearance Rate, Infected Cell Life-Span, and Viral Generation Time , 1996, Science.

[30]  Alan S Perelson,et al.  HIV-1 infection and low steady state viral loads , 2002, Bulletin of mathematical biology.