A domain-oriented approach to the reduction of combinatorial complexity in signal transduction networks

Background:Receptors and scaffold proteins possess a number of distinct domains and bind multiple partners. A common problem in modeling signaling systems arises from a combinatorial explosion of different states generated by feasible molecular species. The number of possible species grows exponentially with the number of different docking sites and can easily reach several millions. Models accounting for this combinatorial variety become impractical for many applications.Results:Our results show that under realistic assumptions on domain interactions, the dynamics of signaling pathways can be exactly described by reduced, hierarchically structured models. The method presented here provides a rigorous way to model a large class of signaling networks using macro-states (macroscopic quantities such as the levels of occupancy of the binding domains) instead of micro-states (concentrations of individual species). The method is described using generic multidomain proteins and is applied to the molecule LAT.Conclusion:The presented method is a systematic and powerful tool to derive reduced model structures describing the dynamics of multiprotein complex formation accurately.

[1]  E. Van Obberghen,et al.  Molecular mechanisms of insulin receptor substrate protein‐mediated modulation of insulin signalling , 2003, FEBS letters.

[2]  R. Iyengar,et al.  Modeling cell signaling networks. , 2004, Biology of the cell.

[3]  J. Schlessinger,et al.  Signaling by Receptor Tyrosine Kinases , 1993 .

[4]  A. Kremling,et al.  Modular analysis of signal transduction networks , 2004, IEEE Control Systems.

[5]  E. Gilles,et al.  Computational modeling of the dynamics of the MAP kinase cascade activated by surface and internalized EGF receptors , 2002, Nature Biotechnology.

[6]  E D Gilles,et al.  Reduction of mathematical models of signal transduction networks: simulation-based approach applied to EGF receptor signalling. , 2004, Systems biology.

[7]  E. Janssen,et al.  Minimal Requirement of Tyrosine Residues of Linker for Activation of T Cells in TCR Signaling and Thymocyte Development1 , 2003, The Journal of Immunology.

[8]  J. Schlessinger.  Cell Signaling by Receptor Tyrosine Kinases , 2000, Cell.

[9]  Burkhart Schraven,et al.  Transmembrane adapters: attractants for cytoplasmic effectors , 2003, Immunological reviews.

[10]  Alberto Isidori,et al.  Nonlinear Control Systems, Third Edition , 1995, Communications and Control Engineering.

[11]  S. Kimura,et al.  A computational model on the modulation of mitogen-activated protein kinase (MAPK) and Akt pathways in heregulin-induced ErbB signalling. , 2003, The Biochemical journal.

[12]  W. S. Hlavacek,et al.  Investigation of Early Events in FcεRI-Mediated Signaling Using a Detailed Mathematical Model1 , 2003, The Journal of Immunology.

[13]  James R Faeder,et al.  Investigation of early events in Fc epsilon RI-mediated signaling using a detailed mathematical model. , 2003, Journal of immunology.

[14]  Burkhart Schraven,et al.  The role of adaptor proteins in lymphocyte activation. , 2004, Molecular immunology.

[15]  Dennis Bray,et al.  Molecular model of a lattice of signalling proteins involved in bacterial chemotaxis , 2000, Nature Cell Biology.

[16]  S. Shen-Orr,et al.  Networks Network Motifs : Simple Building Blocks of Complex , 2002 .

[17]  W. S. Hlavacek,et al.  Mathematical and computational models of immune-receptor signalling , 2004, Nature Reviews Immunology.

[18]  D. Lauffenburger.  Cell signaling pathways as control modules: complexity for simplicity? , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[19]  Julio Saez-Rodriguez,et al.  Dissecting the puzzle of life: modularization of signal transduction networks , 2005, Comput. Chem. Eng..

[20]  B. Kholodenko,et al.  Signaling through receptors and scaffolds: independent interactions reduce combinatorial complexity. , 2005, Biophysical journal.

[21]  D. Lauffenburger,et al.  Computational modeling of the EGF-receptor system: a paradigm for systems biology. , 2003, Trends in cell biology.

[22]  A. Isidori.  Nonlinear Control Systems , 1985 .

[23]  B. Kholodenko,et al.  Quantification of Short Term Signaling by the Epidermal Growth Factor Receptor* , 1999, The Journal of Biological Chemistry.

[24]  H. Sauro,et al.  Quantitative analysis of signaling networks. , 2004, Progress in biophysics and molecular biology.

[25]  T. Pawson,et al.  Assembly of Cell Regulatory Systems Through Protein Interaction Domains , 2003, Science.

[26]  S. Shen-Orr,et al.  Network motifs: simple building blocks of complex networks. , 2002, Science.

[27]  M L Blinov,et al.  Combinatorial complexity and dynamical restriction of network flows in signal transduction. , 2004, Systems biology.

[28]  W. S. Hlavacek,et al.  A network model of early events in epidermal growth factor receptor signaling that accounts for combinatorial complexity. , 2006, Bio Systems.

[29]  James R Faeder,et al.  The complexity of complexes in signal transduction , 2003, Biotechnology and bioengineering.

[30]  U. Bhalla,et al.  Emergent properties of networks of biological signaling pathways. , 1999, Science.

[31]  C. J.,et al.  Predicting Temporal Fluctuations in an Intracellular Signalling Pathway , 1998 .

[32]  K. Kohn.  Molecular interaction map of the mammalian cell cycle control and DNA repair systems. , 1999, Molecular biology of the cell.

[33]  William S. Hlavacek,et al.  BioNetGen: software for rule-based modeling of signal transduction based on the interactions of molecular domains , 2004, Bioinform..