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Kinase Structural Models

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CellSignaling@LANL is a gateway to information about research at Los Alamos National Laboratory focused on cellular signaling and related software and database resources.

Computational Systems Biology

We are developing mathematical models of complex biological systems that play a role in cellular signaling. We are using these models to learn about specific systems, particularly signal transduction systems of the immune system, and also to identify biological design principles. In support of these efforts, we are developing software, such as BioNetGen (a general-purpose tool for computer-aided generation of rule-based deterministic or stochastic models of chemical reaction systems), and various databases, such as EcoTFs, which catalogues data about autoregulation of transcription factors (TFs) in Escherichia coli and the molecular signals that affect the activities of these TFs. One purpose of this web site is to distribute our mathematical models in electronic-exchange format(s), such as SBML, a nascent XML-based standard of the systems biology community. We also provide here material that supplements published reports, e.g., extensive online material related to our model for early events in signaling by FcεRI.

Computational Structural Biology

We are using computational methods, such as sequence alignment and homology modeling, to study the human kinases.

Projects served by this web site:
  1. Understanding the Molecular Mechanisms of Pathogen Recognition by the Immune System: Biothreat Reduction through Predictive Science (LDRD)
  2. Receptor Aggregation and Its Effects (NIH)
  3. Mathematical Modeling of Signal Transduction by a TIR Receptor, a project within the Center for Evolutionary and Theoretical Immunology (NIH)
  4. Design Principles of Genetic Regulatory Networks (LDRD)
  5. Metabolomic Functional Analysis of Bacterial Genomes (DOE)
We study signaling by the following receptors:
  • FcεRI, the high-affinity Fc receptor for IgE antibody,
  • EGFR, the epidermal growth factor (EGF) receptor,
  • IL-1R1, the interleukin-1 (IL-1) receptor type 1,
  • TLR2 and TLR4, the toll-like receptors 2 and 4,
  • BCR, B cell antigen receptor.
Research Highlights

May, 2005

M. L. Blinov, J. R. Faeder, B. Goldstein, W. S. Hlavacek. (2005) A network model of Early Events in Epidermal Growth Factor Receptor Signaling that Accounts for Combinatorial Complexity. (in press) BioSystems. [Reprint (pdf)]
Abstract. We consider a model of early events in signaling by the epidermal growth factor (EGF) receptor (EGFR). The model includes EGF, EGFR, the adapter proteins Grb2 and Shc, and the guanine nucleotide exchange factor Sos, which is activated through EGF-induced formation of EGFR-Grb2-Sos and EGFR-Shc-Grb2-Sos assemblies at the plasma membrane. The protein interactions involved in signaling can potentially generate a diversity of protein complexes and phosphoforms; however, this diversity has been largely ignored in models of EGFR signaling. Here, we develop a model that accounts more fully for potential molecular diversity by specifying rules for protein interactions and then using these rules to generate a reaction network that includes all chemical species and reactions implied by the protein interactions. Read more...

March-April, 2005

J. R. Faeder*, M. L. Blinov*, B. Goldstein, W. S. Hlavacek. (2005) Rule-based modeling of biochemical networks. Complexity 10, 22-41. [Reprint (pdf)]
* These two authors contributed equally

Abstract. We present a method for generating a biochemical reaction network from a description of the interactions of components of biomolecules. The interactions are specified in the form of reaction rules, each of which defines a class of reaction associated with a type of interaction. Reactants within a class have shared properties, which are specified in the rule defining the class. A rule also provides a rate law, which governs each reaction in a class, and a template for transforming reactants into products. A set of reaction rules can be applied to a seed set of chemical species and, subsequently, any new species that are found as products of reactions to generate a list of reactions and a list of the chemical species that participate in these reactions, i.e., a reaction network, which can be translated into a mathematical model.

March, 2005

J. R. Faeder, M. L. Blinov, W. S. Hlavacek, B. Goldstein. (2005) Combinatorial complexity and dynamical restictions of network flows in signal transduction. Syst. Biol. 2, 5-15 [Reprint (pdf)]
Abstract. The activities and interactions of proteins that govern the cellular response to a signal generate a multitude of protein phosphorylation states and heterogeneous protein complexes. Here, using a computational model that accounts for 307 molecular species implied by specified interactions of four proteins involved in signalling by the immunoreceptor Fc&epsilonRI, we determine the relative importance of molecular species that can be generated during signalling, chemical transitions among these species, and reaction paths that lead to activation of the protein tyrosine kinase (PTK) Syk. By all of these measures and over 2- and 10-fold ranges of model parameters--rate constants and initial concentrations--only a small portion of the biochemical network is active. The spectrum of active complexes, however, can be shifted dramatically, even by a change in the concentration of a single protein, which suggests that the network can produce qualitatively different responses under different cellular conditions and in response to different inputs. Reduced models that reproduce predictions of the full model for a particular set of parameters lose their predictive capacity when parameters are varied over 2-fold ranges.

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This site is designed and maintained by Michael L. Blinov and William S. Hlavacek ( Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory). Contributors of content include Michael L. Blinov, James R. Faeder, Byron Goldstein, William S. Hlavacek, Benjamin H. McMahon, Richard G. Posner, Antonio Redondo, and Michael E. Wall
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