Agency Models in Signal Pathway Analysis

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This modeling approach is being incorporated into a new generation of knowledge system at RoboSphere, Sigmoid, to extend the frontiers of a variety of kinomes. Sigmoid uses an innovative agent based …

This modeling approach is being incorporated into a new generation of knowledge system at RoboSphere, Sigmoid, to extend the frontiers of a variety of kinomes. Sigmoid uses an innovative agent based technology, developed at RoboSphere. Sigmoid interactively assists researchers in the hypothesis and evaluation of potential signal transduction systems. Each Sigmoid knowledge agent maintains knowledge of an object, i.e. tissue, cell, kinase, ligand, gene, in a cell signaling environment. Each agent in turn maintains a local environment, i.e. cell environment, to maintain a catalog of constituent objects. Sigmoid provides a veritable silicon laboratory to explore the phenome expression of a variety of signal moieties and molar distributions.

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  • 1. Agency Models in Signal Pathway Analysis RoboSphere Company A Subsidiary of OVium Solutions Wolfgang F. Kraske, PhD Presented at the Phosphorylation Workshop, Asilomar CA December 2003
  • 2. Abstract Pathway diagrams are a useful means of presenting information from signal transduction knowledge bases. These traditional presentations are become increasingly muddled and confusing as the knowledge base rapidly expands. Currently I am developing a modeling approach that exploits the scale-free nature of networks to more simply presents the dynamic spatial (morphological) extents of signal moiety and molarity distributions to a computer user. This approach is ideal for the fusion of functional signal information with anatomical atlas and/or diagnostic imagery; while allowing users to "zoom into" a focal pathway diagram for a specific temporal-spatial coordinate. Furthermore the use of agency, knowledge agents, to dynamically couple the simulation of kinome and phenome metric information will dramatically extend model integrity for embryology, and possibly phylogeny, to the benefit of both the novice and expert.
  • 3. Temporal Morphology Spatial-temporal clocks are essential components for the homeostasis and development of living organisms. biochemical clock mechanisms must therefore influence the presentation and analysis of signal transduction knowledge bases. Three obvious organism cycles come to our immediate attention; cell cycle (cyclin/CDK), circadian clock(mPer/Ck1) and the somite/hox (tyrosine kinase) clock. In each instance kinase and other phosphorylation mechanisms contribute efficiency and reliability to the clock systems. In particular, the circadian clock serves an essential roll in maintaining the metabolism of multi-cellular organisms and has recently been shown to behave as a topologically distributed neural network. Hence spatial temporal processes operate as the basis of this most essential clock mechanism. Alternately a segmentation clock operates during the development of bilaterians with a precise temporal and spatial development algorithm. The segmentation clock specializes in the distribution of somatic cell topology from temporal growth factor cycles and activation of hox genes. Mature organisms my suffer lethal diseases when aspects of the segmentation clock operate erroneously.
  • 4. Murine Circadian Pathway Science STKE Authority: Russell N. Van Gelder
  • 5. Suprachiasmatic Nucleus Circadian Clock Dorsal Ventral mPer1 Dorsal-Medial to Ventral Gradient 0 hr 6 hr 12 hr S. Yamaguchi et. al., Science Vol. 302, pp. 1408
  • 6. Somite Clock FGF8/Wnt Gradient
  • 7. Somite Clock O. Porquie, Science Vol. 301, pp. 328
  • 8. Hox Clock Hox Algorithm Varieties Hox Clock M. Kmita, D. Duboule, Science Vol. 301, pp. 331
  • 9. Filopodia Extend Notch-Delta Epithelial Communication Filopodia consist of moesin protein to transmit signals beyond cell neighborhood C. de Joussineau et. al., Nature vol, 426 pp. 555
  • 10. Knowledge Agent
    • Knowledge agents are software entities that maintain state behavior and communicate in a signal transduction environment.
    • 11. Knowledge agents act collectively as swarms to manifest a group behavior and dynamic environment.
    • 12. Agent swarm communities form neural network environments that can mimic the behavior of inter-cellular tissue, and intra-cellular compartment behavior; gene regulation, cell life-cycle and energy metabolism.
    • 13. Individual agents may represent ligand, receptor, gene or cell compartment (i.e. membrane, gogli body, ER, nucleus) as well as protein complex (e.g. Ig, MAPKn, G, TK, gene regulatory, ribosomal)
    • 14. Nature provides a new knowledge paradigm for computation.
  • 15. Knowledge Agent B = Max{Min{B1, A1}, . . . , Min{Bn, An}} . . . Knowledge Object B, A1 . . . An Knowledge Agent Σ . . . Goal Test . . . { Back Projection - Knowledge Intimacy Adjustments: A1 . . . An Reasoning Mechanism Believability Measure B x x A1 An Knowledge Agent Σ Goal Test B x x K 1 B1 . . . K n Bn Knowledge Agent Σ Goal Test B x x K 1 B1 . . . K n Bn
  • 16. Conclusion This modeling approach is being incorporated into a new generation of knowledge system at RoboSphere, Sigmoid, to extend the frontiers of a variety of kinomes. Sigmoid uses an innovative agent based technology, developed at RoboSphere. Sigmoid interactively assists researchers in the hypothesis and evaluation of potential signal transduction systems. Each Sigmoid knowledge agent maintains knowledge of an object, i.e. tissue, cell, kinase, ligand, gene, in a cell signaling environment. Each agent in turn maintains a local environment, i.e. cell environment, to maintain a catalog of constituent objects. Sigmoid provides a veritable silicon laboratory to explore the phenome expression of a variety of signal moieties and molar distributions.
  • 17. Codon Wheel