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Combining Analysis and Synthesis in a Model of a Biological Cell

Combining Analysis and Synthesis in a Model of a Biological Cell. Ken Webb & Tony White SAC ’04 March 17, 2004. Introduction. In this presentation I will cover: Some background CellAK (Cell Assembly Kit) Autopoiesis and SCL (example/test case) Enhanced CellAK. Background.

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Combining Analysis and Synthesis in a Model of a Biological Cell

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  1. Combining Analysis and Synthesis in a Model of a Biological Cell Ken Webb & Tony White SAC ’04 March 17, 2004

  2. Introduction In this presentation I will cover: • Some background • CellAK (Cell Assembly Kit) • Autopoiesis and SCL (example/test case) • Enhanced CellAK

  3. Background • Started as exercise in bio-inspiration • Getting architectural ideas from biology that can be applied to developing complex computer systems • But there are also aspects of interest to members of cell modeling community • “Whole-cell modeling” (M. Tomita, E-CELL) • “Grand challenge of modeling multi-cellular animal” (D. Harel) • SBML compatible tools

  4. Basic Rationale • In existing cell/biochem modeling tools • Each object is a separate act of human design • Using OO software development techniques • Process of building complex cell models is easier for humans (with reuse) • CellAK: example of such an OO system • Can have very large number of components • Paper accepted by journal BioSystems • Enhanced CellAK: introduced in this paper • Extends CellAK to allow it to model autopoiesis

  5. CellAK • An approach to modeling and simulating cells, and other similar biological and non-biological entities. • Based on: • an object-oriented (OO) paradigm, • UML visual formalism, • ROOM visual formalism. • Prototype implemented using Rational Rose RealTime (RRT)

  6. CellAK – some benefits • Scalable, through use of • Object instantiation from classes, • Multiplicity, • Chemical metaphor. • Easy to implement new behavior • If you know C/C++

  7. CellAK – Multi-step Process CellAK incorporates a top-down process based on current practice in development of embedded and real-time systems. Add more detail at each step. • Identify entities, inheritance and containment hierarchies • Establish relationships between entities • Define entity behavior patterns • Implement detailed behavior • Validate Entities typically all from the biological domain.

  8. 1. UML Inheritance Hierarchy

  9. 1. UML Containment Hierarchy

  10. 1. ROOM Containment Hierarchy

  11. 2. Relationships between entities

  12. 3. Behavior between entities

  13. 3. The configured system

  14. 3. Detailed entity behavior

  15. 4. Implement detailed behavior V * S v = ────── Km + S // Irreversible, 1 Substrate, 1 Product, 0 Activator, 0 Inhibitor, 0 Coenzyme case Irr_Sb1_Pr1_Ac0_In0_Co0: s = sm->molecule[gene->substrateId[0]].get(); nTimes = enzymeLevel * ((gene->substrateV * s) / (gene->substrateK + s)); sm->molecule[gene->substrateId[0]].dec( nTimes ); sm->molecule[gene->productId[0]].inc( nTimes ); break;

  16. 5. Validate

  17. BioEntity • Our name for objects in a model of biological cells, or other similar complex reactive system. • May consist of any combination of: • Behavior • Fine-grained structure • Other bioEntities

  18. BioEntity

  19. BioEntity Types

  20. Autopoiesis • “self-making” • All entities in an autopoietic system or network participate in the creation and continual transformation of other entities • Based on bottom-up synthesis rather than the top-down analysis of original CellAK.

  21. Varela/McMullin SCL Model 3 types of randomly moving entities: • Catalyst CellAK: enzyme • Substrate CellAK: small molecule • Link CellAK: lipid • also Holes CellAK: water molecules

  22. SCL-GRO source: [17] McMullin, B., and Gross, D. Towards the Implementation of Evolving Autopoietic Artificial Agents. http://www.eeng.dcu.ie/~alife/bmcm-ecal-2001/ bmcm-ecal-2001.pdf

  23. CellAK version of SCL • CellAK was unable to model autopoietic systems such as in the SCL model. • An enhanced version of CellAK adds causal dependency to allow this. • In bioEntities that contain both behavior and fine-grained structure (FGS), the behavior may be at least partly dependent on details of that FGS.

  24. BioEntity with Dependency

  25. Generic BioEntity Network

  26. SCL BioEntity Network

  27. CellAK – some limitations • Rigid Structure • Unable to evolve novel structure that can be incorporated into the running system. • Based on proprietary tool (RRT) • But, goal here is to present an approach that can be implemented using a variety of software development languages and tools.

  28. Conclusions • CellAK can model biological systems with many thousands of components • OO/UML top-down decomposition (analysis) • Enhanced CellAK • Adds greater ability for bottom-up synthesis • Allows active objects to influence other active objects by effecting their constiuent parts • Can model autopoietic systems with lots of interdependencies

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