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Natural Modelling of Biological Systems: A Membrane Computing Approach

This platform uses computer science models and software engineering for formal analysis and learning mechanisms in biological systems. It employs membrane systems for simulations, verifications, and automated design. Execution strategies and molecular interactions are modeled, with a focus on gene regulatory networks and formal verification using PRISM. Relevant properties and invariants are checked to analyze relationships between components. Future developments include enhancing property inference and expanding formal verification methods.

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Natural Modelling of Biological Systems: A Membrane Computing Approach

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  1. (Towards a) Modelling Platform for Biological Systems Marian Gheorghe University of Sheffield

  2. What the method does • Use computer science models & concepts and software engineering approach & tools • Formal model – membrane systems: modular and uses “natural” • approach (Nott & Sheff) • Formal analysis + learning mechanisms; • Automated design – structure and parameters • Simulations, verifications, system restructuring and design • FJ Romero-Campero, J Twycross, M Camara, M Bennett, M Gheorghe, N Krasnogor, IJFCS, 2009 • FJ Romero-Campero, N Krasnogor, CiE 2009 • F Bernardini,M Gheorghe,FJ Romero-Campero,N Walkinshaw,WMC 2007

  3. “Natural” modelling -Membrane computing Membranes b a a Objects b a b a c c Regions b Cell Membrane (P) system

  4. What is a (basic) membrane system

  5. Rules and computation • transformation: [a →x]c complex formation/dissociation; activators/inhibitors • communication: a[]c→[a]c, [a]c→ a[]c ; symport, antiport • cell division: [a]c→[b]c [d]c • cell differentiation: [a]c→[b]e • cell death: [a]c→ ; a, b, d, x – multisets • Execution strategies

  6. Modelling molecular interactions

  7. Gene regulatory network - P system model Lac operon in E coli: Hlavacek, Savageau, 1995

  8. Simulations

  9. Invariants of the model Initial values: gene = 1, act = n, rep = m; where n, m either 0 or 10 others = 0 P-invariants PIPE: http://pipe2.sourceforge.net

  10. Property inference

  11. Daikon: Pre-, post-conditions and invariants

  12. Daikon: Pre-, post-conditions and invariants

  13. Daikon: Pre-, post-conditions and invariants 20 !!

  14. Daikon: Pre-, post-conditions and invariants

  15. Formal verification - model checking • Use PRISM – • Probability that the mRNA or the protein is within/under/over some limits • Monotonic increase of some products • Relevant properties • M Kwiatkowska et al 2002

  16. P systems in PRISM P system model PRISM code

  17. Invariants checking – positive regulation … more likely rna’s between 0 and 15, proteins between 0 and 150

  18. Check relationships Relationships between the number of repressors and rna and protein molecules P(prot>rep) P(rna>rep)

  19. Conclusions and further developments

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