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A Computing Framework for Modeling Biological Systems Using Membrane Computing

This document outlines a comprehensive method for modeling biological systems through a novel platform leveraging computer science and software engineering concepts. By utilizing formal model structures such as membrane systems, the approach embodies a natural modeling technique to analyze and simulate complex biological interactions. Key features include automated design, system restructuring, and formal analysis combined with learning mechanisms, allowing for the exploration of cellular behaviors like division and differentiation. The framework is supported by simulations and verifications employing tools like PRISM for formal verification and reliability assessments.

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A Computing Framework for Modeling Biological Systems Using Membrane Computing

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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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