(Towards a) Modelling Platform for Biological Systems

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

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Natural modelling -Membrane computing


Membranes
b
a
a

Objects
b
a
b
a
c
c
Regions
b
Cell
Membrane (P) system
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What is a (basic) membrane system
A membrane system is a computing model consisting of chemicals are modelled as symbols or strings, called abstract objects regions (compartments) contain multisets of objects and other membranes rules are associated to regions system evolves through transitions http//ppage.psystems.eu/ The Oxford Handbook of Membrane Computing To appear 24/12/2009

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Rules and computation

• transformation a ? xc complex
  formation/dissociation activators/inhibitors
• communication ac ? ac, ac ? ac
  symport, antiport
• cell division ac ? bc dc
• cell differentiation ac ? be
• cell death ac ? ? a,
  b, d, x multisets
• Execution strategies

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Modelling molecular interactions
Biochemistry P systems
Compartment Region
Molecules Objects (symbols, strings)
Molecular population Multiset of objects
Biochemical transformations Various rules
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Gene regulatory network - P system model
Lac operon in E coli Hlavacek, Savageau, 1995
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Simulations
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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
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Property inference
Daikon tool Reverse-engineer specifications from software systems as preconditions, postconditions and invariants (Ernst et all, 2001) formal analysis and testing In the context of biological data, it automatically infers invariants to confirm the model behaves as it should - obvious invariants indicate faults anomalous invariants suggest novel relationships

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Daikon Pre-, post-conditions and invariants
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Daikon Pre-, post-conditions and invariants
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Daikon Pre-, post-conditions and invariants
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!!
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Daikon Pre-, post-conditions and invariants
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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

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P systems in PRISM
P system model
PRISM code
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Invariants checking positive regulation
more likely rnas between 0 and 15, proteins
between 0 and 150
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Check relationships
Relationships between the number of repressors
and rna and protein molecules
P(protgtrep)
P(rnagtrep)
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Conclusions and further developments
Integrated engineering approach P systems modelling approach for molecular interactions modular and natural Automated design Property inference Formal verification

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Thanks?