Executable Biology Tutorial

3,208 views
3,047 views

Published on

A tutorial on modeling form Systems and Synthetic Biology. This tutorial was given at the 2nd European Conference on Synthetic Biology, Spain, 2009

Published in: Education, Technology
1 Comment
1 Like
Statistics
Notes
  • I would love to learn more about this. Are there things I can read that introduce this topic in a more easy to understand form?
       Reply 
    Are you sure you want to  Yes  No
    Your message goes here
No Downloads
Views
Total views
3,208
On SlideShare
0
From Embeds
0
Number of Embeds
42
Actions
Shares
0
Downloads
112
Comments
1
Likes
1
Embeds 0
No embeds

No notes for slide

Executable Biology Tutorial

  1. 1. A Gentle Introduction to Executable Biology Natalio Krasnogor ASAP - Interdisciplinary Optimisation Laboratory School of Computer Science and Information Technology Centre for Integrative Systems Biology School of Biology Centre for Healthcare Associated Infections Institute of Infection, Immunity & Inflammation University of Nottingham www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 1 /94
  2. 2. Main Contributors to this Tutorial  Jonathan Blake Integrated Environment  Hongqing Cao Machine Learning & Optimisation Modeling &  Francisco Romero-Campero Model Checking  James Smaldon Dissipative Particle Dynamics Stochastic  Jamie Twycross Simulations www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 2 /94
  3. 3. Outline •Brief Introduction to Computational Modeling •Modeling for Top Down SB •Executable Biology •A pinch of Model Checking •Modeling for the Bottom Up SB •Dissipative Particle Dynamics •Conclusions www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 3 /94
  4. 4. Outline •Brief Introduction to Computational Modeling •Modeling for Top Down SB •Executable Biology •A pinch of Model Checking •Modeling for the Bottom Up SB •Dissipative Particle Dynamics •Conclusions www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 4 /94
  5. 5. InfoBiotics www.infobiotic.net  The utilisation of cutting-edge information processing techniques for biological modelling and synthesis  The understanding of life itself as multi-scale (Spatial/Temporal) information processing systems  Composed of 3 key components:  Executable Biology (or other modeling techniques)  Automated Model and Parameter Estimation  Model Checking (and other formal analysis) www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 5 /94
  6. 6. InfoBiotics There are good reasons to think that information processing is an enabling viewpoint when modeling living systems Life as we know is: • coded in discrete units (DNA, RNA, Proteins) • combinatorially assembles interactions (DNA-RNA, DNA- Proteins,RNA-Proteins , etc) through evolution and self-organisation • Life emerges from these interacting parts • Information is: • transported in time (heredity, memory e.g. neural, immune system, etc) • transported in space (molecular transport processes, channels, pumps, etc) • Transport in time = storage/memory  a computational process • Transport in space = communication  a computational process • Signal Transduction = processing  a computational process www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 6 /94
  7. 7. What is modelling?  Is an attempt at describing in a precise way an understanding of the elements of a system of interest, their states and interactions  A model should be operational, i.e. it should be formal, detailed and “runnable” or “executable”. www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 7 /94
  8. 8. Modeling in Systems & Synthetic Biology Systems Biology Synthetic Biology Colonies • Understanding •Control • Integration • Design • Prediction • Engineering • Life as it is •Life as it could be Cells Computational modelling to Computational modelling to elucidate and characterise engineer and evaluate modular patterns exhibiting possible cellular designs robustness, signal filtering, exhibiting a desired amplification, adaption, behaviour by combining well error correction, etc. studied and characterised Networks cellular modules www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 8 /94
  9. 9. Model Design in Systems/Synthetic Biology It is a hard process to design suitable models in systems/ synthetic biology where one has to consider the choice of the model structure and model parameters at different points repeatedly. Some use of computer simulation has been mainly focused on the computation of the corresponding dynamics for a given model structure and model parameters. Ultimate goal: for a new biological system (spec) one would like to estimate the model structure and model parameters (that match reality/constructible) simultaneously and automatically. Models should be clear & understandable to the biologist www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 9 /94
  10. 10. How you select features, disambiguate and quantify depends on the goals behind your modelling enterprise. Basic goal: to clarify current understandings by formalising what the constitutive elements of a system Systems Biology are and how they interact Intermediate goal: to test current understandings Synthetic Biology against experimental data Advanced goal: to predict beyond current understanding and available data Dream goal: (1) to combinatorially combine in silico well-understood components/models for the design and generation of novel experiments and hypothesis and ultimately (2) to design, program, optimise & control (new) biological systems www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 10 /94
  11. 11. Modelling Approaches There exist many modelling approaches, each with its advantages and disadvantages.  Macroscopic, Microscopic and Mesoscopic  Quantitative and qualitative  Discrete and Continuous  Deterministic and Stochastic  Top-down or Bottom-up E. Klipp et al, Systems Biology in Practice, 2005 www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 11 /94
  12. 12. Tools Suitability and Cost Stochastic ODE uo us Delay Eq. in Co nt PDE Cellular Automata Time Dependent Multi-agents Spatially Structured Monte Carlo Petri Nets te re Disc Π-calculus P-systems Deterministic Jasmin Fisher and Thomas Henzinger. Executable cell biology. Nature Biotechnology, 25, 11, 1239-1249 (2008) www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 12 /94
  13. 13. Modelling Frameworks  Denotational Semantics Models: Set of equations showing relationships between molecular quantities and how they change over time. They are approximated numerically. (I.e. Ordinary Differential Equations, PDEs, etc)  Operational Semantics Models: Algorithm (list of instructions) executable by an abstract machine whose computation resembles the behaviour of the system under study. (I.e. Finite State Machine) Jasmin Fisher and Thomas Henzinger. Executable cell biology. Nature Biotechnology, 25, 11, 1239-1249 (2008) www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 13 /94
  14. 14. Stochasticity in Cellular Systems  Most commonly recognised sources of noise in cellular system are low number of molecules and slow molecular interactions.  Over 80% of genes in E. coli express fewer than a hundred proteins per cell.  Mesoscopic, discrete and stochastic approaches are more suitable:  Only relevant molecules are taken into account.  Focus on the statistics of the molecular interactions and how often they take place. Mads Karn et al. Stochasticity in Gene Expression: From Theories to Phenotypes. Nature Reviews, 6, 451-464 (2005) Purnananda Guptasarma. Does replication-induced transcription regulate synthesis of the myriad low copy number poteins of E. Coli. BioEssays, 17, 11, 987-997 www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 14 /94
  15. 15. Outline •Brief Introduction to Computational Modeling •Modeling for Top Down SB •Executable Biology •A pinch of Model Checking •Modeling for the Bottom Up SB •Dissipative Particle Dynamics •Conclusions www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 15 /94
  16. 16. Executable Biology with P systems  Field of membrane computing initiated by Gheorghe Păun in 2000  Inspired by the hierarchical membrane structure of eukaryotic cells  A formal language: precisely defined and machine processable  An executable biology methodology www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 16 /94
  17. 17. Functional Entities Container • A boundary defining self/non-self (symmetry breaking). • Maintain concentration gradients and avoid environmental damage. Metabolism • Confining raw materials to be processed. • Maintenance of internal structures (autopoiesis). Information • Sensing environmental signals / release of signals. • Genetic information www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 17 /94
  18. 18. Distributed and parallel rewritting systems in compartmentalised hierarchical structures. Objects Compartments Rewriting Rules • Computational universality and efficiency. • Modelling Framework www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 18 /94
  19. 19. P-Systems: Modelling Principles Molecules Objects Structured Molecules Strings Molecular Species Multisets of objects/ strings Membranes/organelles Membrane Biochemical activity rules Biochemical transport Communication rules www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 19 /94
  20. 20. Stochastic P Systems www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 20 /94
  21. 21. Rewriting Rules used by Multi-volume Gillespie’s algorithm www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 21 /94
  22. 22. Molecular Species  A molecular species can be represented using individual objects.  A molecular species with relevant internal structure can be represented using a string. www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 22 /94
  23. 23. Molecular Interactions  Comprehensive and relevant rule-based schema for the most common molecular interactions taking place in living cells. Transformation/Degradation Complex Formation and Dissociation Diffusion in / out Binding and Debinding Recruitment and Releasing Transcription Factor Binding/Debinding Transcription/Translation www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 23 /94
  24. 24. Compartments / Cells  Compartments and regions are explicitly specified using membrane structures. www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 24 /94
  25. 25. Colonies / Tissues  Colonies and tissues are representing as collection of P systems distributed over a lattice.  Objects can travel around the lattice through translocation rules. v www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 25 /94
  26. 26. Molecular Interactions Inside Compartments www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 26 /94
  27. 27. Passive Diffusion of Molecules www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 27 /94
  28. 28. www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 28 /94
  29. 29. Signal Sensing and Active Transport www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 29 /94
  30. 30. Specification of Transcriptional Regulatory Networks www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 30 /94
  31. 31. Transcription as Rewriting Rules on Multisets of Objects and Strings www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 31 /94
  32. 32. Translation as Rewriting Rules on Multisets of Objects and Strings www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 32 /94
  33. 33. Stochastic P Systems  Gillespie Algorithm (SSA) generates trajectories of a stochastic system consisting of modified for multiple compartments/volumes: 1) A stochastic constant is associated with each rule. 2) A propensity is computed for each rule by multiplying the stochastic constant by the number of distinct possible combinations of the elements on the left hand side of the rule. 3) The rule to apply j0 and the waiting time τ for its application are computed by generating two random numbers r1,r2 ~ U(0,1) and using the formulas: F. J. Romero-Campero, J. Twycross, M. Camara, M. Bennett, M. Gheorghe, and N. Krasnogor. Modular assembly of cell systems biology models using p systems. International Journal of Foundations of Computer Science, 2009 www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 33 /94
  34. 34. Multicompartmental Gillespie Algorithm www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 34 /94
  35. 35. Multicompartmental Gillespie Algorithm 1 3 2 www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 34 /94
  36. 36. Multicompartmental Gillespie Algorithm 1 3 r11,…,r1n1 r31,…,r3n3 M3 M1 2 r21,…,r2n2 M2 www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 34 /94
  37. 37. Multicompartmental Gillespie Algorithm 1 3 r11,…,r1n1 r31,…,r3n3 Local Gillespie M3 M1 2 r21,…,r2n2 M2 www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 34 /94
  38. 38. Multicompartmental Gillespie Algorithm 1 3 r11,…,r1n1 ( 1, τ1, r01) r3 1,…,r n3 3 Local Gillespie M3 M1 2 r21,…,r2n2 M2 www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 34 /94
  39. 39. Multicompartmental Gillespie Algorithm 1 3 r11,…,r1n1 ( 1, τ1, r01) r3 1,…,r n3 3 Local Gillespie M3 M1 ( 2, τ2, r02) 2 r21,…,r2n2 M2 www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 34 /94
  40. 40. Multicompartmental Gillespie Algorithm 1 3 r11,…,r1n1 ( 1, τ1, r01) r3 1,…,r n3 3 Local Gillespie M3 M1 ( 2, τ2, r02) 2 ( 3, τ3, r03) r21,…,r2n2 M2 www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 34 /94
  41. 41. Multicompartmental Gillespie Algorithm 1 3 r11,…,r1n1 ( 1, τ1, r01) r3 1,…,r n3 3 Local Gillespie M3 M1 ( 2, τ2, r02) 2 ( 3, τ3, r03) r21,…,r2n2 Sort Compartments M2 τ2 < τ1 < τ3 www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 34 /94
  42. 42. Multicompartmental Gillespie Algorithm 1 3 r11,…,r1n1 ( 1, τ1, r01) r3 1,…,r n3 3 Local Gillespie M3 M1 ( 2, τ2, r02) 2 ( 3, τ3, r03) r21,…,r2n2 Sort Compartments M2 τ2 < τ1 < τ3 ( 2, τ2, r02) ( 1, τ1, r01) ( 3, τ3, r03) www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 34 /94
  43. 43. Multicompartmental Gillespie Algorithm 1 3 r11,…,r1n1 ( 1, τ1, r01) r3 1,…,r n3 3 Local Gillespie M3 M1 ( 2, τ2, r02) 2 ( 3, τ3, r03) r21,…,r2n2 ‘ Sort Compartments M2 τ2 < τ1 < τ3 ( 2, τ2, r02) ( 1, τ1, r01) ( 3, τ3, r03) www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 34 /94
  44. 44. Multicompartmental Gillespie Algorithm 1 3 r11,…,r1n1 ( 1, τ1, r01) r3 1,…,r n3 3 Local Gillespie M3 M1 ( 2, τ2, r02) 2 ( 3, τ3, r03) r21,…,r2n2 ‘ Sort Compartments M2 τ2 < τ1 < τ3 ( 2, τ2, r02) ( 1, τ1-τ2, r01) ( 1, τ1, r01) ( 3, τ3-τ2, r03) ( 3, τ3, r03) Update Waiting Times www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 34 /94
  45. 45. Multicompartmental Gillespie Algorithm 1 3 r11,…,r1n1 ( 1, τ1, r01) r3 1,…,r n3 3 Local Gillespie M3 M1 ( 2, τ2, r02) 2 ( 3, τ3, r03) r21,…,r2n2 ‘ Sort Compartments M2 τ2 < τ1 < τ3 ( 2, τ2, r02) ( 2, τ2’, r02) ( 1, τ1-τ2, r01) ( 1, τ1, r01) ( 3, τ3-τ2, r03) ( 3, τ3, r03) Update Waiting Times www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 34 /94
  46. 46. Multicompartmental Gillespie Algorithm 1 3 r11,…,r1n1 ( 1, τ1, r01) r3 1,…,r n3 3 Local Gillespie M3 M1 ( 2, τ2, r02) 2 ( 3, τ3, r03) r21,…,r2n2 ‘ Sort Compartments M2 τ2 < τ1 < τ3 ( 2, τ2, r02) ( 2, τ2’, r02) ( 1, τ1-τ2, r01) ( 1, τ1, r01) ( 3, τ3-τ2, r03) Insert new triplet ( 3, τ3, r03) τ1-τ2 <τ2’ < τ3-τ2 Update Waiting Times www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 34 /94
  47. 47. Multicompartmental Gillespie Algorithm 1 3 r11,…,r1n1 ( 1, τ1, r01) r31,…,r n3 3 Local Gillespie M3 M1 ( 2, τ2, r02) 2 ( 3, τ3, r03) r21,…,r2n2 ‘ Sort Compartments M2 τ2 < τ1 < τ3 ( 2, τ2, r02) ( 1, τ1-τ2, r01) ( 2, τ2’, r02) ( 1, τ1-τ2, r01) ( 1, τ1, r01) ( 2, τ2’, r02) ( 3, τ3-τ2, r03) ( 3, τ3-τ2, r03) Insert new triplet ( 3, τ3, r03) τ1-τ2 <τ2’ < τ3-τ2 Update Waiting Times www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 34 /94
  48. 48. Scalability through Modularity  Cellular functions arise from orchestrated interactions between motifs consisting of many molecular interacting species.  A P System model is a set of rules representing molecular interactions motifs that appear in many cellular systems. www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 35 /94
  49. 49. Basic P System Modules Used www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 36 /94
  50. 50. Modularity in Gene Regulatory Networks  According to E. Davidson functional cis-regulatory modules are nonrandom clusters of target binding sites for transcription factors regulating the same gene or operon.  A library of modules corresponding to promoters of well studied genes. The activity of these promoters have been modelled mechanistically in terms of rewriting rules representing TF binding and debinding and transcription initiation. E. Davidson, The Regulatory Genome, Gene Regulatory Networks in Development and Evolution, Elsevier. www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain /94
  51. 51. Representing transcriptional fusions and synthetic gene regulatory networks  Variables in our modules can be instantiated with the name of specific genes to represent a construct where the gene is fused to the promoter or cluster of TF binding sites modelled by the module.  These genes can in turn codify other TFs that can interact with other modules producing a synthetic gene regulatory network. www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain /94
  52. 52. Modelling Individual Cells  An individual cell is represented as a P system, a set of compartments where specific objects describing molecular species are placed.  The gene regulatory networks in each cell are represented as a collection of modules and rewriting rules. www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain /94
  53. 53. Modelling Multicellular Systems  The geometry and topology of multicellular systems are described using geometrical lattices over which many copies of the different P systems representing individual cells are distributed. www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain /94
  54. 54.  Using P systems modules one can model a large variety of commonly occurring BRN:  Gene Regulatory Networks  Signaling Networks  Metabolic Networks  This can be done in an incremental way. F. J. Romero-Campero, J. Twycross, M. Camara, M. Bennett, M. Gheorghe, and N. Krasnogor. Modular assembly of cell systems biology models using p systems. International Journal of Foundations of Computer Science, 2009 www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 41 /94
  55. 55. InfoBiotics Pipeline www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 42 /94
  56. 56. SBML from CellDesigner www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 43 /94
  57. 57. Runs simulations and extract data www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 44 /94
  58. 58. Plot Timeseries www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 45 /94
  59. 59. in time and space www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 46 /94
  60. 60. Multi-component negative- feedback oscillator Oscillations caused by time-delayed negative-feedback: Negative-feedback: gene-product that represses it's gene Time-delay: mRNA export, translation and repressor import Novak & Tyson: Design Principles of Biochemical Oscillators. Nat. Rev. Mol. Cell. Biol. 9: 981-991 (2008) www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 47 /94
  61. 61. Multi-component negative- feedback oscillator  Mathematical model − Xc = [mRNA in cytosol] − Yc = [protein in cytosol] − Xn = [mRNA in nucleus] − Yn = [protein in nucleus] − E = [total protease] − p = “integer indicating whether Y binds to DNA as a monomer, trimer, or so on” Executable Biology makes this more obvious: we can vary the value of p and the sequence of binding... www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 48 /94
  62. 62. Single protein represses gene p=1 www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 49 /94
  63. 63. When repression is weak (dissociation rate = 10) No obvious oscillatory behaviour in single simulation www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 50 /94
  64. 64. When repression is weak (dissociation rate = 10) Mean of 100 runs shows convergence to steady state www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 51 /94
  65. 65. When repression is strong (dissociation rate = 0.1) Oscillations evident in single simulation www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 52 /94
  66. 66. When repression is strong (dissociation rate = 0.1) Averging 100 runs dampens oscillations due to different phases but observable. Protein levels steady. www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 53 /94
  67. 67. Repressor binding sequence  When p=2 there are two possible scenarios: – First protein binds to second protein weakly then protein-dimer binds to gene strongly – First protein binds to gene weakly then second protein binds to protein-gene dimer strongly  In the following only the model structure is changed, not the parameters  First dissociation rate = 10  Second dissociation rate = 0.1 www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 54 /94
  68. 68. 1. Protein represses as dimer www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 55 /94
  69. 69. 1. Protein represses as dimer target mRNA levels oscillate ready but protein accumulates in the cytosol www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 56 /94
  70. 70. 2. Proteins repress cooperatively www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 57 /94
  71. 71. 2. Proteins repress cooperatively target Oscillations are steady and protein levels are controlled www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 58 /94
  72. 72. An example: Ron Weiss' Pulse Generator  Two different bacterial strains carrying specific synthetic gene regulatory networks are used.  The first strain produces a diffusible signal AHL.  The second strain possesses a synthetic gene regulatory network which produces a pulse of GFP after AHL sensing.  These two bacterial strains and their respective synthetic networks are modelled as a combination of modules. S. Basu, R. Mehreja, et al. Spatiotemporal control of gene expression with pulse generating networks, PNAS, 101, 6355-6360 www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain /94
  73. 73. Sending Cells www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain /94
  74. 74. Pulse Generating Cells www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain /94
  75. 75. An example: Ron Weiss' Pulse Generator  A rectangular lattice is used over which P systems representing cells sending AHL, cells with the previously introduced pulse generator and environments are distributed. www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain /94
  76. 76. An example: Ron Weiss' Pulse Generator www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain /94
  77. 77. An example: Ron Weiss' Pulse Generator www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain /94
  78. 78. Outline •Brief Introduction to Computational Modeling •Modeling for Top Down SB •Executable Biology •A pinch of Model Checking •Modeling for the Bottom Up SB •Dissipative Particle Dynamics •Conclusions www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 64 /94
  79. 79. Model Checking on the Pulse Generator  The simulation of the Pulse Generator show some interesting properties that were subsequently analysed using model checking.  Due to the complexity of the system (state space explosion) we perform approximate model checking with a precision of 0.01 and a confidence of 0.001 which needed to run 100000 simulations. www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain /94
  80. 80. Model Checking on the Pulse Generator  The simulations show that although the number of signals reaches eventually the same level in all the cells in the lattice those cells that are far from the sending cells produce fewer number of GFP molecules.  The difference between cells close to and far from the sending cells is the rate of increase of the signal AHL.  We study the effect of the rate of increase of the signal AHL in the number of GFP produced. S. Basu, R. Mehreja, et al. Spatiotemporal control of gene expression with pulse generating networks, PNAS, 101, 6355-6360 www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain /94
  81. 81.  We studied the expected number of GFP molecules produced over time for different increase rates of AHL. R = ? [ I = 60 ] rewards molecule = 1 : proteinGFP; endrewards The system is expected to produce longer pulses with lower amplitudes for slow increase rates of AHL signals. www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain /94
  82. 82.  In order to get a clearer idea, the probability distribution of the number of GFP molecules at 60 minutes was computed. P = ? [ true U[60,60] ((proteinGFP > N) & (proteinGFP <= (N + 10))) ] Note that for slow increase rates of AHL the probability of having NO GFP molecules at all is high. www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain /94
  83. 83.  Finally, assuming that for a cell to be fluorescence it needs to have a given number of GFP for an appreciable period of time we studied the expected amount of time a cell have more than 50 GFP molecules during the first 60 minutes after the signals arrive to the cell. R = ? [ C <= 60 ] rewards true : proteinGFP; endrewards www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain /94
  84. 84. Outline •Brief Introduction to Computational Modeling •Modeling for Top Down SB •Executable Biology •A pinch of Model Checking •Modeling for the Bottom Up SB •Dissipative Particle Dynamics •Conclusions www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 70 /94
  85. 85. A (Proto)Cell as an Information Processing Device LeDuc et al. Towards an in vivo biologically inspired nanofactory. Nature (2007) www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 71 /94
  86. 86. a b Transport Modalities a b Antiport channel a b Symport channel a c b a b Promoted symport channel (trap) a b www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 72 /94
  87. 87. Transport Modalities 5 2 1 4 3 Phagocitosys Endocitosys Pinocitosys Exocitosys www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 73 /94
  88. 88. Transport Modalities Highly specific: cell specific & topology specific www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 74 /94
  89. 89. Towards a synthetic cell from the bottom up  Biocompatible vesicles as long-circulating carriers  Polymer self-assembly into higher-order structures  Cell-mimics with hydrophobic ‘cell-wall’ and glycosylated surfaces  Potential for cross-talk with biological cells Pasparakis, G. Angew Chem Int Ed. 2008 47 (26), 4847-4850 www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 75 /94
  90. 90. ‘Talking’ to cell-vesicle aggregates Pasparakis, G. Angew Chem Int Ed. 2008 47 (26), 4847-4850 www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 76 /94
  91. 91. Dissipative Particle Dynamics  Simulate movement of particles which represent several atoms / molecules  Calculate forces acting on particles, integrate equations of motion  Used extensively for investigating the self-assembly of lipid membrane structures at the mesoscale  Typical simulations contain ~105-106 particles, for ~105-106 time steps  Particles interact with each other within a finite radius much smaller than the simulation space, algorithmic optimisations of force calculations are possible www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 77 /94
  92. 92. Dissipative Particle Dynamics  First introduced by Hoogerbrugge and Koelmann in 1992.  Statistical mechanics of the model derived by espanol and warren in 1995.  A coarse graining approach is used so that one simulation particle represents a number of real molecules of a given type.  Since the timescale at which interactions occur is longer than in MD, fewer time-steps are required to simulation the same period of real time.  The short force cut-off radius enables optimisation of the force calculation code to be performed. O H H W O O H H H H www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 78 /94
  93. 93. Dissipative Particle Dynamics Conservative Force i W P Dissipative Force j W P Random Force www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 79 /94
  94. 94. Dissipative Particle Dynamics  Polymers  A number of simulation beads are tied together to represent the original molecule.  Two new forces are introduced between polymer particles, a Hookean spring force and a bond angle force. www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 80 /94
  95. 95. Liposome Formation in DPD www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 81 /94
  96. 96. www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 82 /94
  97. 97. Case Study One: Vesicle Diffusion Polar heads Non polar tails Pores J. Smaldon, J. Blake, D. Lancet, and N. Krasnogor. A multi-scaled approach to artificial life simulation with p systems and dissipative particle dynamics. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2008), ACM Publisher, 2008. www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 83 /94
  98. 98. Case Study One: Vesicle Diffusion  The regions were formed by allowing vesicles to self- assemble from phospholipids in the presence of pore inclusions  Pores are simple channels with an exterior mimicking the hydrophobic/hydrophilic profile of the bilayer www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 84 /94
  99. 99. www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 85 /94
  100. 100. Case Study One: Vesicle Diffusion Tagged solvent particles were placed within the liposome inner volume, the change in concentration due to diffusion of solvent through the membrane pores was measures www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 86 /94
  101. 101. Case Study Two: Liposome Logic  The behaviour of some prokaryotic RNA transcription motifs matches that of boolean logic gates[1]  DPD was extended with mesoscale collision based reactions.  transcriptional logic gates were simulated in bulk solvent and within a liposome core volume. www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 87 /94
  102. 102. www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 88 /94
  103. 103. Case Study Two: Liposome Logic OR gate results for different inputs: (¬X,¬Y) (¬X,Y) (X,¬Y) (X,Y) J. Smaldon, N. Krasnogor, M. Gheorghe, and A. Cameron. Liposome logic. In Proceedings of the 2009 Genetic and Evolutionary Computation Conference (GECCO 2009), 2009 www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 89 /94
  104. 104. Outline •Brief Introduction to Computational Modeling •Modeling for Top Down SB •Executable Biology •A pinch of Model Checking •Modeling for the Bottom Up SB •Dissipative Particle Dynamics •Conclusions www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 90 /94
  105. 105. Summary & Conclusions  This talk has focused on an integrative methodology, InfoBiotics, for Systems & Synthetic Biology  Executable Biology/DPD  Parameter and Model Structure Discovery  Model Checking  Computational models (or executable in Fisher & Henzinger’s jargon) adhere to (a degree) to an operational semantics.  Refer to the excellent review [Fisher & Henzinger, Nature Biotechnology, 2007] www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 91 /94
  106. 106. Summary & Conclusions  Computational models can thus be executed (quite a few tools out there, lots still missing)  Quantitative VS qualitative modelling: computational models can be very useful even when not every detail about a system is known.  Missing Parameters/model structures can sometimes be fitted with of-the-shelf optimisation strategies (e.g. COPASI, GAs, etc)  Computational models can be analysed by model checking: thus they can be used for testing hypothesis and expanding experimental data in a principled way www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 92 /94
  107. 107. Acknowledgements  We would like to acknowledge EPSRC grants EP/E017215/1 & EP/D021847/1 , BBSRC grant BB/F01855X/1 & BB/ D019613/1  Our colleagues in the Centre for Biomolecular Sciences and the Centre for Plant Integrative Biology  ESF for funding ECSB II www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 93 /94
  108. 108. Any Questions? Vacancies: • www.infobiotic.org • PhD in Computational Modeling of root development • Postdoc on Dissipative Particles • www.synbiont.org Dynamics for ProtoCells • ESF Summer School on Plants Bioinformatics, Systems and Synthetic Biology • Nottingham, UK between the 27th and 31st of July 2009 • EU students fully funded! • Limited spaces! apply soon!! www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain 94 /94

×