Computational Synthetic Biology

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These slides were used for a tutorial I gave at GECCO 2010. These are similar, yet not identical, to the other tutorials. The keynote file is too large for slideshare but if anybody needs the original I would be happy to provide a url from where to download it.

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Computational Synthetic Biology

  1. 1. Synthetic Biology Natalio Krasnogor ASAP - Interdisciplinary Optimisation Laboratory School of Computer Science Centre for Integrative Systems Biology School of Biosciences Centre for Healthcare Associated Infections Institute of Infection, Immunity & Inflammation University of Nottingham Copyright is held by the author/owner(s). GECCO’10, July 7–11, 2010, Portland, Oregon, USA. ACM 978-1-4503-0073-5/10/07. 1 /183
  2. 2. Outline •Essential Systems Biology •Synthetic Biology •Computational Modeling for Synthetic Biology •Automated Model Synthesis and Optimisation •A Note on Ethical, Social and Legal Issues •Conclusions 2 /183
  3. 3. The Far Future Chells & The Cellularity Scale L. Cronin, N. Krasnogor, B. G. Davis, C. Alexander, N. Robertson, J.H.G. Steinke, S.L.M. Schroeder, A.N. Khlobystov, G. Cooper, P. Gardner, P. Siepmann, and B. Whitaker. The imitation game - a computational chemical approach to recognizing life. Nature Biotechnology, 24:1203-1206, 2006. 3 /183
  4. 4. Outline •Essential Systems Biology •Synthetic Biology •Computational Modeling for Synthetic Biology •Automated Model Synthesis and Optimisation •A Note on Ethical, Social and Legal Issues •Conclusions 4 /183
  5. 5. Essential Systems Biology •The following slides are based on U. Alon’s papers & excellent introductory text book: “An Introduction to Systems Biology: Design Principles of Biological Circuits” •Also, you may want to check his group’s webpage for up-to-date papers/software: http://www.weizmann.ac.il/mcb/UriAlon/ 5 /183
  6. 6. The Cell as an Information Processing Device LeDuc et al. Towards an in vivo biologically inspired nanofactory. Nature (2007) 6 /183
  7. 7. The Cell as an Intelligent (Evolved) Machine •The Cell senses the environment and its own internal states •Makes Plans, Takes Decisions and Act •Evolution is the master programmer Environmental Inputs Internal States Cell Actions Amir Mitchell, et al., Adaptive prediction of environmental changes by microorganisms. Nature June 2009. 7 /183
  8. 8. The Cell as an Intelligent (Evolved) Machine •The Cell senses the environment and its own internal states •Makes Plans, Takes Decisions and Act •Evolution is the master programmer Environmental Inputs Internal States Cell Actions Wikimedia Commons Amir Mitchell, et al., Adaptive prediction of environmental changes by microorganisms. Nature June 2009. 7 /183
  9. 9. Transcription Networks Environment Signal1 Signal2 Signal3 Signal4 Signal5 ... Signaln Transcription Factors Genome Gene1 Gene2 Gene3 Genek 8 /183
  10. 10. 9 /183
  11. 11. The Basic Unit: A Gene’s Transcription Regulation Mechanics Promoter Protein Y DNA translation Gene Y mRNA RNA Polymerase transcription Y Gene Y Activator X Si X Y X Y Promoter DNA X binding site Gene Y no transcription Protein Y Bound activator Si + translation Protein Y translation Unbound repressor X mRNA mRNA + transcription transcription Bound activator Gene Y Bound activator 10 /183
  12. 12. Network Motifs: Evolution’s Preferred Circuits •Biological networks are complex and vast •To understand their functionality in a scalable way one must choose the correct abstraction “Patterns that occur in the real network significantly more often than in randomized networks are called network motifs” Shai S. Shen-Orr et al., Network motifs in the transcriptional regulation network of Escherichia coli. Nature Genetics 31, 64 - 68 (2002) •Moreover, these patterns are organised in non-trivial/non- random hierarchies Radu Dobrin et al., Aggregation of topological motifs in the Escherichia coli transcriptional regulatory network. BMC Bioinformatics. 2004; 5: 10. •Each network motif carries out a specific information- processing function 11 /183
  13. 13. Y dx Y positively Negative Positive =β−α∗x regulates X autoregulation autoregulation dt β xst = α Negative autoregulation x(t) = xst e−α∗t Simple regulation log 2 Positive autoregulation t1 = 2 α U. Alon. Network motifs: theory and experimental approaches. Nature Reviews Genetics (2007) vol. 8 (6) pp. 450-461 12 /183
  14. 14. A general transcription factor regulating a second TF, called specific TF, such that both regulate effector operon Z. In a coherent FFL, the direct effect of the general transcription factor (X) has the same sign (+/-) than the indirect net effect through Y in the effector operon. If the arrow from X to Z has different sign than the internal ones then the loop is an incoherent FFL Shai S. Shen-Orr et al., Network motifs in the transcriptional regulation network of Escherichia coli. Nature Genetics 31, 64 - 68 (2002) 13 /183
  15. 15. The C1-FFL is a ‘sign-sensitive delay’ element and a persistence detector. The I1-FFL is a pulse generator and response accelerator most common in E. Coli & S. Cerevisiae U. Alon. Network motifs: theory and experimental approaches. Nature Reviews Genetics (2007) vol. 8 (6) pp. 450-461 14 /183
  16. 16. The C1-FFL is a ‘sign-sensitive delay’ element and a persistence detector. If the integration function is “OR” (rather than “AND”), C1-FFL has now delay after stimulation by Sx but, instead, manifests the delay when the stimulation stops. U. Alon. Network motifs: theory and experimental approaches. Nature Reviews Genetics (2007) vol. 8 (6) pp. 450-461 15 /183
  17. 17. The I1-FFL is a pulse generator and response accelerator U. Alon. Network motifs: theory and experimental approaches. Nature Reviews Genetics (2007) vol. 8 (6) pp. 450-461 16 /183
  18. 18. SIM is defined by one TF controlling a set of operons, with the same signs and no additional control. TFs in SIMs are mostly negative autoregulatory (70% in E. coli) Shai S. Shen-Orr et al., Network motifs in the transcriptional regulation network of Escherichia coli. Nature Genetics 31, 64 - 68 (2002) As the activity of the master regulator X changes in time, it crosses the different activation threshold of the genes in the SIM at different times, this prioritizing the activation of the operons U. Alon. Network motifs: theory and experimental approaches. 17 /183 Nature Reviews Genetics (2007) vol. 8 (6) pp. 450-461
  19. 19. U. Alon. Network motifs: theory and experimental approaches. Nature Reviews Genetics (2007) vol. 8 (6) pp. 450-461 18 /183
  20. 20. DORs are layers of dense sets of TFs affecting multiple operons. To understand the specific function of these “gate-arrays” one needs to know the input functions (AND/OR) for each output gene. This data is not currently available in most cases. Shai S. Shen-Orr et al., Network motifs in the transcriptional regulation network of Escherichia coli. Nature Genetics 31, 64 - 68 (2002) 19 /183
  21. 21. •T h e c o r r e c t a b s t r a c t i o n s facilitates understanding in complex systems. •Provide a route to engineering & programming cells. Shai S. Shen-Orr et al., Network motifs in the transcriptional regulation network of Escherichia coli. Nature Genetics 31, 64 - 68 (2002) 20 /183
  22. 22. Outline •Essential Systems Biology •Synthetic Biology •Computational Modeling for Synthetic Biology •Automated Model Synthesis and Optimisation •Conclusions 21 /183
  23. 23. What is Synthetic Biology Synthetic Biology is http://syntheticbiology.org/ A) the design and construction of new biological parts, devices, and systems, and B) the re-design of existing, natural biological systems for useful purposes. 22 /183
  24. 24. What is Synthetic Biology Synthetic Biology is http://syntheticbiology.org/ A) the design and construction of new biological parts, devices, and systems, and B) the re-design of existing, natural biological systems for useful purposes. C) Through rigorous mathematical, computational & engineering routes 22 /183
  25. 25. Synthetic Biology • Aims at designing, constructing and developing artificial biological systems •Offers new routes to ‘genetically modified’ organisms, synthetic living entities, smart drugs and hybrid computational-biological devices. • Potentially enormous societal impact, e.g., healthcare, environmental protection and remediation, etc • Synthetic Biology's basic assumption: •Methods readily used to build non-biological systems could also be use to specify, design, implement, verify, test and deploy novel synthetic biosystems. •These method come from computer science, engineering and maths. •Modeling and optimisation run through all of the above. 23 /183
  26. 26. Synthetic & Systems Biology: Sisters Disciplines 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 24 /183
  27. 27. Top-Down Synthetic Biology: An Approach to Engineering Biology  Cells are information processors. DNA is their Pseudomonas programming language. Discosoma sp. aeruginosa Aequorea victoria  DNA sequencing and PCR: Vibrio fischeri Identification and isolation of cellular parts.  Recombinant DNA and DNA synthesis : Combination E. coli of DNA and construction of new systems.  Tools to make biology easier to engineer: Standardisation, encapsulation and abstraction (blueprints). 25 /183 20
  28. 28. Top-Down Synthetic Biology: An Approach to Engineering Biology  Cells are information processors. DNA is their Pseudomonas programming language. Discosoma sp. aeruginosa Aequorea victoria  DNA sequencing and PCR: Vibrio fischeri Identification and isolation of cellular parts.  Recombinant DNA and DNA synthesis : Combination E. coli of DNA and construction of new systems.  Tools to make biology easier to engineer: Standardisation, encapsulation and abstraction (blueprints). 25 /183 20
  29. 29. Top-Down Synthetic Biology: An Approach to Engineering Biology  Cells are information processors. DNA is their Pseudomonas programming language. Discosoma sp. aeruginosa Aequorea victoria  DNA sequencing and PCR: Vibrio fischeri Identification and isolation of cellular parts.  Recombinant DNA and DNA synthesis : Combination E. coli of DNA and construction of new systems. plasmids  Tools to make biology easier to engineer: Standardisation, encapsulation and abstraction (blueprints). 25 /183 20
  30. 30. Top-Down Synthetic Biology: An Approach to Engineering Biology  Cells are information processors. DNA is their Pseudomonas programming language. Discosoma sp. aeruginosa Aequorea victoria  DNA sequencing and PCR: Vibrio fischeri Identification and isolation of cellular parts.  Recombinant DNA and DNA synthesis : Combination E. coli of DNA and construction of new systems. plasmids  Tools to make biology easier to engineer: Standardisation, encapsulation DNA synthesis and abstraction (blueprints). 25 /183 20
  31. 31. Top-Down Synthetic Biology: An Approach to Engineering Biology  Cells are information processors. DNA is their Pseudomonas programming language. Discosoma sp. aeruginosa Aequorea victoria  DNA sequencing and PCR: Vibrio fischeri Identification and isolation of cellular parts.  Recombinant DNA and DNA synthesis : Combination E. coli of DNA and construction of new systems. plasmids  Tools to make biology easier to engineer: Standardisation, encapsulation Chassis DNA synthesis and abstraction (blueprints). 25 /183 20
  32. 32. Top-Down Synthetic Biology: An Approach to Engineering Biology  Cells are information processors. DNA is their Pseudomonas programming language. Discosoma sp. aeruginosa Aequorea victoria  DNA sequencing and PCR: Vibrio fischeri Identification and isolation of cellular parts.  Recombinant DNA and DNA synthesis : Combination E. coli of DNA and construction of new systems. plasmids  Tools to make biology easier to engineer: Standardisation, encapsulation Chassis DNA synthesis Circuit Blueprint and abstraction (blueprints). 25 /183 20
  33. 33. Synthetic Biology’s Brick & Mortar (I) D. Sprinzak & M.B. Elowitz (2005). Reconstruction of genetic circuits, Nature 438:24, 443-448. 26 /183
  34. 34. Synthetic Biology’s Brick & Mortar (II) D. Sprinzak & M.B. Elowitz (2005). Reconstruction of genetic circuits, Nature 438:24, 443-448. 27 /183
  35. 35. Example I: Elowitz & Leibler Represilator M.B. Elowitz & S. Leibler (2000). A Synthetic Oscilatory Network of Transcriptional Regulators. Nature, 403:20, 335-338 28 /183
  36. 36. An Example: Elowitz & Leibler Represilator M.B. Elowitz & S. Leibler (2000). A Synthetic Oscillatory Network of Transcriptional Regulators. Nature, 403:20, 335-338 29 /183
  37. 37. Example II: Combinatorial Synthetic Logic C.C. Guet et al., Combinatorial Synthesis of Genetic Networks, Science 296, 1466-1470, 2002 30 /183
  38. 38. Example II: Combinatorial Synthetic Logic C.C. Guet et al., Combinatorial Synthesis of Genetic Networks, Science 296, 1466-1470, 2002 31 /183
  39. 39. Example II: Combinatorial Synthetic Logic C.C. Guet et al., Combinatorial Synthesis of Genetic Networks, Science 296, 1466-1470, 2002 32 /183
  40. 40. Example III: Push-on/Push-off circuit C. Lou et al., Synthesizing a novel genetic sequential logic circuit: a push-on push-off switch, Molecular Systems Biology 6; Article number 350 33 /183
  41. 41. Example III: Push-on/Push-off circuit C. Lou et al., Synthesizing a novel genetic sequential logic circuit: a push-on push-off switch, Molecular Systems Biology 6; Article number 350 34 /183
  42. 42. Axample IV: 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 within a range of values (Band Pass). S. Basu, R. Mehreja, et al. (2004) Spatiotemporal control of gene expression with pulse generating networks, PNAS, 101, 6355-6360 35 /183
  43. 43. 36 /183
  44. 44. Outline •Essential Systems Biology •Synthetic Biology •Computational Modeling for Synthetic Biology •Automated Model Synthesis and Optimisation •Conclusions 37 /183
  45. 45. What is modeling? • 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”. 38 /183
  46. 46. •“feature selection” is the first issue one must confront when building a model •One starts from a system of interest and then a decision should be taken as to what will the model include/leave out •That is, at what level the model will be built 39 /183
  47. 47. The goals of Modelling •To capture the essential features of a biological entity/phenomenon •To disambiguate the understanding behind those features and their interactions •To move from qualitative knowledge towards quantitative knowledge 40 /183
  48. 48. 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 41 /183
  49. 49. There is potentially a distinction between modeling for Synthetic Biology vs Systems Biology: •Systems Biology is concerned with Biology as it is •Synthetic Biology is concerned with Biology as it could be “Our view of engineering biology focuses on the abstraction and standardization of biological components” by R. Rettberg @ MIT newsbite August 2006. “Well-characterized components help lower the barriers to modeling. The use of control elements (such as temperature for a temperature-sensitive protein, or an exogenous small molecule affecting a reaction) helps model validation” by Di Ventura et al, Nature, 2006 Co-design of parts and their models hence improving and making both more reliable 42 /183
  50. 50. Model Development From [E. Klipp et al, Systems Biology in Practice, 2005]: 1) Formulation of the problem 2) Verification of available information 3) Selection of model structure 4) Establishing a simple model 5) Sensitivity analysis 6) Experimental tests of the model predictions 7) Stating the agreements and divergences between experimental and modelling results 8) Iterative refinement of model 43 /183
  51. 51. The Challenge of Scales Within a cell the dissociation constants of DNA/ transcription factor binding to specific/ non-specific sites differ by 4-6 orders of magnitude DNA protein binding occurs at 1-10s time scale very fast in comparison to a cell’s life cycle. R. Milo, et al., BioNumbers—the database of key numbers in molecular and cell biology. Nucleic Acids Research, 1–4 (2009) http://bionumbers.hms.harvard.edu/default.aspx 44 /183
  52. 52. The Scale Separation Map sa cs s*103 m QS mol diff in colony Temporal scale (log) transcription PD translation s D vesicle dynamics ms diff. small mol micelle dynamics µs ns energy transfer ps bond vibration fs nm µm mm Couplings Spatial scale (log) 45 /183
  53. 53. The Challenge of Small Numbers 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 46 /183
  54. 54. Modelling Approaches There exist many modeling 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 47 /183
  55. 55. 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) D. Harel, "A Grand Challenge for Computing: Full Reactive Modeling of a Multi-Cellular Animal", Bulletin of the EATCS , European Association for Theoretical Computer Science, no. 81, 2003, pp. 226-235 A. Regev, E. Shapiro. The π-calculus as an abstraction for biomolecular systems. Modelling in Molecular Biology., pages 1–50. Springer Berlin., 2004. Jasmin Fisher and Thomas Henzinger. Executable cell biology. Nature Biotechnology, 25, 11, 1239-1249 (2008) 48 /183
  56. 56. Tools Suitability and Cost •From [D.E Goldberg, 2002] (adapted): “Since science and math are in the description business, the model is the thing…The engineer or inventor has much different motives. The engineered object is the thing” ε, error Synthetic Biologist Computer Scientist/Mathematician C, cost of modelling 49 /183
  57. 57. 50 /183
  58. 58. There are good reasons to think that information processing is a key viewpoint to take when modeling 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 51 /183
  59. 59. Computer Science Contributions Methodologies designed to cope with: • Languages to cope with complex, concurrent, systems of parts: J.Twycross, L.R. Band, M. J. Bennett, J.R. • ∏-calculus King, and N. Krasnogor. Stochastic and deterministic multiscale models for • Process Calculi systems biology: an auxin-transport case study. BMC Systems Biology, 4(:34), • P Systems March 2010 • Tools to analyse and optimise: • EA, ML • Model Checking 52 /183
  60. 60. InfoBiotics Workbench and Dashboard www.infobiotics.net 53 /183
  61. 61. InfoBiotics Workbench and Dashboard www.infobiotics.net Specification 53 /183
  62. 62. 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 54 /183
  63. 63. 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 55 /183
  64. 64. Distributed and parallel rewritting systems in compartmentalised hierarchical structures. Objects Compartments Rewriting Rules • Computational universality and efficiency. • Modelling Framework 56 /183
  65. 65. Cell-like P systems Intuitive Visual representation as a Venn diagram with a unique superset and without intersected sets. the classic P system diagram appearing in most papers (Păun) 57 /183
  66. 66. Cell-like P systems Intuitive Visual representation as a Venn diagram with a unique superset and without intersected sets. formally equivalent to a tree: 1 2 4 3 7 5 6 the classic P system diagram appearing in most papers (Păun) 8 9 57 /183
  67. 67. Cell-like P systems Intuitive Visual representation as a Venn diagram with a unique superset and without intersected sets. formally equivalent to a tree: 1 2 4 3 7 5 6 the classic P system diagram appearing in most papers (Păun) 8 9 • a string of matching parentheses: [ 1 [2 ] 2 [ 3 ] 3 [4 [5 ] 5 [6 [ 8 ] 8 [9 ] 9 ]6 [7 ]7 ]4 ]1 57 /183
  68. 68. 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 58 /183
  69. 69. Stochastic P Systems 59 /183
  70. 70. Rewriting Rules used by Multi-volume Gillespie’s algorithm 60 /183
  71. 71. Molecular Species  A molecular species can be represented using individual objects.  A molecular species with relevant internal structure can be represented using a string. 61 /183
  72. 72. 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 62 /183
  73. 73. Compartments / Cells  Compartments and regions are explicitly specified using membrane structures. 63 /183
  74. 74. 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 64 /183
  75. 75. Molecular Interactions Inside Compartments 65 /183
  76. 76. Passive Diffusion of Molecules 66 /183
  77. 77. Signal Sensing and Active Transport 67 /183
  78. 78. Specification of Transcriptional Regulatory Networks 68 /183
  79. 79. Post-Transcriptional Processes  For each protein in the system, post-transcriptional processes like translational initiation, messenger and protein degradation, protein dimerisation, signal sensing, signal diffusion etc are represented using modules of rules.  Modules can have also as parameters the stochastic kinetic constants associated with the corresponding rules in order to allow us to explore possible mutations in the promoters and ribosome binding sites in order to optimise the behaviour of the system. 69 /183
  80. 80. 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. 70 /183
  81. 81. Basic P System Modules Used 71 /183
  82. 82. Characterisation/Encapsulation of Cellular Parts: Gene Promoters  A modeling language for the design of synthetic bacterial colonies.  A module, set of rules describing the molecular interactions involving a cellular part, provides encapsulation and abstraction.  Collection or libraries of reusable cellular parts and reusable models. E. Davidson (2006) The Regulatory Genome, Gene Regulation Networks in Development and Evolution, Elsevier 72 /183 21
  83. 83. Characterisation/Encapsulation of Cellular Parts: Gene Promoters  A modeling language for the design of synthetic bacterial colonies. 01101110100001010100011110001011101010100011010100  A module, set of rules describing the molecular interactions involving a cellular part, provides encapsulation and abstraction.  Collection or libraries of reusable cellular parts and reusable models. E. Davidson (2006) The Regulatory Genome, Gene Regulation Networks in Development and Evolution, Elsevier 72 /183 21
  84. 84. Characterisation/Encapsulation of Cellular Parts: Gene Promoters  A modeling language for the design of synthetic bacterial colonies.  A module, set of rules describing the molecular interactions involving a cellular part, provides encapsulation and abstraction.  Collection or libraries of reusable cellular parts and reusable models. E. Davidson (2006) The Regulatory Genome, Gene Regulation Networks in Development and Evolution, Elsevier 72 /183 21
  85. 85. Characterisation/Encapsulation of Cellular Parts: Gene Promoters AHL LuxR CI  A modeling language for the design of synthetic bacterial colonies.  A module, set of rules describing the molecular interactions involving a cellular part, provides encapsulation and abstraction.  Collection or libraries of reusable cellular parts and reusable models. E. Davidson (2006) The Regulatory Genome, Gene Regulation Networks in Development and Evolution, Elsevier 72 /183 21
  86. 86. Characterisation/Encapsulation of Cellular Parts: Gene Promoters AHL LuxR CI  A modeling language for the design of synthetic bacterial colonies. PluxOR1({X},{c1, c2, c3, c4, c5, c6, c7, c8, c9},{l}) = {  A module, set of rules type: promoter describing the molecular sequence: ACCTGTAGGATCGTACAGGTTTACGCAAGAA interactions involving a ATGGTTTGTATAGTCGAATACCTCTGGCGGTGATA cellular part, provides encapsulation and rules: abstraction. r1: [ LuxR2 + PluxPR.X ]_l -c1-> [ PluxPR.LuxR2.X ]_l r2: [ PluxPR.LuxR2.X ]_l -c2-> [ LuxR2 + PluxPR.X ]_l ...  Collection or libraries of r5: [ CI2 + PluxPR.X ]_l -c5-> [ PluxPR.CI2.X ]_l reusable cellular parts and r6: [ PluxPR.CI2.X ]_l -c6-> [ CI2 + PluxPR.X ]_l reusable models. ... r9: [ PluxPR.LuxR2.X ]_l -c9-> [ PluxPR.LuxR2.X + RNAP.X ]_l } E. Davidson (2006) The Regulatory Genome, Gene Regulation Networks in Development and Evolution, Elsevier 72 /183 21
  87. 87. Module Variables: Recombinant DNA, Directed Evolution, Chassis selection 73 /183 22
  88. 88. Module Variables: Recombinant DNA, Directed Evolution, Chassis selection  Recombinant DNA: Objects variables can be instantiated with the name of specific genes. 73 /183 22
  89. 89. Module Variables: Recombinant DNA, Directed Evolution, Chassis selection  Recombinant DNA: Objects variables can be instantiated with the name of specific genes. 73 /183 22
  90. 90. Module Variables: Recombinant DNA, Directed Evolution, Chassis selection  Recombinant DNA: Objects variables can be instantiated with the name of specific genes. PluxOR1({X=tetR}) 73 /183 22
  91. 91. Module Variables: Recombinant DNA, Directed Evolution, Chassis selection  Recombinant DNA: Objects variables can be instantiated with the name of specific genes. 73 /183 22
  92. 92. Module Variables: Recombinant DNA, Directed Evolution, Chassis selection  Recombinant DNA: Objects variables can be instantiated with the name of specific genes. PluxOR1({X=GFP}) 73 /183 22
  93. 93. Module Variables: Recombinant DNA, Directed Evolution, Chassis selection  Recombinant DNA: Objects variables can be instantiated with the name of specific genes. PluxOR1({X=GFP}) 73 /183 22
  94. 94. Module Variables: Recombinant DNA, Directed Evolution, Chassis selection  Recombinant DNA: Objects variables can be instantiated with the name of specific genes. PluxOR1({X=GFP})  Directed evolution: Variables for stochastic constants can be instantiated with specific values. 73 /183 22
  95. 95. Module Variables: Recombinant DNA, Directed Evolution, Chassis selection  Recombinant DNA: Objects variables can be instantiated with the name of specific genes. PluxOR1({X=GFP})  Directed evolution: Variables for stochastic constants can be instantiated with specific values. A 73 /183 22
  96. 96. Module Variables: Recombinant DNA, Directed Evolution, Chassis selection  Recombinant DNA: Objects variables can be instantiated with the name of specific genes. PluxOR1({X=GFP})  Directed evolution: Variables for stochastic constants can be instantiated with specific values. A PluxOR1({X=GFP},{...,c4=10,...}) 73 /183 22
  97. 97. Module Variables: Recombinant DNA, Directed Evolution, Chassis selection  Recombinant DNA: Objects variables can be instantiated with the name of specific genes. PluxOR1({X=GFP})  Directed evolution: Variables for stochastic constants can be instantiated with specific values. A PluxOR1({X=GFP},{...,c4=10,...})  Chassis Selection: The variable for the label can be instantiated with the name of a chassis. PluxOR1({X=GFP},{...,c4=10,...},{l=DH5α }) 73 /183 22
  98. 98. Characterisation/Encapsulation of Cellular Parts: Riboswitches  A riboswitch is a piece of RNA that folds in different ways depending on the presence of absence of specific molecules regulating translation. ToppRibo({X},{c1, c2, c3, c4, c5, c6},{l}) = { type: riboswitch sequence:GGTGATACCAGCATCGTCTTGATGCCCTTGG CAGCACCCCGCTGCAAGACAACAAGATG rules: r1: [ RNAP.ToppRibo.X ]_l -c1-> [ ToppRibo.X ]_l r2: [ ToppRibo.X ]_l -c2-> [ ]_l r3: [ ToppRibo.X + theop ]_l –c3-> [ ToppRibo*.X ]_l r4: [ ToppRibo*.X ]_l –c4-> [ ToppRibo.X + theop ]_l r5: [ ToppRibo*.X ]_l –c5-> [ ]_l r6: [ ToppRibo*.X ]_l –c6-> [ToppRibo*.X + Rib.X ]_l } 74 /183 23
  99. 99. Characterisation/Encapsulation of Cellular Parts: Degradation Tags  Degradation tags are amino acid sequences recognised by proteases. Once the corresponding DNA sequence is fused to a gene the half life of the protein is reduced considerably. degLVA({X},{c1, c2},{l}) = { type: degradation tag sequence: CAGCAAACGACGAAAACTACGCTTTAGTAGCT rules: r1: [ Rib.X.degLVA ]_l -c1-> [ X.degLVA ]_l r2: [ X.degLVA ]_l -c2-> [ ]_l } 75 /183 24
  100. 100. Higher Order Modules: Building Synthetic Gene Circuits PluxOR1 ToppRibo geneX degLVA 76 /183 25
  101. 101. Higher Order Modules: Building Synthetic Gene Circuits PluxOR1 ToppRibo geneX degLVA 3OC6_repressible_sensor({X}) = { 76 /183 25
  102. 102. Higher Order Modules: Building Synthetic Gene Circuits PluxOR1 ToppRibo geneX degLVA 3OC6_repressible_sensor({X}) = { PluxOR1({X=ToppRibo.geneX.degLVA},{...},{l=DH5α}) 76 /183 25
  103. 103. Higher Order Modules: Building Synthetic Gene Circuits PluxOR1 ToppRibo geneX degLVA 3OC6_repressible_sensor({X}) = { PluxOR1({X=ToppRibo.geneX.degLVA},{...},{l=DH5α}) ToppRibo({X=geneX.degLVA},{...},{l=DH5α}) 76 /183 25
  104. 104. Higher Order Modules: Building Synthetic Gene Circuits PluxOR1 ToppRibo geneX degLVA 3OC6_repressible_sensor({X}) = { PluxOR1({X=ToppRibo.geneX.degLVA},{...},{l=DH5α}) ToppRibo({X=geneX.degLVA},{...},{l=DH5α}) degLVA({X},{...},{l=DH5α}) 76 /183 25
  105. 105. Higher Order Modules: Building Synthetic Gene Circuits PluxOR1 ToppRibo geneX degLVA 3OC6_repressible_sensor({X}) = { PluxOR1({X=ToppRibo.geneX.degLVA},{...},{l=DH5α}) ToppRibo({X=geneX.degLVA},{...},{l=DH5α}) degLVA({X},{...},{l=DH5α}) } 76 /183 25
  106. 106. Higher Order Modules: Building Synthetic Gene Circuits PluxOR1 ToppRibo geneX degLVA 3OC6_repressible_sensor({X}) = { PluxOR1({X=ToppRibo.geneX.degLVA},{...},{l=DH5α}) ToppRibo({X=geneX.degLVA},{...},{l=DH5α}) X=GFP degLVA({X},{...},{l=DH5α}) } 76 /183 25
  107. 107. Higher Order Modules: Building Synthetic Gene Circuits PluxOR1 ToppRibo geneX degLVA 3OC6_repressible_sensor({X}) = { PluxOR1({X=ToppRibo.geneX.degLVA},{...},{l=DH5α}) ToppRibo({X=geneX.degLVA},{...},{l=DH5α}) X=GFP degLVA({X},{...},{l=DH5α}) } 76 /183 25
  108. 108. Higher Order Modules: Building Synthetic Gene Circuits PluxOR1 ToppRibo geneX degLVA 3OC6_repressible_sensor({X}) = { PluxOR1({X=ToppRibo.geneX.degLVA},{...},{l=DH5α}) ToppRibo({X=geneX.degLVA},{...},{l=DH5α}) X=GFP degLVA({X},{...},{l=DH5α}) } Plux({X=ToppRibo.geneCI.degLVA},{...},{l=DH5α}) ToppRibo({X=geneCI.degLVA},{...},{l=DH5α}) degLVA({CI},{...},{l=DH5α}) PtetR({X=ToppRibo.geneLuxR.degLVA},{...},{l=DH5α}) Weiss_RBS({X=LuxR},{...},{l=DH5α}) Deg({X=LuxR},{...},{l=DH5α}) 76 /183 25
  109. 109. AHL Specification of Multi-cellular Systems: LPP systems AHL GFP AHL LuxR Pconst PluxOR1 luxR gfp LuxI AHL CI Pconst luxI Plux cI 77 /183
  110. 110. Infobiotics: An Integrated Framework http://www.infobiotics.org/infobiotics-workbench/ Cellular Parts 78 /183 29
  111. 111. Infobiotics: An Integrated Framework http://www.infobiotics.org/infobiotics-workbench/ Cellular Parts Libraries of Modules 78 /183 29
  112. 112. Infobiotics: An Integrated Framework http://www.infobiotics.org/infobiotics-workbench/ Cellular Parts Libraries of Module Modules Combinations 78 /183 29
  113. 113. Infobiotics: An Integrated Framework http://www.infobiotics.org/infobiotics-workbench/ Cellular Synthetic Parts Circuits Libraries of Module Modules Combinations 78 /183 29
  114. 114. Infobiotics: An Integrated Framework http://www.infobiotics.org/infobiotics-workbench/ Cellular Synthetic Parts Circuits Libraries of Module Modules Combinations 78 /183 29
  115. 115. Infobiotics: An Integrated Framework http://www.infobiotics.org/infobiotics-workbench/ Cellular Synthetic Parts Circuits Libraries of Module P systems Modules Combinations 78 /183 29
  116. 116. Infobiotics: An Integrated Framework http://www.infobiotics.org/infobiotics-workbench/ Cellular Synthetic Single Parts Circuits Cells Libraries of Module P systems Modules Combinations 78 /183 29
  117. 117. Infobiotics: An Integrated Framework http://www.infobiotics.org/infobiotics-workbench/ Cellular Synthetic Single Parts Circuits Cells Libraries of Module P systems Modules Combinations 78 /183 29
  118. 118. Infobiotics: An Integrated Framework http://www.infobiotics.org/infobiotics-workbench/ Cellular Synthetic Single Parts Circuits Cells Libraries of Module P systems LPP systems Modules Combinations 78 /183 29
  119. 119. Infobiotics: An Integrated Framework http://www.infobiotics.org/infobiotics-workbench/ Cellular Synthetic Single Synthetic Multi- Parts Circuits Cells cellular Systems Libraries of Module P systems LPP systems Modules Combinations 78 /183 29
  120. 120. Infobiotics: An Integrated Framework http://www.infobiotics.org/infobiotics-workbench/ Cellular Synthetic Single Synthetic Multi- Parts Circuits Cells cellular Systems Libraries of Module P systems LPP systems Modules Combinations 78 /183 29
  121. 121. Infobiotics: An Integrated Framework http://www.infobiotics.org/infobiotics-workbench/ Cellular Synthetic Single Synthetic Multi- Parts Circuits Cells cellular Systems Libraries of Module P systems LPP systems Modules Combinations A compiler based on a BNF grammar 78 /183 29
  122. 122. Infobiotics: An Integrated Framework http://www.infobiotics.org/infobiotics-workbench/ Cellular Synthetic Single Synthetic Multi- Parts Circuits Cells cellular Systems Libraries of Module P systems LPP systems Modules Combinations A compiler based on a BNF grammar Multi Compartmental Stochastic Simulations based on Gillespie’s algorithm 78 /183 29
  123. 123. Infobiotics: An Integrated Framework http://www.infobiotics.org/infobiotics-workbench/ Cellular Synthetic Single Synthetic Multi- Parts Circuits Cells cellular Systems Libraries of Module P systems LPP systems Modules Combinations A compiler based on a BNF grammar Multi Compartmental Spatio-temporal Stochastic Simulations Dynamics Analysis based on Gillespie’s using Model Checking algorithm with PRISM and MC2 78 /183 29
  124. 124. Infobiotics: An Integrated Framework http://www.infobiotics.org/infobiotics-workbench/ Cellular Synthetic Single Synthetic Multi- Parts Circuits Cells cellular Systems Libraries of Module P systems LPP systems Modules Combinations A compiler based on a BNF grammar Multi Compartmental Spatio-temporal Automatic Design of Stochastic Simulations Dynamics Analysis Synthetic Gene based on Gillespie’s using Model Checking Regulatory Circuits using algorithm with PRISM and MC2 Evolutionary Algorithms 78 /183 29
  125. 125. Stochastic P Systems Are Executable Programs The virtual machine running these programs is a “Gillespie Algorithm (SSA)”. It generates trajectories of a stochastic syste: A stochastic constant is associated with each rule. 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. 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 79 /183
  126. 126. 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 80 /183
  127. 127. An Important Difference with “Normal” Programs •Executable Stochastic P systems are not programs with stochastic behavior function f1(p1,p2,p3,p4) function f1(p1,p2,p3,p4) { { if (p1<p2) and (rand<0.5) if (p1<p2) RND print p3 print p3 RND else else RND print p4 print p4 RND } } •A cell is a living example of distributed stochastic computing. 81 /183
  128. 128. Rapid
Model
Prototyping •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 82 /183
  129. 129. Specification 83 /183 InfoBiotics Workbench and Dashboard
  130. 130. Specification Simulation Analysis 83 /183 InfoBiotics Workbench and Dashboard
  131. 131. An example: A 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 within a range of values (Band Pass). S. Basu, R. Mehreja, et al. (2004) Spatiotemporal control of gene expression with pulse generating networks, PNAS, 101, 6355-6360 84 /183
  132. 132. Sender Cells AHL SenderCell()= LuxI AHL { Pconst Pconst({X = luxI },…) luxI PostTransc({X=LuxI},{c1=3.2,…}) Diff({X=AHL},{c=0.1}) } 85 /183
  133. 133. Pulse Generating Cells AHL AHL PulseGenerator()= LuxR GFP { PluxOR1 Pconst({X=luxR},…) Pconst gfp luxR PluxOR1({X=gfp},…) Plux({X=cI},…) CI … Plux … cI Diff({X=AHL},…) } 86 /183
  134. 134. AHL Spatial Distribution of Senders and Pulse Generators AHL GFP AHL LuxR Pconst PluxOR1 luxR gfp LuxI AHL CI Pconst luxI Plux cI 87 /183
  135. 135. Pulse propagation - simulation I Simulation I 88 /183
  136. 136. Pulse Generating Cells With Relay AHL LuxR AHL PulseGenerator(X ) = PluxOR1 { Pconst luxR Pconst({X=luxR},…) PluxOR1({X},…) , Plux CI Plux({X=cI},…) , luxI … Plux LuxI cI Diff({X=AHL},…) , Plux({X=luxI},…) AHL } 89 /183
  137. 137. Pulse propagation & Rely- simulation II Simulation II 90 /183 36
  138. 138. A Signal Translator for Pattern Formation Pact1 FP1 Prep2 Pact1 Prep1 Prep3 act1 rep1 rep3 I2 Prep1 Pact2 Prep2 Prep4 act2 rep2 rep4 I1 Pact2 FP2 91 /183
  139. 139. Uniform Spatial Distribution of Signal Translators for Pattern Formation 92 /183
  140. 140. Pattern Formation in synthetic bacterial colonies Simulation III 93 /183
  141. 141. Pattern Formation in synthetic bacterial colonies 94 /183
  142. 142. Si Spatial Distribution of Signal Translators and propagators Si LuxR GFP Pconst PluxOR1 luxR gfp Plux CI luxI Plux cI LuxI Si 95 /183
  143. 143. Alternating signal pulses in synthetic bacterial colonies Simulation IV 96 /183
  144. 144. 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 •Automated Model Synthesis and Optimisation •Conclusions 97 /183
  145. 145. Specification Simulation Analysis 98 /183 InfoBiotics Workbench and Dashboard
  146. 146. Specification Optimisation Simulation Analysis 98 /183 InfoBiotics Workbench and Dashboard
  147. 147. Automated Model Synthesis and Optimisation • Modeling is an intrinsically difficult process • It involves “feature selection” and disambiguation • Model Synthesis requires • design the topology or structure of the system in terms of molecular interactions • estimate the kinetic parameters associated with each molecular interaction • All the above iterated 99 /183
  148. 148. • Once a model has been prototyped, whether derived from existing literature or “ab initio” ➡ Use some optimisation method to fine tune parameters/model structure • adopts an incremental methodology, namely starting from very simple P system modules (BioBricks) specifying basic molecular interactions, more complicated modules are produced to model more complex molecular systems. 100 /183
  149. 149. Large Literature on Model Synthesis • Mason et al. use a random Local Search (LS) as the mutation to evolve electronic networks with desired dynamics • Chickarmane et al. use a standard GA to optimize the kinetic parameters of a population of ODE-based reaction networks having the desired topology. • Spieth et al. propose a memetic algorithm to find gene regulatory networks from experimental DNA microarray data where the network structure is optimized with a GA and the parameters are optimized with an Evolution Strategy (ES). • Etc 101 /183
  150. 150. Evolutionary Algorithms for Automated Model Synthesis and Optimisation EA are potentially very useful for AMSO • There’s a substantial amount of work on: • using GP-like systems to evolve executable structures • using EAs for continuous/discrete optimisation • An EA population represents alternative models (could lead to different experimental setups) • EAs have the potential to capture, rather than avoid, evolvability of models 102 /183
  151. 151. Evolving Executable Biology Models • The main idea is to use a nested evolutionary algorithm where the first layer evolves model structures while the inner layer acts as a local search for the parameters of the model. • It uses stochastic P systems as a computational, modular and discrete-stochastic modelling framework. • It adopts an incremental methodology, namely starting from very simple P system modules specifying basic molecular interactions, more complicated modules are produced to model more complex molecular systems. •Successfully validated evolved models can then be added to the models library 103 /183
  152. 152. Nested EA for Model Synthesis F. Romero-Campero, H.Cao, M. Camara, and N. Krasnogor. Structure and parameter estimation for cell systems biology models. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2008), pages 331-338. ACM Publisher, 2008. H. Cao, F.J. Romero- Campero, S. Heeb, M. Camara, and N. Krasnogor. Evolving cell models for systems and synthetic biology. Systems and Synthetic Biology , 2009 104 /183
  153. 153. Fitness Evaluation 105 /183
  154. 154. Representation 106 /183
  155. 155. 107 /183
  156. 156. Structural Operators 108 /183
  157. 157. Structural Operators 109 /183
  158. 158. Parameter Optimisation 110 /183
  159. 159. Parameter Optimisation 111 /183
  160. 160. Simple Illustrative Case studies • Case 1: molecular complexation • Case 2: enzymatic reaction • Case 3: autoregulation in transcriptional networks •Case 3.1. negative autoregulation •Case 3.2. positive autoregulation 112 /183
  161. 161. Case 1: Molecular Complexation Target P System Model Structure c1 c2 Target P System Model Parameters 113 /183
  162. 162. Case 1: Experimental Results 50/50 runs found the same P system model structure as the target one Target and Evolved Time Series 114 /183
  163. 163. Case 1: Experimental Results 50/50 runs found the same P system model structure as the target one Target Model Parameters Best Model Parameters Target and Evolved Time Series 115 /183
  164. 164. Case 2: Enzymatic Reaction Target P System Model Structure c1 c2 c3 Target P System Model Parameters 116 /183
  165. 165. Case 2: Experimental Results Target and Evolved Time Series 117 /183
  166. 166. Case 2: Experimental Results Target and Evolved Time Series Target Model Parameters Best Model Parameters Two Approaches Using Basic Adding the Newly Modules Found in Case 1 Number of runs the target 12/50 39/50 P system model was found Mean RMSE 3.8 2.85 118 /183
  167. 167. Case 3.1: Negative Autoregulation Target P System Model Structure Target P System Model Parameters c1 c2 c3 c4 c5 c6 119 /183
  168. 168. Case 3.1: Experimental Results Design Design 1 Design 2 Model Number of runs 46/50 4/50 Mean +/- STD 4.11+/- 11.73 4.63 +/- 2.91 Best Model in Design 1 Best Model in Design 2 120 /183
  169. 169. Case 3.2: Positive Autoregulation Target P System Model Structure Target P System Model Parameters c1 c2 c3 c4 c5 c6 c7 121 /183
  170. 170. Case 3.2: Experimental Results Design Design 1 Design 2 Model Number of runs 30/50 20/50 Mean +/- STD 16.36 +/- 3.03 20.14 +/- 5.13 Best Model in Design 1 122 /183
  171. 171. The Fitness Function • Multiple time-series per target • Different time series have very different profiles, e.g., response time or maxima occur at different times/places • Transient states (sometimes) as important as steady states •RMSE will mislead search H. Cao, F. Romero-Campero, M.Camara, N.Krasnogor (2009). Analysis of Alternative Fitness Methods for the Evolutionary Synthesis of Cell Systems Biology Models. 123 /183
  172. 172. Four Fitness Functions N time series M time points in each time series Simulated data Target data F1 F2 F2 normalises whithin a time series between [0,1] 124 /183
  173. 173. Four Fitness Functions N time series M time points in each time series Simulated data Target data Randomly generated normalised vector with: F3 F3, unlike F1, does not assume an equal weighting of all the errors. It produces a randomised average of weights. 125 /183
  174. 174. Four Fitness Functions N time series M time points in each time series Simulated data Target data F4 F4 simply multiplies errors hence even small ones within a set with multiple orders of maginitudes can still have an effect. Indeed, this might lead to numerical instabilities due to nonlinear effects. 126 /183
  175. 175. Problem Specification 127 /183
  176. 176. Case Studies 128 /183
  177. 177. 129 /183
  178. 178. 130 /183
  179. 179. 131 /183
  180. 180. 132 /183
  181. 181. 133 /183
  182. 182. 134 /183
  183. 183. 135 /183
  184. 184. Results Study Case 1 136 /183
  185. 185. Results Study Case 2 137 /183
  186. 186. Results Study Case 3 138 /183
  187. 187. Target model Alternative, biologically valid, modules 139 /183
  188. 188. 140 /183
  189. 189. Results Study Case 4 141 /183
  190. 190. Comparisons of the constants between the best fitness models obtained by F1, F4 and the target model for Test Case 4 (Values different from the target are in bold and underlined) 142 /183
  191. 191. 143 /183
  192. 192. 144 /183
  193. 193. 145 /183
  194. 194. 146 /183
  195. 195. 147 /183
  196. 196. Target 148 /183
  197. 197. Target 149 /183
  198. 198. The evolving curve of average fitness by four fitness methods (F1 ∼ F4) in 20 runs for Test Case 4. 150 /183
  199. 199. The evolving curve of average model diversity by four fitness methods (F1 ∼ F4) in 20 runs for Test Case 4 151 /183
  200. 200. 152 /183
  201. 201. Multi-Objective Optimisation in Morphogenesis The following slides are based on Rui Dilão, Daniele Muraro, Miguel Nicolau, Marc Schoenauer. Validation of a morphogenesis model of Drosophila early development by a multi-objective evolutionary optimization algorithm. Proc. 7th European Conference on Evolutionary Computation, ML and Data Mining in BioInformatics (EvoBIO'09), April 2009. 153 /183
  202. 202. • The authors investigate the use of MOEA for modeling morphogenesis • The model organism is drosophila embrios Rui Dilão, Daniele Muraro, Miguel Nicolau, Marc Schoenauer. Validation of a morphogenesis model of Drosophila early development by a multi-objective evolutionary optimization algorithm. Proc. 7th European Conference on Evolutionary Computation, ML and Data Mining in BioInformatics (EvoBIO'09), April 2009 154 /183
  203. 203. Initial Conditions PDE model 155 /183
  204. 204. Target By Evolving: abcd, acad, Dbcd/Dcad, r, mRNA distributions and t However: Goal is not perfect fit but rather robust fit 156 /183
  205. 205. • The authors use both Single and Multi-objective optimisation • CMA-ES is at the core of both • CMA-ES is a (µ,λ)-ES that employs multivariate Gaussian distributions • Uses “cumulative path” for co-variantly adapting this distribution • For the MO case it uses the global pareto dominance based selection Objective Function Bi-Objective Function 157 /183
  206. 206. 158 /183
  207. 207. 159 /183
  208. 208. 160 /183
  209. 209. Parameter Optimisation in Metabolic Models A. Drager et al. (2009). Modeling metabolic networks in C. glutamicum: a comparison of rate laws in combination with various parameter optimization strategies. BMC Systems Biol ogy 2009, 3:5 161 /183
  210. 210. Outline •Essential Systems Biology •Synthetic Biology •Computational Modeling for Synthetic Biology •Automated Model Synthesis and Optimisation •A Note on Ethical, Social and Legal Issues •Conclusions 162 /183
  211. 211. Synthetic Biology The new science of synthetic biology aims to re-engineer life at the molecular level and even create completely new forms of life. It has the potential to create new medicines, biofuels, assist climate change through carbon capture, and develop solutions to help clean up the environment. 163 /183
  212. 212. What is Synthetic Biology? Synthetic Biology is http://syntheticbiology.org/ A) the design and construction of new biological parts, devices, and systems, and B) the re-design of existing, natural biological systems for useful purposes. 164 /183
  213. 213. Been There, Done That 165 /183
  214. 214. The Unjustified Fear of Chimera and Foreign Genes Transplantation Itaya, M., Tsuge, K. Koizumi, M., and Fujita, K. Combining two genomes in one + = Cell: Stable cloning of the Synechosystis PCC6803 genome in the Bacillus subtilis 168 genome.Proc. Natl. Acad. Sci., USA, 102, 15971-15976 (2005) 150 times larger than the human genome 166 /183
  215. 215. A Technology Not a Product “ The problem of deaths and injury as a result of road accidents is now acknowledge to be a global phenomenon.... publications show that in 1990 road accidents as a cause of death or dissability were in ninth place out of a total of over 100 identified causes.... by 2020 forecasts suggest... road accidents will move up to sixth...” Estimating global road fatalities. G. Jacobs & A. Aeron-Thomas. Global Road Safety Partnership 167 /183
  216. 216. A Technology Not a Product “ The problem of deaths and injury as a result of road accidents is now acknowledge to be a global phenomenon.... publications show that in 1990 road accidents as a cause of death or dissability were in ninth place out of a total of over 100 identified causes.... by 2020 forecasts suggest... road accidents will move up to sixth...” And yet nobody seriously considers banning mechanical engineering Estimating global road fatalities. G. Jacobs & A. Aeron-Thomas. Global Road Safety Partnership 167 /183
  217. 217. A Technology Not a Product 168 /183
  218. 218. A Technology Not a Product And yet nobody seriously considers banning printing technology 168 /183
  219. 219. But .... •Technologies are regulated: •Cars have seat belts and laws establish speed limits •Main Kampf is banned in Germany and by, e.g., restricting google, China bans uncountable written material of all sorts! •Societies must establish an informed dialogue involving: •tax payers •Scientists •Lobbies of all sorts •Government 169 /183
  220. 220. What IS Synthetic Biology? Synthetic Biology is http://syntheticbiology.org/ A) the design and construction of new biological parts, devices, and systems, and B) the re-design of existing, natural biological systems for useful purposes. C) Through rigorous mathematical, computational engineering routes 170 /183
  221. 221. Biology only smarter, safer and clearer 171 /183
  222. 222. Outline •Essential Systems Biology •Synthetic Biology •Computational Modeling for Synthetic Biology •Automated Model Synthesis and Optimisation •A Note on Ethical, Social and Legal Issues •Conclusions 172 /183
  223. 223. Summary & Conclusions •This talk has focused on an integrative methodology for Systems & Synthetic Biology •Executable Biology •Parameter and Model Structure Discovery •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] 173 /183
  224. 224. Summary & Conclusions •The gap present in mathematical models between the model and its algorithmic implementation disappears in computational models as all of them are algorithms. •A new gap appears between the biology and the modeling technique and this can be solved by a judicious “feature selection”, i.e. the selection of the correct abstractions •Good computational models are more intuitive and analysable 174 /183
  225. 225. 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 175 /183
  226. 226. Summary & Conclusions A nested evolutionary algorithm is proposed to automatically develop and optimise the modular structure and parameters of cellular models based on stochastic P systems. Several case studies with incremental model complexity demonstrate the effectiveness of our algorithm. The fact that this algorithm produces alternative models for a specific biological signature is very encouraging as it could help biologists to design new experiments to discriminate among competing hypothesis (models). Comparing results by only using the elementary modules and by adding newly found modules to the library shows the obvious advantage of the incremental methodology with modules. This points out the great potential to automatically design more complex cellular models in the future by using a modular approach. 176 /183
  227. 227. Summary & Conclusions •Synthetising Synthetic Biology Models is more like evolving general GP programs and less like fitting regresion or inter/extra- polation •We evolve executable structures, distributed programs(!) •These are noisy and expensive to execute •Like in GP programs, executable biology models might achieve similar behaviour through different program “structure” •Prone to bloat •Like in GP, complex relation between diversity and solution quality •However, diverse solutions of similar fit might lead to interesting experimental routes •Co-desig of models and wetware. 177 /183
  228. 228. Summary & Conclusions •Some really nice tutorials and other sources: •Luca Caderlli’s BraneCalculus & BioAmbients •Simulating Biological Systems in the Stochastic π−calculus by Phillips and Cardelli •From Pathway Databases to Network Models by Aguda and Goryachev •Modeling and analysis of biological processes by Brane Calculi and Membrane Systems by Busi and Zandron •D. Gilbert’s website contain several nice papers with related methods and tutorials 178 /183
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