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Synthetic Biology -  Modeling and Optimisation
 

Synthetic Biology - Modeling and Optimisation

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This tutorial was given at GECCO 2009. It updates and extends the slideshow "Executable Biology Tutorial"

This tutorial was given at GECCO 2009. It updates and extends the slideshow "Executable Biology Tutorial"

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    Synthetic Biology -  Modeling and Optimisation Synthetic Biology - Modeling and Optimisation Presentation Transcript

    • Synthetic Biology: Modelling and Optimisation Natalio Krasnogor ASAP - Interdisciplinary Optimisation Laboratory School of Computer Science Centre for Integrative Systems Biology School of Biology Centre for Healthcare Associated Infections Institute of Infection, Immunity & Inflammation University of Nottingham Copyright is held by the author/owner(s). GECCO’09, July 8–12, 2009, Montréal Québec, Canada. ACM 978-1-60558-505-5/09/07. 1 /203 Thursday, 9 July 2009
    • 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 2 /203 Thursday, 9 July 2009
    • 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 3 /203 Thursday, 9 July 2009
    • 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 commonly 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. • Modelling and optimisation run through all of the above. 4 /203 Thursday, 9 July 2009
    • Models and Reality •The use of models is intrinsic to any scientific activity. •Models are abstractions of the real-world that highlight some key features while ignoring others that are assumed to be not relevant. •A model should not be seen or presented as representations of the truth, but instead as a statement of our current knowledge. 5 /203 Thursday, 9 July 2009
    • 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”. 6 /203 Thursday, 9 July 2009
    • •“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 7 /203 Thursday, 9 July 2009
    • 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 8 /203 Thursday, 9 July 2009
    • •There is potentially a distinction between modelling for Synthetic Biology and 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 modelling. 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 9 /203 Thursday, 9 July 2009
    • •There is potentially a distinction between modelling for Synthetic Biology and 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 modelling. 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 9 /203 Thursday, 9 July 2009
    • Thus, Multi-Scale Modelling in the 2 SBs seek to produce computable understanding integrating massive datasets at various levels of details simultaneously Progress Organ Individual Cell colony Cells Regulatory Networks Proteins DNA/RNA Time 10 /203 Thursday, 9 July 2009
    • The Pragmalogical Problem of Modelling in XXI century Biology • XXI century Biology brings to the fore the ubiquitous philosophical questions in complex systems, that of emergent behavior and the tension between reductionism and holistic approaches to science. • Synthetic Biology (and SysBio) has, however, a very pragmatic agenda: the engineering and control of novel biological systems • The pragmalogical problem: If each subcomponent of a living system (and processes/components therein) are understood… Can we say that the system is understood? That is, can we assume that the system = ∑parts ? • More importantly: can we control that biosystem? 11 /203 Thursday, 9 July 2009
    • The Pragmalogical Problem of Modelling in XXI century Biology • XXI century Biology brings to the fore the ubiquitous philosophical questions in complex systems, that of emergent behavior and the tension between reductionism and holistic approaches to science. & Integrative • Synthetic Biology (and SysBio) has, however, a very pragmatic agenda: the engineering and control of novel biological systems • The pragmalogical problem: If each subcomponent of a living system (and processes/components therein) are understood… Can we say that the system is understood? That is, can we assume that the system = ∑parts ? • More importantly: can we control that biosystem? 11 /203 Thursday, 9 July 2009
    •  Modelling relies on rigorous computational, engineering and mathematical tools & techniques  However, the act of modelling remains at the interface between art and science  Undoubtedly, a multidisciplinary endeavour 12 /203 Thursday, 9 July 2009
    • Modelling as a constrained scientific art  Although modelling lies at the interface of art and science there are guidelines we can follow  Some examples:  The scale separation map [Hoekstra et al, LNCS 4487, 2007]  Tools suitability & cost [Goldberg, 2002] 13 /203 Thursday, 9 July 2009
    • The Scale Separation Map  The Scale Separation Map is an abstraction recently proposed by Hoekstra and co-workers [Hoekstra et al, LNCS 4487, 2007]  Introduced in the context of Multi-scale modelling with cellular automata but the core concepts still valid for other modelling techniques 14 /203 Thursday, 9 July 2009
    • The Scale Separation Map  A Cellular Automata is defined as: C= < A(Δx, Δt,L,T), S, R, G, F > A is a spatial domain made of cells of size Δx with a total size of L The simulation clock ticks every Δt units for a total of T units T We can simulate processes: Δt  as fast as Δt for as long as T units  ranging from Δx to L sizes. Δx L L 15 /203 Thursday, 9 July 2009
    •  A Scale Separation Map (SSM) is a two dimensional map with horizontal axis representing time and vertical axis representing space 1 0 B ξB A ξA τB Spatial scale (log) τA 3.1 2 3.2 Temporal scale (log) 16 /203 Thursday, 9 July 2009
    •  A Scale Separation Map (SSM) is a two dimensional map with horizontal axis representing time and vertical axis representing space • Region 0: A and B overlap  single scale multi-science 1 0 model A ξA • Region 1: ξA ≈ ξB ^ τA > τB B ξB  temporal scale separation Spatial scale (log) τA • Region 2: ξA > ξB ^ τB ≈ τA τB  coarse and fine structures 3.1 2 3.2 in similar timescales • Region 3.1: ξA > ξB ^ τB < τA  familiar micro-macro models • Region 3.2: ξA > ξB ^ τB > τA  small and slow process linked to a fast and Temporal scale (log) large process (e.g. Blood flood and artery repair) 16 /203 Thursday, 9 July 2009
    •  A Scale Separation Map (SSM) is a two dimensional map with horizontal axis representing time and vertical axis representing space • Region 0: A and B overlap  single scale multi-science 1 0 model A ξA • Region 1: ξA ≈ ξB ^ τA > τB B ξB  temporal scale separation Spatial scale (log) τA • Region 2: ξA > ξB ^ τB ≈ τA τB  coarse and fine structures 3.1 2 3.2 in similar timescales • Region 3.1: ξA > ξB ^ τB < τA  familiar micro-macro models • Region 3.2: ξA > ξB ^ τB > τA  small and slow process linked to a fast and Temporal scale (log) large process (e.g. Blood flood and artery repair) 16 /203 Thursday, 9 July 2009
    •  A Scale Separation Map (SSM) is a two dimensional map with horizontal axis representing time and vertical axis representing space • Region 0: A and B overlap  single scale multi-science 1 0 model A ξA • Region 1: ξA ≈ ξB ^ τA > τB  temporal scale separation Spatial scale (log) τA • Region 2: ξA > ξB ^ τB ≈ τA  coarse and fine structures 3.1 2 3.2 in similar timescales • Region 3.1: ξA > ξB ^ τB < τA  familiar micro-macro models B ξB • Region 3.2: ξA > ξB ^ τB > τB τA  small and slow process linked to a fast and Temporal scale (log) large process (e.g. Blood flood and artery repair) 16 /203 Thursday, 9 July 2009
    •  A Scale Separation Map (SSM) is a two dimensional map with horizontal axis representing time and vertical axis representing space • Region 0: A and B overlap  single scale multi-science 1 0 model A ξA • Region 1: ξA ≈ ξB ^ τA > τB  temporal scale separation Spatial scale (log) τA • Region 2: ξA > ξB ^ τB ≈ τA  coarse and fine structures 3.1 2 3.2 in similar timescales • Region 3.1: ξA > ξB ^ τB < τA  familiar micro-macro models • Region 3.2: ξA > ξB ^ τB > B ξB τA  small and slow τB process linked to a fast and Temporal scale (log) large process (e.g. Blood flood and artery repair) 16 /203 Thursday, 9 July 2009
    •  A Scale Separation Map (SSM) is a two dimensional map with horizontal axis representing time and vertical axis representing space • Region 0: A and B overlap  single scale multi-science 1 0 model A ξA • Region 1: ξA ≈ ξB ^ τA > τB  temporal scale separation Spatial scale (log) τA • Region 2: ξA > ξB ^ τB ≈ τA  coarse and fine structures 3.1 2 3.2 in similar timescales • Region 3.1: ξA > ξB ^ τB < τA  familiar micro-macro models B ξB • Region 3.2: ξA > ξB ^ τB > τB τA  small and slow process linked to a fast and Temporal scale (log) large process (e.g. Blood flood and artery repair) 16 /203 Thursday, 9 July 2009
    • Even within a single cell the space & time scale separations are important E.g.: • 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. [F.J. Romero Campero, 2007] 17 /203 Thursday, 9 July 2009
    • The Scale Separation Map • With sufficient data each process can be assigned its space-time region unambiguously Couplings, e.g. F • A given process may well have its Δx (respectively Δt) > than another’s ξA (respectively τA) Spatial scale (log) • Hence different processes in the SSM might require different modelling techniques Temporal scale (log) 18 /203 Thursday, 9 July 2009
    • 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 19 /203 Thursday, 9 July 2009
    • 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) 20 /203 Thursday, 9 July 2009
    • 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 21 /203 Thursday, 9 July 2009
    • Tools Suitability and Cost Low cost/ High cost/ High error Low error Adapted from [Goldberg 2002] Unarticulated Articulated Dimensional Facetwise Equations wisdom Qualitative models models Of motion models Chemical Bioinformatic Biopolimer Microarrays and G.E. Markup Language Sequence Markup Markup Language Markup Language (CML) Language (BSML) (BioML) (MAGEML) Cell Systems Biology Mathematics Markup Language Markup Language Markup Language (MathML) (SBML) (MathML) 22 /203 Thursday, 9 July 2009
    • From [Di Ventura et al., Nature, 2006] Low cost/ High cost/ High error Low error Unarticulated Dimensional Facetwise Equations wisdom models models Of motion  Formalism-independent errors  Formalism-dependent errors 23 /203 Thursday, 9 July 2009
    • From [Di Ventura et al., Nature, 2006] Low cost/ High cost/ High error Low error Unarticulated Dimensional Facetwise Equations wisdom models models Of motion  Formalism-independent errors  Formalism-dependent errors 23 /203 Thursday, 9 July 2009
    • From [Di Ventura et al., Nature, 2006] Low cost/ High cost/ High error Low error Unarticulated Dimensional Facetwise Equations wisdom models models Of motion  Formalism-independent errors  Formalism-dependent errors 23 /203 Thursday, 9 July 2009
    • 24 /203 Thursday, 9 July 2009
    • 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 25 /203 Thursday, 9 July 2009
    • Towards Executable Modells for SBs “Although the road ahead is long and winding, it leads to a future where biology and medicine are transformed into precision engineering.” - Hiroaki Kitano.  Synthetic Biology and Systems biology promise more than integrated understanding: it promises systematic control of biological systems: 1. From an experimental viewpoint: Improved data acquisition 2. From a bioinformatics viewpoint: Improved data analysis tools 3. From a conceptual viewpoint: move from a science of mass-action/ energy-conversion to a science of information processing through multiple heterogeneous medium 26 /203 Thursday, 9 July 2009
    • 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 27 /203 Thursday, 9 July 2009
    •  It thus makes sense to use methodologies designed to cope with complex, concurrent, interactive systems of parts as found in computer sciences (e.g.):  Petri Nets  Process Calculi  P-Systems 28 /203 Thursday, 9 July 2009
    • 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) 29 /203 Thursday, 9 July 2009
    • 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 30 /203 Thursday, 9 July 2009
    • 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 31 /203 Thursday, 9 July 2009
    • 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 32 /203 Thursday, 9 July 2009
    • 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 33 /203 Thursday, 9 July 2009
    • 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 34 /203 Thursday, 9 July 2009
    • 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 35 /203 Thursday, 9 July 2009
    • 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 36 /203 Thursday, 9 July 2009
    • Distributed and parallel rewritting systems in compartmentalised hierarchical structures. Objects Compartments Rewriting Rules • Computational universality and efficiency. • Modelling Framework 37 /203 Thursday, 9 July 2009
    • 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) 38 /203 Thursday, 9 July 2009
    • 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 38 /203 Thursday, 9 July 2009
    • 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 38 /203 Thursday, 9 July 2009
    • 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 39 /203 Thursday, 9 July 2009
    • Stochastic P Systems 40 /203 Thursday, 9 July 2009
    • Rewriting Rules used by Multi-volume Gillespie’s algorithm 41 /203 Thursday, 9 July 2009
    • Molecular Species  A molecular species can be represented using individual objects.  A molecular species with relevant internal structure can be represented using a string. 42 /203 Thursday, 9 July 2009
    • 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 43 /203 Thursday, 9 July 2009
    • Compartments / Cells  Compartments and regions are explicitly specified using membrane structures. 44 /203 Thursday, 9 July 2009
    • 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 45 /203 Thursday, 9 July 2009
    • Molecular Interactions Inside Compartments 46 /203 Thursday, 9 July 2009
    • Passive Diffusion of Molecules 47 /203 Thursday, 9 July 2009
    • 48 /203 Thursday, 9 July 2009
    • a b Transport Modalities a b Antiport channel a b Symport channel a c b a b Promoted symport channel (trap) a b 49 /203 Thursday, 9 July 2009
    • Transport Modalities 5 2 1 4 3 Phagocitosys Endocitosys Pinocitosys Exocitosys 50 /203 Thursday, 9 July 2009
    • Transport Modalities Highly specific: cell specific & topology specific 51 /203 Thursday, 9 July 2009
    • Signal Sensing and Active Transport 52 /203 Thursday, 9 July 2009
    • Specification of Transcriptional Regulatory Networks 53 /203 Thursday, 9 July 2009
    • Transcription as Rewriting Rules on Multisets of Objects and Strings 54 /203 Thursday, 9 July 2009
    • Translation as Rewriting Rules on Multisets of Objects and Strings 55 /203 Thursday, 9 July 2009
    • 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. 56 /203 Thursday, 9 July 2009
    • 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. 57 /203 Thursday, 9 July 2009
    • Basic P System Modules Used 58 /203 Thursday, 9 July 2009
    • Modularity in Gene Regulatory Networks  Cis-regulatory modules are nonrandom clusters of target binding sites for transcription factors regulating the same gene or operon.  A P system module is a set of rewriting rules containing variables that can be instantiated with specific objects, stochastic constants and membrane labels. E. Davidson (2006) The Regulatory Genome, Gene Regulation Networks in Development and Evolution, Elsevier 59 /203 Thursday, 9 July 2009
    • Modularity in Gene Regulatory Networks AHL LuxR CI  Cis-regulatory modules are nonrandom clusters of target binding sites for transcription factors regulating the same gene or operon.  A P system module is a set of rewriting rules containing variables that can be instantiated with specific objects, stochastic constants and membrane labels. E. Davidson (2006) The Regulatory Genome, Gene Regulation Networks in Development and Evolution, Elsevier 59 /203 Thursday, 9 July 2009
    • Modularity in Gene Regulatory Networks AHL LuxR CI  Cis-regulatory modules are nonrandom clusters of target binding sites for transcription factors regulating the same gene or operon.  A P system module is a set of rewriting rules containing variables that can be instantiated with specific objects, stochastic constants and membrane labels. E. Davidson (2006) The Regulatory Genome, Gene Regulation Networks in Development and Evolution, Elsevier 59 /203 Thursday, 9 July 2009
    • Representing transcriptional fusions  Objects Variables 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 specified by the module. 60 /203 Thursday, 9 July 2009
    • Representing transcriptional fusions  Objects Variables 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 specified by the module. 60 /203 Thursday, 9 July 2009
    • Representing transcriptional fusions  Objects Variables 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 specified by the module. 60 /203 Thursday, 9 July 2009
    • Representing transcriptional fusions  Objects Variables 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 specified by the module. 60 /203 Thursday, 9 July 2009
    • Representing transcriptional fusions  Objects Variables 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 specified by the module. 60 /203 Thursday, 9 July 2009
    • Representing transcriptional fusions  Objects Variables 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 specified by the module. 60 /203 Thursday, 9 July 2009
    • Representing transcriptional fusions  Objects Variables 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 specified by the module. 60 /203 Thursday, 9 July 2009
    • Representing Directed Evolution  Variables for stochastic constants can be instantiated with specific values in order to represent directed evolution. 61 /203 Thursday, 9 July 2009
    • Representing Directed Evolution  Variables for stochastic constants can be instantiated with specific values in order to represent directed evolution. 61 /203 Thursday, 9 July 2009
    • Representing Directed Evolution  Variables for stochastic constants can be instantiated with specific values in order to represent directed evolution. A 61 /203 Thursday, 9 July 2009
    • Representing Directed Evolution  Variables for stochastic constants can be instantiated with specific values in order to represent directed evolution. A 61 /203 Thursday, 9 July 2009
    • Representing synthetic transcriptional networks  The genes used to instantiate variables in our modules can codify other TFs that interact with other modules or promoters producing a synthetic gene regulatory network. 62 /203 Thursday, 9 July 2009
    • Representing synthetic transcriptional networks  The genes used to instantiate variables in our modules can codify other TFs that interact with other modules or promoters producing a synthetic gene regulatory network. 62 /203 Thursday, 9 July 2009
    • 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 63 /203 Thursday, 9 July 2009
    • Multicompartmental Gillespie Algorithm 64 /203 Thursday, 9 July 2009
    • Multicompartmental Gillespie Algorithm 1 3 2 64 /203 Thursday, 9 July 2009
    • Multicompartmental Gillespie Algorithm 1 3 r11,…,r1n1 r31,…,r3n3 M3 M1 2 r21,…,r2n2 M2 64 /203 Thursday, 9 July 2009
    • Multicompartmental Gillespie Algorithm 1 3 r11,…,r1n1 r31,…,r3n3 Local Gillespie M3 M1 2 r21,…,r2n2 M2 64 /203 Thursday, 9 July 2009
    • Multicompartmental Gillespie Algorithm 1 3 r11,…,r1n1 ( 1, τ1, r01) r31,…,r n3 3 Local Gillespie M3 M1 2 r21,…,r2n2 M2 64 /203 Thursday, 9 July 2009
    • Multicompartmental Gillespie Algorithm 1 3 r11,…,r1n1 ( 1, τ1, r01) r31,…,r n3 3 Local Gillespie M3 M1 ( 2, τ2, r02) 2 r21,…,r2n2 M2 64 /203 Thursday, 9 July 2009
    • 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 M2 64 /203 Thursday, 9 July 2009
    • 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 64 /203 Thursday, 9 July 2009
    • 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, r01) ( 3, τ3, r03) 64 /203 Thursday, 9 July 2009
    • 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, r01) ( 3, τ3, r03) 64 /203 Thursday, 9 July 2009
    • 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) ( 1, τ1, r01) ( 3, τ3-τ2, r03) ( 3, τ3, r03) Update Waiting Times 64 /203 Thursday, 9 July 2009
    • 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) ( 2, τ2’, r02) ( 1, τ1-τ2, r01) ( 1, τ1, r01) ( 3, τ3-τ2, r03) ( 3, τ3, r03) Update Waiting Times 64 /203 Thursday, 9 July 2009
    • 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) ( 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 64 /203 Thursday, 9 July 2009
    • 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 64 /203 Thursday, 9 July 2009
    •  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 65 /203 Thursday, 9 July 2009
    • InfoBiotics Pipeline 66 /203 Thursday, 9 July 2009
    • SBML from CellDesigner 67 /203 Thursday, 9 July 2009
    • Runs simulations and extract data 68 /203 Thursday, 9 July 2009
    • Plot Timeseries 69 /203 Thursday, 9 July 2009
    • in time and space 70 /203 Thursday, 9 July 2009
    • Synthetic Biology Examples 71 /203 Thursday, 9 July 2009
    • 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) 72 /203 Thursday, 9 July 2009
    • 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... 73 /203 Thursday, 9 July 2009
    • Single protein represses gene p=1 74 /203 Thursday, 9 July 2009
    • When repression is weak (dissociation rate = 10) No obvious oscillatory behaviour in single simulation 75 /203 Thursday, 9 July 2009
    • When repression is weak (dissociation rate = 10) Mean of 100 runs shows convergence to steady state 76 /203 Thursday, 9 July 2009
    • When repression is strong (dissociation rate = 0.1) Oscillations evident in single simulation 77 /203 Thursday, 9 July 2009
    • When repression is strong (dissociation rate = 0.1) Averging 100 runs dampens oscillations due to different phases but observable. Protein levels steady. 78 /203 Thursday, 9 July 2009
    • 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 79 /203 Thursday, 9 July 2009
    • 1. Protein represses as dimer 80 /203 Thursday, 9 July 2009
    • 1. Protein represses as dimer target mRNA levels oscillate ready but protein accumulates in the cytosol 81 /203 Thursday, 9 July 2009
    • 2. Proteins repress cooperatively 82 /203 Thursday, 9 July 2009
    • 2. Proteins repress cooperatively target Oscillations are steady and protein levels are controlled 83 /203 Thursday, 9 July 2009
    • 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. (2004) Spatiotemporal control of gene expression with pulse generating networks, PNAS, 101, 6355-6360 84 /203 Thursday, 9 July 2009
    • Sending Cells 85 /203 Thursday, 9 July 2009
    • Sending Cells 85 /203 Thursday, 9 July 2009
    • Sending Cells Pconst 85 /203 Thursday, 9 July 2009
    • Sending Cells Pconst luxI 85 /203 Thursday, 9 July 2009
    • Sending Cells Pconst({X = luxI },…) Pconst luxI 85 /203 Thursday, 9 July 2009
    • Sending Cells AHL Pconst({X = luxI },…) LuxI AHL PostTransc({X=LuxI},{c1=3.2,…}) Diff({X=AHL},{c=0.1}) Pconst luxI 85 /203 Thursday, 9 July 2009
    • Pulse Generating Cells 86 /203 Thursday, 9 July 2009
    • Pulse Generating Cells 86 /203 Thursday, 9 July 2009
    • Pulse Generating Cells LuxR Pconst luxR 86 /203 Thursday, 9 July 2009
    • Pulse Generating Cells AHL AHL LuxR Pconst luxR 86 /203 Thursday, 9 July 2009
    • Pulse Generating Cells AHL AHL LuxR GFP PluxOR1 Pconst gfp luxR 86 /203 Thursday, 9 July 2009
    • Pulse Generating Cells AHL AHL LuxR GFP PluxOR1 Pconst gfp luxR Plux cI 86 /203 Thursday, 9 July 2009
    • Pulse Generating Cells AHL AHL LuxR GFP PluxOR1 Pconst gfp luxR CI Plux cI 86 /203 Thursday, 9 July 2009
    • Pulse Generating Cells AHL AHL Pconst({X=luxR},…) LuxR GFP PluxOR1({X=gfp},…) PluxOR1 Plux({X=cI},…) Pconst gfp luxR … … CI Diff({X=AHL},…) Plux cI 86 /203 Thursday, 9 July 2009
    • Spatial Distribution of Senders and Pulse Generators AHL GFP AHL LuxR Pconst PluxOR1 luxR gfp LuxI AHL CI Pconst luxI Plux cI 87 /203 Thursday, 9 July 2009
    • AHL Spatial Distribution of Senders and Pulse Generators AHL GFP AHL LuxR Pconst PluxOR1 luxR gfp LuxI AHL CI Pconst luxI Plux cI 87 /203 Thursday, 9 July 2009
    • Wave propagation simulation I SIMULATION I 88 /203 Thursday, 9 July 2009
    • Pulse Generating Cells AHLWith Relay AHL LuxR GFP PluxOR1 Pconst gfp luxR CI Plux cI 89 /203 Thursday, 9 July 2009
    • Pulse Generating Cells AHLWith Relay AHL LuxR GFP PluxOR1 Pconst gfp luxR Plux CI luxI LuxI Plux cI AHL 89 /203 Thursday, 9 July 2009
    • Pulse Generating Cells AHLWith Relay AHL Pconst({X=luxR},…) LuxR GFP PluxOR1 Pconst gfp luxR Plux CI luxI LuxI Plux cI AHL 89 /203 Thursday, 9 July 2009
    • Pulse Generating Cells AHLWith Relay AHL Pconst({X=luxR},…) LuxR GFP PluxOR1({X=gfp},…) PluxOR1 Pconst gfp luxR Plux CI luxI LuxI Plux cI AHL 89 /203 Thursday, 9 July 2009
    • Pulse Generating Cells AHLWith Relay AHL Pconst({X=luxR},…) LuxR GFP PluxOR1({X=gfp},…) PluxOR1 Plux({X=cI},…) Pconst gfp luxR Plux CI luxI LuxI Plux cI AHL 89 /203 Thursday, 9 July 2009
    • Pulse Generating Cells AHLWith Relay AHL Pconst({X=luxR},…) LuxR GFP PluxOR1({X=gfp},…) PluxOR1 Plux({X=cI},…) Pconst gfp luxR … Plux CI luxI LuxI Plux cI AHL 89 /203 Thursday, 9 July 2009
    • Pulse Generating Cells AHLWith Relay AHL Pconst({X=luxR},…) LuxR GFP PluxOR1({X=gfp},…) PluxOR1 Plux({X=cI},…) Pconst gfp luxR … … Plux CI luxI LuxI Plux cI AHL 89 /203 Thursday, 9 July 2009
    • Pulse Generating Cells AHLWith Relay AHL Pconst({X=luxR},…) LuxR GFP PluxOR1({X=gfp},…) PluxOR1 Plux({X=cI},…) Pconst gfp luxR … … Plux CI luxI Diff({X=AHL},…) LuxI Plux cI AHL 89 /203 Thursday, 9 July 2009
    • Pulse Generating Cells AHLWith Relay AHL Pconst({X=luxR},…) LuxR GFP PluxOR1({X=gfp},…) PluxOR1 Plux({X=cI},…) Pconst gfp luxR … … Plux CI luxI Diff({X=AHL},…) Plux Plux({X=luxI},…) LuxI cI AHL 89 /203 Thursday, 9 July 2009
    • Wave propagation simulation II SIMULATION II 90 /203 36 Thursday, 9 July 2009
    • AHL Spatial Distribution of Pulse Generators and Seed AHL LuxR GFP Pconst PluxOR1 luxR gfp Plux CI luxI Plux cI LuxI AHL 91 /203 Thursday, 9 July 2009
    • Wave propagation with Four Droplets of Signal SIMULATION III 92 /203 38 Thursday, 9 July 2009
    • Pulse Generating Cells AHLWith Relay LuxR AHL PulseGenerator(X ) = PluxOR1 { Pconst({X=luxR},…) , Pconst luxR PluxOR1({X},…) , Plux({X=cI},…) , Plux … CI luxI Diff({X=AHL},…) , LuxI Plux cI Plux({X=luxI},…) } AHL 93 /203 Thursday, 9 July 2009
    • Combining Complex Modules AHL AHL Inverter(X ) = LuxR { Pconst({X=luxR},…) , PluxOR1({X=lacI},…) , Pconst PluxOR1 Plac({X},…) , luxR lacI … LacI Diff({X=AHL},…)} Plac 94 /203 Thursday, 9 July 2009
    • Combining Complex Modules AHL AHL Inverter(X ) = LuxR { Pconst({X=luxR},…) , PluxOR1({X=lacI},…) , Pconst PluxOR1 Plac({X},…) , luxR lacI … LacI Diff({X=AHL},…)} Plac PulseGenerator({X=lacI}) Inverter({X=gfP}) 94 /203 Thursday, 9 July 2009
    • Inversion Through a Propagating Wave SIMULATION IV 95 /203 41 Thursday, 9 July 2009
    • 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 96 /203 Thursday, 9 July 2009
    • Probabilistic Model Checking  A precise computational/mathematical model allows us to perform formal verification techniques: Probabilistic model checking.  Properties are expressed formally using temporal logic and analysed.  The fundamental components of the PRISM language are modules, variables and commands. • A model is composed of a number of modules which can interact with each other. • A module contains a number of local variables and commands. 97 /203 Thursday, 9 July 2009
    • P Systems and PRISM P System Component PRISM Component Membrane Module Multisets of Objects Local Variables Rewriting rules Commands  Rewards/Costs are associated with states and transitions representing the number of objects and the application of rules.  Some Properties:  Expected Number of objects over time: R = ? [ I = T ]  Expected Number of rule application over time: R = ? [ C <= T ]  Expected Time to reach a state: R = ? [ F molec_1 = K ]  Transient properties: P = ? [ true U[t_1 t_2] molec_1 >= K_1 ]  Steady State/Long run properties: S = ? [ molec_1 >= K_1] PRISM is used as an example. Other model checkers are more appropriate for larger systems 98 /203 Thursday, 9 July 2009
    • Post-transcriptional Regulation Post-transcriptional regulation [ gene ]b  [ gene + rna ]b ctrc ctrc*ctrl = 1.13 [ rna ]b  [ ]b c2=0.3465 10 proteins in [ rna ]b  [ rna + Protein ]b ctrl steady State [ Protein ]b  [ ]b c4=0.3465 99 /203 Thursday, 9 July 2009
    • Post-transcriptional Regulation R = ? [ I = 240 ] P = ? [ true U[240,240] Proteins = N ] 100 /203 Thursday, 9 July 2009
    • Positive Regulation [ TF + gene ] b  [ TF.gene ] b con [ TF.gene ] b  [ TF + gene ] b coff [ gene ]b  [ gene + rna ]b ctrc [ rna ]b  [ ]b c2 [ rna ]b  [ rna + Protein ]b ctrl [ Protein ]b  [ ]b c4 101 /203 Thursday, 9 July 2009
    • Positive Regulation R = ? [ C <= 100 ] R = ? [ C <= 100 ] P = ? [ true U[60,60] Proteins = N ] 102 /203 Thursday, 9 July 2009
    • 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. 103 /203 Thursday, 9 July 2009
    • 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 104 /203 Thursday, 9 July 2009
    •  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. 105 /203 Thursday, 9 July 2009
    •  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. 106 /203 Thursday, 9 July 2009
    •  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 107 /203 Thursday, 9 July 2009
    • 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 108 /203 Thursday, 9 July 2009
    • A (Proto)Cell as an Information Processing Device LeDuc et al. Towards an in vivo biologically inspired nanofactory. Nature (2007) 109 /203 Thursday, 9 July 2009
    • 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 110 /203 Thursday, 9 July 2009
    • Vesicle Biorecognition Pasparakis, G. et al, Angew Chem Int Ed. 2008 47 (26), 4847-4850 111 /203 Thursday, 9 July 2009
    • ‘Talking’ to cell-vesicle aggregates Pasparakis, G. Angew Chem Int Ed. 2008 47 (26), 4847-4850 112 /203 Thursday, 9 July 2009
    • 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 113 /203 Thursday, 9 July 2009
    • 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 114 /203 Thursday, 9 July 2009
    • 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 115 /203 Thursday, 9 July 2009
    • Dissipative Particle Dynamics Conservative Force i W P Dissipative Force j W P Random Force 116 /203 Thursday, 9 July 2009
    • 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. 117 /203 Thursday, 9 July 2009
    • Liposome Formation in DPD 118 /203 Thursday, 9 July 2009
    • 119 /203 Thursday, 9 July 2009
    • 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. 120 /203 Thursday, 9 July 2009
    • 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 121 /203 Thursday, 9 July 2009
    • 122 /203 Thursday, 9 July 2009
    • 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 123 /203 Thursday, 9 July 2009
    • 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. 124 /203 Thursday, 9 July 2009
    • 125 /203 Thursday, 9 July 2009
    • 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 126 /203 Thursday, 9 July 2009
    • Parallel DPD  In order to simulate large systems, the DPD method can be parallelised using spatial decomposition techniques  Each processor is assigned a subvolume of the 3D simulation space  Communication required when particles move between subvolumes  Communication performed using MPI  10x-15x faster than the single processor version of DPD (Jupiter implementation) 127 /203 Thursday, 9 July 2009
    • Compute Unified Device Architecture (CUDA) DPD  Algorithms can be written in C code for execution on an Nvidia G8X/GT200 GPU.  Modern CPUs have 2-4 cores, modern GPUs have hundreds. Highly parallel SIMD computing.  Several orders of magnitude calculation speed increase for MD.  The DPD algorithm was expressed in a manner enabling parallel execution with CUDA. 128 /203 Thursday, 9 July 2009
    • CUDA DPD Implementation CUDA DPD is ~27 times faster than single processor 129 /203 Thursday, 9 July 2009
    • 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 130 /203 Thursday, 9 July 2009
    • 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 131 /203 Thursday, 9 July 2009
    •  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. 132 /203 Thursday, 9 July 2009
    • 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 133 /203 Thursday, 9 July 2009
    • 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 134 /203 Thursday, 9 July 2009
    • 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 135 /203 Thursday, 9 July 2009
    • 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. Best Paper award at the Bioinformatics track. 136 /203 Thursday, 9 July 2009
    • Fitness Evaluation 137 /203 Thursday, 9 July 2009
    • Representation 138 /203 Thursday, 9 July 2009
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    • Structural Operators 140 /203 Thursday, 9 July 2009
    • Structural Operators 141 /203 Thursday, 9 July 2009
    • Parameter Optimisation 142 /203 Thursday, 9 July 2009
    • Parameter Optimisation 143 /203 Thursday, 9 July 2009
    • 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 144 /203 Thursday, 9 July 2009
    • Case 1: Molecular Complexation Target P System Model Structure c1 c2 Target P System Model Parameters 145 /203 Thursday, 9 July 2009
    • Case 1: Experimental Results 50/50 runs found the same P system model structure as the target one Target and Evolved Time Series 146 /203 Thursday, 9 July 2009
    • Case 1: Experimental Results 50/50 runs found the same P system model structure as the target one Target Model Parameters Best Model Parameters 147 /203 Target and Evolved Time Series Thursday, 9 July 2009
    • Case 2: Enzymatic Reaction Target P System Model Structure c1 c2 c3 Target P System Model Parameters 148 /203 Thursday, 9 July 2009
    • Case 2: Experimental Results Target and Evolved Time Series 149 /203 Thursday, 9 July 2009
    • 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 2.85 150 /203 Thursday, 9 July 2009
    • Case 3.1: Negative Autoregulation Target P System Model Structure Target P System Model Parameters c1 c2 c3 c4 c5 c6 151 /203 Thursday, 9 July 2009
    • 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 2 152 /203 Thursday, 9 July 2009
    • Case 3.2: Positive Autoregulation Target P System Model Structure Target P System Model Parameters c1 c2 c3 c4 c5 c6 c7 153 /203 Thursday, 9 July 2009
    • 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 1 154 /203 Thursday, 9 July 2009
    • Revisiting 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. Analysis of Alternative Fitness Methods for the Evolutionary Synthesis of Cell Systems Biology Models. Submitted (2009) 155 /203 Thursday, 9 July 2009
    • 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] 156 /203 Thursday, 9 July 2009
    • 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. 157 /203 Thursday, 9 July 2009
    • 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. 158 /203 Thursday, 9 July 2009
    • Further Case Studies 159 /203 Thursday, 9 July 2009
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    • Results Study Case 1 167 /203 Thursday, 9 July 2009
    • Results Study Case 2 168 /203 Thursday, 9 July 2009
    • Results Study Case 3 169 /203 Thursday, 9 July 2009
    • Target model Alternative, biologically valid, modules 170 /203 Thursday, 9 July 2009
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    • Results Study Case 4 172 /203 Thursday, 9 July 2009
    • 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) 173 /203 Thursday, 9 July 2009
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    • Target 179 /203 Thursday, 9 July 2009
    • Target 180 /203 Thursday, 9 July 2009
    • The evolving curve of average fitness by four fitness methods (F1 ∼ F4) in 20 runs for Test Case 4. 181 /203 Thursday, 9 July 2009
    • The evolving curve of average model diversity by four fitness methods (F1 ∼ F4) in 20 runs for Test Case 4 182 /203 Thursday, 9 July 2009
    • 183 /203 Thursday, 9 July 2009
    • 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. 184 /203 Thursday, 9 July 2009
    • • 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 185 /203 Thursday, 9 July 2009
    • Initial Conditions PDE model 186 /203 Thursday, 9 July 2009
    • Target By Evolving: abcd, acad, Dbcd/Dcad, r, mRNA distributions and t However: Goal is not perfect fit but rather robust fit 187 /203 Thursday, 9 July 2009
    • • 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 188 /203 Thursday, 9 July 2009
    • 189 /203 Thursday, 9 July 2009
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    • 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 192 /203 Thursday, 9 July 2009
    • 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] 193 /203 Thursday, 9 July 2009
    • 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 modelling 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 194 /203 Thursday, 9 July 2009
    • 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 195 /203 Thursday, 9 July 2009
    • 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. 196 /203 Thursday, 9 July 2009
    • 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. 197 /203 Thursday, 9 July 2009
    • 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 198 /203 Thursday, 9 July 2009
    • Other Sources 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, (to appear), 2009. F.J. Romero-Camero and N. Krasnogor. An approach to biomodel engineering based on p systems. In Proceedings of Computation In Europe (CIE 2009), 2009. J. Smaldon, N. Krasnogor, M. Gheorghe, and A. Cameron. Liposome logic. In Proceedings of the 2009 Genetic and Evolutionary Computation Conference (GECCO 2009), 2009 F. Romero-Campero, H.Cao, M. Camara, and N. Krasnogor. Structure and parameter estimation for cell systems biology models. In Maarten Keijzer et.al, editor, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2008), pages 331-338. ACM Publisher, 2008. This paper won the Best Paper award at the Bioinformatics track. 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. 199 /203 Thursday, 9 July 2009
    • Other Sources Păun, Gh. Computing with membranes. Journal of Computer and System Sciences 61 (2000) 108-143 P Systems Web Page http://psystems.disco.unimib.it/ Bianco L. Membrane Models of Biological Systems PhD thesis 2007 Bernardini F, Gheorghe M, Krasnogor N, Terrazas G. Membrane Computing - Current Results and Future Problems. CiE 2005 49-53 Bernardini F, Gheorghe M, Krasnogor N. Quorum sensing P systems. Theoretical Computer Science 371 (2007) 20-33 Miguel Nicolau, Marc Schoenauer. Evolving Specific Network Statistical Properties using a Gene Regulatory Network Model. In ECCS 2008, 5th European Conference on Complex Systems, 2008. Miguel Nicolau, Marc Schoenauer. Evolving Scale-Free Topologies using a Gene Regulatory Network Model. CEC 2008, IEEE Congress on Evolutionary Computation, pp. 3748-3755, IEEE Press, 2008. 200 /203 Thursday, 9 July 2009
    • Other Sources A. Ridwan. A parallel implementation of Gillespie's Direct Method. Proc. of the International Conference on Computational Science, p.284-291, Krakow, Poland, June 2004. G. C. Ewing et al. Akaroa2: Exploiting network computing by distributing stochastic simulation. Proc. of the European Simulation Multiconference, p. 175-181, Warsaw, June 1999. P. Hellekalek. Don't trust parallel Monte Carlo! ACM SIGSIM Simulation Digest, 28(1):82-89, 1998. M. Schwehm. Parallel stochastic simulation of whole cell models. Proc. of the Second International Conference on Systems Biology, p.333-341, CalTech, C.A., November 2001 201 /203 Thursday, 9 July 2009
    • Acknowledgements Members of my team working on SB2 EP/E017215/1  Jonathan Blake Integrated Environment EP/D021847/1  Hongqing Cao Machine Learning & Optimisation BB/F01855X/1 BB/D019613/1  Francisco Romero-Campero Modeling & Model Checking  James Smaldon Dissipative Particle Dynamics My colleagues in the Centre for  Jamie Twycross Biomolecular Sciences and the Stochastic Simulations Centre for Plant Integrative Biology at Nottingham • GECCO 2009 organisers for inviting this tutorial, specially Martin Butz and Gunther Raidl • You for listening! 202 /203 Thursday, 9 July 2009
    • Any Questions? • www.infobiotic.org • www.synbiont.org Become a member and have access to $$$ for engaging in SB research. Contact me if interested 203 /203 Thursday, 9 July 2009