Building Executable Biology Models for Synthetic Biology

Building Executable Biology Models for Synthetic Biology 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 1 /57 Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel Tuesday, 30 June 2009

Based on 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. 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 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. Analysis of Alternative Fitness Methods for the Evolutionary Synthesis of Cell Systems Biology Models. F. Romero-Campero, H.Cao, M. Camara, and N. Krasnogor. Submitted. 2 /57 Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel Tuesday, 30 June 2009

Outline •Brief Introduction to Computational Modeling •Modeling for Top Down SB •Executable Biology •Automated Model Synthesis and Optimisation •Conclusions 3 /57 Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel Tuesday, 30 June 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, deploy and maintain novel synthetic biosystems. • These method come from computer science, engineering and maths. • Modelling and optimisation run through all of the above. 4 /57 Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel Tuesday, 30 June 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 5 /57 Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel Tuesday, 30 June 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 6 /57 Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel Tuesday, 30 June 2009

 Modeling relies on rigorous computational, engineering and mathematical tools & techniques  However, the act of modeling remains at the interface between art and science  Undoubtedly, a multidisciplinary endeavour 7 /57 Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel Tuesday, 30 June 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 8 /57 Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel Tuesday, 30 June 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 9 /57 Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel Tuesday, 30 June 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) 10 /57 Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel Tuesday, 30 June 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) 11 /57 Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel Tuesday, 30 June 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] 12 /57 Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel Tuesday, 30 June 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 13 /57 Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel Tuesday, 30 June 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 14 /57 Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel Tuesday, 30 June 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) 15 /57 Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel Tuesday, 30 June 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 16 /57 Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel Tuesday, 30 June 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 17 /57 Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel Tuesday, 30 June 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 19 /57 Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel Tuesday, 30 June 2009

Distributed and parallel rewritting systems in compartmentalised hierarchical structures. Objects Compartments Rewriting Rules • Computational universality and efficiency. • Modelling Framework 20 /57 Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel Tuesday, 30 June 2009

Stochastic P Systems 21 /57 Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel Tuesday, 30 June 2009

Rewriting Rules used by Multi-volume Gillespie’s algorithm 22 /57 Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel Tuesday, 30 June 2009

Molecular Interactions Inside Compartments 23 /57 Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel Tuesday, 30 June 2009

Passive Diffusion of Molecules 24 /57 Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel Tuesday, 30 June 2009

Signal Sensing and Active Transport 25 /57 Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel Tuesday, 30 June 2009

Specification of Transcriptional Regulatory Networks 26 /57 Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel Tuesday, 30 June 2009

Scalability through Modularity 27 /57 Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel Tuesday, 30 June 2009

Modularity in Gene Regulatory Networks  According to E. Davidson functional cis-regulatory modules are nonrandom clusters of target binding sites for transcription factors regulating the same gene or operon.  A library of modules corresponding to promoters of well studied genes. The activity of these promoters have been modelled mechanistically in terms of rewriting rules representing TF binding and debinding and transcription initiation. E. Davidson, The Regulatory Genome, Gene Regulatory Networks in Development and Evolution, Elsevier. 28 /57 Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel Tuesday, 30 June 2009

Modelling Individual Cells  An individual cell is represented as a P system, a set of compartments where specific objects describing molecular species are placed.  The gene regulatory networks in each cell are represented as a collection of modules and rewriting rules. 29 /57 Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel Tuesday, 30 June 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 30 /57 Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel Tuesday, 30 June 2009

InfoBiotics Pipeline 31 /57 Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel Tuesday, 30 June 2009

Quick Demo  Simulator-results-rescaled.html  Cie-model22-rescaled.html 32 /57 Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel Tuesday, 30 June 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 34 /57 Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel Tuesday, 30 June 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). • Jaramillo et al. use Simulated Annealing as the main search strategy for model inference based on (O)DEs 35 /57 Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel Tuesday, 30 June 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 36 /57 Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel Tuesday, 30 June 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. 37 /57 Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel Tuesday, 30 June 2009

Fitness Evaluation 38 /57 Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel Tuesday, 30 June 2009

The Objective (Fitness) Function • Multiple time-series per target • Different time series have very different profiles, e.g., maxima occur at different times/places • Transient states (sometimes) as important as steady states •RMSE might 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) 39 /57 Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel Tuesday, 30 June 2009

A Few Examples 40 /57 Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel Tuesday, 30 June 2009

41 /57 Problem Specification Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel Tuesday, 30 June 2009

42 /57 Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel Tuesday, 30 June 2009

Results Study Case 4 43 /57 Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel Tuesday, 30 June 2009

Target 49 /57 Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel Tuesday, 30 June 2009

Target 50 /57 Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel Tuesday, 30 June 2009

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. 51 /57 Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel Tuesday, 30 June 2009

Summary & Conclusions  This talk has focused on an integrative methodology, InfoBiotics, for Systems & Synthetic Biology  Executable Biology  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] 53 /57 Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel Tuesday, 30 June 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 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 54 /57 Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel Tuesday, 30 June 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  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. 55 /57 Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel Tuesday, 30 June 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 Dissipative Particle Dynamics My colleagues in the Centre for  James Smaldon Biomolecular Sciences and the Centre for Plant Integrative Biology  Jamie Twycross Stochastic Simulations at Nottingham Thanks also go to: Ben Gurion University of the Negev’s Distinguished Scientists Visitor Program Professor Dr. Moshe Sipper 56 /57 Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel Tuesday, 30 June 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 57 /57 Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel Tuesday, 30 June 2009

Building Executable Biology Models for Synthetic Biology

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