Building Executable Biology Models for Synthetic Biology

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

Natalio KrasnogorASAP - Interdisciplinary Optimisation LaboratorySchool of Computer Science

Centre for Integrative Systems BiologySchool of Biology

Centre for Healthcare Associated InfectionsInstitute of Infection, Immunity & Inflammation

University of Nottingham

Building Executable Biology Models for Synthetic Biology

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Tuesday, 30 June 2009


Based onF. 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.

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Outline•Brief Introduction to Computational Modeling

•Modeling for Top Down SB•Executable Biology

•Automated Model Synthesis and Optimisation

•Conclusions

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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.

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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

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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

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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

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Modelling Approaches

There exist many modelling approaches, each with its advantages and disadvantages.

Macroscopic, Microscopic and MesoscopicQuantitative and qualitativeDiscrete and ContinuousDeterministic and StochasticTop-down or Bottom-up

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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, e

rror

C, cost of modelling

Synthetic Biologist

Computer Scientist/Mathematician

Tools Suitability and Cost

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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)

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Temporal scale (log)

Spa

tial s

cale

(log

)

• With sufficient data each process can be assigned its space-time region unambiguously

• A given process may well have its Δx (respectively Δt) > than another’s ξA (respectively τA)

• Hence different processes in the SSM might require different modelling techniques

Couplings, e.g. F

The Scale Separation Map

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Even within a single cell the space & time scale separations are important

[F.J. Romero Campero, 2007]

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.

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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

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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

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InfoBioticswww.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)

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http://www.infobiotic.net

http://www.infobiotic.net


Cells

Colonies

Modeling in Systems & Synthetic Biology

Networks

Systems Biology Synthetic Biology

• Understanding• Integration• Prediction• Life as it is

•Control• Design• Engineering•Life as it could be

Computational modelling toelucidate and characterisemodular patterns exhibitingrobustness, signal filtering,amplification, adaption, error correction, etc.

Computational modelling toengineer and evaluate possible cellular designsexhibiting a desiredbehaviour by combining well studied and characterised cellular modules

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Model Development From [E. Klipp et al, Systems Biology in Practice,

2005]1. Formulation of the problem2. Verification of available information3. Selection of model structure4. Establishing a simple model5. Sensitivity analysis6. Experimental tests of the model predictions7. Stating the agreements and divergences between

experimental and modelling results8. Iterative refinement of model

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•Conclusions

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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

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Distributed and parallel rewritting systems in compartmentalised hierarchical structures.

Compartments

Objects

Rewriting Rules

• Computational universality and efficiency.

• Modelling Framework

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Stochastic P Systems

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Rewriting Rules

used by Multi-volume Gillespie’s algorithm

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Molecular Interactions Inside Compartments

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Passive Diffusion of Molecules

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Signal Sensing and Active Transport

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Specification of Transcriptional Regulatory Networks

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Scalability through Modularity

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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.

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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.

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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

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InfoBiotics Pipeline

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Quick Demo

Simulator-results-rescaled.html Cie-model22-rescaled.html

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•Conclusions

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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 iterated34



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

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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

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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.

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Fitness Evaluation

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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)

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A Few Examples

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Problem Specification41


/57 Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel42



Results Study Case 4

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Target

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Target

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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.

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•Conclusions

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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]

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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

Summary & Conclusions

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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.

Summary & Conclusions

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Acknowledgements

Jonathan Blake

Hongqing Cao

Francisco Romero-Campero

James Smaldon

Jamie Twycross

Integrated Environment

Machine Learning & Optimisation

Modeling & Model Checking

Dissipative Particle Dynamics

Stochastic Simulations

Members of my team working on SB2

EP/E017215/1

EP/D021847/1

BB/F01855X/1

BB/D019613/1

My colleagues in the Centre for Biomolecular Sciences and the Centre for Plant Integrative Biology at Nottingham

Thanks also go to:

Ben Gurion University of the Negev’s Distinguished Scientists Visitor Program

Professor Dr. Moshe Sipper56



Any Questions?

•www.infobiotic.org

•www.synbiont.org Become a member and have access to $$$ forengaging in SB research. Contact me if interested

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Building Executable Biology Models for Synthetic Biology

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