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.


Outline
•Brief Introduction to Computational Modeling
•Modeling for Top Down SB
•Executable Biology
•Automated Model Synthesis and Optimisation
•Conclusions



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.


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


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



 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



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



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



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)



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)



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]


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



 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



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)


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



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



Outline

•Conclusions



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



Distributed and parallel rewritting systems in
compartmentalised hierarchical structures.

Objects

Compartments

Rewriting Rules

• Computational universality and efficiency.

• Modelling Framework



Stochastic P Systems



Rewriting Rules

used by Multi-volume Gillespie’s algorithm


Molecular Interactions
Inside Compartments



Passive Diffusion of Molecules



Signal Sensing and
Active Transport



Specification of Transcriptional
Regulatory Networks



Scalability through Modularity



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.



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.



 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



InfoBiotics
Pipeline



Quick Demo
 Simulator-results-rescaled.html
 Cie-model22-rescaled.html



Outline

•Conclusions



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


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



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


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.



Fitness Evaluation



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)


A Few Examples



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


Results Study Case 4



Target



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.



Outline

•Conclusions



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]



 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



 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.


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


Any Questions?

• www.infobiotic.org

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



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