Computational Synthetic Biology

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Natalio KrasnogorASAP - Interdisciplinary Optimisation LaboratorySchool of Computer Science

Centre for Integrative Systems BiologySchool of Biosciences

Centre for Healthcare Associated InfectionsInstitute of Infection, Immunity & Inflammation

University of Nottingham

Synthetic Biology

1

Copyright is held by the author/owner(s).GECCO’10, July 7–11, 2010, Portland, Oregon, USA.ACM 978-1-4503-0073-5/10/07.

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Outline

•Essential Systems Biology

•Synthetic Biology

•Computational Modeling for Synthetic Biology

•Automated Model Synthesis and Optimisation

•A Note on Ethical, Social and Legal Issues

•Conclusions2

L. Cronin, N. Krasnogor, B. G. Davis, C. Alexander, N. Robertson, J.H.G. Steinke, S.L.M. Schroeder, A.N. Khlobystov, G. Cooper, P. Gardner, P. Siepmann, and B. Whitaker. The imitation game - a computational chemical approach to recognizing life. Nature Biotechnology, 24:1203-1206, 2006.

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The Far Future

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Chells & The Cellularity Scale

http://localhost/~nxk/MOVIES/cellularityScale.htm

http://localhost/~nxk/MOVIES/cellularityScale.htm

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Outline






•Conclusions4

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Essential Systems Biology•The following slides are based on U. Alon’s papers & excellent introductory text book:

“An Introduction to Systems Biology: Design Principles of Biological Circuits”

•Also, you may want to check his group’s webpage for up-to-date papers/software:

http://www.weizmann.ac.il/mcb/UriAlon/

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The Cell as an Information Processing Device

LeDuc et al. Towards an in vivo biologically inspired nanofactory. Nature (2007)

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•The Cell senses the environment and its own internal states•Makes Plans, Takes Decisions and Act•Evolution is the master programmer

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The Cell as an Intelligent (Evolved) Machine

Cell

Internal States

Environmental Inputs

Actions

Amir Mitchell, et al., Adaptive prediction of environmental changes by microorganisms. Nature June 2009.

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•The Cell senses the environment and its own internal states•Makes Plans, Takes Decisions and Act•Evolution is the master programmer

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The Cell as an Intelligent (Evolved) Machine

Cell

Internal States

Environmental Inputs

Actions

Amir Mitchell, et al., Adaptive prediction of environmental changes by microorganisms. Nature June 2009.

Wikimedia Commons

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Transcription Networks

8

Gene1 Gene2 Gene3 GenekGenome

Transcription Factors

Signal2 Signal5Signal1 Signal3 Signal4 Signaln...Environment

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The Basic Unit: A Gene’s Transcription Regulation Mechanics

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Gene YDNA

Promoter

Gene Y

mRNAtranscription

translation

Protein Y

RNA Polymerase

Y

Gene YDNA

PromoterY

X binding site

X

Gene Y

mRNA+ transcription

+ translationProtein Y

Activator X

Si

Bound activator

YXSi

Bound activator

no transcription

Bound activator

Unbound repressor X

mRNAtranscription

translationProtein Y

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Network Motifs: Evolution’s Preferred Circuits•Biological networks are complex and vast•To understand their functionality in a scalable way one must choose the correct abstraction

•Moreover, these patterns are organised in non-trivial/non-random hierarchies

•Each network motif carries out a specific information-processing function

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“Patterns that occur in the real network significantly more often than in randomized networks are called network motifs” Shai S. Shen-Orr et al., Network motifs in the transcriptional regulation

network of Escherichia coli. Nature Genetics 31, 64 - 68 (2002)

Radu Dobrin et al., Aggregation of topological motifs in the Escherichia coli transcriptional regulatory network. BMC Bioinformatics. 2004; 5: 10.

x(t) = xste!!"t

dx

dt= ! ! " " x

t 12

=log 2!

xst =!

"

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Y positively regulates X

Negative autoregulation

Positive autoregulation

Negative autoregulation

Simple regulation

Positive autoregulation

U. Alon. Network motifs: theory and experimental approaches. Nature Reviews Genetics (2007) vol. 8 (6) pp. 450-461

Y

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Shai S. Shen-Orr et al., Network motifs in the transcriptional regulation network of Escherichia coli. Nature Genetics 31, 64 - 68 (2002)

A general transcription factor regulating a second TF, called specific TF, such that both regulate effector operon Z.

In a coherent FFL, the direct effect of the general transcription factor (X) has the same sign (+/-) than the indirect net effect through Y in the effector operon.

If the arrow from X to Z has different sign than the internal ones then the loop is an incoherent FFL

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most commonin E. Coli & S. Cerevisiae

The C1-FFL is a ‘sign-sensitive delay’ element and a persistence detector.

The I1-FFL is a pulse generator and response accelerator


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The C1-FFL is a ‘sign-sensitive delay’ element and a persistence detector.

If the integration function is “OR” (rather than “AND”), C1-FFL has now delay after stimulation by Sx but, instead, manifests the delay when the stimulation stops.


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The I1-FFL is a pulse generator and response accelerator


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SIM is defined by one TF controlling a set of operons, with the same signs and no additional control.

TFs in SIMs are mostly negative autoregulatory (70% in E. coli)


As the activity of the master regulator X changes in time, it crosses the different activation threshold of the genes in the SIM at different times, this prioritizing the activation of the operons

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DORs are layers of dense sets of TFs affecting multiple operons.

To understand the specific function of these “gate-arrays” one needs to know the input functions (AND/OR) for each output gene. This data is not currently available in most cases.

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•The correct abstract ions facilitates understanding in complex systems.

•Provide a route to engineering & programming cells.

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Outline





•Conclusions

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What is Synthetic Biology

Synthetic Biology is

A) the design and construction of new biological parts, devices, and systems, and

B) the re-design of existing, natural biological systems for useful purposes.

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http://syntheticbiology.org/

http://syntheticbiology.org


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What is Synthetic Biology

Synthetic Biology is

A) the design and construction of new biological parts, devices, and systems, and

B) the re-design of existing, natural biological systems for useful purposes.

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C) Through rigorous mathematical, computational & engineering routes



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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 readily used to build non-biological systems could also be use to specify, design, implement, verify, test and deploy novel synthetic biosystems. •These method come from computer science, engineering and maths.•Modeling and optimisation run through all of the above.

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Sys

tem

s B

iolo

gy

Syn

thet

ic B

iolo

gy

Basic goal: to clarify current understandings by formalising what the constitutive elements of a system are and how they interact

Intermediate goal: to test current understandings 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

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Synthetic & Systems Biology: Sisters Disciplines

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Top-Down Synthetic Biology: An Approach to Engineering Biology

Cells are information processors. DNA is their programming language. DNA sequencing and PCR: Identification and isolation of cellular parts.

Recombinant DNA and DNA synthesis : Combination of DNA and construction of new systems.

Tools to make biology easier to engineer: Standardisation, encapsulation and abstraction (blueprints).

E. coli

Vibrio fischeri

Pseudomonas aeruginosaDiscosoma sp.

Aequoreavictoria

25

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E. coli

Vibrio fischeri

Pseudomonas aeruginosaDiscosoma sp.

Aequoreavictoria

25

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E. coli

Vibrio fischeri

Pseudomonas aeruginosa

plasmids

Discosoma sp.

Aequoreavictoria

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E. coli

Vibrio fischeri


plasmids

DNA synthesis

Discosoma sp.

Aequoreavictoria

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E. coli

Vibrio fischeri


plasmids

DNA synthesis

Discosoma sp.

Aequoreavictoria

Chassis

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E. coli

Vibrio fischeri


plasmids

DNA synthesis

Discosoma sp.

Aequoreavictoria

Circuit BlueprintChassis

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Synthetic Biology’s Brick & Mortar (I)

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D. Sprinzak & M.B. Elowitz (2005). Reconstruction of genetic circuits, Nature 438:24, 443-448.

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Synthetic Biology’s Brick & Mortar (II)

D. Sprinzak & M.B. Elowitz (2005). Reconstruction of genetic circuits, Nature 438:24, 443-448.

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Example I: Elowitz & Leibler Represilator

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M.B. Elowitz & S. Leibler (2000). A Synthetic Oscilatory Network of Transcriptional Regulators. Nature, 403:20, 335-338

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An Example: Elowitz & Leibler Represilator

M.B. Elowitz & S. Leibler (2000). A Synthetic Oscillatory Network of Transcriptional Regulators. Nature, 403:20, 335-338

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Example II: Combinatorial Synthetic Logic

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C.C. Guet et al., Combinatorial Synthesis of Genetic Networks, Science 296, 1466-1470, 2002

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Example III: Push-on/Push-off circuit

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C. Lou et al., Synthesizing a novel genetic sequential logic circuit: a push-on push-off switch, Molecular Systems Biology 6; Article number 350

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Example III: Push-on/Push-off circuit

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C. Lou et al., Synthesizing a novel genetic sequential logic circuit: a push-on push-off switch, Molecular Systems Biology 6; Article number 350

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• Two different bacterial strains carrying specific synthetic gene regulatory networks are used.

• The first strain produces a diffusible signal AHL.

• The second strain possesses a synthetic gene regulatory network which produces a pulse of GFP after AHL sensing within a range of values (Band Pass).

Axample IV: Ron Weiss' Pulse Generator

S. Basu, R. Mehreja, et al. (2004) Spatiotemporal control of gene expression with pulse generating networks, PNAS, 101, 6355-6360

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Outline





•Conclusions

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What is modeling?

• Is an attempt at describing in a precise way an understanding of the elements of a system of interest, their states and interactions

• A model should be operational, i.e. it should be formal, detailed and “runnable” or “executable”.

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•“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

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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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Cells

Colonies

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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Modeling in Systems & Synthetic Biology

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There is potentially a distinction between modeling for Synthetic Biology vs Systems Biology:

•Systems Biology is concerned with Biology as it is•Synthetic Biology is concerned with Biology as it could be

“Our view of engineering biology focuses on the abstraction and standardization of biological components” by R. Rettberg @ MIT newsbite August 2006.

“Well-characterized components help lower the barriers to modeling. The use of control elements (such as temperature for a temperature-sensitive protein, or an exogenous small molecule affecting a reaction) helps model validation” by Di Ventura et al, Nature, 2006

Co-design of parts and their models hence improving and making both more reliable

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Model DevelopmentFrom [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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The Challenge of Scales

Within a cell the dissociation constants of DNA/ transcription factor binding to specific/non-specific sites differ by 4-6 orders of magnitude

DNA protein binding occurs at 1-10s time scale very fast in comparison to a cell’s life cycle.

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R. Milo, et al., BioNumbers—the database of key numbers in molecular and cell biology. Nucleic Acids

Research, 1–4 (2009) http://bionumbers.hms.harvard.edu/default.aspx

http://bionumbers.hms.harvard.edu/default.aspx

http://bionumbers.hms.harvard.edu/default.aspx

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Tem

pora

l sca

le (l

og)

Spatial scale (log)Couplings

The Scale Separation Map

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nm µm mm

fsps

nsµs

ms

ss*

103

bond vibration

energy transfer

micelle dynamics

vesicle dynamicsdiff. small mol

transcription translation

QS mol diff in colony

DPD

mcssa

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The Challenge of Small Numbers in Cellular Systems

Most commonly recognised sources of noise in cellular system are low number of molecules and slow molecular interactions.

Over 80% of genes in E. coli express fewer than a hundred proteins per cell.

Mesoscopic, discrete and stochastic approaches are more suitable: Only relevant molecules are taken into account. Focus on the statistics of the molecular interactions and how often they

take place.

Mads Karn et al. Stochasticity in Gene Expression: From Theories to Phenotypes. Nature Reviews, 6, 451-464 (2005)

Purnananda Guptasarma. Does replication-induced transcription regulate synthesis of the myriad low copy number poteins of E. Coli. BioEssays, 17, 11, 987-997

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Modelling Approaches

There exist many modeling 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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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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A. Regev, E. Shapiro. The π-calculus as an abstraction for biomolecular systems. Modelling in Molecular Biology., pages 1–50. Springer Berlin., 2004.

D. Harel, "A Grand Challenge for Computing: Full Reactive Modeling of a Multi-Cellular Animal", Bulletin of the EATCS , European Association for Theoretical Computer Science, no. 81, 2003, pp. 226-235

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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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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

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Computer Science ContributionsMethodologies designed to cope with: • Languages to cope with complex, concurrent, systems of parts:

• ∏-calculus• Process Calculi• P Systems

• Tools to analyse and optimise:• EA, ML• Model Checking

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J.Twycross, L.R. Band, M. J. Bennett, J.R. King, and N. Krasnogor. Stochastic and deterministic multiscale models for systems biology: an auxin-transport case study. BMC Systems Biology, 4(:34), March 2010

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InfoBiotics Workbench and Dashboardwww.infobiotics.net

http://www.infobiotics.net


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InfoBiotics Workbench and Dashboard

Spec

ifica

tion www.infobiotics.net



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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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Functional EntitiesContainer

• 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

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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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Cell-like P systems

the classic P system diagram appearing in most papers (Păun)

Intuitive Visual representation as a Venn diagram with a unique superset and without intersected sets.

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Cell-like P systems

1

23

4

75

8

6

9

formally equivalent to a tree:



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Cell-like P systems

• a string of matching parentheses: [1 [2 ]2 [3 ]3 [4 [5 ]5 [6 [8 ]8 [9 ]9 ]6

[7 ]7 ]4 ]1

1

23

4

75

8

6

9

formally equivalent to a tree:



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P-Systems: Modelling PrinciplesMoleculesStructured Molecules

ObjectsStrings

Molecular Species Multisets of objects/strings

Membranes/organelles Membrane

Biochemical activity rules

Biochemical transport Communication rules

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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 Species A molecular species can be represented using

individual objects.

A molecular species with relevant internal structure can be represented using a string.

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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

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Compartments / Cells Compartments and regions are explicitly

specified using membrane structures.

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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

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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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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.

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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.

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Basic P System Modules Used

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Characterisation/Encapsulation of Cellular Parts: Gene Promoters

A modeling language for the design of synthetic bacterial colonies.

A module, set of rules describing the molecular interactions involving a cellular part, provides encapsulation and abstraction. Collection or libraries of reusable cellular parts and reusable models.

E. Davidson (2006) The Regulatory Genome, Gene Regulation Networks in Development and Evolution, Elsevier

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01101110100001010100011110001011101010100011010100


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LuxRAHL

CI


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LuxRAHL

CI

PluxOR1({X},{c1, c2, c3, c4, c5, c6, c7, c8, c9},{l}) = {

type: promoter

sequence: ACCTGTAGGATCGTACAGGTTTACGCAAGAA ATGGTTTGTATAGTCGAATACCTCTGGCGGTGATA

rules: r1: [ LuxR2 + PluxPR.X ]_l -c1-> [ PluxPR.LuxR2.X ]_l r2: [ PluxPR.LuxR2.X ]_l -c2-> [ LuxR2 + PluxPR.X ]_l ... r5: [ CI2 + PluxPR.X ]_l -c5-> [ PluxPR.CI2.X ]_l r6: [ PluxPR.CI2.X ]_l -c6-> [ CI2 + PluxPR.X ]_l ... r9: [ PluxPR.LuxR2.X ]_l -c9-> [ PluxPR.LuxR2.X + RNAP.X ]_l}


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Module Variables: Recombinant DNA, Directed Evolution, Chassis

selection

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selection Recombinant DNA: Objects variables can be instantiated with the name of specific

genes.

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genes.

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genes.

PluxOR1({X=tetR})

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genes.

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genes.

PluxOR1({X=GFP})

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genes.

PluxOR1({X=GFP})

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selection

Directed evolution: Variables for stochastic constants can be instantiated with specific values.

Recombinant DNA: Objects variables can be instantiated with the name of specific genes.

PluxOR1({X=GFP})

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selection

A



PluxOR1({X=GFP})

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selection

A



PluxOR1({X=GFP})

PluxOR1({X=GFP},{...,c4=10,...})

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selection

A



PluxOR1({X=GFP})

PluxOR1({X=GFP},{...,c4=10,...}) Chassis Selection: The variable for the label can be instantiated with the name of a

chassis.

PluxOR1({X=GFP},{...,c4=10,...},{l=DH5α })

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Characterisation/Encapsulation of Cellular Parts: Riboswitches

A riboswitch is a piece of RNA that folds in different ways depending on the presence of absence of specific molecules regulating translation.

ToppRibo({X},{c1, c2, c3, c4, c5, c6},{l}) = {

type: riboswitch

sequence:GGTGATACCAGCATCGTCTTGATGCCCTTGG CAGCACCCCGCTGCAAGACAACAAGATG rules: r1: [ RNAP.ToppRibo.X ]_l -c1-> [ ToppRibo.X ]_l r2: [ ToppRibo.X ]_l -c2-> [ ]_l r3: [ ToppRibo.X + theop ]_l –c3-> [ ToppRibo*.X ]_l r4: [ ToppRibo*.X ]_l –c4-> [ ToppRibo.X + theop ]_l r5: [ ToppRibo*.X ]_l –c5-> [ ]_l r6: [ ToppRibo*.X ]_l –c6-> [ToppRibo*.X + Rib.X ]_l}

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Characterisation/Encapsulation of Cellular Parts: Degradation Tags

Degradation tags are amino acid sequences recognised by proteases. Once the corresponding DNA sequence is fused to a gene the half life of the protein is reduced considerably.

degLVA({X},{c1, c2},{l}) = {

type: degradation tag

sequence: CAGCAAACGACGAAAACTACGCTTTAGTAGCT

rules: r1: [ Rib.X.degLVA ]_l -c1-> [ X.degLVA ]_l r2: [ X.degLVA ]_l -c2-> [ ]_l}

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Higher Order Modules: Building Synthetic Gene Circuits

PluxOR1 geneXToppRibo degLVA

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3OC6_repressible_sensor({X}) = {

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3OC6_repressible_sensor({X}) = { PluxOR1({X=ToppRibo.geneX.degLVA},{...},{l=DH5α})

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3OC6_repressible_sensor({X}) = { PluxOR1({X=ToppRibo.geneX.degLVA},{...},{l=DH5α}) ToppRibo({X=geneX.degLVA},{...},{l=DH5α})

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3OC6_repressible_sensor({X}) = { PluxOR1({X=ToppRibo.geneX.degLVA},{...},{l=DH5α}) ToppRibo({X=geneX.degLVA},{...},{l=DH5α}) degLVA({X},{...},{l=DH5α})

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3OC6_repressible_sensor({X}) = { PluxOR1({X=ToppRibo.geneX.degLVA},{...},{l=DH5α}) ToppRibo({X=geneX.degLVA},{...},{l=DH5α}) degLVA({X},{...},{l=DH5α})}

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X=GFP

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X=GFP

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X=GFP

Plux({X=ToppRibo.geneCI.degLVA},{...},{l=DH5α}) ToppRibo({X=geneCI.degLVA},{...},{l=DH5α}) degLVA({CI},{...},{l=DH5α})

PtetR({X=ToppRibo.geneLuxR.degLVA},{...},{l=DH5α}) Weiss_RBS({X=LuxR},{...},{l=DH5α}) Deg({X=LuxR},{...},{l=DH5α})

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luxIPconst

LuxI AHL

AHL

luxRPconst

cIPlux

gfpPluxOR1

LuxR

CI

GFPAHL

AHLSpecification of Multi-cellular

Systems: LPP systems

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Infobiotics: An Integrated Frameworkhttp://www.infobiotics.org/infobiotics-workbench/

Cellular Parts

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Libraries of Modules

Cellular Parts

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Cellular Parts

Module Combinations

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Cellular Parts

Synthetic Circuits

Module Combinations

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Cellular Parts

Synthetic Circuits

Module Combinations

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P systems

Cellular Parts

Synthetic Circuits

Module Combinations

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P systems

Single Cells

Cellular Parts

Synthetic Circuits

Module Combinations

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P systems

Single Cells

Cellular Parts

Synthetic Circuits

Module Combinations

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P systems LPP systems

Single Cells

Cellular Parts

Synthetic Circuits

Module Combinations

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Synthetic Multi-cellular Systems



Single Cells

Cellular Parts

Synthetic Circuits

Module Combinations

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Single Cells

Cellular Parts

Synthetic Circuits

Module Combinations

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A compiler based on a BNF grammar

Single Cells

Cellular Parts

Synthetic Circuits

Module Combinations

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Multi Compartmental Stochastic Simulations

based on Gillespie’s algorithm


Single Cells

Cellular Parts

Synthetic Circuits

Module Combinations

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Spatio-temporal Dynamics Analysis

using Model Checking with PRISM and MC2


Single Cells

Cellular Parts

Synthetic Circuits

Module Combinations

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Spatio-temporal Dynamics Analysis

using Model Checking with PRISM and MC2

Automatic Design of Synthetic Gene

Regulatory Circuits using Evolutionary Algorithms


Single Cells

Cellular Parts

Synthetic Circuits

Module Combinations

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Stochastic P Systems Are Executable Programs

The virtual machine running these programs is a “Gillespie Algorithm (SSA)”. It generates trajectories of a stochastic syste:

A stochastic constant is associated with each rule.A propensity is computed for each rule by multiplying the stochastic constant by the number of distinct possible combinations of the elements on the left hand side of the rule.

F. J. Romero-Campero, J. Twycross, M. Camara, M. Bennett, M. Gheorghe, and N. Krasnogor. Modular assembly of cell systems biology models using p systems. International Journal of Foundations of Computer Science, 2009

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Multicompartmental Gillespie Algorithm

1

2

3 r11,…,r1

n1

M1

r21,…,r2

n2

M2

r31,…,r3

n3

M3

( 1, τ1, r01)

( 2, τ2, r02)

( 3, τ3, r03)

( 2, τ2, r02)

( 1, τ1, r01)

( 3, τ3, r03)

Sort Compartments τ2 < τ1 < τ3

Local Gillespie

( 1, τ1-τ2, r01)

( 3, τ3-τ2, r03)

Update Waiting Times

( 2, τ2’, r02)( 1, τ1-τ2, r0

1)

( 2, τ2’, r02)

( 3, τ3-τ2, r03)

Insert new triplet τ1-τ2 <τ2’ < τ3-τ2

‘

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An Important Difference with “Normal” Programs

•Executable Stochastic P systems are not programs with stochastic behavior

•A cell is a living example of distributed stochastic computing.

81

function f1(p1,p2,p3,p4){if (p1<p2) RND print p3 RNDelse RND print p4 RND}

function f1(p1,p2,p3,p4){if (p1<p2) and (rand<0.5) print p3else print p4}

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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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RapidModelPrototyping

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• Two different bacterial strains carrying specific synthetic gene regulatory networks are used.

• The first strain produces a diffusible signal AHL.

• The second strain possesses a synthetic gene regulatory network which produces a pulse of GFP after AHL sensing within a range of values (Band Pass).

An example: A Pulse Generator

S. Basu, R. Mehreja, et al. (2004) Spatiotemporal control of gene expression with pulse generating networks, PNAS, 101, 6355-6360

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Sender Cells

Pconst

LuxI AHL

AHL

SenderCell()=

{

Pconst({X = luxI },…)

PostTransc({X=LuxI},{c1=3.2,…})

Diff({X=AHL},{c=0.1})

}

luxI

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luxRPconst

cIPlux

gfpPluxOR1

LuxR

CI

GFPAHL

AHL

PulseGenerator()=

{

Pconst({X=luxR},…)

PluxOR1({X=gfp},…)

Plux({X=cI},…)

…

…

Diff({X=AHL},…)

}

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Pulse Generating Cells

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Spatial Distribution of Senders and Pulse Generators

luxIPconst

LuxI AHL

AHL

AHL

87

luxRPconst

cIPlux

gfpPluxOR1

LuxR

CI

GFPAHL

AHL

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Pulse propagation - simulation I

88

Simulation I

http://localhost/~nxk/MOVIES/pulseGenerator2.htm


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Pulse Generating Cells With Relay

luxRPconst

cIPlux

PluxOR1

LuxR

CI

AHL

AHL

luxIPlux

LuxI

AHL

PulseGenerator(X ) =

{ Pconst({X=luxR},…)

PluxOR1({X},…) ,

Plux({X=cI},…) ,

…

Diff({X=AHL},…) ,

Plux({X=luxI},…)

}89

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Simulation II

Pulse propagation & Rely- simulation II



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A Signal Translatorfor Pattern Formation

act1Prep2

act2Prep1

rep1Pact1

rep2Pact2

rep3Prep1

rep4Prep2

I2Prep3

I1Prep4

FP2Pact2

FP1Pact1

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Uniform Spatial Distribution of Signal Translators for Pattern Formation

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Pattern Formation in synthetic bacterial colonies

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Simulation III

http://localhost/~nxk/MOVIES/patternFormation.htm

http://localhost/~nxk/MOVIES/patternFormation.htm

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Pattern Formation in synthetic bacterial colonies

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Spatial Distribution of Signal Translators and propagators

luxRPconst

cIPlux

gfpPluxOR1

LuxR GFPSi

Si

luxI

LuxI

Si

Plux

95

CI

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Alternating signal pulses in synthetic bacterial colonies

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Simulation IV

http://localhost/~nxk/MOVIES/alternatingPulses.htm

http://localhost/~nxk/MOVIES/alternatingPulses.htm

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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


•Conclusions97


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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 iterated99

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• 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.

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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

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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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• 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

Evolving Executable Biology 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.

H. Cao, F.J. Romero-Campero, S. Heeb, M. Camara, and N. Krasnogor. Evolving cell models for systems and synthetic biology. Systems and Synthetic Biology , 2009

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Fitness Evaluation

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Representation

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Structural Operators

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Structural Operators

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Parameter Optimisation

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Parameter Optimisation

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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

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Case 1: Molecular ComplexationTarget P System Model Structure

Target P System Model Parameters

c1

c2

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Case 1: Experimental Results

50/50 runs found the same P system model structure as the target one

Target and Evolved Time Series 114

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50/50 runs found the same P system model structure as the target one

Best Model Parameters

Target Model Parameters

Target and Evolved Time Series 115

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Case 2: Enzymatic ReactionTarget P System Model Structure

Target P System Model Parameters

c1

c2

c3

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Case 2: Experimental ResultsTarget and Evolved Time Series

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Best Model Parameters

Target Model Parameters Target and Evolved Time Series

Two Approaches Using Basic Modules

Adding the Newly Found in Case 1

Number of runs the target P system model was

found

12/50 39/50

Mean RMSE 3.8 2.85118

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Case 3.1: Negative AutoregulationTarget P System Model Structure Target P System Model Parameters

c1

c2

c3

c4

c5

c6

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Case 3.1: Experimental Results

Best Model in Design 1

Design Design 1 Design 2

Model

Number of runs 46/50 4/50

Mean +/- STD 4.11+/- 11.73 4.63 +/- 2.91


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Case 3.2: Positive AutoregulationTarget P System Model Structure Target P System Model Parameters

c1

c2

c3

c4

c5

c6

c7

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Case 3.2: Experimental ResultsDesign Design 1 Design 2

Model

Number of runs 30/50 20/50

Mean +/- STD 16.36 +/- 3.03 20.14 +/- 5.13


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The Fitness Function• Multiple time-series per target

• Different time series have very different profiles, e.g., response time or maxima occur at different times/places

• Transient states (sometimes) as important as steady states

•RMSE will mislead search

H. Cao, F. Romero-Campero, M.Camara, N.Krasnogor (2009). Analysis of Alternative Fitness Methods for the Evolutionary Synthesis of Cell Systems Biology Models.

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Four Fitness FunctionsN time seriesM time points in each time series Simulated data Target data

F1

F2

F2 normalises whithin a time series between [0,1]

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Four Fitness FunctionsN time seriesM 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.

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Four Fitness FunctionsN time seriesM 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.

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Problem Specification

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Case Studies

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Results Study Case 1

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Alternative, biologically valid, modules

Target model

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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)

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Target

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Target

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The evolving curve of average fitness by four fitness methods (F1 ∼ F4) in 20 runs for Test Case 4.

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The evolving curve of average model diversity by four fitness methods (F1 ∼ F4) in 20 runs for Test Case 4

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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.

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• 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

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Initial Conditions

PDE model

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Target

By Evolving:abcd, acad, Dbcd/Dcad, r, mRNA distributions and t

However: Goal is not perfect fit but rather robust fit

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• 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

Bi-Objective Function

Objective Function

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Parameter Optimisation in Metabolic Models

161

A. Drager et al. (2009). Modeling metabolic networks in C. glutamicum: a comparison of rate laws in combination with various parameter optimization strategies. BMC Systems Biol ogy 2009, 3:5

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Outline






•Conclusions162

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Synthetic BiologyThe new science of synthetic biology aims to re-engineer life at the molecular level and even create completely new forms of life. It has the potential to create new medicines, biofuels, assist climate change through carbon capture, and develop solutions to help clean up the environment.

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What is Synthetic Biology?Synthetic Biology isA) the design and construction of new biological parts, devices, and systems, andB) the re-design of existing, natural biological systems for useful purposes.


164



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Been There, Done That

165

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The Unjustified Fear of Chimera and Foreign Genes

TransplantationItaya, M., Tsuge, K. Koizumi, M., and Fujita, K. Combining two genomes in one C e l l : S t a b l e c l o n i n g o f t h e Synechosystis PCC6803 genome in the Bacillus subtilis 168 genome.Proc. Natl. Acad. Sci., USA, 102, 15971-15976 (2005)

+ =

150 times larger than the human genome

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A Technology Not a Product“ The problem of deaths and injury as a result of road accidents is now acknowledge to be a global phenomenon.... publications show that in 1990 road accidents as a cause of death or dissability were in ninth place out of a total of over 100 identified causes.... by 2020 forecasts suggest... road accidents will move up to sixth...”

Estimating global road fatalities. G. Jacobs & A. Aeron-Thomas. Global Road Safety Partnership

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A Technology Not a Product“ The problem of deaths and injury as a result of road accidents is now acknowledge to be a global phenomenon.... publications show that in 1990 road accidents as a cause of death or dissability were in ninth place out of a total of over 100 identified causes.... by 2020 forecasts suggest... road accidents will move up to sixth...”

And yet nobody seriously considers banning mechanical engineering

Estimating global road fatalities. G. Jacobs & A. Aeron-Thomas. Global Road Safety Partnership

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A Technology Not a Product

168

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A Technology Not a Product

And yet nobody seriously considers banning printing technology

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But ....•Technologies are regulated:

•Cars have seat belts and laws establish speed limits•Main Kampf is banned in Germany and by, e.g., restricting google, China bans uncountable written material of all sorts!

•Societies must establish an informed dialogue involving:•tax payers•Scientists•Lobbies of all sorts•Government

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What IS Synthetic Biology?Synthetic Biology isA) the design and construction of new biological parts, devices, and systems, andB) the re-design of existing, natural biological systems for useful purposes.


C) Through rigorous mathematical, computational engineering routes

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Biology only smarter, safer and clearer

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Outline






•Conclusions172

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Summary & Conclusions•This talk has focused on an integrative methodology for Systems & Synthetic Biology

•Executable Biology•Parameter and Model Structure Discovery

•Computational models (or executable in Fisher & Henzinger’s jargon) adhere to (a degree) to an operational semantics.

•Refer to the excellent review [Fisher & Henzinger, Nature Biotechnology, 2007]

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•The gap present in mathematical models between the model and its algorithmic implementation disappears in computational models as all of them are algorithms.•A new gap appears between the biology and the modeling technique and this can be solved by a judicious “feature selection”, i.e. the selection of the correct abstractions•Good computational models are more intuitive and analysable

Summary & Conclusions

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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 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


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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.


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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, 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.


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Summary & Conclusions•Some really nice tutorials and other sources:

•Luca Caderlli’s BraneCalculus & BioAmbients•Simulating Biological Systems in the Stochastic π−calculus by Phillips and Cardelli•From Pathway Databases to Network Models by Aguda and Goryachev•Modeling and analysis of biological processes by Brane Calculi and Membrane Systems by Busi and Zandron•D. Gilbert’s website contain several nice papers with related methods and tutorials

178

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Other SourcesF. 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.

179

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Other SourcesPăun, 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.

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http://psystems.disco.unimib.it/

http://psystems.disco.unimib.it/

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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

Other Sources

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Acknowledgements

•Jonathan Blake

•Claudio Lima

•Francisco Romero-Campero

•Karima Righetti

•Jamie Twycross

Integrated Environment

Machine Learning & Optimisation

Modeling & Model Checking

Molecular Micro-Biology

Stochastic Simulations

Members of my team working on SB2

EP/E017215/1

EP/H024905/1

BB/F01855X/1

BB/D019613/1

University of NottinghamProf. M. Camara, Dr. S. Heeb, Dr. G. Rampioni, Prof. P. WilliamsWeizmann Institute of ScienceProf. D. Lancet, Prof. I. Pilpel

GECCO 2010 organisers for inviting this tutorial.

You for listening!

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