Towards a Rapid Model Prototyping Strategy for Systems & Synthetic Biology

Towards a Rapid Model Prototyping
Strategy for Systems & 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

BEACON, Michigan State University, July 2010 1 /82

Outline
•Conceptual Viewpoint & CAD tools
•Model Specification Framework
•Examples of Modeling for Top Down SB
• In Silico Model Evolution
•Conclusions


A (Proto)Cell as an Information
Processing Device

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


Functional Entities
Container
• A boundary defining self/non-self (symmetry breaking).
• Maintain concentration gradients and avoid environmental damage.

Metabolism

• Energy utilisation
• Confining raw materials to be processed.
• Maintenance of internal structures (autopoiesis).

Information

• Sensing environmental signals / release of signals.
• Genetic information


The Cell as an Intelligent (Evolved)
Machine
•The Cell senses the environment and its own
internal states
•Makes Plans, Takes Decisions and Acts
•Evolution is the master programmer
Environmental Inputs

Internal States

Cell
Actions

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

The Cell as an Intelligent (Evolved)
Machine
•The Cell senses the environment and its own
internal states
•Makes Plans, Takes Decisions and Acts
•Evolution is the master programmer
Environmental Inputs

Internal States

Cell
Actions

Wikimedia Commons

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

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

“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)

•Moreover, these patterns are organised in non-trivial/non-
random hierarchies Radu Dobrin et al., Aggregation of topological motifs in the Escherichia
coli transcriptional regulatory network. BMC Bioinformatics. 2004; 5: 10.

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


Y positively Negative Positive
regulates X autoregulation autoregulation

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

The C1-FFL is a ‘sign-sensitive delay’ element and a persistence detector.
The I1-FFL is a pulse generator and response accelerator
U. Alon. Network motifs: theory and experimental approaches. Nature Reviews Genetics (2007) vol. 8 (6) pp. 450-461

•Cells (and most
biologists) don’t do
differential calculus!
•P systems are a
executable specifications
that closely mimic
biological reality.
•These are programs
that explicitly mimic the
internal behavior of cell
systems.
•These programs are
executed in a virtual
machine that captures
the intrinsic
stochasticity inherent in
biology

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)
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

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

Jasmin Fisher and Thomas Henzinger. Executable cell biology. Nature Biotechnology, 25, 11, 1239-1249
9 /82
(2008)
BEACON, Michigan State University, July 2010

Properties of Petri Net Models
•A readable language but still a formal language with unambiguous
semantics
•Close to graphical depictions of metabolic networks normally used
by biologists

Erythrocyte cell’s
glycolytic and pentose
phospate pathway from
[Reddy et al., Comput.
Biol. Med., 1996]


Some Limitations
•Not designed to handle spatial events or
spatial processes such as diffusion, mechanical
transduction, Brownian motion, collisions,
transport, etc.
•Stochasticity is loaded onto W, i.e. it is not a
property of rules or interactions
•Although very elegant computing invariants and
other properties can really only be done for small
systems.
•Although due to composability, to certain extent,
you can “divide & conquer”


Elementary Concepts in π-calculus
•A program specifies a network of interacting
processes
•Processes are defined by their potential
communication activities
•Communication occurs on complementary
channels, identified by names
•Message content: Channel name
Biological reactions are thus
abstracted as communications.

π-calculus
syntax
Process expressions
P ::= 0 a null process
π.P
P+P mutually exclusive processes
P|P concurrent processes
(νx)P new channel x in P
!P create a copy of P

Action prefixes
π ::= x? (y) receive y along x
x! <y> send y along x
τ
unobservable (internal) action

π-calculus
syntax
Process expressions a molecule / domain
π.P
(νx)P new channel x in P

Action prefixes
τ

π-calculus
syntax
π.P
A set of
(νx)P new channel x in P molecules /
domains

Action prefixes
τ

π-calculus
syntax
π.P
A set of
(νx)P new channel x in P molecules /
domains

Action prefixes
π ::= x? (y) receive y along x a capability to do
x! <y> send y along x something
(e.g. phosporilate,
τ
cleave, etc)


π-calculus
syntax
π.P
This process
(enzyme)
knows how to P|P concurrent processes
do action π A set of
(e.g. ligate) (νx)P new channel x in P molecules /
domains

Action prefixes
π ::= x? (y) receive y along x a capability to do
x! <y> send y along x something
(e.g. phosporilate,
τ
cleave, etc)


Some Limitations
•Non-mobile/mobile approaches offer a tradeoff
between relevance and utility.
•Computational problem: each molecule is an
instance of a process.
•Some abstraction are obscure:
•private channels for compartments is cumbersome,
requires multi-step encoding of the formation of
complexes, not really a nature encoding of proximity
and biochemical complementarity.
•Communication always involves exactly two
processes.


Biochemical Process Calculi
based on π-calculus

 π-calculus (Stochastic, Causal)
 BioAmbients (mobile compartments)
 Brane calculi (membranes, Projective)
 Beta Binders (dynamic compartments)

increasing emphasis
on intracellular organisation


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

BU/TD Synthetic & Systems Biologist

Computer Scientist/Engineer

Mathematician

C, cost of modelling


InfoBiotics Workbench and Dashboard www.infobiotics.net
Management

Specification

Optimisation
Simulation

Verification
Analysis


Outline
•Conclusions


InfoBiotics Workbench and Dashboard



Specification


Model Specification 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
G. Paun. Computing with membranes. Journal of Computer and System Sciences, 61(1):108–143, 2000

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, 20(3):427-442, 2009

Distributed and parallel rewritting systems in
What is a P Systems? compartmentalised hierarchical structures.

Objects

Compartments

Rewriting Rules

• Computational universality and efficiency.

• Modelling Framework

P-Systems’ Principal Entities
Molecules (ATP, ADP, Letters (objects) from an
OHL, etc) alphabet
Structured Molecules Strings in the alphabet
(DNA, RNA,Proteins,
etc)
Molecules in “bulk” Multisets of objects/strings
Membranes/ Membrane
organelles
Biochemical activity Rules
Biochemical transport Communication rules

Rewriting Rules

used by Multi-volume Gillespie’s algorithm


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


Compartments / Cells
 Compartments and regions are explicitly
specified using membrane structures.


Colonies / Tissues
 Colonies and tissues are representing as a
collection of P systems distributed over a lattice.

 Objects can travel around the lattice through
translocation rules.

v


Molecular Interactions
Inside Compartments


Passive Diffusion of Molecules


Specification of Transcriptional
Regulatory Networks


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 may also have (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.


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 modules of
rules representing molecular interactions
(network) motifs that appear in many cellular
systems.


Basic P System Modules Used


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

BEACON, Michigan State University, July 2010 33 /82 21

bacterial colonies.
01101110100001010100011110001011101010100011010100

encapsulation and
abstraction.

reusable models.

Evolution, Elsevier


AHL
LuxR CI
bacterial colonies.

encapsulation and
abstraction.

reusable models.

Evolution, Elsevier


AHL
LuxR CI
bacterial colonies.
PluxOR1({X},{c1, c2, c3, c4, c5, c6, c7, c8, c9},{l}) = {
 A module, set of rules type: promoter
sequence: ACCTGTAGGATCGTACAGGTTTACGCAAGAA
ATGGTTTGTATAGTCGAATACCTCTGGCGGTGATA
encapsulation and rules:
abstraction. r1: [ LuxR2 + PluxPR.X ]_l -c1-> [ PluxPR.LuxR2.X ]_l
r2: [ PluxPR.LuxR2.X ]_l -c2-> [ LuxR2 + PluxPR.X ]_l
...
 Collection or libraries of r5: [ CI2 + PluxPR.X ]_l -c5-> [ PluxPR.CI2.X ]_l
reusable cellular parts and r6: [ PluxPR.CI2.X ]_l -c6-> [ CI2 + PluxPR.X ]_l
reusable models. ...
r9: [ PluxPR.LuxR2.X ]_l -c9-> [ PluxPR.LuxR2.X + RNAP.X ]_l
}
Evolution, Elsevier


Module Variables: Recombinant DNA, Directed
Evolution, Chassis selection


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


genes.

PluxOR1({X=tetR})


genes.

PluxOR1({X=GFP})


genes.

PluxOR1({X=GFP})
 Directed evolution: Variables for stochastic constants can be instantiated with specific
values.


genes.

PluxOR1({X=GFP})
values.

A


genes.

PluxOR1({X=GFP})
values.

A

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


genes.

PluxOR1({X=GFP})
values.

A

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 })


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
}


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
}


Higher Order Modules: Building Synthetic
Gene Circuits
PluxOR1 ToppRibo geneX degLVA


Gene Circuits

3OC6_repressible_sensor({X}) = {


Gene Circuits

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


Gene Circuits

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


Gene Circuits

degLVA({X},{...},{l=DH5α})


Gene Circuits

}


Gene Circuits

ToppRibo({X=geneX.degLVA},{...},{l=DH5α}) X=GFP
}


Gene Circuits

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


AHL
Specification of Multi-cellular
Systems: LPP systems
AHL
GFP AHL
LuxR

Pconst PluxOR1
luxR gfp LuxI AHL

CI Pconst luxI
Plux
cI


Infobiotics: An Integrated Framework
http://www.infobiotics.org/infobiotics-workbench/

Cellular Synthetic Single Synthetic Multi-
Parts Circuits Cells cellular Systems

Libraries of Module P systems LPP systems
Modules Combinations

A compiler based on
a BNF grammar

Multi Compartmental Spatio-temporal Automatic Design of
Stochastic Simulations Dynamics Analysis Synthetic Gene
based on Gillespie’s using Model Checking Regulatory Circuits using
algorithm with PRISM and MC2 Evolutionary Algorithms


Stochastic P Systems Are
Executable Programs

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

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


•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, N.
Krasnogor. Modular Assembly of Cell Systems Biology Models Using P Systems.
Internatnional Journal of Foundations of Computer Science, 20(3):427-442,
2009.

Degano P., Prandi D., Priami C., and Quaglia P. Beta-binders for biological quantitative experiments.
ENTCS, 164:101-117, 2006. Proc. 4th Workshop on Quantitative Aspects of Programming
Languages, QAPL 2006.


Outline
•Conclusions



Specification



Specification
Simulation

Analysis


An example: Ron Weiss' Pulse Generator

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

 The first strain produces a diffusible signal AHL.

 The second strain possesses a synthetic gene regulatory
network which produces a pulse of GFP after AHL sensing.

 These two bacterial strains and their respective synthetic
networks are modelled as a combination of modules.

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


An example: Ron Weiss' Pulse Generator

Pulse Generator
Signal Sender
AHL
AHL
AHL GFP
LuxR
PluxOR
LuxI AHL Pconst gfp
luxR
Pconst
luxI
Plux
cI CI
Pconst({X = luxI },…)
PostTransc({X=LuxI},{c1=3.2,…}) Pconst({X=luxR},…)
Diff({X=AHL},{c=0.1}) PluxOR1({X=gfp},…)
Plux({X=cI},…) …


AHL
Spatial Distribution of Senders
and Pulse Generators
AHL
GFP AHL
LuxR

Pconst PluxOR1
luxR gfp LuxI AHL

Pconst luxI
CI
Plux
cI


Pulse propagation - simulation I

Simulation I


Pulse Generating Cells
With Relay
AHL

AHL
LuxR GFP

PluxOR1
Pconst gfp
luxR

CI

Plux
cI


With Relay
AHL

AHL
LuxR GFP

PluxOR1
Pconst gfp
luxR

Plux CI
luxI

LuxI
Plux
cI

AHL


With Relay
AHL

AHL Pconst({X=luxR},…)
LuxR GFP

PluxOR1
Pconst gfp
luxR

Plux CI
luxI

LuxI
Plux
cI

AHL


With Relay
AHL

LuxR GFP
PluxOR1({X=gfp},…)
PluxOR1
Pconst gfp
luxR

Plux CI
luxI

LuxI
Plux
cI

AHL


With Relay
AHL

LuxR GFP
PluxOR1 Plux({X=cI},…)
Pconst gfp
luxR

Plux CI
luxI

LuxI
Plux
cI

AHL


With Relay
AHL

LuxR GFP
Pconst gfp
luxR
…

Plux CI
luxI

LuxI
Plux
cI

AHL


With Relay
AHL

LuxR GFP
Pconst gfp
luxR
…
…
Plux CI
luxI

LuxI
Plux
cI

AHL


With Relay
AHL

LuxR GFP
Pconst gfp
luxR
…
…
Plux CI
luxI Diff({X=AHL},…)

LuxI
Plux
cI

AHL


With Relay
AHL

LuxR GFP
Pconst gfp
luxR
…
…
Plux CI
luxI Diff({X=AHL},…)

LuxI
Plux Plux({X=luxI},…)
cI

AHL


Pulse propagation & Rely-
simulation II

Simulation II


A Signal Translator
for Pattern Formation

Pact1
FP1

Prep2 Pact1 Prep1 Prep3
act1 rep1 rep3 I2

Prep1 Pact2 Prep2 Prep4
act2 rep2 rep4 I1
Pact2
FP2


Uniform Spatial Distribution of
Signal Translators for Pattern Formation


Pattern Formation in
synthetic bacterial colonies

Simulation III


Si
Spatial Distribution of Signal
Translators and propagators
Si
LuxR GFP

Pconst PluxOR1
luxR gfp

Plux CI
luxI
Plux
cI
LuxI

Si


Alternating signal pulses in
synthetic bacterial colonies

Simulation IV


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


Multi-Objective Optimisation in
Morphogenesis

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


Parameter Optimisation in
Metabolic Models

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


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
evolvability of models/network motifs

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.)

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, 4(1), 2010.

C. Garcia-Martinez, C. Lima, J.
Twycross, M. Lozano, N.
Krasnogor. P System Model
Optimisation by Means of
Evolutionary Based Search
Algorithms. Proceedings of the
2010 Genetic and Evolutionary
Computation Conference
(GECCO-2010). (Nominated for
best paper award at the
Bioinformatics track.)

Fitness Evaluation


The 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 not the only
possibility

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, 4(1), 2010.

Some Case Studies


Problem Specification


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).

Alternative models can be formally analysed and compared
based on other than “fitness” criteria:

★Sensitivity analysis / Robustness
★Average properties (model checking)
★Parsimony
Comparing results by only using elementary modules or by
adding newly found modules to the library shows the obvious
advantage of the incremental methodology with modules.


Outline
•Conclusions


Management

Specification

Optimisation
Simulation

Verification
Analysis

BEACON, Michigan State University, July InfoBiotics Workbench
2010 75 /82 and Dashboard

TAKE HOME MESSAGE

• P systems are a handy way of specifying discrete and stochastic
rule-based compartmental models.

• Modularity in P systems as a design principle for synthetic
networks that enables reusability, hierarchical abstraction and
standardisation.

• Can be linked to DPD-type simulations.
• Model Checking for the formal analysis of our models.
• Automated explorations (evolutionary search) on models’
structure and parameters.

• Computer Aided analysis of modular and alternative designs (e.g.
synthetic network functionality).


An Important Difference with “Normal” Programs:
a GP challenge?
•Executable Stochastic P systems are not programs
with stochastic behavior
function f1(p1,p2,p3,p4) function f1(p1,p2,p3,p4)
{ {
if (p1<p2) and (rand<0.5) if (p1<p2) RND
print p3 print p3 RND
else else RND
print p4 print p4 RND
} }

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

•How does this impact GP techniques? can they
cope? scale? are they parsimonious?

Crazy Idea: Evolution by Natural Selection
as a Search Algorithm
•Three Key Ingredients
•Inheritable traits
•Variation
•Selection Pressure


A More Fundamental Question
•Fundamental CS results: Averaged over all
problems, all search methods perform equally well
(or rather bad). Wolpert, D.H., Macready, W.G. (1997), "No Free Lunch Theorems for
Optimization," IEEE Transactions on Evolutionary Computation 1, 67

•So why does Nature use Evolution as its search
mechanism? why not other methods?

•In Synt Bio people worry a lot about evolvability
(cellularity scale video)

•Can we engineer in vivo new search mechanism?
e.g. a biological Variable Neighborhood Search?

Could, e.g., an in vivo VNS “defeat”
Evolution? in which environments?

• Perhaps SB rather than trying to stop evolution should try to
outsmart/outpace it!

Acknowledgements
Members of my team working on SB2
EP/E017215/1
•Jonathan Blake Infobiotics Dashboard, Integrated Environment
EP/D021847/1

•Claudio Lima Infobiotics Workbench Machine Learning & Optimisation BB/F01855X/1
BB/D019613/1
•Francisco Romero-Campero Infobiotics Modeling & Model Checking

•James Smaldon Infobiotics Dissipative Particle Dynamics

•Jamie Twycross Infobiotics Stochastic Simulations

•Karima Righetti
Molecular Micro-Biology

• Prof. M. Camara (CBS, UoN)
• Prof. C. Alexander (Pharmacy , UoN)
• Dr. S. Heeb (CBS, UoN)
• Dr. G. Rampioni (CBS, UoN)

Towards a Rapid Model Prototyping Strategy for Systems & Synthetic Biology

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