Computational Synthetic Biology

Synthetic Biology
Natalio Krasnogor
ASAP - Interdisciplinary Optimisation Laboratory
School of Computer Science

Centre for Integrative Systems Biology
School of Biosciences

Centre for Healthcare Associated Infections
Institute of Infection, Immunity & Inflammation

University of Nottingham

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
•Conclusions
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The Far Future

Chells & The Cellularity Scale

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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Outline

•Conclusions
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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 as an Intelligent (Evolved)
Machine
•The Cell senses the environment and its own
internal states
•Makes Plans, Takes Decisions and Act
•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.
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The Cell as an Intelligent (Evolved)
Machine
•The Cell senses the environment and its own
internal states
•Makes Plans, Takes Decisions and Act
•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.
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Transcription Networks

Environment Signal1 Signal2 Signal3 Signal4 Signal5 ... Signaln

Transcription Factors

Genome
Gene1 Gene2 Gene3 Genek

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The Basic Unit: A Gene’s Transcription Regulation Mechanics

Promoter Protein Y
DNA
translation
Gene Y

mRNA
RNA Polymerase
transcription

Y
Gene Y

Activator X Si
X Y X Y
Promoter
DNA
X binding site Gene Y no transcription

Protein Y Bound activator
Si + translation Protein Y
translation
Unbound repressor X
mRNA mRNA
+ transcription
transcription

Bound activator Gene Y Bound activator
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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

“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

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Y

dx
Y positively Negative Positive
=β−α∗x
regulates X autoregulation autoregulation
dt

β
xst =
α

Negative autoregulation
x(t) = xst e−α∗t
Simple regulation

log 2
Positive autoregulation t1 =
2 α

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

Shai S. Shen-Orr et al., Network motifs in the transcriptional regulation

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

The I1-FFL is a pulse generator
and response accelerator

most common
in E. Coli & S.
Cerevisiae

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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

U. Alon. Network motifs: theory and
experimental approaches. Nature Reviews
Genetics (2007) vol. 8 (6) pp. 450-461
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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
U. Alon. Network motifs: theory and experimental approaches.
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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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•T h e c o r r e c t a b s t r a c t i o n s
facilitates understanding in
complex systems.

•Provide a route to engineering
& programming cells. Shai S. Shen-Orr et al., Network motifs in the
transcriptional regulation network of Escherichia
coli. Nature Genetics 31, 64 - 68 (2002)
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Outline

•Conclusions

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What is Synthetic Biology

Synthetic Biology is http://syntheticbiology.org/

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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What is Synthetic Biology


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.

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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Synthetic & Systems Biology:
Sisters Disciplines
Basic goal: to clarify current understandings by
formalising what the constitutive elements of a system
Systems Biology

are and how they interact
Intermediate goal: to test current understandings
Synthetic Biology

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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Top-Down Synthetic Biology:
An Approach to Engineering Biology
 Cells are information
processors. DNA is their
Pseudomonas programming language.
Discosoma sp. aeruginosa
Aequorea
victoria
 DNA sequencing and PCR:
Vibrio fischeri
Identification and isolation of
cellular parts.

 Recombinant DNA and
DNA synthesis : Combination
E. coli of DNA and construction of
new systems.

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

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Aequorea
victoria
Vibrio fischeri
cellular parts.

new systems.

plasmids  Tools to make biology
easier to engineer:

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Aequorea
victoria
Vibrio fischeri
cellular parts.

new systems.

easier to engineer:
DNA synthesis and abstraction (blueprints).

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Aequorea
victoria
Vibrio fischeri
cellular parts.

new systems.

easier to engineer:
Chassis
DNA synthesis and abstraction (blueprints).

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Aequorea
victoria
Vibrio fischeri
cellular parts.

new systems.

easier to engineer:
Chassis
DNA synthesis
Circuit Blueprint

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Synthetic Biology’s Brick & Mortar (I)

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

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

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

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Example II: Combinatorial Synthetic Logic


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Example II: Combinatorial Synthetic
Logic


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Example III: Push-on/Push-off circuit

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

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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Axample IV: 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
within a range of values (Band Pass).

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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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

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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 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
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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.

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
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The Scale Separation Map

sa
cs
s*103

m
QS mol diff in colony
Temporal scale (log)

transcription

PD
translation
s

D
vesicle dynamics
ms

diff. small mol
micelle dynamics
µs
ns

energy transfer
ps

bond vibration
fs

nm µm mm

Couplings Spatial scale (log)

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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 Mesoscopic
Quantitative and qualitative
Discrete and Continuous
Deterministic and Stochastic
Top-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)
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
(2008)
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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

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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 Contributions
Methodologies designed to cope with:

• Languages to cope with complex, concurrent,
systems of parts:
J.Twycross, L.R. Band, M. J. Bennett, J.R.
• ∏-calculus King, and N. Krasnogor. Stochastic and
deterministic multiscale models for
• Process Calculi systems biology: an auxin-transport case
study. BMC Systems Biology, 4(:34),

• P Systems March 2010

• Tools to analyse and optimise:
• EA, ML
• Model Checking
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InfoBiotics Workbench and Dashboard
www.infobiotics.net

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InfoBiotics Workbench and Dashboard
www.infobiotics.net

Specification

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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 Entities
Container
• 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.

Objects

Compartments

Rewriting Rules

• Computational universality and efficiency.

• Modelling Framework

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Cell-like P systems
Intuitive Visual representation
as a Venn diagram with a
unique superset and without
intersected sets.

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

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Cell-like P systems
intersected sets.

formally equivalent to a tree:
1

2 4
3
7
5 6
(Păun)
8 9

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Cell-like P systems
intersected sets.

formally equivalent to a tree:
1

2 4
3
7
5 6
(Păun)
8 9

• a string of matching parentheses: [ 1 [2 ] 2 [ 3 ] 3 [4 [5 ] 5 [6 [ 8 ] 8 [9 ] 9 ]6
[7 ]7 ]4 ]1

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P-Systems: Modelling Principles
Molecules Objects
Structured Molecules Strings
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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bacterial colonies.
01101110100001010100011110001011101010100011010100

encapsulation and
abstraction.

reusable models.

Evolution, Elsevier

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AHL
LuxR CI
bacterial colonies.

encapsulation and
abstraction.

reusable models.

Evolution, Elsevier

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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

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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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selection
genes.

PluxOR1({X=tetR})

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selection
genes.

PluxOR1({X=GFP})

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selection
genes.

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

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selection
genes.

PluxOR1({X=GFP})
values.

A

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selection
genes.

PluxOR1({X=GFP})
values.

A

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

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selection
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 })

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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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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 ToppRibo geneX degLVA

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3OC6_repressible_sensor({X}) = {

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

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

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degLVA({X},{...},{l=DH5α})

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}

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

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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})

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

Pconst PluxOR1
luxR gfp LuxI AHL

CI Pconst luxI
Plux
cI

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

Cellular
Parts

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Cellular
Parts

Libraries of
Modules

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Cellular
Parts

Libraries of Module
Modules Combinations

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Cellular Synthetic
Parts Circuits

Libraries of Module

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Cellular Synthetic
Parts Circuits

Libraries of Module P systems

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Cellular Synthetic Single
Parts Circuits Cells

Libraries of Module P systems

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Cellular Synthetic Single
Parts Circuits Cells

Libraries of Module P systems LPP systems

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Cellular Synthetic Single Synthetic Multi-
Parts Circuits Cells cellular Systems


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A compiler based on
a BNF grammar

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A compiler based on
a BNF grammar

Multi Compartmental
Stochastic Simulations
based on Gillespie’s
algorithm

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A compiler based on
a BNF grammar

Multi Compartmental Spatio-temporal
Stochastic Simulations Dynamics Analysis
based on Gillespie’s using Model Checking
algorithm with PRISM and MC2

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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

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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
3 r11,…,r1n1 ( 1, τ1, r01)
r31,…,r n3
3
Local Gillespie
M3 M1 ( 2, τ2, r02)

2 ( 3, τ3, r03)
r21,…,r2n2
‘ Sort Compartments
M2
τ2 < τ1 < τ3

( 2, τ2, r02)
( 1, τ1-τ2, r01) ( 2, τ2’, r02)
( 1, τ1-τ2, r01)
( 1, τ1, r01)
( 2, τ2’, r02)
( 3, τ3-τ2, r03)
( 3, τ3-τ2, r03) Insert new triplet ( 3, τ3, r03)
τ1-τ2 <τ2’ < τ3-τ2 Update Waiting Times

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An Important Difference with “Normal”
Programs
•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.
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Rapid Model Prototyping
•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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Specification

83 /183 InfoBiotics Workbench and Dashboard

Specification
Simulation

Analysis


An example: A 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
within a range of values (Band Pass).

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
AHL

SenderCell()=
LuxI AHL
{

Pconst Pconst({X = luxI },…)
luxI
PostTransc({X=LuxI},{c1=3.2,…})
Diff({X=AHL},{c=0.1})
}

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Pulse Generating Cells
AHL

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

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

Pconst PluxOR1
luxR gfp LuxI AHL

CI Pconst luxI
Plux
cI

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Pulse propagation - simulation I

Simulation I

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Pulse Generating Cells
With Relay
AHL

LuxR
AHL
PulseGenerator(X ) =

PluxOR1
{
Pconst
luxR Pconst({X=luxR},…)

PluxOR1({X},…) ,
Plux CI Plux({X=cI},…) ,
luxI
…
Plux
LuxI cI Diff({X=AHL},…) ,
Plux({X=luxI},…)
AHL
}
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Pulse propagation & Rely-
simulation II

Simulation II

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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

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Uniform Spatial Distribution of
Signal Translators for Pattern Formation

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Pattern Formation in
synthetic bacterial colonies

Simulation III

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Pattern Formation in

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Si
Spatial Distribution of Signal
Translators and propagators
Si
LuxR GFP

Pconst PluxOR1
luxR gfp

Plux CI
luxI
Plux
cI
LuxI

Si

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Alternating signal pulses in

Simulation IV

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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
•Conclusions
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Specification
Simulation

Analysis


Specification

Optimisation
Simulation

Analysis


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
99 /183

• 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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Evolving Executable Biology Models

• 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

103 /183

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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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 Complexation
Target P System Model Structure

c1

c2

Target P System Model Parameters

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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

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

Best Model Parameters

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Case 2: Enzymatic Reaction
Target P System Model Structure

c1
c2

c3

Target P System Model Parameters

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Target Model Parameters

Best Model Parameters

Two Approaches Using Basic Adding the Newly
Modules Found in Case 1
Number of runs the target 12/50 39/50
P system model was
found
Mean RMSE 3.8 2.85
118 /183

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

c1

c2

c3

c4

c5

c6

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Case 3.1: Experimental Results
Design Design 1 Design 2

Model

Number of runs 46/50 4/50

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

Best Model in Design 1 Best Model in Design 2

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

c1

c2
c3

c4
c5

c6
c7

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Case 3.2: Experimental Results
Design Design 1 Design 2

Model

Number of runs 30/50 20/50

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

Best Model in Design 1

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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.
123 /183

Four Fitness Functions
N time series
M 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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N 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.
125 /183

N 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.

126 /183

Problem Specification

127 /183

Results Study Case 1

136 /183


137 /183


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Target model

Alternative, biologically valid, modules

139 /183


141 /183

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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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
156 /183

• 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

Objective
Function

Bi-Objective
Function

157 /183

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

161 /183

Outline

•Conclusions
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Synthetic Biology
The 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?

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.

164 /183

Been There, Done That

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The Unjustified Fear of Chimera
and Foreign Genes
Transplantation
Itaya, M., Tsuge, K. Koizumi, M., and
Fujita, K. Combining two genomes in one
+ = Cell: Stable cloning of the
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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“ 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

167 /183


168 /183


And yet nobody seriously
considers banning printing
technology

168 /183

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

169 /183

What IS Synthetic Biology?

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.

C) Through rigorous mathematical, computational engineering routes

170 /183

Biology only smarter, safer and
clearer

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Outline

•Conclusions
172 /183

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

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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

175 /183

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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•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

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Computational Synthetic Biology

Computational Synthetic Biology

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