4.16.24 21st Century Movements for Black Lives.pptx
Synthetic Biology - Modeling and Optimisation
1. Synthetic Biology: Modelling and
Optimisation
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
Copyright is held by the author/owner(s).
GECCO’09, July 8–12, 2009, Montréal Québec, Canada.
ACM 978-1-60558-505-5/09/07.
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2. 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
•Automated Model Synthesis and Optimisation
•Conclusions
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3. 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
•Automated Model Synthesis and Optimisation
•Conclusions
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4. Synthetic Biology
• Aims at designing, constructing and developing artificial biological
systems
•Offers new routes to ‘genetically modified’ organisms, synthetic living
entities, smart drugs and hybrid computational-biological devices.
• Potentially enormous societal impact, e.g., healthcare, environmental
protection and remediation, etc
• Synthetic Biology's basic assumption:
• Methods commonly used to build non-biological systems could
also be use to specify, design, implement, verify, test and deploy
novel synthetic biosystems.
• These method come from computer science, engineering and
maths.
• Modelling and optimisation run through all of the above.
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5. Models and Reality
•The use of models is intrinsic to any
scientific activity.
•Models are abstractions of the real-world
that highlight some key features while
ignoring others that are assumed to be not
relevant.
•A model should not be seen or presented
as representations of the truth, but instead
as a statement of our current knowledge.
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6. What is modelling?
• 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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7. •“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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8. 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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9. •There is potentially a distinction between modelling for Synthetic Biology
and 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 modelling. 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
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10. •There is potentially a distinction between modelling for Synthetic Biology
and 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 modelling. 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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11. Thus, Multi-Scale Modelling in the 2 SBs seek
to produce computable understanding
integrating massive datasets at various levels
of details simultaneously
Progress
Organ Individual
Cell colony
Cells
Regulatory
Networks
Proteins
DNA/RNA
Time
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12. The Pragmalogical Problem of
Modelling in XXI century Biology
• XXI century Biology brings to the fore the ubiquitous philosophical
questions in complex systems, that of emergent behavior and the
tension between reductionism and holistic approaches to science.
• Synthetic Biology (and SysBio) has, however, a very pragmatic
agenda: the engineering and control of novel biological systems
• The pragmalogical problem: If each subcomponent of a living system
(and processes/components therein) are understood… Can we say that
the system is understood? That is, can we assume that the system =
∑parts ?
• More importantly: can we control that biosystem?
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13. The Pragmalogical Problem of
Modelling in XXI century Biology
• XXI century Biology brings to the fore the ubiquitous philosophical
questions in complex systems, that of emergent behavior and the
tension between reductionism and holistic approaches to science.
& Integrative
• Synthetic Biology (and SysBio) has, however, a very pragmatic
agenda: the engineering and control of novel biological systems
• The pragmalogical problem: If each subcomponent of a living system
(and processes/components therein) are understood… Can we say that
the system is understood? That is, can we assume that the system =
∑parts ?
• More importantly: can we control that biosystem?
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14. Modelling relies on rigorous computational,
engineering and mathematical tools &
techniques
However, the act of modelling remains at the
interface between art and science
Undoubtedly, a multidisciplinary endeavour
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15. Modelling as a constrained
scientific art
Although modelling lies at the interface of art
and science there are guidelines we can
follow
Some examples:
The scale separation map [Hoekstra et al, LNCS 4487, 2007]
Tools suitability & cost [Goldberg, 2002]
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16. The Scale Separation Map
The Scale Separation Map is an
abstraction recently proposed by Hoekstra
and co-workers [Hoekstra et al, LNCS
4487, 2007]
Introduced in the context of Multi-scale
modelling with cellular automata but the
core concepts still valid for other modelling
techniques
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17. The Scale Separation Map
A Cellular Automata is defined as:
C= < A(Δx, Δt,L,T), S, R, G, F >
A is a spatial domain made of cells of size Δx with a total size of L
The simulation clock ticks every Δt units for a total of T units
T
We can simulate processes: Δt
as fast as Δt for as long as T units
ranging from Δx to L sizes. Δx
L
L
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18. A Scale Separation Map (SSM) is a two dimensional
map with horizontal axis representing time and vertical
axis representing space
1 0 B ξB
A ξA τB
Spatial scale (log)
τA
3.1 2 3.2
Temporal scale (log)
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19. A Scale Separation Map (SSM) is a two dimensional
map with horizontal axis representing time and vertical
axis representing space
• Region 0: A and B overlap
single scale multi-science
1 0 model
A ξA • Region 1: ξA ≈ ξB ^ τA > τB
B ξB temporal scale separation
Spatial scale (log)
τA • Region 2: ξA > ξB ^ τB ≈ τA
τB coarse and fine structures
3.1 2 3.2 in similar timescales
• Region 3.1: ξA > ξB ^ τB <
τA familiar micro-macro
models
• Region 3.2: ξA > ξB ^ τB >
τA small and slow
process linked to a fast and
Temporal scale (log) large process (e.g. Blood
flood and artery repair)
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20. A Scale Separation Map (SSM) is a two dimensional
map with horizontal axis representing time and vertical
axis representing space
• Region 0: A and B overlap
single scale multi-science
1 0 model
A ξA • Region 1: ξA ≈ ξB ^ τA > τB
B ξB temporal scale separation
Spatial scale (log)
τA • Region 2: ξA > ξB ^ τB ≈ τA
τB coarse and fine structures
3.1 2 3.2 in similar timescales
• Region 3.1: ξA > ξB ^ τB <
τA familiar micro-macro
models
• Region 3.2: ξA > ξB ^ τB >
τA small and slow
process linked to a fast and
Temporal scale (log) large process (e.g. Blood
flood and artery repair)
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21. A Scale Separation Map (SSM) is a two dimensional
map with horizontal axis representing time and vertical
axis representing space
• Region 0: A and B overlap
single scale multi-science
1 0 model
A ξA • Region 1: ξA ≈ ξB ^ τA > τB
temporal scale separation
Spatial scale (log)
τA • Region 2: ξA > ξB ^ τB ≈ τA
coarse and fine structures
3.1 2 3.2 in similar timescales
• Region 3.1: ξA > ξB ^ τB <
τA familiar micro-macro
models
B ξB • Region 3.2: ξA > ξB ^ τB >
τB τA small and slow
process linked to a fast and
Temporal scale (log) large process (e.g. Blood
flood and artery repair)
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22. A Scale Separation Map (SSM) is a two dimensional
map with horizontal axis representing time and vertical
axis representing space
• Region 0: A and B overlap
single scale multi-science
1 0 model
A ξA • Region 1: ξA ≈ ξB ^ τA > τB
temporal scale separation
Spatial scale (log)
τA • Region 2: ξA > ξB ^ τB ≈ τA
coarse and fine structures
3.1 2 3.2 in similar timescales
• Region 3.1: ξA > ξB ^ τB <
τA familiar micro-macro
models
• Region 3.2: ξA > ξB ^ τB >
B ξB
τA small and slow
τB process linked to a fast and
Temporal scale (log) large process (e.g. Blood
flood and artery repair)
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23. A Scale Separation Map (SSM) is a two dimensional
map with horizontal axis representing time and vertical
axis representing space
• Region 0: A and B overlap
single scale multi-science
1 0 model
A ξA • Region 1: ξA ≈ ξB ^ τA > τB
temporal scale separation
Spatial scale (log)
τA • Region 2: ξA > ξB ^ τB ≈ τA
coarse and fine structures
3.1 2 3.2 in similar timescales
• Region 3.1: ξA > ξB ^ τB <
τA familiar micro-macro
models
B ξB • Region 3.2: ξA > ξB ^ τB >
τB τA small and slow
process linked to a fast and
Temporal scale (log) large process (e.g. Blood
flood and artery repair)
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24. Even within a single cell the space & time
scale separations are important
E.g.:
• Within a cell the dissociation
constants of DNA/ transcription
factor binding to specific/non-
specific sites differ by 4-6 orders of
magnitude
• DNA protein binding occurs at 1-10s
time scale very fast in comparison
to a cell’s life cycle.
[F.J. Romero Campero, 2007]
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25. The Scale Separation Map
• With sufficient data each process can be
assigned its space-time region
unambiguously
Couplings, e.g. F • A given process may well have its Δx
(respectively Δt) > than another’s ξA
(respectively τA)
Spatial scale (log)
• Hence different processes in the SSM might
require different modelling techniques
Temporal scale (log)
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26. Modelling Approaches
There exist many modelling approaches, each with its
advantages and disadvantages.
Macroscopic, Microscopic and Mesoscopic
Quantitative and qualitative
Discrete and Continuous
Deterministic and Stochastic
Top-down or Bottom-up
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27. 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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28. 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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29. Tools Suitability and Cost
Low cost/ High cost/
High error Low error
Adapted from [Goldberg 2002]
Unarticulated Articulated Dimensional Facetwise Equations
wisdom Qualitative models models Of motion
models
Chemical Bioinformatic Biopolimer Microarrays and G.E.
Markup Language Sequence Markup Markup Language Markup Language
(CML) Language (BSML) (BioML) (MAGEML)
Cell Systems Biology Mathematics
Markup Language Markup Language Markup Language
(MathML) (SBML) (MathML)
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30. From [Di Ventura et al., Nature, 2006]
Low cost/ High cost/
High error Low error
Unarticulated Dimensional Facetwise Equations
wisdom models models Of motion
Formalism-independent errors
Formalism-dependent errors
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31. From [Di Ventura et al., Nature, 2006]
Low cost/ High cost/
High error Low error
Unarticulated Dimensional Facetwise Equations
wisdom models models Of motion
Formalism-independent errors
Formalism-dependent errors
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32. From [Di Ventura et al., Nature, 2006]
Low cost/ High cost/
High error Low error
Unarticulated Dimensional Facetwise Equations
wisdom models models Of motion
Formalism-independent errors
Formalism-dependent errors
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34. Stochasticity in Cellular Systems
Most commonly recognised sources of noise in cellular system are low
number of molecules and slow molecular interactions.
Over 80% of genes in E. coli express fewer than a hundred proteins per cell.
Mesoscopic, discrete and stochastic approaches are more suitable:
Only relevant molecules are taken into account.
Focus on the statistics of the molecular interactions and how often they
take place.
Mads Karn et al. Stochasticity in Gene Expression: From Theories to Phenotypes. Nature Reviews, 6,
451-464 (2005)
Purnananda Guptasarma. Does replication-induced transcription regulate synthesis of the myriad low
copy number poteins of E. Coli. BioEssays, 17, 11, 987-997
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35. Towards Executable Modells for SBs
“Although the road ahead is long and winding, it leads to a
future where biology and medicine are transformed into
precision engineering.” - Hiroaki Kitano.
Synthetic Biology and Systems biology promise more than
integrated understanding: it promises systematic control of
biological systems:
1. From an experimental viewpoint: Improved data acquisition
2. From a bioinformatics viewpoint: Improved data analysis tools
3. From a conceptual viewpoint: move from a science of mass-action/
energy-conversion to a science of information processing through
multiple heterogeneous medium
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36. 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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37. It thus makes sense to use methodologies
designed to cope with complex,
concurrent, interactive systems of parts as
found in computer sciences (e.g.):
Petri Nets
Process Calculi
P-Systems
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38. InfoBiotics
www.infobiotic.net
•The utilisation of cutting-edge information
processing techniques for biological modelling and
synthesis
•The understanding of life itself as multi-scale
(Spatial/Temporal) information processing systems
•Composed of 3 key components:
•Executable Biology (or other modeling
techniques)
•Automated Model and Parameter Estimation
•Model Checking (and other formal analysis)
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39. 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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40. Model Design in Systems/Synthetic Biology
• It is a hard process to design suitable models in systems/
synthetic biology where one has to consider the choice of the
model structure and model parameters at different points
repeatedly.
• Some use of computer simulation has been mainly focused on
the computation of the corresponding dynamics for a given
model structure and model parameters.
• Ultimate goal: for a new biological system (spec) one would like
to estimate the model structure and model parameters (that
match reality/constructible) simultaneously and automatically.
• Models should be clear & understandable to the biologist
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41. How you select features, disambiguate and
quantify depends on the goals behind your
modelling enterprise.
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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42. 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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43. 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
•Automated Model Synthesis and Optimisation
•Conclusions
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44. 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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45. 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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46. Distributed and parallel rewritting systems in
compartmentalised hierarchical structures.
Objects
Compartments
Rewriting Rules
• Computational universality and efficiency.
• Modelling Framework
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47. 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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48. Cell-like P systems
Intuitive Visual representation
as a Venn diagram with a
unique superset and without
intersected sets.
formally equivalent to a tree:
1
2 4
3
7
5 6
the classic P system diagram appearing in most papers
(Păun)
8 9
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49. Cell-like P systems
Intuitive Visual representation
as a Venn diagram with a
unique superset and without
intersected sets.
formally equivalent to a tree:
1
2 4
3
7
5 6
the classic P system diagram appearing in most papers
(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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50. 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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52. Rewriting Rules
used by Multi-volume Gillespie’s algorithm
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53. 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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54. 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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55. Compartments / Cells
Compartments and regions are explicitly
specified using membrane structures.
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56. 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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66. Translation as Rewriting Rules on
Multisets of Objects and Strings
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67. 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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68. 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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69. Basic P System Modules Used
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70. Modularity in Gene Regulatory
Networks
Cis-regulatory modules
are nonrandom clusters of
target binding sites for
transcription factors
regulating the same gene
or operon.
A P system module is a
set of rewriting rules
containing variables that
can be instantiated with
specific objects, stochastic
constants and membrane
labels.
E. Davidson (2006) The Regulatory Genome, Gene Regulation Networks in Development and
Evolution, Elsevier
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71. Modularity in Gene Regulatory
Networks AHL
LuxR CI
Cis-regulatory modules
are nonrandom clusters of
target binding sites for
transcription factors
regulating the same gene
or operon.
A P system module is a
set of rewriting rules
containing variables that
can be instantiated with
specific objects, stochastic
constants and membrane
labels.
E. Davidson (2006) The Regulatory Genome, Gene Regulation Networks in Development and
Evolution, Elsevier
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Thursday, 9 July 2009
72. Modularity in Gene Regulatory
Networks AHL
LuxR CI
Cis-regulatory modules
are nonrandom clusters of
target binding sites for
transcription factors
regulating the same gene
or operon.
A P system module is a
set of rewriting rules
containing variables that
can be instantiated with
specific objects, stochastic
constants and membrane
labels.
E. Davidson (2006) The Regulatory Genome, Gene Regulation Networks in Development and
Evolution, Elsevier
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73. Representing transcriptional
fusions
Objects Variables can be instantiated with the name of specific genes to
represent a construct where the gene is fused to the promoter or cluster of TF
binding sites specified by the module.
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74. Representing transcriptional
fusions
Objects Variables can be instantiated with the name of specific genes to
represent a construct where the gene is fused to the promoter or cluster of TF
binding sites specified by the module.
60 /203
Thursday, 9 July 2009
75. Representing transcriptional
fusions
Objects Variables can be instantiated with the name of specific genes to
represent a construct where the gene is fused to the promoter or cluster of TF
binding sites specified by the module.
60 /203
Thursday, 9 July 2009
76. Representing transcriptional
fusions
Objects Variables can be instantiated with the name of specific genes to
represent a construct where the gene is fused to the promoter or cluster of TF
binding sites specified by the module.
60 /203
Thursday, 9 July 2009
77. Representing transcriptional
fusions
Objects Variables can be instantiated with the name of specific genes to
represent a construct where the gene is fused to the promoter or cluster of TF
binding sites specified by the module.
60 /203
Thursday, 9 July 2009
78. Representing transcriptional
fusions
Objects Variables can be instantiated with the name of specific genes to
represent a construct where the gene is fused to the promoter or cluster of TF
binding sites specified by the module.
60 /203
Thursday, 9 July 2009
79. Representing transcriptional
fusions
Objects Variables can be instantiated with the name of specific genes to
represent a construct where the gene is fused to the promoter or cluster of TF
binding sites specified by the module.
60 /203
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80. Representing Directed Evolution
Variables for stochastic constants can be instantiated
with specific values in order to represent directed
evolution.
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81. Representing Directed Evolution
Variables for stochastic constants can be instantiated
with specific values in order to represent directed
evolution.
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82. Representing Directed Evolution
Variables for stochastic constants can be instantiated
with specific values in order to represent directed
evolution.
A
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83. Representing Directed Evolution
Variables for stochastic constants can be instantiated
with specific values in order to represent directed
evolution.
A
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84. Representing synthetic
transcriptional networks
The genes used to instantiate variables in our modules can
codify other TFs that interact with other modules or promoters
producing a synthetic gene regulatory network.
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85. Representing synthetic
transcriptional networks
The genes used to instantiate variables in our modules can
codify other TFs that interact with other modules or promoters
producing a synthetic gene regulatory network.
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86. Stochastic P Systems
Gillespie Algorithm (SSA) generates trajectories of a stochastic
system consisting of modified for multiple compartments/volumes:
1) A stochastic constant is associated with each rule.
2) 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.
3) The rule to apply j0 and the waiting time τ for its application
are computed by generating two random numbers r1,r2 ~ U(0,1)
and using the formulas:
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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101. 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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102. InfoBiotics
Pipeline
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108. Multi-component negative-
feedback oscillator
Oscillations caused by time-delayed negative-feedback:
Negative-feedback: gene-product that represses it's gene
Time-delay: mRNA export, translation and repressor import
Novak & Tyson: Design Principles of Biochemical Oscillators. Nat. Rev. Mol. Cell. Biol. 9: 981-991 (2008)
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109. Multi-component negative-
feedback oscillator
Mathematical model
− Xc = [mRNA in cytosol]
− Yc = [protein in cytosol]
− Xn = [mRNA in nucleus]
− Yn = [protein in nucleus]
− E = [total protease]
− p = “integer indicating
whether Y binds to DNA as a
monomer, trimer, or so on”
Executable Biology makes this more obvious:
we can vary the value of p and the sequence of binding...
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111. When repression is weak
(dissociation rate = 10)
No obvious oscillatory behaviour in single simulation
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112. When repression is weak
(dissociation rate = 10)
Mean of 100 runs shows convergence to steady state
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113. When repression is strong
(dissociation rate = 0.1)
Oscillations evident in single simulation
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114. When repression is strong
(dissociation rate = 0.1)
Averging 100 runs dampens oscillations due to different
phases but observable. Protein levels steady.
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115. Repressor binding sequence
When p=2 there are two possible scenarios:
– First protein binds to second protein weakly then
protein-dimer binds to gene strongly
– First protein binds to gene weakly then second
protein binds to protein-gene dimer strongly
In the following only the model structure is
changed, not the parameters
First dissociation rate = 10
Second dissociation rate = 0.1
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119. 2. Proteins repress cooperatively
target
Oscillations are steady and protein levels are controlled
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120. 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
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132. Pulse Generating Cells
AHL
AHL
LuxR GFP
PluxOR1
Pconst gfp
luxR
Plux
cI
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133. Pulse Generating Cells
AHL
AHL
LuxR GFP
PluxOR1
Pconst gfp
luxR
CI
Plux
cI
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134. Pulse Generating Cells
AHL
AHL Pconst({X=luxR},…)
LuxR GFP
PluxOR1({X=gfp},…)
PluxOR1 Plux({X=cI},…)
Pconst gfp
luxR
…
…
CI
Diff({X=AHL},…)
Plux
cI
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135. 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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136. 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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137. Wave propagation
simulation I
SIMULATION I
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138. Pulse Generating Cells
AHLWith Relay
AHL
LuxR GFP
PluxOR1
Pconst gfp
luxR
CI
Plux
cI
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139. Pulse Generating Cells
AHLWith Relay
AHL
LuxR GFP
PluxOR1
Pconst gfp
luxR
Plux CI
luxI
LuxI
Plux
cI
AHL
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140. Pulse Generating Cells
AHLWith Relay
AHL Pconst({X=luxR},…)
LuxR GFP
PluxOR1
Pconst gfp
luxR
Plux CI
luxI
LuxI
Plux
cI
AHL
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141. Pulse Generating Cells
AHLWith Relay
AHL Pconst({X=luxR},…)
LuxR GFP
PluxOR1({X=gfp},…)
PluxOR1
Pconst gfp
luxR
Plux CI
luxI
LuxI
Plux
cI
AHL
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142. Pulse Generating Cells
AHLWith Relay
AHL Pconst({X=luxR},…)
LuxR GFP
PluxOR1({X=gfp},…)
PluxOR1 Plux({X=cI},…)
Pconst gfp
luxR
Plux CI
luxI
LuxI
Plux
cI
AHL
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143. Pulse Generating Cells
AHLWith Relay
AHL Pconst({X=luxR},…)
LuxR GFP
PluxOR1({X=gfp},…)
PluxOR1 Plux({X=cI},…)
Pconst gfp
luxR
…
Plux CI
luxI
LuxI
Plux
cI
AHL
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147. Wave propagation
simulation II
SIMULATION II
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36
Thursday, 9 July 2009
148. AHL
Spatial Distribution of
Pulse Generators and Seed
AHL
LuxR GFP
Pconst PluxOR1
luxR gfp
Plux CI
luxI
Plux
cI
LuxI
AHL
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149. Wave propagation with
Four Droplets of Signal
SIMULATION III
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38
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153. Inversion Through a
Propagating Wave
SIMULATION IV
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41
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154. 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
•Automated Model Synthesis and Optimisation
•Conclusions
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155. Probabilistic Model Checking
A precise computational/mathematical model allows
us to perform formal verification techniques:
Probabilistic model checking.
Properties are expressed formally using temporal
logic and analysed.
The fundamental components of the PRISM language
are modules, variables and commands.
• A model is composed of a number of modules which can
interact with each other.
• A module contains a number of local variables and commands.
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156. P Systems and PRISM
P System Component PRISM Component
Membrane Module
Multisets of Objects Local Variables
Rewriting rules Commands
Rewards/Costs are associated with states and transitions representing
the number of objects and the application of rules.
Some Properties:
Expected Number of objects over time: R = ? [ I = T ]
Expected Number of rule application over time: R = ? [ C <= T ]
Expected Time to reach a state: R = ? [ F molec_1 = K ]
Transient properties: P = ? [ true U[t_1 t_2] molec_1 >= K_1 ]
Steady State/Long run properties: S = ? [ molec_1 >= K_1]
PRISM is used as an example. Other model checkers are more appropriate for larger systems
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159. Positive Regulation
[ TF + gene ] b [ TF.gene ] b con
[ TF.gene ] b [ TF + gene ] b coff
[ gene ]b [ gene + rna ]b ctrc
[ rna ]b [ ]b c2
[ rna ]b [ rna + Protein ]b ctrl
[ Protein ]b [ ]b c4
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160. Positive Regulation
R = ? [ C <= 100 ] R = ? [ C <= 100 ]
P = ? [ true U[60,60] Proteins = N ]
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161. Model Checking on the Pulse
Generator
The simulation of the Pulse Generator show some interesting
properties that were subsequently analysed using model checking.
Due to the complexity of the system (state space explosion) we
perform approximate model checking with a precision of 0.01 and a
confidence of 0.001 which needed to run 100000 simulations.
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162. Model Checking on the Pulse
Generator
The simulations show that although the number of signals
reaches eventually the same level in all the cells in the lattice
those cells that are far from the sending cells produce fewer
number of GFP molecules.
The difference between cells close to and far from the
sending cells is the rate of increase of the signal AHL.
We study the effect of the rate of increase of the signal AHL
in the number of GFP produced.
S. Basu, R. Mehreja, et al. Spatiotemporal control of gene expression with pulse generating
networks, PNAS, 101, 6355-6360
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163. We studied the expected number of GFP molecules produced over time for
different increase rates of AHL.
R = ? [ I = 60 ]
rewards
molecule = 1 : proteinGFP;
endrewards
The system is expected to
produce longer pulses with
lower amplitudes for slow
increase rates of AHL
signals.
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164. In order to get a clearer idea, the probability distribution of the number of
GFP molecules at 60 minutes was computed.
P = ? [ true U[60,60] ((proteinGFP > N) & (proteinGFP <= (N + 10))) ]
Note that for slow
increase rates of AHL
the probability of having
NO GFP molecules at
all is high.
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165. Finally, assuming that for a cell to be fluorescence it needs to have a given
number of GFP for an appreciable period of time we studied the expected
amount of time a cell have more than 50 GFP molecules during the first 60
minutes after the signals arrive to the cell.
R = ? [ C <= 60 ]
rewards
true : proteinGFP;
endrewards
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166. 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
•Automated Model Synthesis and Optimisation
•Conclusions
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167. A (Proto)Cell as an Information
Processing Device
LeDuc et al. Towards an in vivo biologically inspired nanofactory. Nature (2007)
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168. Towards a synthetic cell from
the bottom up
Biocompatible vesicles as long-circulating carriers
Polymer self-assembly into higher-order structures
Cell-mimics with hydrophobic ‘cell-wall’ and glycosylated
surfaces
Potential for cross-talk with biological cells
Pasparakis, G. Angew Chem Int Ed. 2008 47 (26), 4847-4850
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169. Vesicle Biorecognition
Pasparakis, G. et al, Angew Chem Int Ed. 2008 47 (26), 4847-4850
111 /203
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170. ‘Talking’ to cell-vesicle aggregates
Pasparakis, G. Angew Chem Int Ed. 2008 47 (26), 4847-4850
112 /203
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171. 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
•Automated Model Synthesis and Optimisation
•Conclusions
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172. Dissipative Particle Dynamics
Simulate movement of particles which represent several
atoms / molecules
Calculate forces acting on particles, integrate equations of
motion
Used extensively for investigating the self-assembly of lipid
membrane structures at the mesoscale
Typical simulations contain ~105-106 particles, for ~105-106 time
steps
Particles interact with each other within a finite radius much
smaller than the simulation space, algorithmic optimisations of
force calculations are possible
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173. Dissipative Particle Dynamics
First introduced by Hoogerbrugge and Koelmann in 1992.
Statistical mechanics of the model derived by espanol and warren in
1995.
A coarse graining approach is used so that one simulation particle
represents a number of real molecules of a given type.
Since the timescale at which interactions occur is longer than in MD,
fewer time-steps are required to simulation the same period of real time.
The short force cut-off radius enables optimisation of the force calculation
code to be performed.
O
H H W
O O
H H H H
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174. Dissipative Particle Dynamics
Conservative Force
i W
P
Dissipative Force
j W
P
Random Force
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175. Dissipative Particle Dynamics
Polymers
A number of simulation beads are tied together to
represent the original molecule.
Two new forces are introduced between polymer
particles, a Hookean spring force and a bond angle
force.
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178. Case Study One: Vesicle Diffusion
Polar heads
Non polar tails
Pores
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.
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179. Case Study One: Vesicle
Diffusion
The regions were formed by allowing vesicles to self-
assemble from phospholipids in the presence of pore
inclusions
Pores are simple channels with an exterior mimicking
the hydrophobic/hydrophilic profile of the bilayer
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181. Case Study One: Vesicle Diffusion
Tagged solvent particles were placed within the liposome inner
volume, the change in concentration due to diffusion of solvent
through the membrane pores was measures
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182. Case Study Two: Liposome
Logic
The behaviour of some prokaryotic RNA
transcription motifs matches that of
boolean logic gates[1]
DPD was extended with mesoscale
collision based reactions.
transcriptional logic gates were simulated
in bulk solvent and within a liposome core
volume.
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