Towards a Rapid Model Prototyping Strategy for Systems & Synthetic Biology
1. Towards a Rapid Model Prototyping
Strategy for Systems & Synthetic Biology
Natalio Krasnogor
ASAP - Interdisciplinary Optimisation Laboratory
School of Computer Science
Centre for Integrative Systems Biology
School of Biology
Centre for Healthcare Associated Infections
Institute of Infection, Immunity & Inflammation
University of Nottingham
BEACON, Michigan State University, July 2010 1 /82
2. Outline
•Conceptual Viewpoint & CAD tools
•Model Specification Framework
•Examples of Modeling for Top Down SB
• In Silico Model Evolution
•Conclusions
BEACON, Michigan State University, July 2010 2 /82
3. A (Proto)Cell as an Information
Processing Device
LeDuc et al. Towards an in vivo biologically inspired nanofactory. Nature (2007)
BEACON, Michigan State University, July 2010 3 /82
4. Functional Entities
Container
• A boundary defining self/non-self (symmetry breaking).
• Maintain concentration gradients and avoid environmental damage.
Metabolism
• Energy utilisation
• Confining raw materials to be processed.
• Maintenance of internal structures (autopoiesis).
Information
• Sensing environmental signals / release of signals.
• Genetic information
BEACON, Michigan State University, July 2010 4 /82
5. The Cell as an Intelligent (Evolved)
Machine
•The Cell senses the environment and its own
internal states
•Makes Plans, Takes Decisions and Acts
•Evolution is the master programmer
Environmental Inputs
Internal States
Cell
Actions
Amir Mitchell, et al., Adaptive prediction of environmental changes by microorganisms. Nature June 2009.
BEACON, Michigan State University, July 2010 5 /82
6. The Cell as an Intelligent (Evolved)
Machine
•The Cell senses the environment and its own
internal states
•Makes Plans, Takes Decisions and Acts
•Evolution is the master programmer
Environmental Inputs
Internal States
Cell
Actions
Wikimedia Commons
Amir Mitchell, et al., Adaptive prediction of environmental changes by microorganisms. Nature June 2009.
BEACON, Michigan State University, July 2010 5 /82
7. 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
BEACON, Michigan State University, July 2010 6 /82
8. Y positively Negative Positive
regulates X autoregulation autoregulation
Shai S. Shen-Orr et al., Network motifs in the transcriptional regulation
network of Escherichia coli. Nature Genetics 31, 64 - 68 (2002)
The C1-FFL is a ‘sign-sensitive delay’ element and a persistence detector.
The I1-FFL is a pulse generator and response accelerator
U. Alon. Network motifs: theory and experimental approaches. Nature Reviews Genetics (2007) vol. 8 (6) pp. 450-461
BEACON, Michigan State University, July 2010 7 /82
11. •Cells (and most
biologists) don’t do
differential calculus!
•P systems are a
executable specifications
that closely mimic
biological reality.
•These are programs
that explicitly mimic the
internal behavior of cell
systems.
•These programs are
executed in a virtual
machine that captures
the intrinsic
stochasticity inherent in
biology
BEACON, Michigan State University, July 2010 8 /82
12. Modelling Frameworks
•Denotational Semantics Models:
Set of equations showing relationships between molecular
quantities and how they change over time.
They are approximated numerically.
(I.e. Ordinary Differential Equations, PDEs, etc)
•Operational Semantics Models:
Algorithm (list of instructions) executable by an abstract machine
whose computation resembles the behaviour of the system under
study. (i.e. Finite State Machine)
D. Harel, "A Grand Challenge for Computing: Full Reactive Modeling of a Multi-Cellular Animal", Bulletin of the
EATCS , European Association for Theoretical Computer Science, no. 81, 2003, pp. 226-235
A. Regev, E. Shapiro. The π-calculus as an abstraction for biomolecular systems. Modelling in Molecular
Biology., pages 1–50. Springer Berlin., 2004.
Jasmin Fisher and Thomas Henzinger. Executable cell biology. Nature Biotechnology, 25, 11, 1239-1249
9 /82
(2008)
BEACON, Michigan State University, July 2010
13. Properties of Petri Net Models
•A readable language but still a formal language with unambiguous
semantics
•Close to graphical depictions of metabolic networks normally used
by biologists
Erythrocyte cell’s
glycolytic and pentose
phospate pathway from
[Reddy et al., Comput.
Biol. Med., 1996]
BEACON, Michigan State University, July 2010 10 /82
14. Properties of Petri Net Models
•A readable language but still a formal language with unambiguous
semantics
•Close to graphical depictions of metabolic networks normally used
by biologists
Erythrocyte cell’s
glycolytic and pentose
phospate pathway from
[Reddy et al., Comput.
Biol. Med., 1996]
BEACON, Michigan State University, July 2010 10 /82
15. Some Limitations
•Not designed to handle spatial events or
spatial processes such as diffusion, mechanical
transduction, Brownian motion, collisions,
transport, etc.
•Stochasticity is loaded onto W, i.e. it is not a
property of rules or interactions
•Although very elegant computing invariants and
other properties can really only be done for small
systems.
•Although due to composability, to certain extent,
you can “divide & conquer”
BEACON, Michigan State University, July 2010 11 /82
16. Elementary Concepts in π-calculus
•A program specifies a network of interacting
processes
•Processes are defined by their potential
communication activities
•Communication occurs on complementary
channels, identified by names
•Message content: Channel name
Biological reactions are thus
abstracted as communications.
BEACON, Michigan State University, July 2010 12 /82
17. π-calculus
syntax
Process expressions
P ::= 0 a null process
π.P
P+P mutually exclusive processes
P|P concurrent processes
(νx)P new channel x in P
!P create a copy of P
Action prefixes
π ::= x? (y) receive y along x
x! <y> send y along x
τ
unobservable (internal) action
BEACON, Michigan State University, July 2010 13 /82
18. π-calculus
syntax
Process expressions a molecule / domain
P ::= 0 a null process
π.P
P+P mutually exclusive processes
P|P concurrent processes
(νx)P new channel x in P
!P create a copy of P
Action prefixes
π ::= x? (y) receive y along x
x! <y> send y along x
τ
unobservable (internal) action
BEACON, Michigan State University, July 2010 13 /82
19. π-calculus
syntax
Process expressions a molecule / domain
P ::= 0 a null process
π.P
P+P mutually exclusive processes
P|P concurrent processes
A set of
(νx)P new channel x in P molecules /
domains
!P create a copy of P
Action prefixes
π ::= x? (y) receive y along x
x! <y> send y along x
τ
unobservable (internal) action
BEACON, Michigan State University, July 2010 13 /82
20. π-calculus
syntax
Process expressions a molecule / domain
P ::= 0 a null process
π.P
P+P mutually exclusive processes
P|P concurrent processes
A set of
(νx)P new channel x in P molecules /
domains
!P create a copy of P
Action prefixes
π ::= x? (y) receive y along x a capability to do
x! <y> send y along x something
(e.g. phosporilate,
τ
unobservable (internal) action
cleave, etc)
BEACON, Michigan State University, July 2010 13 /82
21. π-calculus
syntax
Process expressions a molecule / domain
P ::= 0 a null process
π.P
This process
(enzyme)
P+P mutually exclusive processes
knows how to P|P concurrent processes
do action π A set of
(e.g. ligate) (νx)P new channel x in P molecules /
domains
!P create a copy of P
Action prefixes
π ::= x? (y) receive y along x a capability to do
x! <y> send y along x something
(e.g. phosporilate,
τ
unobservable (internal) action
cleave, etc)
BEACON, Michigan State University, July 2010 13 /82
22. Some Limitations
•Non-mobile/mobile approaches offer a tradeoff
between relevance and utility.
•Computational problem: each molecule is an
instance of a process.
•Some abstraction are obscure:
•private channels for compartments is cumbersome,
requires multi-step encoding of the formation of
complexes, not really a nature encoding of proximity
and biochemical complementarity.
•Communication always involves exactly two
processes.
BEACON, Michigan State University, July 2010 14 /82
23. Biochemical Process Calculi
based on π-calculus
π-calculus (Stochastic, Causal)
BioAmbients (mobile compartments)
Brane calculi (membranes, Projective)
Beta Binders (dynamic compartments)
increasing emphasis
on intracellular organisation
BEACON, Michigan State University, July 2010 15 /82
24. Tools Suitability and Cost
•From [D.E Goldberg, 2002] (adapted):
“Since science and math are in the description
business, the model is the thing…The engineer
or inventor has much different motives. The
engineered object is the thing” ε, error
BU/TD Synthetic & Systems Biologist
Computer Scientist/Engineer
Mathematician
C, cost of modelling
BEACON, Michigan State University, July 2010 16 /82
25. InfoBiotics Workbench and Dashboard www.infobiotics.net
Management
Specification
Optimisation
Simulation
Verification
Analysis
BEACON, Michigan State University, July 2010 17 /82
26. Outline
•Conceptual Viewpoint & CAD tools
•Model Specification Framework
•Examples of Modeling for Top Down SB
• In Silico Model Evolution
•Conclusions
BEACON, Michigan State University, July 2010 18 /82
28. InfoBiotics Workbench and Dashboard
Specification
BEACON, Michigan State University, July 2010 19 /82
29. Model Specification with P systems
•Field of membrane computing initiated by
Gheorghe Păun in 2000
•Inspired by the hierarchical membrane
structure of eukaryotic cells
•A formal language: precisely defined and
machine processable
•An executable biology methodology
G. Paun. Computing with membranes. Journal of Computer and System Sciences, 61(1):108–143, 2000
F. J. Romero-Campero, J. Twycross, M. Camara, M. Bennett, M. Gheorghe, and N. Krasnogor. Modular
assembly of cell systems biology models using p systems. International Journal of Foundations of Computer
Science, 20(3):427-442, 2009
BEACON, Michigan State University, July 2010 20 /82
30. Distributed and parallel rewritting systems in
What is a P Systems? compartmentalised hierarchical structures.
Objects
Compartments
Rewriting Rules
• Computational universality and efficiency.
• Modelling Framework
BEACON, Michigan State University, July 2010 21 /82
31. P-Systems’ Principal Entities
Molecules (ATP, ADP, Letters (objects) from an
OHL, etc) alphabet
Structured Molecules Strings in the alphabet
(DNA, RNA,Proteins,
etc)
Molecules in “bulk” Multisets of objects/strings
Membranes/ Membrane
organelles
Biochemical activity Rules
Biochemical transport Communication rules
BEACON, Michigan State University, July 2010 22 /82
32. Rewriting Rules
used by Multi-volume Gillespie’s algorithm
BEACON, Michigan State University, July 2010 23 /82
33. 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
BEACON, Michigan State University, July 2010 24 /82
34. Compartments / Cells
Compartments and regions are explicitly
specified using membrane structures.
BEACON, Michigan State University, July 2010 25 /82
35. Colonies / Tissues
Colonies and tissues are representing as a
collection of P systems distributed over a lattice.
Objects can travel around the lattice through
translocation rules.
v
BEACON, Michigan State University, July 2010 26 /82
36. Molecular Interactions
Inside Compartments
BEACON, Michigan State University, July 2010 27 /82
37. Passive Diffusion of Molecules
BEACON, Michigan State University, July 2010 28 /82
39. Post-Transcriptional Processes
For each protein in the system, post-transcriptional processes like
translational initiation, messenger and protein degradation, protein
dimerisation, signal sensing, signal diffusion etc are represented using
modules of rules.
Modules may also have (as parameters) the stochastic kinetic constants
associated with the corresponding rules in order to allow us to explore
possible mutations in the promoters and ribosome binding sites in order to
optimise the behaviour of the system.
BEACON, Michigan State University, July 2010 30 /82
40. Scalability through Modularity
• Cellular functions arise from orchestrated
interactions between motifs consisting of
many molecular interacting species.
A P System model is a set of modules of
rules representing molecular interactions
(network) motifs that appear in many cellular
systems.
BEACON, Michigan State University, July 2010 31 /82
41. Basic P System Modules Used
BEACON, Michigan State University, July 2010 32 /82
42. Characterisation/Encapsulation of Cellular
Parts: Gene Promoters
A modeling language for
the design of synthetic
bacterial colonies.
A module, set of rules
describing the molecular
interactions involving a
cellular part, provides
encapsulation and
abstraction.
Collection or libraries of
reusable cellular parts and
reusable models.
E. Davidson (2006) The Regulatory Genome, Gene Regulation Networks in Development and
Evolution, Elsevier
BEACON, Michigan State University, July 2010 33 /82 21
43. Characterisation/Encapsulation of Cellular
Parts: Gene Promoters
A modeling language for
the design of synthetic
bacterial colonies.
01101110100001010100011110001011101010100011010100
A module, set of rules
describing the molecular
interactions involving a
cellular part, provides
encapsulation and
abstraction.
Collection or libraries of
reusable cellular parts and
reusable models.
E. Davidson (2006) The Regulatory Genome, Gene Regulation Networks in Development and
Evolution, Elsevier
BEACON, Michigan State University, July 2010 33 /82 21
44. Characterisation/Encapsulation of Cellular
Parts: Gene Promoters
A modeling language for
the design of synthetic
bacterial colonies.
A module, set of rules
describing the molecular
interactions involving a
cellular part, provides
encapsulation and
abstraction.
Collection or libraries of
reusable cellular parts and
reusable models.
E. Davidson (2006) The Regulatory Genome, Gene Regulation Networks in Development and
Evolution, Elsevier
BEACON, Michigan State University, July 2010 33 /82 21
45. Characterisation/Encapsulation of Cellular
Parts: Gene Promoters
AHL
LuxR CI
A modeling language for
the design of synthetic
bacterial colonies.
A module, set of rules
describing the molecular
interactions involving a
cellular part, provides
encapsulation and
abstraction.
Collection or libraries of
reusable cellular parts and
reusable models.
E. Davidson (2006) The Regulatory Genome, Gene Regulation Networks in Development and
Evolution, Elsevier
BEACON, Michigan State University, July 2010 33 /82 21
46. Characterisation/Encapsulation of Cellular
Parts: Gene Promoters
AHL
LuxR CI
A modeling language for
the design of synthetic
bacterial colonies.
PluxOR1({X},{c1, c2, c3, c4, c5, c6, c7, c8, c9},{l}) = {
A module, set of rules type: promoter
describing the molecular
sequence: ACCTGTAGGATCGTACAGGTTTACGCAAGAA
interactions involving a
ATGGTTTGTATAGTCGAATACCTCTGGCGGTGATA
cellular part, provides
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
}
E. Davidson (2006) The Regulatory Genome, Gene Regulation Networks in Development and
Evolution, Elsevier
BEACON, Michigan State University, July 2010 33 /82 21
47. Module Variables: Recombinant DNA, Directed
Evolution, Chassis selection
BEACON, Michigan State University, July 2010 34 /82 22
48. Module Variables: Recombinant DNA, Directed
Evolution, Chassis selection
Recombinant DNA: Objects variables can be instantiated with the name of specific
genes.
BEACON, Michigan State University, July 2010 34 /82 22
49. Module Variables: Recombinant DNA, Directed
Evolution, Chassis selection
Recombinant DNA: Objects variables can be instantiated with the name of specific
genes.
BEACON, Michigan State University, July 2010 34 /82 22
50. Module Variables: Recombinant DNA, Directed
Evolution, Chassis selection
Recombinant DNA: Objects variables can be instantiated with the name of specific
genes.
PluxOR1({X=tetR})
BEACON, Michigan State University, July 2010 34 /82 22
51. Module Variables: Recombinant DNA, Directed
Evolution, Chassis selection
Recombinant DNA: Objects variables can be instantiated with the name of specific
genes.
BEACON, Michigan State University, July 2010 34 /82 22
52. Module Variables: Recombinant DNA, Directed
Evolution, Chassis selection
Recombinant DNA: Objects variables can be instantiated with the name of specific
genes.
PluxOR1({X=GFP})
BEACON, Michigan State University, July 2010 34 /82 22
53. Module Variables: Recombinant DNA, Directed
Evolution, Chassis selection
Recombinant DNA: Objects variables can be instantiated with the name of specific
genes.
PluxOR1({X=GFP})
BEACON, Michigan State University, July 2010 34 /82 22
54. Module Variables: Recombinant DNA, Directed
Evolution, Chassis selection
Recombinant DNA: Objects variables can be instantiated with the name of specific
genes.
PluxOR1({X=GFP})
Directed evolution: Variables for stochastic constants can be instantiated with specific
values.
BEACON, Michigan State University, July 2010 34 /82 22
55. Module Variables: Recombinant DNA, Directed
Evolution, Chassis selection
Recombinant DNA: Objects variables can be instantiated with the name of specific
genes.
PluxOR1({X=GFP})
Directed evolution: Variables for stochastic constants can be instantiated with specific
values.
A
BEACON, Michigan State University, July 2010 34 /82 22
56. Module Variables: Recombinant DNA, Directed
Evolution, Chassis selection
Recombinant DNA: Objects variables can be instantiated with the name of specific
genes.
PluxOR1({X=GFP})
Directed evolution: Variables for stochastic constants can be instantiated with specific
values.
A
PluxOR1({X=GFP},{...,c4=10,...})
BEACON, Michigan State University, July 2010 34 /82 22
57. Module Variables: Recombinant DNA, Directed
Evolution, Chassis selection
Recombinant DNA: Objects variables can be instantiated with the name of specific
genes.
PluxOR1({X=GFP})
Directed evolution: Variables for stochastic constants can be instantiated with specific
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 })
BEACON, Michigan State University, July 2010 34 /82 22
58. Characterisation/Encapsulation of Cellular
Parts: Riboswitches
A riboswitch is a piece of RNA that folds in different ways depending on the
presence of absence of specific molecules regulating translation.
ToppRibo({X},{c1, c2, c3, c4, c5, c6},{l}) = {
type: riboswitch
sequence:GGTGATACCAGCATCGTCTTGATGCCCTTGG
CAGCACCCCGCTGCAAGACAACAAGATG
rules:
r1: [ RNAP.ToppRibo.X ]_l -c1-> [ ToppRibo.X ]_l
r2: [ ToppRibo.X ]_l -c2-> [ ]_l
r3: [ ToppRibo.X + theop ]_l –c3-> [ ToppRibo*.X ]_l
r4: [ ToppRibo*.X ]_l –c4-> [ ToppRibo.X + theop ]_l
r5: [ ToppRibo*.X ]_l –c5-> [ ]_l
r6: [ ToppRibo*.X ]_l –c6-> [ToppRibo*.X + Rib.X ]_l
}
BEACON, Michigan State University, July 2010 35 /82 23
59. Characterisation/Encapsulation of Cellular
Parts: Degradation Tags
Degradation tags are amino acid sequences recognised by proteases. Once the
corresponding DNA sequence is fused to a gene the half life of the protein is
reduced considerably.
degLVA({X},{c1, c2},{l}) = {
type: degradation tag
sequence: CAGCAAACGACGAAAACTACGCTTTAGTAGCT
rules:
r1: [ Rib.X.degLVA ]_l -c1-> [ X.degLVA ]_l
r2: [ X.degLVA ]_l -c2-> [ ]_l
}
BEACON, Michigan State University, July 2010 36 /82 24
60. Higher Order Modules: Building Synthetic
Gene Circuits
PluxOR1 ToppRibo geneX degLVA
BEACON, Michigan State University, July 2010 37 /82 25
61. Higher Order Modules: Building Synthetic
Gene Circuits
PluxOR1 ToppRibo geneX degLVA
3OC6_repressible_sensor({X}) = {
BEACON, Michigan State University, July 2010 37 /82 25
62. Higher Order Modules: Building Synthetic
Gene Circuits
PluxOR1 ToppRibo geneX degLVA
3OC6_repressible_sensor({X}) = {
PluxOR1({X=ToppRibo.geneX.degLVA},{...},{l=DH5α})
BEACON, Michigan State University, July 2010 37 /82 25
63. Higher Order Modules: Building Synthetic
Gene Circuits
PluxOR1 ToppRibo geneX degLVA
3OC6_repressible_sensor({X}) = {
PluxOR1({X=ToppRibo.geneX.degLVA},{...},{l=DH5α})
ToppRibo({X=geneX.degLVA},{...},{l=DH5α})
BEACON, Michigan State University, July 2010 37 /82 25
64. Higher Order Modules: Building Synthetic
Gene Circuits
PluxOR1 ToppRibo geneX degLVA
3OC6_repressible_sensor({X}) = {
PluxOR1({X=ToppRibo.geneX.degLVA},{...},{l=DH5α})
ToppRibo({X=geneX.degLVA},{...},{l=DH5α})
degLVA({X},{...},{l=DH5α})
BEACON, Michigan State University, July 2010 37 /82 25
65. Higher Order Modules: Building Synthetic
Gene Circuits
PluxOR1 ToppRibo geneX degLVA
3OC6_repressible_sensor({X}) = {
PluxOR1({X=ToppRibo.geneX.degLVA},{...},{l=DH5α})
ToppRibo({X=geneX.degLVA},{...},{l=DH5α})
degLVA({X},{...},{l=DH5α})
}
BEACON, Michigan State University, July 2010 37 /82 25
66. Higher Order Modules: Building Synthetic
Gene Circuits
PluxOR1 ToppRibo geneX degLVA
3OC6_repressible_sensor({X}) = {
PluxOR1({X=ToppRibo.geneX.degLVA},{...},{l=DH5α})
ToppRibo({X=geneX.degLVA},{...},{l=DH5α}) X=GFP
degLVA({X},{...},{l=DH5α})
}
BEACON, Michigan State University, July 2010 37 /82 25
67. Higher Order Modules: Building Synthetic
Gene Circuits
PluxOR1 ToppRibo geneX degLVA
3OC6_repressible_sensor({X}) = {
PluxOR1({X=ToppRibo.geneX.degLVA},{...},{l=DH5α})
ToppRibo({X=geneX.degLVA},{...},{l=DH5α}) X=GFP
degLVA({X},{...},{l=DH5α})
}
BEACON, Michigan State University, July 2010 37 /82 25
68. Higher Order Modules: Building Synthetic
Gene Circuits
PluxOR1 ToppRibo geneX degLVA
3OC6_repressible_sensor({X}) = {
PluxOR1({X=ToppRibo.geneX.degLVA},{...},{l=DH5α})
ToppRibo({X=geneX.degLVA},{...},{l=DH5α}) X=GFP
degLVA({X},{...},{l=DH5α})
}
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})
BEACON, Michigan State University, July 2010 37 /82 25
69. AHL
Specification of Multi-cellular
Systems: LPP systems
AHL
GFP AHL
LuxR
Pconst PluxOR1
luxR gfp LuxI AHL
CI Pconst luxI
Plux
cI
BEACON, Michigan State University, July 2010 38 /82
70. Infobiotics: An Integrated Framework
http://www.infobiotics.org/infobiotics-workbench/
Cellular Synthetic Single Synthetic Multi-
Parts Circuits Cells cellular Systems
Libraries of Module P systems LPP systems
Modules Combinations
A compiler based on
a BNF grammar
Multi Compartmental Spatio-temporal Automatic Design of
Stochastic Simulations Dynamics Analysis Synthetic Gene
based on Gillespie’s using Model Checking Regulatory Circuits using
algorithm with PRISM and MC2 Evolutionary Algorithms
BEACON, Michigan State University, July 2010 39 /82 29
71. Stochastic P Systems Are
Executable Programs
The virtual machine running these programs is a “Gillespie
Algorithm (SSA)”. It generates trajectories of a stochastic system:
A stochastic constant is associated with each rule.
A propensity is computed for each rule by multiplying the
stochastic constant by the number of distinct possible
combinations of the elements on the left hand side of the rule.
F. J. Romero-Campero, J. Twycross, M. Camara, M. Bennett, M. Gheorghe, and N. Krasnogor.
Modular assembly of cell systems biology models using p systems. International Journal of
Foundations of Computer Science, 2009
BEACON, Michigan State University, July 2010 40 /82
72. •Using P systems modules one can model a large variety of
commonly occurring BRN:
•Gene Regulatory Networks
•Signaling Networks
•(Metabolic Networks)
•This can be done in an incremental way.
F.J. Romero-Campero, J. Twycross, M. Camara, M. Bennett, M. Gheorghe, N.
Krasnogor. Modular Assembly of Cell Systems Biology Models Using P Systems.
Internatnional Journal of Foundations of Computer Science, 20(3):427-442,
2009.
Degano P., Prandi D., Priami C., and Quaglia P. Beta-binders for biological quantitative experiments.
ENTCS, 164:101-117, 2006. Proc. 4th Workshop on Quantitative Aspects of Programming
Languages, QAPL 2006.
BEACON, Michigan State University, July 2010 41 /82
73. Outline
•Conceptual Viewpoint & CAD tools
•Model Specification Framework
•Examples of Modeling for Top Down SB
• In Silico Model Evolution
•Conclusions
BEACON, Michigan State University, July 2010 42 /82
74. InfoBiotics Workbench and Dashboard
Specification
BEACON, Michigan State University, July 2010 43 /82
75. InfoBiotics Workbench and Dashboard
Specification
Simulation
Analysis
BEACON, Michigan State University, July 2010 43 /82
76. 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
BEACON, Michigan State University, July 2010 44 /82
77. An example: Ron Weiss' Pulse Generator
Pulse Generator
Signal Sender
AHL
AHL
AHL GFP
LuxR
PluxOR
LuxI AHL Pconst gfp
luxR
Pconst
luxI
Plux
cI CI
Pconst({X = luxI },…)
PostTransc({X=LuxI},{c1=3.2,…}) Pconst({X=luxR},…)
Diff({X=AHL},{c=0.1}) PluxOR1({X=gfp},…)
Plux({X=cI},…) …
BEACON, Michigan State University, July 2010 45 /82
78. AHL
Spatial Distribution of Senders
and Pulse Generators
AHL
GFP AHL
LuxR
Pconst PluxOR1
luxR gfp LuxI AHL
Pconst luxI
CI
Plux
cI
BEACON, Michigan State University, July 2010 46 /82
79. Pulse propagation - simulation I
Simulation I
BEACON, Michigan State University, July 2010 47 /82
80. Pulse Generating Cells
With Relay
AHL
AHL
LuxR GFP
PluxOR1
Pconst gfp
luxR
CI
Plux
cI
BEACON, Michigan State University, July 2010 48 /82
81. Pulse Generating Cells
With Relay
AHL
AHL
LuxR GFP
PluxOR1
Pconst gfp
luxR
Plux CI
luxI
LuxI
Plux
cI
AHL
BEACON, Michigan State University, July 2010 48 /82
82. Pulse Generating Cells
With Relay
AHL
AHL Pconst({X=luxR},…)
LuxR GFP
PluxOR1
Pconst gfp
luxR
Plux CI
luxI
LuxI
Plux
cI
AHL
BEACON, Michigan State University, July 2010 48 /82
83. Pulse Generating Cells
With Relay
AHL
AHL Pconst({X=luxR},…)
LuxR GFP
PluxOR1({X=gfp},…)
PluxOR1
Pconst gfp
luxR
Plux CI
luxI
LuxI
Plux
cI
AHL
BEACON, Michigan State University, July 2010 48 /82
84. Pulse Generating Cells
With Relay
AHL
AHL Pconst({X=luxR},…)
LuxR GFP
PluxOR1({X=gfp},…)
PluxOR1 Plux({X=cI},…)
Pconst gfp
luxR
Plux CI
luxI
LuxI
Plux
cI
AHL
BEACON, Michigan State University, July 2010 48 /82
85. Pulse Generating Cells
With Relay
AHL
AHL Pconst({X=luxR},…)
LuxR GFP
PluxOR1({X=gfp},…)
PluxOR1 Plux({X=cI},…)
Pconst gfp
luxR
…
Plux CI
luxI
LuxI
Plux
cI
AHL
BEACON, Michigan State University, July 2010 48 /82
86. Pulse Generating Cells
With Relay
AHL
AHL Pconst({X=luxR},…)
LuxR GFP
PluxOR1({X=gfp},…)
PluxOR1 Plux({X=cI},…)
Pconst gfp
luxR
…
…
Plux CI
luxI
LuxI
Plux
cI
AHL
BEACON, Michigan State University, July 2010 48 /82
87. Pulse Generating Cells
With Relay
AHL
AHL Pconst({X=luxR},…)
LuxR GFP
PluxOR1({X=gfp},…)
PluxOR1 Plux({X=cI},…)
Pconst gfp
luxR
…
…
Plux CI
luxI Diff({X=AHL},…)
LuxI
Plux
cI
AHL
BEACON, Michigan State University, July 2010 48 /82
88. Pulse Generating Cells
With Relay
AHL
AHL Pconst({X=luxR},…)
LuxR GFP
PluxOR1({X=gfp},…)
PluxOR1 Plux({X=cI},…)
Pconst gfp
luxR
…
…
Plux CI
luxI Diff({X=AHL},…)
LuxI
Plux Plux({X=luxI},…)
cI
AHL
BEACON, Michigan State University, July 2010 48 /82
89. Pulse propagation & Rely-
simulation II
Simulation II
BEACON, Michigan State University, July 2010 49 /82 36
90. 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
BEACON, Michigan State University, July 2010 50 /82
91. Uniform Spatial Distribution of
Signal Translators for Pattern Formation
BEACON, Michigan State University, July 2010 51 /82
92. Pattern Formation in
synthetic bacterial colonies
Simulation III
BEACON, Michigan State University, July 2010 52 /82
93. Si
Spatial Distribution of Signal
Translators and propagators
Si
LuxR GFP
Pconst PluxOR1
luxR gfp
Plux CI
luxI
Plux
cI
LuxI
Si
BEACON, Michigan State University, July 2010 53 /82
94. Alternating signal pulses in
synthetic bacterial colonies
Simulation IV
BEACON, Michigan State University, July 2010 54 /82
95. Outline
•Conceptual Viewpoint & CAD tools
•Model Specification Framework
•Examples of Modeling for Top Down SB
• In Silico Model Evolution
•Conclusions
BEACON, Michigan State University, July 2010 55 /82
96. 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
BEACON, Michigan State University, July 2010 56 /82
97. Large Literature on Model Synthesis
• Mason et al. use a random Local Search (LS) as the mutation to
evolve electronic networks with desired dynamics
• Chickarmane et al. use a standard GA to optimize the kinetic
parameters of a population of ODE-based reaction networks having
the desired topology.
• Spieth et al. propose a Memetic Algorithm to find gene regulatory
networks from experimental DNA microarray data where the network
structure is optimized with a GA and the parameters are optimized
with an Evolution Strategy (ES).
• Jaramillo et al. use Simulated Annealing as the main search strategy
for model inference based on (O)DEs
BEACON, Michigan State University, July 2010 57 /82
98. Multi-Objective Optimisation in
Morphogenesis
Rui Dilão, Daniele Muraro, Miguel Nicolau, Marc
Schoenauer. Validation of a morphogenesis model of
Drosophila early development by a multi-objective
evolutionary optimization algorithm. Proc. 7th
European Conference on Evolutionary Computation,
ML and Data Mining in BioInformatics (EvoBIO'09),
April 2009
BEACON, Michigan State University, July 2010 58 /82
99. 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
BEACON, Michigan State University, July 2010 59 /82
100. Evolutionary Algorithms for Automated
Model Synthesis and Optimisation
EA are potentially very useful for AMSO
• There’s a substantial amount of work on:
• using GP-like systems to evolve executable
structures
• using EAs for continuous/discrete
optimisation
• An EA population represents alternative
models (could lead to different experimental
setups)
• EAs have the potential to capture
evolvability of models/network motifs
BEACON, Michigan State University, July 2010 60 /82
101. Nested EA for Model Synthesis
F. Romero-Campero, H.Cao, M.
Camara, and N. Krasnogor.
Structure and parameter
estimation for cell systems
biology models. Proceedings of
the Genetic and Evolutionary
Computation Conference
(GECCO-2008), pages
331-338. ACM Publisher, 2008.
(Best Paper award at the
Bioinformatics track.)
H. Cao, F.J. Romero-Campero,
S. Heeb, M. Camara, and N.
Krasnogor. Evolving cell models
for systems and synthetic
biology. Systems and Synthetic
Biology, 4(1), 2010.
C. Garcia-Martinez, C. Lima, J.
Twycross, M. Lozano, N.
Krasnogor. P System Model
Optimisation by Means of
Evolutionary Based Search
Algorithms. Proceedings of the
2010 Genetic and Evolutionary
Computation Conference
(GECCO-2010). (Nominated for
best paper award at the
Bioinformatics track.)
BEACON, Michigan State University, July 2010 61 /82
103. The Fitness Function
• Multiple time-series
per target
• Different time series
have very different
profiles, e.g., maxima
occur at different
times/places
• Transient states
(sometimes) as
important as steady
states
•RMSE not the only
possibility
H. Cao, F.J. Romero-Campero, S. Heeb, M. Camara, and N. Krasnogor. Evolving cell models for systems and synthetic
biology. Systems and Synthetic Biology, 4(1), 2010.
BEACON, Michigan State University, July 2010 63 /82
113. The fact that this algorithm produces alternative models for a
specific biological signature is very encouraging as it could
help biologists to design new experiments to discriminate
among competing hypothesis (models).
Alternative models can be formally analysed and compared
based on other than “fitness” criteria:
★Sensitivity analysis / Robustness
★Average properties (model checking)
★Parsimony
Comparing results by only using elementary modules or by
adding newly found modules to the library shows the obvious
advantage of the incremental methodology with modules.
BEACON, Michigan State University, July 2010 73 /82
114. Outline
•Conceptual Viewpoint & CAD tools
•Model Specification Framework
•Examples of Modeling for Top Down SB
• In Silico Model Evolution
•Conclusions
BEACON, Michigan State University, July 2010 74 /82
115. Management
Specification
Optimisation
Simulation
Verification
Analysis
BEACON, Michigan State University, July InfoBiotics Workbench
2010 75 /82 and Dashboard
116. TAKE HOME MESSAGE
• P systems are a handy way of specifying discrete and stochastic
rule-based compartmental models.
• Modularity in P systems as a design principle for synthetic
networks that enables reusability, hierarchical abstraction and
standardisation.
• Can be linked to DPD-type simulations.
• Model Checking for the formal analysis of our models.
• Automated explorations (evolutionary search) on models’
structure and parameters.
• Computer Aided analysis of modular and alternative designs (e.g.
synthetic network functionality).
BEACON, Michigan State University, July 2010 76 /82
117. An Important Difference with “Normal” Programs:
a GP challenge?
•Executable Stochastic P systems are not programs
with stochastic behavior
function f1(p1,p2,p3,p4) function f1(p1,p2,p3,p4)
{ {
if (p1<p2) and (rand<0.5) if (p1<p2) RND
print p3 print p3 RND
else else RND
print p4 print p4 RND
} }
•A cell is a living example of distributed stochastic
computing.
•How does this impact GP techniques? can they
cope? scale? are they parsimonious?
BEACON, Michigan State University, July 2010 77 /82
118. Crazy Idea: Evolution by Natural Selection
as a Search Algorithm
•Three Key Ingredients
•Inheritable traits
•Variation
•Selection Pressure
BEACON, Michigan State University, July 2010 78 /82
119. Crazy Idea: Evolution by Natural Selection
as a Search Algorithm
•Three Key Ingredients
•Inheritable traits
•Variation
•Selection Pressure
BEACON, Michigan State University, July 2010 78 /82
120. A More Fundamental Question
•Fundamental CS results: Averaged over all
problems, all search methods perform equally well
(or rather bad). Wolpert, D.H., Macready, W.G. (1997), "No Free Lunch Theorems for
Optimization," IEEE Transactions on Evolutionary Computation 1, 67
•So why does Nature use Evolution as its search
mechanism? why not other methods?
•In Synt Bio people worry a lot about evolvability
(cellularity scale video)
•Can we engineer in vivo new search mechanism?
e.g. a biological Variable Neighborhood Search?
BEACON, Michigan State University, July 2010 79 /82
121. Could, e.g., an in vivo VNS “defeat”
Evolution? in which environments?
• Perhaps SB rather than trying to stop evolution should try to
outsmart/outpace it!
BEACON, Michigan State University, July 2010 80 /82
122. Could, e.g., an in vivo VNS “defeat”
Evolution? in which environments?
• Perhaps SB rather than trying to stop evolution should try to
outsmart/outpace it!
BEACON, Michigan State University, July 2010 80 /82
123. Could, e.g., an in vivo VNS “defeat”
Evolution? in which environments?
• Perhaps SB rather than trying to stop evolution should try to
outsmart/outpace it!
BEACON, Michigan State University, July 2010 80 /82
124. Could, e.g., an in vivo VNS “defeat”
Evolution? in which environments?
• Perhaps SB rather than trying to stop evolution should try to
outsmart/outpace it!
BEACON, Michigan State University, July 2010 80 /82
125. Acknowledgements
Members of my team working on SB2
EP/E017215/1
•Jonathan Blake Infobiotics Dashboard, Integrated Environment
EP/D021847/1
•Claudio Lima Infobiotics Workbench Machine Learning & Optimisation BB/F01855X/1
BB/D019613/1
•Francisco Romero-Campero Infobiotics Modeling & Model Checking
•James Smaldon Infobiotics Dissipative Particle Dynamics
•Jamie Twycross Infobiotics Stochastic Simulations
•Karima Righetti
Molecular Micro-Biology
• Prof. M. Camara (CBS, UoN)
• Prof. C. Alexander (Pharmacy , UoN)
• Dr. S. Heeb (CBS, UoN)
• Dr. G. Rampioni (CBS, UoN)
BEACON, Michigan State University, July 2010 81 /82