Lecture 2 of a course given at Universidad Nacional de Tucuman on Computation and Synthetic Biology

1. The Infobiotics BioProgramming Language &
Workbench
Computer-Aided Design for Synthetic Biology
Harold Fellermann & Nat Krasnogor

2. Motivation CAD for Synthetic Biology
l Most synthetic biology designs are currently developed
by laborious trial-and-error in the wet lab.
l Synthetic biology attempts to engineer elaborate
biological circuits from non-engineered biological parts.
l Unconsidered interactions among these parts often
prevent reliable scaling of designs.
l We develop systematic engineering approaches for
synthetic biology.

3. Motivation CAD for Synthetic Biology
Computer-Aided Design software for Synthetic Biology that
integrates for the following iterative work flow:

4. TheInfobioticsWorkbench The Infobiotics Workbench
Project
navigation simulation
results
simulation
controller
IDE
features
Perspectives:
simulation
verification
compilation
IBL
code
Targeted users:
l computational synthetic biologists
l “people who use CoPaSi”
Typical IDE (integrated development environment)
interface:

5. Motivation IBW Workflow

6. TheInfobioticsLanguage The Infobiotics Language (IBL)
l domain specific programming language for SB
entities
l provides statements to
l define SB parts and systems
l define rules and rates for stochastic simulation
l annotate designs with verification statements
l annotate designs with genetic sequence
information
l supports scalable designs through modularity
l declarative language with Java-like syntax

7. TheInfobioticsLanguage Coding Molecular Interactions
Example: mRNA transcription from up-regulated promoter
Promoter
RNAsignal
PBAD = PROMOTER()
Ara = MOLECULE()
rnaP = MOLECULE()
rna = RNA()
RULE regulation: PBAD + Ara <==> PBAD~Ara
RULE transcriptionInitiation: PBAD~Ara + rnaP <==> PBAD~Ara~rnaP
RULE transcriptionStep: PBAD~Ara~rnaP => PBAD + Ara + rnaP + rna
RULE rnaDegradation: rna =>
parts
interactions

8. define ActivatedTranscription typeof PROCESS(
PROMOTER promoter: input,
MOLECULE signal: input,
RNA rna: output
) {
// RULEs here
} using rnaP
trans = ActivatedTranscription(
promoter=PBAD, signal=Ara, rna=rna
)
TheInfobioticsLanguage Abstraction and Encapsulation: Processes
Loosely correspond to functions in other languages
l Definition:
l Usage:
encapsulating rules

9. TheInfobioticsLanguage Abstraction and Encapsulation: Processes
trans1 = ActivatedTranscription(
promoter=PBAD, signal=Ara, rna=rna
)
trans2 = ActivatedTranscription(
promoter=PLas, signal=aTc, rna=rna
)

10. TheInfobioticsLanguage Putting it all together: Devices
A device in the SB sense: a continuous piece of DNA
DEVICE(
parts = [lacI, pLs1con, pTrc2, cI, gfpmut3],
input = [aTc, IPTG], output = [LacI, CI, GFPmut3]
) {
mrna_LacI = RNA() // a local variable
// PROCESSES and RULES here
}
GFPmut3cI
LacI pTrc2
pLs1con
IPTG
aTc
collection of parts,
rules, and processes
that characterize the
piece of DNA

11. TheInfobioticsLanguage Topological Organization: Cells and Regions
Cells introduce compartments with physical
boundaries
Cells are embedded in regions (e.g. wells)
define Ecoli typeof CELL() {
AHL = MOLECULE()
RULE diffusion: AHL <-> OUTSIDE
}
define site typeof REGION() {
AHL = MOLECULE()
mycell = Ecoli()
}
collection of devices,
parts, rules, and
processes that
characterize a cell

12. Motivation IBW Workflow
2

13. TheInfobioticsLanguage Stochastic Simulation
Stochastic rate annotation is part of IBL
l Rate constants given in physical units.
l Converted into Markov process for stochastic
simulation.
P = PROMOTER()
rnaP = MOLECULE()
Rna = RNA()
RULE transcriptionInitiation: P + rnaP <==> P~rnaP
transcriptionInitiation.forwardRate = 0.1 M-1 s-1
transcriptionInitiation.backwardRate = 50 s-1
RULE transcriptionStep: P~rnaP => P + rnaP + rna
transcriptionStep.forwardRate = 10 s-1
RULE rnaDegradation: rna =>
rnaDegradation.forwardRate = 0.01 s-1

14. TheInfobioticsWorkbench Stochastic Simulation
Stochastic simulation is delegated to ngss
(next generation stochastic simulator, D.
Sanassy)
l implemented for best performance
l currently implements nine different SSAs
l automatic algorithm selection
l MPI support to distribute
l simultaneous runs

15. Motivation IBW Workflow
3

16. TheInfobioticsLanguage Verification through Model Checking
Verification statements are part of IBL
l Natural language like syntax
l Properties obtained from mining biological literature
l Can be converted into temporal logic clauses and fed
into different model checkers (PRISM, NuSVM, MS2)
VERIFY [ GFP > 0 uM ] EVENTUALLY HOLDS
VERIFY [ GFP > 0 uM ] EVENTUALLY HOLDS WITH PROBABILITY > 0.9
VERIFY [ GFP > 0 uM ] ALWAYS HOLDS
VERIFY [ GFP > 2*RFP ] NEVER HOLDS
VERIFY [ GFP > 0 uM ] HOLDS WITHIN 60 s
VERIFY [ AHL > 0 uM ] IS FOLLOWED BY [ GFP > 0 uM ]
VERIFY [ GFP > 0 uM ] EVENTUALLY HOLDS,
UNTIL THEN [ NahR = 0 uM ] HOLDS

17. TheInfobioticsWorkbench Verification through Model Checking
Verification is performed in the defining context:
Allows scaling of model checking to systems with many
components.
Verification of RULEs
Verification of CELLs
Verification of DEVICEs
define myDevice typeof DEVICE() {
// RULEs tested during verification
VERIFY [ GFP > 0 uM ] EVENTUALLY HOLDS
}
// other DEVICEs and RULEs, not tested during verification

18. Motivation IBW Workflow
4

19. TheInfobioticsLanguage Biomatter Compilation
Biomatter compilation directives are part of IBL
Sequence information can be pulled in from standard
repositories
(e.g. biobricks, virtualParts), in-house databases, or defined
manually.
PnahR = PROMOTER(URI = "biobricks://BB_0132")
nahR = GENE(URI = "biobricks://BB_08431")
lasR = GENE(sequence= "TACGTTGACCA...")
myDevice = DEVICE(parts=[PnahR, nahR, lasR], output=[NahR, LasR]) {
ATCG ARRANGE nahR lasR
// PROCESSES and RULES here
}
ATCG DEVICE myDevice CLONING SITES: 2

20. TheInfobioticsWorkbench Biomatter Compilation
Biomatter compilation is delegated to ATGC
(assistent to genetic compilation, C. Ladroue)
l Completes IBL designs with terminators, spacers, RBS's
l Calculates ideal RBS's from given IBL transcription rate
l Arranges parts according to given constraints
l Adds cloning sites from specified library
l Generates final sequence in standard formats (e.g. SBOL)

21. TheInfobioticsWorkbench The IBW Workflow
l IBW cannot guarantee that defined RULES correctly
reflect the mechanism of a specified biopart
l The user is responsible for defining correct RULES
l Version management can help in an iterative design
process
l toward a correct overall design

22. TheInfobioticsWorkbench The IBW Implementation
planned
ngss PRISM, NuSVM ATGC
IBW is implemented as Ecplise Rich Client Application
using XText to define IBL.

23. Acknowledgments
l Christophe Ladroue
l Laurentiu Mierla
l Jonny Naylor
l Daven Sanassy
l Marian Gheorghe
l Sara Kalvala
l Savas Konur
l Natalio Krasnogor
l varaious members of ICOS