1. Forward Engineering of
Synthetic Bio-Logical AND Gates
Jonathan R. Tomshine, KavitaIyer, Jennifer A. Maynard, Yiannis N. Kaznessis
University of Minnesota, Minneapolis

2. Modeling: Approaches
and Goals
Two basic philosophies:

Assist in analysis and design of proposed system

Describe & summarize behavior of existing system

Summary much easier than prediction, can use

anything that works
Prediction must be built on more fundamental

principles that are understood before-hand

3. Digression: Civil
Engineering
Bridges: a sketch can summarize a

shape, but not enough to build:
True “engineering” requires a

detailed model based on physical
principles
Model (known) behavior of individual

beams
Understand composite behavior of the

whole bridge

4. In SilicoDesign in
Synthetic Gene Networks
Don’t *know* behavior of proposed system

design based on intuition

Better understanding of generic lower-level

processes:
Transcription

Translation

Degradation

Induction

Etc., etc.

Solution: build engineering model from bottom-

up, rather than top-down
try to predict complex behavior from simple components


5. Gene Expression as
Chemical Reactions
Represent mechanisms as networks of elementary

chemical reactions – a general approach:
For Example,
Nature of Reactions* Number
Dimerization:
of Rxns
2
Repressor Protein Dimerization
k
araC2araC + araC
2
Repressor / Operator Binding
2
RNAp / Promoter Binding k
1
araC+ araC araC2
Bound RNAp Conformational Change
1
RNAp moving to coding DNA
1*
Transcription Elongation
1
mRNA / Ribosome Binding
1
Ribosome Moves Off of Ribosome Binding Site
1*
Translation Elongation
4
Degradation

6. Simulation of Networks:
Stochastic Cells
Cells Small: 1×10-15 liters (bacteria)

Reactants Scarce: perhaps 1 molecule of a DNA site

per cell
Far from thermodynamic limit; cannot use ODE’s

Example One Trajectory Many (1000) Trajectories
System:
A0 = 1000
A0 = 3 A0 = 75
A B
B0 = 0
B0 = 0 B0 = 0
BA
Stochastic
Deterministic (ODE)

7. The AND Gate: a Simple
Case
One promoter, two different

types of operator sites (Lac,
Tet) – similar to lac/ara of
Lutz, Bujard (1997)
RNAp should not bind if

either operator occupied
With three positions and two

types of operators, 6
different promoter
configurations

8. AND Gate Modeling ab
initio: Will it Work?
Created a model with literature kinetics

apply IPTG and aTc in silico, check GFP levels

AND Gate Model,
First Iteration
Looked promising: almost no GFP w/o inducer

Induced to a high level at realistic

concentrations (max: 200 ng/mLaTc, 1 mM
IPTG)
…but no way to differentiate (say) LTT from

TTL!

9. Constructing & Evaluating
the AND Gate
Okay in silico, but will it run in vivo?


10. Leakiness of LacI:
Refining the Model
Count individual cells (flow cytometry) to quantify

Not all promoter configurations created equal

Could not predict with modeling (lack of parameters)


11. Model Refinements
Added additional term for “leaky”

Experiment
expression: RNAp can knock a
single LacI off of the promoter:
RNAp + P:O:LacIRNAp:P:O +

LacI
Rate constant depends on

promoter configuration; different for
each promoter
Model
Calibrated levels of LacI, TetR in

(Final Iteration)
our cell line
Affects all model variations

Alters induction thresholds


12. Points of Agreement
Model captures observed trend in

promoter activity (LTT > TLT >
TTL)
Model captures trend in

decreasing leakiness with
decreasing activity (fit new
parameter)
Model captures the ability of 2-

tetO systems to induce, and the
failure of 2-lacO systems

13. What Was Learned
AND Gate promoter model can be applied to larger

designs
Components of AND Gate (Lac, Tet repressors &

binding sites) better understood for future
modeling
Models build on themselves; next round more

sophisticated with more confidence

14. SynBioSS Desktop:
Making Simulation Easier
http://synbioss.sourceforge.net/
Accepts models in SBML or

NetCDF format
Applies Gillespie’s SSA or

Hybrid Stochastic-Discrete /
Stochastic Continuous
Methods
Completely graphical &

platform independent (Python
GTK GUI), Open Source
(GPL)
Fast (math in Fortran 90/95)


15. SynBioSS Wiki: The
Problem of Kinetics
https://kaznessis.msi.umn.edu/wiki
Kinetic data scattered throughout

literature
Detailed models require lots of

data – tedious to gather together
Wiki provides single repository for

k-values, searchable by
interacting species, with
references, etc.
Point-and-click model construction

– “shopping cart” style

16. SynBioSS Designer:
Automatic Models
Generation of new models

difficult w/o experience – a
niche market
Expression of individual genes

systematic
Solution: generate models

automatically based on physical
brick sequence & a few
parameters

17. Thank you!
Acknowledgements

Minnesota Supercomputing

Institute
UofM Digital Technology Center

Tony Hill

Howard Salis

Emma Weeding

Vassilis Sotiropoulos

KavitaIyer
