Rodríguez-Patón Aradas, Alfonso

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Biocircuits engineering and bio-design automation: some recent results

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Biocircuits engineering and bio-design automation: 
some recent results”
Alfonso Rodríguez-Patón
arpaton@fi.upm.es
@LIA_UPM
Newcastle University,  June 15, 2015 
LIA members
• Professors:	
– Andrei Paun
– Petr Sosík
– Iván	Pau
– Daniel	Manrique
• PhD	students:	
– Martín	Gutiérrez
– Paula	Gregorio
– Guillermo	Pérez
– Ismael	Gómez
– Vishal Gupta
– Antonio	García
– Marcos	Rodríguez
• Master	students:	Irene	Serrano,	Laura	Puente,	Luis	Enrique	Muñoz
Outline
• Systems Biology: GRO simulator
• Synthetic Biology: PLASWIRES project, Directed
Evolution.
• DNA Computing: Inference with DNA 
molecules.
• Lab automation: EVOPROG project
One-gene genetic oscillator with a positive 
feedback loop and a negative interaction
Miró-Bueno JM, Rodríguez-Patón A (2011) A Simple Negative Interaction in the Positive 
Transcriptional Feedback of a Single Gene Is Sufficient to Produce Reliable Oscillations. PLoS
ONE 6(11): e27414. doi:10.1371/journal.pone.0027414 
One-gene genetic oscillator with a positive 
feedback loop and a negative interaction
PLoS ONE 6(11):	e27414.	
doi:10.1371/journal.pone.0027414
DNA	Computing	in	LIA	group
Sainz	de	Murieta,	I.,	&	Rodriguez-Paton,	A.	
(2014).	Probabilistic reasoning with a	Bayesian
DNA	device based on strand displacement.	
NATURAL	COMPUTING,	13(4),	549-557.
Rodríguez-Patón,	A.,	de	Murieta,	I.	S.,	&	Sosík,	
P.	(2014).	DNA	strand displacement system
running logic programs.	Biosystems,	115,	5-12.
Chicago
Computational modelling
The most frequent approaches for simulating genetic
circuits are:
• Differential Equations (DEs)
– High precision at single-cell level
– Scale poorly to large-scale colonies and spatial
component is not easily reproduced
• Agent/Individual based Models (IbMs)
– Perform better at a large scale
– Not as precise as DEs (most IbMs are rule based)
– Provide a good spatial environment
LIA
• We are interested in designing, simulating and 
studying multicellular genetic circuits that run in 
bacterial colonies.
• We wan to simulate cell-cell communication
based on conjugation: space is important
-------------------------------------------------------------
• Agent/Individual based Models (IbMs): are best
suited for this purpose.
State of the Art: IbMs
• CellModeller
– Python library for simulating bacterial colonies
developed by U. Cambridge and Microsoft 
Research.
– Simulates 2D or 3D colonies of rod-shaped
bacteria.
– Simulations are implemented through DEs or
through rules.
– Runs on OpenCL and reaches up to 32000 
simulated bacteria in 30 mins.
State of the Art: IbMs (II)
• iDynoMiCS
– Developed by J.U. Kreft lab at University of 
Birmingham
– Simulates 2D and 3D bacterial colonies.
– Simulations are implemented through rules (XML 
parametrization and Java).
– Goal is to study biofilm formation.
State of the Art: IbMs (III)
• BactoSIM
– Developed by LIA – UPM.
– Simulates 2D spherical bacterial colonies.
– Simulations are implemented through rules (based
on Repast - Java).
– Reaches 106 bacteria in 1 hour.
– The goal of BactoSIM is to study bacterial
conjugation.
State of the Art: IbMs (IV)
• GRO 
– Developed by Klavins lab at University of 
Washington.
– Simulates 2D rod-shaped bacterial colonies.
– Simulations are implemented through rules (gro 
language – based on guarded commands).
– Reaches around 104 bacteria in 1 hour.
– Aimed at simulating multicellular behaviors.
IbMs
• At LIA, we (have) work(ed) with these IbMs:
– iDynoMiCS
– BactoSIM
– GRO
• A brief summary of our work will now be 
presented.
iDynoMiCS
iDynoMiCS - Conjugation
AND gate
iDynoMiCS – Growth
Bacteria growing with rod-shaped bacteria and shoving
BactoSIM
BactoSIM – Measuring phage infectivity
Phage
Uninfected bacterium
Infected bacterium
T = 0 mins T = 125 mins
GRO
GRO
GRO
GRO
• Developed at Klavins Lab
(University of Washington)
• IbM based on guarded
commands and functional
programming.
• GRO is mainly concerned
with studying bacterial colony
growth and multicellular
behaviors based on signals.
• Chipmunk acts as GRO’s
physics engine.
Some limitations of GRO
• Bacterial conjugation is not implemented.
• GRO is slow, it reaches about 20000 bacteria in 
4 hours.
• Describing an experiment with guarded
commands is unnatural for biologists.
• Colony growth does not take into account
nutrient uptake.
GRO, improved by LIA
• We have implemented the following
functionalities for GRO:
– Bacterial conjugation
– New shoving algorithm (CellEngine)
– New genetic module
– New nutrient uptake and growth module 
(CellSignal)
Implementation of conjugation process
• Conjugation was implemented atop the
modified GRO (using CellEngine)
“Aura” calculation to retrieve a bacterium’s neighbors
25
Conjugative plasmid:	will you survive?
Implementation of conjugation process
• GRO’s source code was modified to include
conjugation process in its workflow
Integrate growth
and contact
dynamics
Update cell state
For each cell:
a) Conjugate (if it’s time)
b) Increase volume
c) Evaluate guarded
commands
d) Check for divisions and 
create daughter cells if
necessaryRemove dead
cells
Integrate diffusion
and degradation of 
signals
Adapted from Jang et al., 2012 27
GRO – Conjugation rules
• A bacterium conjugates in GRO under the
following circumstances:
– It has a conjugative plasmid
– A probability of conjugation is calculated from a 
ratio between the # of average conjugations per life
cycle, the # of timesteps and the # of neighbor
bacteria.
28
CellEngine: a new fast shoving algorithm
• CellEngine, a new shoving algorithm
• Based on the grouping of bacteria in rings
CellEngine: algorithm
1. Find bacteria in the edge of the colony
2. Recursively create rings of bacteria of a certain
width w inwards until reaching the center of the
colony
3. Once the center is found, repeat for all rings
starting at the center and moving outwards:
1. Relax overlaps of ring i as if it were an independent
colony. Assume the inner ring (i+1) as a wall and the
outer ring (i-1) as non-existent.
2. Relocate ring i-1 outwards around ring i.
CellEngine execution
Ring calculation
CellEngine execution
Ring calculation and growth (large scale)
CellEngine vs. Chipmunk
GRO (CellEngine) GRO (Chipmunk)
Growth test
CellEngine execution
Bacterial colony growth
CellEngine execution
Cell alignment calculation
CellEngine – some numbers
GRO (Chipmunk) GRO (CellEngine)
Cells Total time (hours) Total time (hours)
1 0.00 0.00
500 0.00 0.00
1000 0.01 0.00
2000 0.02 0.00
4000 0.08 0.00
8000 0.41 0.00
16000 2.14 0.00
20000 4.02 0.01
100000 > 168 0.05
Genetic module
• Boolean values for the proteins: 0/1 
• Boolean values for the state of the genes: ON/OFF
• How long must be a gene ON to produce enough protein to
be considered as 1? Half-life activation time. 
• How long must be a gene OFF to consider its associated
protein takes value 0? Half-life degradation time. 
• Promoters: Boolean logic gates: AND, OR, etc.
• Noise: Probability that a gene ON(OFF) switch to OFF(ON) 
respectively, without any change in the inputs
• Similar to a Probabilistic Asynchronous Boolean Network 
with delays. Or a piecewise-linear differential equation. 
Example: Repressilator
Original design of the Repressilator
Example: Repressilator
Guarded command based source code for the Repressilator
Example: Repressilator
Genetic design based source code for the Repressilator
Example: Repressilator
Repressilator in GRO Wet-lab Repressilator
Example: Edge detector
Plasmid-plasmid interaction
Nutrient uptake and growth module
Colony growth with underlying nutrient uptake
We’re working on more features…
• A simplified and faster version of signal diffusion
• Phage (lytic and non-lytic) dynamics.
• A new genetic module more “precise” with
probabilities and threshold values of time for
0/1 protein states.
• 3D simulations: Morpheus, Biocellion
Perspectives
GRO
SBOL
Quantitative data
GRO source
code
Outline
• Systems Biology: GRO simulator
• Synthetic Biology: PLASWIRES 
project, Directed Evolution.
• DNA Computing: Inference with DNA 
molecules.
• Lab automation: EVOPROG project
PLASWIRES project

Outline
• Systems Biology: GRO simulator
• Synthetic Biology: PLASWIRES project, 
Directed Evolution.
• DNA Computing: Inference with DNA 
molecules.
• Lab automation: EVOPROG project
An Autonomous In Vivo Dual Selection Protocol
for Boolean Genetic Circuits
Beneš, D., Sosík, P., & Rodríguez-Patón, A. (2015). An Autonomous In Vivo Dual Selection Protocol for
Boolean Genetic Circuits. Artificial Life 21: 247–260 (2015)  doi:10.1162/ARTL_a_00160 
An Autonomous In Vivo Dual Selection Protocol
for Boolean Genetic Circuits
Beneš, D., Sosík, P., & Rodríguez-Patón, A. (2015). An Autonomous In Vivo Dual Selection Protocol for
Boolean Genetic Circuits. Artificial Life 21: 247–260 (2015)  doi:10.1162/ARTL_a_00160 
Outline
• Systems Biology: GRO simulator
• Synthetic Biology: PLASWIRES project, 
Directed Evolution.
• DNA Computing: Inference with
DNA molecules.
• Lab automation: EVOPROG project
Inference with DNA molecules
Basic Inference rules: modus ponens and 
modus tollens
Modus Ponens states that from P and the implication 
P → Q one can deduce Q. 
If P, then Q
P.
Therefore, Q 
Modus Tollens states that from NOT-Q and  the 
implication P → Q one can deduce NOT-P.
If P, then Q
Not-Q.
Therefore, Not-P
Previous works: Inference with DNA molecules
DNA strand displacement and competitive
hybridization
Inference with DNA strand displacement
Special case of resolution: Modus Ponens
Modus	Ponens
Special case of resolution: Modus Tollens
Modus	Tollens
Solving SAT applying resolution with 
autonomous 4-way branch migration
Examples:
F1= B∧¬B is unsatisfiable. 
F2 = (A ∨ B) ∧ (¬ B ∨ C) is satisfiable. 
F3= ¬A ∧ (A ∨ B) ∧ ¬B is unsatisfiable. 
Applying resolution to all the clauses, if a refutation can be 
derived from the initial formula, then the formula is 
unsatisfiable. A refutation is a sequence of clauses 
obtained by iterated application of the resolution rule that 
finish in the empty clause.
In our experimental set-up the cover strands contains a 
fluorescent marker so the empty clause corresponds to a 
non-fluorescent double-stranded molecule with nicks 
between all parts encoding variables. 
4-way DNA branch migration: B∧¬B
Autonomous	resolution	determines	that F1=	B∧¬B is	unsatisfiable:	we	get	the	empty	clause	
4-way	branch	migration	 Strand	exchangeAnnealing	
Solving SAT applying resolution with 
autonomous 4-way branch migration
Resolvant clause:	A∨ C
No	empty	clause.	F2 is	satisfiable.
Resolution
Empty	clause.	
F3 is	unsatisfiable
Resolution
Resolution
Solving SAT applying resolution with 
autonomous 4-way branch migration
Outline
• Systems Biology: GRO simulator
• Synthetic Biology: PLASWIRES project, 
Directed Evolution.
• DNA Computing: Inference with
DNA molecules.
• Lab automation: EVOPROG project
Applying ICT for biology Automation
A general view
Automated	Equipment	
Pipelines
(Transcriptics,	ECL)
Open	Source	Pipetting	
Robots
(OpenTrons,	Modular	Science)
Bio-Design	and	Workflow	
Language
(Autoprotocol,	Antha)
Flow	control	on	Fluidic	
Devices
(Aqua,	EvoCoder)
Graphical	Language	for	
Protocol	Description
Automated Protocol Execution in Biology-
Graphical languages for Lab protocol description
Graphical languages for Lab protocol description
Team of	biologists
Experimental	protocol
Working on each stage of the process…
Lab Platform Lab Interface Lab Automation
Execution
EVOPROG Project
EVOPROG project: LIA’s Tasks
Evoprog biological
machine
(Programmable
Evolution Machine)
Parameters
Standard
Architecture for
(Definition of	ISA)
Program based on
Instruction Set	
Architecture
High	Level
Language
Biologist
We	are	
here!!!
Task 3:	Compiler to	
Machine	code
Living	matter
Transforming biologists’	thoughts and	designs in	real	instructions
and	parameters for the Evoprog machine
Task 1:	Visual	Language
for Biologists
Task 2:	Generic high-
level language


http://oa.upm.es/42545/6/INVE_MEM_2015_229816.pdf

Biocircuits engineering and bio-design automation: some recent results

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