P
System
Model
Op/misa/on
by

Means
of
Evolu/onary
Based
Search

           Algorithms
       C.
García‐Mar+nez,
C.
Lima,
...
Outline
• Mo+va+on:
Systems
&
Synthe+c
Biology,
P

  Systems
based
modeling
• Methods
&
Experimental
Setup
• Results
and
D...
The Cell as an Intelligent (Evolved)
             Machine
•The Cell senses the environment and its own
internal states
•Ma...
The Cell as an Intelligent (Evolved)
             Machine
•The Cell senses the environment and its own
internal states
•Ma...
Network Motifs: Evolution’s Preferred Circuits
•Biological networks are complex and vast
•To understand their functionalit...
Y positively        Negative                 Positive
     regulates X         autoregulation           autoregulation

Sh...
•T h e correct abstractions
facilitates understanding in
complex systems.

•Provide a route to engineering ,
programming  ...
7
7
•   Cells
(and
most
biologists)

    don’t
do
differen/al

    calculus!

•   P
systems
are
a
executable

    specifica/ons
...
Fundamental
EC
Challenge
• Learning
a
program
with
stochas/c
behavior

  vs.
learning
a
P
system.

    function f1(p1,p2,p...
Modular
Assembly
of
P
Systems
• Modules:
set
of
rules
represen/ng
molecular
interac/ons

  that
occur
oNen.
• Elemental
mo...
Multi-Objective Optimisation in
       Morphogenesis
                 Rui Dilão, Daniele Muraro, Miguel
                 N...
Parameter Optimisation in
    Metabolic Models

                   A. Drager et al. (2009).
                   Modeling me...
Evolving P Systems Structures
                    F. Romero-Campero,
                    H.Cao, M. Camara, and N.
        ...
Outline
• Mo/va/on:
Systems
&
Synthe/c
Biology,
P

  Systems
based
modeling
• Methods
&
Experimental
Setup
• Results
and
D...
Methods
&
Experimental
Setup
• Compare
different
evolu/onary
algorithms
to
op/mise

  parameters
(kine/c
constants)
in
P
sy...
Target
Models




                15
Target
Models




•
Highly
Dimensional
•
Noisy
&
Uncertain
outcomes
•
Non‐lineari+es
•
Expensive
Func+on
evalua+ons
      ...
Target
Models




•
Highly
Dimensional
•
Noisy
&
Uncertain
outcomes     Op+misa+on
Hell!
•
Non‐lineari+es
•
Expensive
Func...
Evolu/onary
Algorithms
• Covariance
Matrix
Adapta/on
Algorithm
(CMA‐
  ES)
• Differen/al
Evolu/on
(DE)
• Opposi/on‐Based
Di...
Experimental
Details
• Fitness
of
a
given
candidate
solu/on
is
given
by:
    1. Run
the
corresponding
P
system
with
the

 ...
Outline
• Mo/va/on:
Systems
&
Synthe/c
Biology,
P

  Systems
based
modeling
• Experimental
Setup
• Results
and
Discussion
...
Results




          19
Discussion
• Algorithms
ranked
according
to
RMSE.
• Mann‐Whitney
U
test
with
p‐value=0.05
to

  determine
which
algorithms...
Discussion
• For
TC3,
many
algorithms
perform
similar,
but
DE,

  ODE,
and
GA
seem
to
perform
slightly
beer.

  Similar
to...
Discussion
• In
general,
GA,
ODE,
and
DE
perform
beer
for

  problems
with
few
parameters
(13
and
18).
GA

  performs
beer...
Best
Fitness




               23
Best
Fitness
• Two
important
observa/ons:
  1. For
TC4
(larger
number
of
parameters),
GA,
DE,
and

     ODE
reduce
their
c...
Average
Model
Fit
• Test
Case
1




• Test
Case
2




                                25
Average
Model
Fit
• Test
Case
3


For
protein1,
all

algorithms
have
similar

output
to
the
target.




                  ...
Average
Model
Fit
• Test
Case
4




                                27
Outline
• Mo/va/on:
Systems
&
Synthe/c
Biology,
P

  Systems
based
modeling
• Methods
&
Experimental
Setup
• Results
and
D...
Conclusions
• Considered
4
test
cases
of
increasing
difficulty.
• Limited
computa/onal
resources
(1000
evalua/ons)
have

  b...
Acknowledgements
Members of my team working on SB2                                University of Nottingham
               ...
31

     31
31

     31
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P
 Systems 
Model 
Optimisation 
by
 Means 
of 
Evolutionary 
Based 
Search
 Algorithms

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This talk was presented at the Bioinformatics track at GECCO 2010. The associated paper was nominated for best paper award

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P
 Systems 
Model 
Optimisation 
by
 Means 
of 
Evolutionary 
Based 
Search
 Algorithms

  1. 1. P
System
Model
Op/misa/on
by
 Means
of
Evolu/onary
Based
Search
 Algorithms C.
García‐Mar+nez,
C.
Lima,
 J.
Twycross,
M.
Lozano,
N.
Krasnogor 1
  2. 2. Outline • Mo+va+on:
Systems
&
Synthe+c
Biology,
P
 Systems
based
modeling • Methods
&
Experimental
Setup • Results
and
Discussion • Conclusions 2
  3. 3. The Cell as an Intelligent (Evolved) Machine •The Cell senses the environment and its own internal states •Makes Plans, Takes Decisions and Act •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. 3 /136 3
  4. 4. The Cell as an Intelligent (Evolved) Machine •The Cell senses the environment and its own internal states •Makes Plans, Takes Decisions and Act •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. 3 /136 3
  5. 5. 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 4 /136 4
  6. 6. 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 5 /136 5
  7. 7. •T h e correct abstractions facilitates understanding in complex systems. •Provide a route to engineering , programming and evolving Shai S. Shen-Orr et al., Network motifs in the transcriptional regulation network of Escherichia cells and their models coli. Nature Genetics 31, 64 - 68 (2002) 6 /136 6
  8. 8. 7
  9. 9. 7
  10. 10. • Cells
(and
most
biologists)
 don’t
do
differen/al
 calculus!
 • P
systems
are
a
executable
 specifica/ons
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
stochas5city
 inherent
in
biology 7
  11. 11. Fundamental
EC
Challenge • Learning
a
program
with
stochas/c
behavior
 vs.
learning
a
P
system.
 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. 8
  12. 12. Modular
Assembly
of
P
Systems • Modules:
set
of
rules
represen/ng
molecular
interac/ons
 that
occur
oNen. • Elemental
modules:
Degrada/on,
complexa/on,
 unregulated
gene
expression,
nega/ve
gene
expression,
etc. • Combinatorics:
Combina/on
of
basic
modules
(building‐ blocks)
originates
more
complex
modules,
allowing
modular
 and
hierarchical
modelling
with
P
systems. • Challenge:
Explore
the
large
combinatorial
space
of
modules
 and
corresponding
parameters. 9
  13. 13. 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. 10 /136 10
  14. 14. 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 11 /136 11
  15. 15. Evolving P Systems Structures 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. 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 , 2009 12 /136 12
  16. 16. Outline • Mo/va/on:
Systems
&
Synthe/c
Biology,
P
 Systems
based
modeling • Methods
&
Experimental
Setup • Results
and
Discussion • Conclusions 13
  17. 17. Methods
&
Experimental
Setup • Compare
different
evolu/onary
algorithms
to
op/mise
 parameters
(kine/c
constants)
in
P
systems. • Four
test
cases
of
increasing
difficulty
and
dimension: 1. TC1:
Pulse
generator
for
different
ini/al
condi/ons
(13
 parameters). 2. TC2:
Same
problem
as
TC1
but
with
a
larger
parameters’
 domain. 3. TC3:
More
general
pulse
generator:
feed‐forward
loop
mo/f
 (18
parameters). 4. TC4:
Bandwidth
detector
(34
parameters). • Experimental
budget
was
restricted
to
1000
func/on
 evalua/ons. 14
  18. 18. Target
Models 15
  19. 19. Target
Models •
Highly
Dimensional •
Noisy
&
Uncertain
outcomes •
Non‐lineari+es •
Expensive
Func+on
evalua+ons 15
  20. 20. Target
Models •
Highly
Dimensional •
Noisy
&
Uncertain
outcomes Op+misa+on
Hell! •
Non‐lineari+es •
Expensive
Func+on
evalua+ons 15
  21. 21. Evolu/onary
Algorithms • Covariance
Matrix
Adapta/on
Algorithm
(CMA‐ ES) • Differen/al
Evolu/on
(DE) • Opposi/on‐Based
Differen/al
Evolu/on
(ODE) • Real‐Coded
Gene/c
Algorithm
(GA) • Variable
Neighbourhood
Search
with
 Evolu/onary
Components
(VNS‐ECs
v1
and
v2) 16
  22. 22. Experimental
Details • Fitness
of
a
given
candidate
solu/on
is
given
by: 1. Run
the
corresponding
P
system
with
the
 mul/compartment
Gillespie
stochas/c
simula/on
 algorithm
(20
runs). 2. Average
the
output
/me
series
of
all
runs
and
 calculate
the
difference
to
the
target
series,
using
the
 randomly
weighted
sum
method. • Beer
(for
guiding
the
search)
than
simple
 considering
the
RMSE,
par/cularly
when
/me
 series
ranger
over
different
scales. • Op/misa/on
results
are
averaged
over
50
runs. 17
  23. 23. Outline • Mo/va/on:
Systems
&
Synthe/c
Biology,
P
 Systems
based
modeling • Experimental
Setup • Results
and
Discussion • Conclusions 18
  24. 24. Results 19
  25. 25. Discussion • Algorithms
ranked
according
to
RMSE. • Mann‐Whitney
U
test
with
p‐value=0.05
to
 determine
which
algorithms
perform
significantly
 beer
than
others. • For
TC1,
most
algorithms
perform
equally
well,
 with
excep/on
to
CMA‐ES
and
VNS‐EC1. • For
TC2,
we
can
find
significant
differences
 between
algorithms,
where
GA
is
the
beer. • Reducing
biological
knowledge
(from
TC1
to
TC2)
 clearly
affects
the
performance
of
the
algorithms. 20
  26. 26. Discussion • For
TC3,
many
algorithms
perform
similar,
but
DE,
 ODE,
and
GA
seem
to
perform
slightly
beer.
 Similar
to
TC1
but
now
VNS‐EC2
performs
 considerably
worse. • For
TC4,
where
there
is
a
larger
number
of
 parameters,
results
are
significantly
different
from
 other
problems. • VNS‐ECs
now
perform
significantly
beer
than
 remaining
approaches. • CMA‐ES,
ODE,
and
DE
perform
similarly,
while
GA
 is
the
least
compe//ve. 21
  27. 27. Discussion • In
general,
GA,
ODE,
and
DE
perform
beer
for
 problems
with
few
parameters
(13
and
18).
GA
 performs
beer
when
biological
knowledge
is
 reduced. • On
the
other
hand,
VNS‐ECs
perform
beer
for
 the
larger
problem
(38
parameters). • Why
is
this?
The
number
of
evalua/ons
 allowed
is
small
(1000).
Let’s
have
a
look… 22
  28. 28. Best
Fitness 23
  29. 29. Best
Fitness • Two
important
observa/ons: 1. For
TC4
(larger
number
of
parameters),
GA,
DE,
and
 ODE
reduce
their
convergence
speeds
because
 evolving
popula/ons
of
individuals
consumes
many
 resources.
However,
VNS‐ECs
which
focus
the
search
 on
one
solu/on
make
a
beer
usage
of
the
reduced
 budget. 2. When
the
problem
involves
fewer
parameters,
the
 allowed
budget
is
enough
to
properly
converge
a
 popula/on
of
solu/ons.
In
this
case,
VNS‐ECs
are
not
 compe//ve
anymore. 24
  30. 30. Average
Model
Fit • Test
Case
1 • Test
Case
2 25
  31. 31. Average
Model
Fit • Test
Case
3 For
protein1,
all
 algorithms
have
similar
 output
to
the
target. 26
  32. 32. Average
Model
Fit • Test
Case
4 27
  33. 33. Outline • Mo/va/on:
Systems
&
Synthe/c
Biology,
P
 Systems
based
modeling • Methods
&
Experimental
Setup • Results
and
Discussion • Conclusions 28
  34. 34. Conclusions • Considered
4
test
cases
of
increasing
difficulty. • Limited
computa/onal
resources
(1000
evalua/ons)
have
 been
imposed
given
the
increased
/me
to
evaluate
 candidate
solu/ons. • For
this
experimental
setup,
it
has
been
found
that: 1. When
number
of
kine/c
constants
is
small,
GA,
DE,
and
ODE
are
 robust
op/misers. 2. When
number
of
parameters
increases,
the
VNS‐Ecs
obtain
 beer
results. • DE
(and,
not
reported,
PSO)
give
a
good
compromise
of
quality/speed
 and
configura/on
effort
for
small
to
medium
size
problems. • For
larger
problems,
VNS‐Ecs
(without
too
many
parameters)
seem
 the
way
to
go. • Fitness
criterion
must
be
revisited!!! 29
  35. 35. Acknowledgements Members of my team working on SB2 University of Nottingham Prof. M. Camara, Dr. S. Heeb, Dr. G. •Jonathan Blake Integrated Environment Rampioni, Prof. P. Williams •Claudio Lima Weizmann Institute of Science Machine Learning & Optimisation Prof. D. Lancet, Prof. I. Pilpel •Francisco Romero-Campero Modeling & Model Checking •Karima Righetti Molecular Micro-Biology •Jamie Twycross Stochastic Simulations BB/F01855X/1 BB/D019613/1 EP/E017215/1 EP/H024905/1 30 /136 30
  36. 36. 31 31
  37. 37. 31 31

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