Presentation summarising the 2013 ICSB conference in Copenhagen, a requirement of James Hutton Institute Visits Abroad funding. Presented at the Cellular and Molecular Sciences seminar series.

1. 14th
Interna+onal
Conference
on
Systems
Biology,
Copenhagen
(Visits
Abroad)
Leighton
Pritchard

2. ICSB2013
l Copenhagen,
Denmark
Friday
30th
August-­‐Tuesday
3rd
September
l Website:
hBp://www.icsb2013.dk/
l TwiBer
hashtag:
#ICSB13
l Three
Parallel
sessions
(50
talks
aBended):
l Metagenomics;
Industrial
applicaPons;
Temporal
phenomena
across
biological
Pmescales
l Cancer
and
Stem
cells;
Physiology-­‐based
modelling
of
disease;
Metabolomics
l Precision
medicine;
Health
care
data;
Network
engineering

3. ICSB2013
l Three
parallel
sessions:
l Strategies
for
modelling
biological
systems;
Protein
interacPon
networks;
Drug
discovery
l GenePc
networks;
Complete
cell
modelling;
Host-­‐pathogen
interac+ons
l Signalling
networks;
Cells
to
Pssues;
Very
large
scale
data
visualisa+on
l GenePcs
and
imaging;
Complex
genePc
traits;
Synthe+c
biology
l Two
poster
sessions:
l Session
1:
191
posters
l Session
2:
195
posters
l My
poster:
hBp://shar.es/EQuFG
and
hBp://dx.doi.org/10.6084/m9.figshare.767275

4. ICSB2013:
Keynotes
l Included:
l Stuart
Kauffmann:
hBp://en.wikipedia.org/wiki/Stuart_Kauffman
„ TheorePcal
biologist,
introduced
Boolean
networks
to
biology
l Marc
Vidal:
hBp://dms.hms.harvard.edu/BBS/fac/Vidal.php
„ Pioneering
invesPgaPon
of
protein-­‐protein
interacPon
networks
l Gene
Myers:
hBp://en.wikipedia.org/wiki/Eugene_Myers
„ Comp.
sci/bioinformaPcs,
co-­‐inventor
of
BLAST
l Wendell
Lim:
hBp://en.wikipedia.org/wiki/Wendell_Lim
„ Signalling
networks;
optogenePcs
l Peer
Bork:
hBp://en.wikipedia.org/wiki/Peer_Bork
„ Human
gut
microbiome;
Tree
of
Life
etc.
l Ruedi
Aebersold:
hBp://en.wikipedia.org/wiki/Ruedi_Aebersold
„ Pioneer
of
mass
spectrometry
in
biology
l Chris
Voigt:
hBp://en.wikipedia.org/wiki/Christopher_Voigt
„ Engineering
logic
circuits
in
bacteria
l Bernhard
Palsson:
hBp://en.wikipedia.org/wiki/Bernhard_Palsson
„ Whole-­‐cell
modelling;
introducPon
of
linear
programming
to
biological
cell
modelling

5. ICSB2013:
Impressions
l Wet-­‐lab/dry
compuPng
integraPon
is
leading
biological
understanding
–
SysBio
a
lot
further
on
than
you
might
think
l Whole-­‐system
measurement
(sequencing,
proteomics,
metabolomics
etc.)
l Modelling
and
predicPon
l Cataloguing
is
not
understanding
l Biology
is
dynamic
l You
cannot
intuiPvely
understand
a
cell
at
the
molecular
level
l Few
speakers
were
working
on
plants
or
plant
pathogens
l More
money
(and
acceptance…)
in
cancer
and
human
health?
l Opportunity
for
us?
l Single-­‐cell
studies
waiPng
in
the
wings
l “Network
state
determines
phenotype”
–
Ruedi
Aebersold

6. ICSB2013:
Human
Gut
(1)
l Parallels
with
soil
microbiome
and
interac+on
with
plants
obvious!
l Bjorn
Neilsen,
CBS
Lyngby
l Sequenced
396
human
faecal
samples
(MetaHit2
database)
l IdenPfied
bacteria
by
coabundance
gene
groups
(CAGs)
–
741
‘species’
l Found
community-­‐wide
dependency
networks
l Damian
Plichta,
CBS
Lyngby
l FuncPonal
expression
(array)
profiles
in
233
stool
samples
consistent
across
variaPon
in
species
mix;
most
transcribed
funcPons
unknown!
l DifferenPal
acPvaPon/silencing
depending
on
companion
species.
l hBp://www.nature.com/nature/journal/v493/n7430/full/nature11711.html
l hBp://www.nature.com/nature/journal/v500/n7464/full/nature12506.html

7. ICSB2013:
Human
Gut
(2)
l Mani
Arumugam,
University
of
Copenhagen
l Microbiome
ferments
undigested
carbohydrates
to
produce
SCFA.
l Microbiome
richness
and
bacteroides/firmicute
balance
shired
in
overweight/obese
individuals.
l Yuri
Kosinsky,
NovarPs
l Constructed
semi-­‐mechanisPc
4-­‐compartment
spaPal
model
of
metabolism
in
gut
species.
l Model
predicted
differenPal
butyrate
flux
from
gut
to
plasma
as
a
result
of
change
in
bacteroides/firmicute
balance.
l Gijs
den
Besten,
University
of
Groningen
l 13C-­‐labelled
SCFA
(acetate/propionate/butyrate)
introduced
into
mouse.
l ODE
modelling
of
SCFA/insulin
in
response
to
dietary
fibre
and
fat;
SCFA
flux
–
not
concentraPon
-­‐
the
significant
influence
on
body
weight.

8. ICSB2013:
Human
Gut
(3)
l Peer
Bork,
EMBL
l MetaHit
database
has
over
10m
genes
idenPfied
from
human
gut
microbiome
analysis
(hBp://www.metahit.eu/)
l Have
species-­‐based
‘diagnosPcs’
for
colorectal
cancer,
type
II
diabetes,
Crohn’s
etc.
(AUC≈0.8,
n>120).
l Need
beBer
funcPonal
annotaPon.
Some
signal
from
anPbioPc
resistance
potenPal:
most
are
resistances
to
veterinary
anPbioPcs;
geographical
variaPon
with
anPbioPc
use.
l Donate
your
own
poo:
hBp://microbes.eu/
l Microbiota
and
SCFA:
hBp://onlinelibrary.wiley.com/doi/10.1038/oby.2009.167/full
l CompePPon
in
faBy
acid
oxidaPon:
hBp://www.ploscompbiol.org/arPcle/info%3Adoi%2F10.1371%2Fjournal.pcbi.1003186
l Human
gut
microbiome:
hBp://www.nature.com/nature/journal/v464/n7285/full/nature08821.html

9. ICSB2013:
Networks
(1)
l Ruedi
Aebersold,
University
of
Zurich
l mRNA
is
stochasPc
(42k
copies
per
cell),
proteins
are
not
(95m
copies
per
cell).
Focus
on
transcripts
misses
a
lot
of
biology.
hBp://www.cell.com/retrieve/pii/S0092867412011269
l Developing
SWATH-­‐MS:
records
complete,
Pme-­‐resolved,
high
accuracy
fragment
ion
spectrum.
Aiming
for
complete
proteome
measurement.
hBp://www.imsb.ethz.ch/researchgroup/malars/research/
openswath
l Measured
phosphorylaPon
states
of
1015
proteins
as
consequence
of
co-­‐sPmulaPon
of
two
signalling
pathways
–
validated
against
prior
publicaPons.
Over
1000
‘response
surfaces’
revealing
pathway
crosstalk
(see
hBp://onlinelibrary.wiley.com/doi/10.1111/j.
1742-­‐4658.2006.05359.x)

10. ICSB2013:
Networks
(2)
l Anne-­‐Claude
Gavin,
EMBL
l Over
1600
‘omics
datasets
for
M.pneumoniae,
including
phosphorylaPon
dynamics:
hBp://www.nature.com/msb/journal/v8/n1/full/
msb20124.html
l Has
developed
a
liposome
array
to
study
protein
recruitment
to
biological
membranes
(patent
applicaPon
GB1212896.3)
l Pierre
Millard,
University
of
Manchester
l ThEcoli
–
a
kinePc
(ODE)
model
of
E.coli
central
metabolism.
2
compartments,
52
metabolites,
76
reacPons,
56
allosteric
interacPons,
490
parameters.
l Validated
against
experimental
data
obtained
in
the
project
(sugar
pulses/substrate
limitaPon)
–
see
also
hBp://www.sciencedirect.com/science/arPcle/pii/
S1096717613000074
.

11. ICSB2013:
Networks
(3)
l Jasmin
Fisher,
Microsor
l ‘Executable
biology’:
the
logic
and
calculus
of
biological
molecular
decision-­‐making
l BioModelAnalyzer
to
make
models
accessible
to
wet-­‐lab
biologists
(hBp://biomodelanalyzer.research.microsor.com/)
l Julio
Saez-­‐Rodriguez,
EBI
l Building
large-­‐scale
models
of
signalling
in
disease
from
pathway/phosphoproteomic
data.
l ExhausPvely
characterising
feasible
logic
models
of
a
signalling
network:
hBp://bioinformaPcs.oxfordjournals.org/content/
29/18/2320.long

12. ICSB2013:
Highlight
(1)
l Rama
Ranganathan,
SouthWestern
University
l Protein
residue
coevoluPon
(my
PhD
topic)
associaPon
with
thermodynamic
coupling:
hBp://www.sciencemag.org/content/286/5438/295
l Coevolving
protein
residues
define
‘sectors’
of
structure
with
independent
divergence:
hBp://www.cell.com/retrieve/pii/S0092867409009635
l ‘Sectors’
define
spaPal
architecture
of
protein
funcPon
and
adaptaPon
–
all
residues
subsPtuted
to
all
other
residues
in
combinaPon
to
define
funcPonal
sequence
constraints:
hBp://www.nature.com/nature/journal/vaop/ncurrent/full/
nature11500.html
l EpistaPc
posiPons
provide
opportuniPes
for
adaptaPon
to
novel
funcPons
(novel
ligand
binding)
l Applica+on
to
effector/resistance
protein
func+on
obvious!
l (See
also
Pritchard
&
Duron,
2000…
hBp://www.sciencedirect.com/science/arPcle/pii/
S0022519399910433)

13. ICSB2013:
Highlight
(2)
l Wendell
Lim,
University
of
California
San
Franciso
l OptogenePcs:
use
light
sPmulus
to
acPvate/inacPvate
a
network
component.
Enables
Pme-­‐variant
signals
to
control
network
intervenPon.
l Based
on
phytochrome.
Demonstrated
light-­‐gated
YFP
membrane
recruitment,
and
applicaPon
to
Ras
signalling.
Also
light-­‐dependent
BFP-­‐Erk
transfer
to
the
nucleus.
l OptogenePcs
enables
live
cell
modificaPon
and
readout
l IdenPfied
‘gated
signalling’
–
differenPal
response
to
signal
frequency,
not
on/off
l Obvious
applica+ons
to
plant
pathology!
l hBp://www.nature.com/ncb/journal/v9/n3/abs/ncb1543.html
l hBp://www.cell.com/molecular-­‐cell/retrieve/pii/S1097276511009506

14. ICSB2013:
Highlight
(3a)
l Chris
Voigt,
MIT
l “…biology
is
a
mess…constantly
peeling
the
onion”
l Took
Klebsiella
N-­‐fixaPon
nif
cluster
as
basis
to
engineer
N-­‐
fixaPon
into
crop
plants.
l “Refactored”
the
cluster
–
removed
ncDNA,
non-­‐essenPal
genes,
TFs
etc.;
randomised
all
codons.
Reorganised
into
arPficial
operons
under
control
of
known
(BioBrick:
hBp://biobricks.org/)
regulatory
systems.
l That
part
took
seven
years.
l Cluster
had
7.3%
of
WT
efficiency
l Then
undertook
massively-­‐parallel
design
tesPng
with
tens
of
thousands
of
modified
constructs
of
the
cluster
in
an
automated
fashion.

15. ICSB:
Highlight
(3b)
l Developed
a
novel
rule-­‐based
design
language
to
direct
evoluPon
of
the
cluster
by
direcPng
rounds
of
permutaPon
of
components.
l Used
machine
learning
to
infer
what
worked
l IniPal
cluster
had
7.3%
of
WT
N-­‐fixaPon
efficiency
l Final
cluster
recovered
100%
of
WT
N-­‐fixaPon
efficiency
l Surprises:
l Nearly
all
the
original/WT
operon
structure
is
unnecessary
l CrypPc
RNAs
in
the
WT
genes
interact
with
porins,
making
some
genes
appear
lethal
l CollaboraPon
with
John
Innes
Centre
(Giles
Oldroyd)

16. ICSB2013:
Personal
ego
bonus
l MASS
Toolbox
l Mass
AcPon
Stoichiometric
SimulaPon
by
Nik
Sonnenschein
(Bernhard
Palsson’s
group),
UCSD
l Uses
one
of
my
published
models
as
its
tutorial
example
(as
do
MathWorks
for
their
SimBiology
training)
„ Pritchard
&
Kell
(2002)
doi:
10.1046/j.1432-­‐1033.2002.03055.x

17. Acknowledgements
l Visits
Abroad
Fund
l Seedcorn
Funding