What happened to computing 1930-80 is now happening to biology
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Transcript of What happened to computing 1930-80 is now happening to biology
What happened to computing 1930-80 is now happening to biology
Friden mechanical calculator: 1930-1966
Friden electronic calculator: 1965Intel 4004
proc:1971
Sharp single chip calculator: 1977
What happened to computing 1930-80 is now happening to biology- Example: electroporation
Pro
port
ion
of
cells
tra
nsf
ect
ed
Pre-2000
Today
Tomorrow
The complete protocol
% c
ells
dam
ag
ed
by t
ran
sfect
ion
Another example: automated parallel mini-preps
Pathway/ interaction
DBs / literature
data analysis
model construction
model analysis
hypothesis formulation
experimentalplanning
experiments to test hypothesesBiology
Technology
Computation
The geek shall inherit (systems) biology
cell-specific, comprehensive,
kinetic and quantitative data
Top-down, global, systematic
• Discovery science via high throughput ‘omic technologies
• Screening- multi-target/multi-component drugs- multi-parameter disease signatures
• Observation & data-driven
• Focus on nonlinear interactions
• Study irreducible systems
• Analyzing emergent (difficult-to-predict, nonlinear) properties
• Hypothesis and model-driven
Bottom-up, local dynamics
2 complementary approaches in systems biology:
global climate local weather
The yeast galactose utilization pathway – bottom up viewd
e A
tau
ri e
t a
l B
ioc
he
m J
. 2
00
5
Galactose
Galactose 1-phosphate
Glucose 1-phosphate
Glucose 6-phosphate
UDP-galactose
PGM1PGM2
UDP- glucose
UGP1
1,3-â-Glucan
Glycogen synthesis
Threhalose synthesis
GPH1
GSY1GSY2
GSC2FKS1FKS3TPS1
TSL1TPS3
Galactose
Gal10p(epimerase)
Gal7p(transferase)
Gal1p(kinase)
Gal2p(transporter)
Gal80
Gal3
Gal2
Gal 1,7,10
Gal3p
Gal3p*
Gal80p
+ feedback
+ feedback
- feedback
Gal4p
Gal4p
Gal4p
Gal4p
The yeast galactose utilization pathway – top down view
How do we make sense of this?
A network graph viewed in Cytoscape; what does it do?
. • • ••••
•
••
•
in1
in2
out = XOR
Vdd
Gnd
T1
T2
T3
T4
T5
T6
T7
T8
T9
T10
T11
T12
T13
T14
1
0
2
3
4
5
6
7
8
NOR (1) NAND (2) NOT (3) NOR (4)
in1in2
out
• • 1
2 3
4
in1 in2 NOR 1 NAND 2
NOT 3 NOR 4
0 0 1 1 0 0
0 1 0 1 0 1
1 0 0 1 0 1
1 1 0 0 1 0
out = XOR
An Exclusive OR gate
p38pathway
Phagocytosis
Cell adhesion
Translation
Fas
mRNA stability
Actin polymerization
Transfac documentedP-DNA interaction
Transfac/HPRD/in-house documented P-P interaction
Red nodes: down-regulated
Green nodes: up-regulated
“Guilt by association” network analysis
Uri Alon & colleagues Rick Young & colleagues
Ecoli
yeast
Community effect
Nonlinear switchRegulated &/or rapid response
sea urchin functional building blocks
Bolouri & DavidsonBioEssays 2002
Unidirectional switch
Network motifs & functional building blocks
. • • ••••
•
••
•
in1
in2
T1
T2
T3
T4
T5
T6
T8
T9
T10
T11
T12
T13
T14
1
0
2
5
7
•
•T1
1
T1
2
T1
3
7
B A if (select1=on & select2=on) •
select1select2
A
Cannot guess function from topology alone
Network topology:
equivalent network motifs/modules
Possible (mis)interpretation:
Network motifs have many additional inputs and interactions
Alon & colleagues, Nature Genetics, 2002. 31(1): p. 64-8
see also:
The feed forward motif:
A topological motif may implement different functions
A coherent feedforward motif acting as a gradient sensor
Need a functional abstraction hierarchy
(B)
(A)
(C)
(D)
(E)
(g)
Potential genetic regulatory functional building blocks
(F)
BioTapestry.org
PSS = (ks/kdp).mRNA
mRNASS= (kt/kdm).Y
At steady state:
GataE
Otx
GCM
P
mRNA
mRNAss
Pss1st o
rder
2 cooperative site
s
kt.ks/2.kdm.kdp
Kd
iss
(B)
(A)
(C)
(D)
(E)
(g)
Potential genetic regulatory functional building blocks
(F)
positive intercellular feedback
positive driver
cell type specific gene battery
(1) (2) (3)
-catenin/Wnt8 ‘Community Effect’ filters out expression variability
Cells in a reinforcing loop
- all cells in group adopt same fate
- sharp boundary between cell types
- insensitive to level of “driver”
Simulated time
blue
gen
e ac
tivi
ty le
vel
_ cell 1_ cell 2_ cell 3
cell 1 driver gene
NTCF
frizzledGSK-3
Wnt8
c
Krox
c
Intercellular positive feedback: The community effect
Gurdon ‘88, Nature, 336, 772-4
Data from Davidson lab
(B)
(A)
(C)
(D)
(E)
(g)
Potential genetic regulatory functional building blocks
(F)
Intracellular negative feedback
(A) tuned for regulated level
(C) tuned for rapid response
(B) single transcriptional pulseSee also Ashburner et al 1973-1981 (see Cell, 1990. 61(1):1-3)
x
(D) tuned for long lasting oscillation
FoxA
(B)
(A)
(C)
(D)
(E)
(g)
Potential genetic regulatory functional building blocks
(F)
NkdTCF
EnTCF
Wnt pathway
Wnt
Ptc
Cell 1 (posterior)Cell 2 (anterior)
SlpCiHh
Dlp
Ptc: :H
hS
mo
_I
Cos2::GSK3
Sm
oCiA CiR
Sm
o::C
os2Su(Fu) Fu
Ci::Su(Fu)
u
Hh pathway
0
1000
2000
3000
4000
5000
6000
1 9 17 25 33 41 49 57 65 73 81 89 97 105 113 121 129 137 145 153 161 169 177 185 193 201
DshA
DshA2
En
En2
Mutual exclusion in a mammalian adult cell specification process
ratedecayK
rateproduction
Diss _*
_
ratedecayK
rateproduction
Diss _*
_
gene 1
gene 2
Mutual exclusion operator
after Cherry & Adler, JTB 2000
cooperativity factor = 2
cooperativity factor = 3
cooperativity factor = 4
Multi-cellular mutual exclusion
(B)
(A)
(C)
(D)
(E)
(g)
Potential genetic regulatory functional building blocks
(F)
Alon & colleagues, Nature Genetics, 2002. 31(1): p. 64-8
see also:
The feed forward motif:
100 molecules
0
5000
10000
15000
20000
25000
30000
35000
40000
45000
6000 7000 8000 9000
protein_A
protein_AA
protein_B
0
2000
4000
6000
8000
10000
0 2001.334
protein_A
protein_AA
protein_B
yeast
M
~ 2 days
(B)
(A)
(C)
(D)
(E)
(g)
Potential genetic regulatory functional building blocks
(F)
activator levelstead
y s
tate
gen
e 2
activator levelstead
y s
tate
gen
e 2
Direct activation Repression cascade
gene2gene1
gene2gene1
gene2, cell2
gene2, cell1
timetimetime
gene2gene1
activator
gene2gene1
activator gene3
cell type 1 cell type 2
U
U
Regulatory circuit elements: boundary detection switches
(B)
(A)
(C)
(D)
(E)
(g)
Potential genetic regulatory functional building blocks
(F)
(B)
(A)
(C)
(D)
(E)
(g)
Potential genetic regulatory functional building blocks
(F)
ATF3NFB
IL6, IL12b…
TLR4
A feedforward motif functioning as a reset (homeostasis) mechanism
NFkB
ATF3
ATF3
TLR4 genes
Gilchrist et al, Nature, 2006
NFkB
quantitative measure of behavior
P1
P2
Convex:
• Large stability margins
• Parameter independence
Robust behavior in parameter space
P1
P2
Concave:
• Poor stability margins
• Parameter interdependence
Note also rate of change of ‘desired’ behavior away from operating point
The same genes take part in different processes and functional blocksDifferent genes implement the same functional building blocks
Data from Davidson lab
Heart specification functional module:conserved from flies to humans?
vertebrate
fly
maveric, multi-talented enthusiastsearly adopters standardize,
develop “Killer Apps”
performance, reliability, usability and manufacturability
diverse, specialized suppliers ofmodular, standardized parts
http://magnet.systemsbiology.net/software/Pointillist/
http://www.septicshock.org/
http://sugp.caltech.edu/endomes/
Gilchrist et al, Nature 2006 Davidson et al, Science 2002Hwang et al, PNAS 2005a, bOrrell et al, Physica D, 2006Ramsey et al, Nat. Gen. 2006
Steve Ramsey Daehee Hwang
Alistair Rust
Christophe Battail
Mark Robinson
Bin Li
Jennifer Smith, Deena Leslie, Andrea Weston, Marcello Marelli, Tim Petersen
Andy Siegel, John Aitchison, Lee Hood
Ben Buelow, Mark Gilchrist, Katy Kennedy, Adrian Ozinsky,
Jared Roach, Carrie Baldwin, Natalya Yudkovsly
Alan Aderem
Bill Longabaugh
Davidson Lab & Eric Davidson
Vesteinn ThorssonMartin Korb
David Orrell Pedro de Atauri
Department of Cellular & Physiological Sciences University of British ColumbiaLife Sciences Centre2350 Health Sciences MallVancouver, BC Canada V6T 1Z3
Recruiting:
- Graduate students- Post docs- Technicians- Professional staff
Subjects:
- molecular & cell biology- transcriptional regulation- single cell assays- math, stats- physics, engineering- computer science- software engineering