Download Circuit Engineers Doing Biology

Bio-Design Automation
EE5393 – University of Minnesota
Brian’s Automated Modular Biochemical Instantiator
ECE
What,
Expense
How, Why,
Reports
…
What are we doing?
• Investigating design strategies for generating “netlists” of
protein-protein biochemical reactions.
How are we going about it?
• Applying circuit CAD methodologies:
modularity / abstractions / hierarchical designs.
Why are we bothering?
• Such tools and methods will revolutionize the way
synthetic biology is done.
Playing by the Rules
Playing by the Rules
Rules for integrated circuits:
amplifier
v1 1 0
rin1 1 0 9e12
rjump 1 4 1e-12
rin2 4 0 9e12
e1 3 0 1 2 999k
e2 6 0 4 5 999k
e3 9 0 8 7 999k
rload 9 0 10k
r1 2 3 10k
rgain 2 5 10k
r2 5 6 10k
r3 3 7 10k
r4 7 9 10k
r5 6 8 10k
r6 8 0 10k
.dc v1 0 10 1
.print dc v(9)
.end
circuit
netlist
SPICE
waveforms
Playing by the Rules
Rules for biochemistry:
Gillespie’s
SPICE
SSA
X=100, Y = 30
Xa = Xb = Xn= 0
Y =0
biochemical reactions
and initial quantities of proteins
histogram: resulting
quantities of proteins
Playing by the Rules
Rules for biochemistry:
Gillespie’s
SPICE
SSA
algorithms widely studied
data structures (Gibson & Bruck, Fett & Riedel); approximation
methods (Petzold); hybrid discrete/continuous methods (Kaznessis); …
dynamics well studied
mathematics (Tyson, Khammash, Doyle, …);
biology (Arkin, Endy, Brent); …
computation (Winfree, Shapiro); …
Gillespie’s
SPICE
SSA
X=100, Y = 30
Xa = Xb = Xn= 0
Y =0
Biochemical Netlists
Netlists found in nature:
• Elucidated by biologists.
X=100, Y = 30
Xa = Xb = Xn= 0
Y =0
New Netlists:
• Designed by skilled experimentalists
(by tinkering with existing mechanisms).
Where does the netlist come from?
Synthetic Biology
• Positioned as an engineering discipline.
– “Novel functionality through design”.
– Repositories of standardized parts.
• Driven by experimental expertise in
particular domains of biology.
– Modify gene regulation, signaling pathways,
metabolic pathways…
Building Bridges
"Think of how engineers build bridges. They design quantitative
models to help them understand what sorts of pressure and weight the
bridge can withstand, and then use these equations to improve the
actual physical model. [In our work on memory in yeast cells] we really
did the same thing.”
– Pam Silver, Harvard 2007
Engineering Design
• Quantitative modeling.
• Mathematical analysis.
• Incremental and iterative design changes.
Synthetic Biology
Feats of synthetic bio-engineering:
• Cellulosic ethanol (Nancy Ho, Purdue, ’04)
• Anti-malarial drugs (Jay Keasling, UC Berkeley, ‘06)
• Tumor detection (Chris Voigt, UCSF ‘06)
Strategy: apply experimental expertise; formulate ad-hoc
designs; perform extensive simulations.
Building Digital Circuits
inputs
outputs
x1
f1 ( x1 ,K, xm)
f 2 ( x1 ,K, xm)
x2
..
.
xm
digital circuit
..
.
f n ( x1 ,K, xm )
• Design is driven by the input/output specification.
• CAD tools are not part of the design process; they are
the design process.
[computational]
[computational]
Synthetic
Analysis
Biology
“There are known ‘knowns’; and there are unknown
‘unknowns’; but today I’ll speak of the known ‘unknowns’.”
– Donald Rumsfeld, 2004
Molecular
Inputs
Known /
Known
Unknown
Biological
Process
Molecular
Products
Given
Unknown
Unknown
Known
Gene Regulation
Hard to tinker with this; but it’s the way computation is done…
Biochemistry in a Nutshell
Nucleotides:
{ A, C , T , G}
DNA: string of n nucleotides (n ≈ 109)
... ACCGTTGAATGACG...
Amino acid: coded by a sequence of 3 nucleotides.
{ A, C , T , G }3  {a1 ,K , a20 }
Proteins: produced from a sequence of m amino
acids (m ≈ 103) called a “gene”.
{a1 ,K , a20 } m  protein
Custom Gene Synthesis
Going from reading genetic codes to writing them.
US Patent 20070122826 (pending):
“The present invention relates to a
minimal set of protein-coding genes
which provides the information required
for replication of a free-living organism in
a rich bacterial culture medium.”
– J. Craig Venter Institute
Custom Gene Synthesis
Going from reading genetic codes to writing them.
Moderator:
“Some people have accused you
of playing God.”
J. Craig Venter:
“Oh no, we’re not playing.”
Biochemical Netlists
What are we doing?
• Figuring out how to design netlists in
terms of abstract protein types so that
we meet desired specs.
X=100, Y = 30
Xa = Xb = Xn= 0
Y =0
Why?
• Implement computation & signal
processing on protein quantities.
Ok, but how?
Playing by the Rules
Biochemical Reactions: how types of molecules combine.
2a +
+
b
c
Biochemical Reactions
+
species count
cell
9
8
6
5
7
9
Discrete chemical kinetics; spatial homogeneity.
Biochemical Reactions
Relative rates or (reaction propensities):
slow
+
medium
+
fast
+
Discrete chemical kinetics; spatial homogeneity.
Stochastic Kinetics
The probability that a given
reaction is the next to fire is
proportional to:
• Its rate.
• The number of ways that the
reactants can combine.
R1
R2
R3
k1
2A  B  3 C
k2
B  2C 
k3
3A
A  C  2B
See D. Gillespie, “Stochastic Chemical Kinetics”, 2006.
Stochastic Kinetics
For each reaction
Ri
let
k
n1X1  n2 X2   
 
 X1  X2 
i  k    
 n1  n2 
Choose the next reaction according to:
i
Pr( Ri ) 
 j
j
Design Automation for Integrated Circuits
Behavioral Specification
(e.g., DSP function)
Register
Level
Design
Structural Description
(e.g., memory and functional units)
Logic
Synthesis
Circuit-Level Description
(e.g., NAND2 and D flip-flops)
SPICE
waveforms
Design Automation for Integrated
Biochemistry
Circuits
Behavioral Specification
(e.g., DSP function)
Register
Level
Design
Structural Description
(e.g., memory and functional units)
Logic
Biochemical
Synthesis
Biochemical Netlist
(e.g., Proteins, Enzymes)
SPICE
STA
Engine
waveforms
PSB 2009: “Stochastic Transient Analysis Biochemical Systems”
Design Automation for Integrated
Biochemistry
Circuits
Behavioral Specification
(e.g., DSP function)
Register
Level
Design
Structural Description
(e.g., memory and functional units)
Logic
Biochemical
Synthesis
Biochemical Netlist
(e.g., Proteins, Enzymes)
SPICE
STA
Engine
DAC 07, SB 3.0: “The Synthesis
of Stochastic Biochemical Systems”
waveforms
Design Automation for Integrated
Biochemistry
Circuits
Behavioral Specification
(e.g., DSP function)
Register
Level
Design
Joint work with Keshab Parhi’s group.
Structural Description
(e.g., memory and functional units)
Biochemical
Synthesis
Biochemical Netlist
(e.g., Proteins, Enzymes)
SPICE
STA
Engine
Brian’s Automated Modular Biochemical Instantiator
waveforms