Transcript Circuit Engineers Doing Biology

EE 5393: Circuits, Computation and Biology
Marc D. Riedel
Assistant Professor, ECE University of Minnesota
x1
x2
AND
x3
OR
f1
AND
f2
f3
Who is this guy?
• Most of the cells in his body
are not his own!
• Most of the cells in his body
are not even human!
• Most of the DNA in his body
is alien!
“Minnesota Farmer”
Who is this guy?
He’s a human-bacteria hybrid:
[like all of us]
• 100 trillion bacterial cells of
at least 500 different types
inhabit his body.
vs.
• only 1 trillion human cells of
210 different types.
“Minnesota Farmer”
Who
What’s
is in
this
hisguy?
gut?
He’s a human-bacteria hybrid:
[like all of us]
• 100 trillion bacterial cells of
at least 500 different types
inhabit his body.
vs.
• only 1 trillion human cells of
210 different types.
“Minnesota Farmer”
What’s in his gut?
“E. coli, a self-replicating object only a thousandth of a millimeter in
size, can swim 35 diameters a second, taste simple chemicals in its
environment, and decide whether life is getting better or worse.”
– Howard C. Berg
About 3 pounds of bacteria!
flagellum
Bacterial Motor
Bacterial Motor
Electron Microscopic Image
We should put
these critters to
work…
“Stimulus, response! Stimulus
response! Don’t you ever think!”
Synthetic Biology
• Positioned as an engineering discipline.
– “Novel functionality through design”.
– Repositories of standardized parts.
• Driven by experimental expertise in particular
domains of biology.
– Gene-regulation, signaling, metabolism,
protein structures …
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.
Building Digital Circuits
Intel 4004
(1971)
~2000 gates
Intel “Nehalem”
(2008)
~2 billion gates
Building Digital Circuits
inputs
outputs
x1
a f1 ( x1 , 
… , xm )
x2
… , xm )
a f 2 ( x1 , 
..
.
xm
digital circuit
a
f n ( x1 , 
… , xm )
• Design is driven by the input/output specification.
• CAD tools are not part of the design process; they are
the design process.
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.
From ad hoc to Systematic…
“A Symbolic Analysis of Relay and
Switching Circuits,”
“A Mathematical
Theory of
M.S. Thesis,
MIT, 1937
Communication,”
Bell System
Technical
Claude E. Shannon
1916 –2001
Journal, 1948.
of all digital
computation.
BasisBasis
of information
theory,
coding theory
and all communication systems.
[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
Artificial Life
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
Artificial Life
Going from reading genetic codes to write them.
Moderator:
“Some people have accused you of
playing God.”
J. Craig Venter:
“Oh no, we’re not playing.
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 , , a20 }
Proteins: produced from a sequence of m amino
acids (m ≈ 103).
{a1 , , a20 } m  protein
The (nano) Structural Landscape
“You see things; and you say ‘Why?’ But I dream things that
never were; and I say ‘Why not?’"
– George Bernard Shaw, 1925
Novel Materials…
Novel biological
functions…
Novel biochemistry…
Jargon vs.Terminology
“Now this end is called the thagomizer, after the
late Thag Simmons.”
The Computational Landscape
“There are known ‘knowns’; and there are unknown
‘unknowns’; but today I’ll speak of the known ‘unknowns’.”
– Donald Rumsfeld, 2002
Semiconductors:
exponentially smaller, faster, cheaper – forever?
1 transistor (1960’s)
2000 transistors
(Intel 4004, 1971)
800 million transistors
(Intel Penryn, 2007)
The Computational Landscape
“There are known ‘knowns’; and there are unknown
‘unknowns’; but today I’ll speak of the known ‘unknowns’.”
– Donald Rumsfeld, 2002
Semiconductors:
exponentially smaller, faster, cheaper – forever?
• Abutting true physical
limits.
• Cost and complexity
are starting to
overwhelm.
The Computational Landscape
“There are known ‘knowns’; and there are unknown
‘unknowns’; but today I’ll speak of the known ‘unknowns’.”
– Donald Rumsfeld, 2002
Potential Solutions:
• Multiple cores?
• Parallel Computing?
The Computational Landscape
“There are known ‘knowns’; and there are unknown
‘unknowns’; but today I’ll speak of the known ‘unknowns’.”
– Donald Rumsfeld, 2002
Potential Solutions:
• Novel Materials?
• Novel Function?
c
a
b
?
The Computational Landscape
“There are known ‘knowns’; and there are unknown
‘unknowns’; but today I’ll speak of the known ‘unknowns’.”
– Donald Rumsfeld, 2002
output
protein
RNAp
gene
The Computational Landscape
“There are known ‘knowns’; and there are unknown
‘unknowns’; but today I’ll speak of the known ‘unknowns’.”
– Donald Rumsfeld, 2002
RNAp
repressor
protein
gene
Biological computation?
nada
Research Activities in my Lab
Our research activities encompass topics in logic synthesis and
verification, as well as in synthetic and computational biology. A
broad theme is the application of expertise from the realm of circuit
design to the analysis and synthesis of biological systems. Current
projects include:
•
•
•
•
The concurrent logical and physical design of nanoscale digital circuitry.
The synthesis of stochastic logic for robust polynomial arithmetic.
Feedback in combinational circuits.
High-performance computing for the stochastic simulation of
biochemical reactions.
• The analysis and synthesis of stochasticity in biochemical systems.
Research Activities in my Lab
Circuits
• We’re studying the mathematical functions for digital circuits.
• We’re writing computer programs to automatically design such
circuits.
Biology
• We’re studying the concepts, mechanisms, and dynamics of
intracellular biochemistry.
• We’re writing computer programs for analyzing and
synthesizing these dynamics.
Two Made-Up Facts
[well, abstractions, really…]
Logic Gates
x1
g
x2
Biochemical Reactions
+
Logic Gates
“AND” gate
x1
g
x2
x1 x2 g
0 0 0
0 1 0
1 0 0
1 1 1
Logic Gates
“XOR” gate
x1
g
x2
x1 x2 g
0 0 0
0 1 1
1 0 1
1 1 0
Digital Circuit
inputs
outputs
x1
a f1 ( x1 ,  , xm )
x2
a f 2 ( x1 ,  , xm )

a f n ( x1 ,  , xm )

xm
circuit
Digital Circuit
inputs
outputs
x1
a f1 ( x1 ,  , xm )
x2
a f 2 ( x1 ,  , xm )
a f ( x1 ,  , xm )

a f n ( x1 ,  , xm )

xm
gate
circuit
Digital Circuit
x11
1
x02
NAND
1
x03
OR
0
x14
0
AND
AND
0
x15
NOR
1
x16
AND
My PhD Dissertation
[yes, in one slide…]
x3
x2
x1
x1
x1
x1
x2
x3
It’s not a bug, it’s a feature.
Current Research
Model defects, variations, uncertainty, etc.:
inputs
outputs
0
circuit
1
Characterize probability of outcomes.
Current Research
Model defects, variations, uncertainty, etc.:
inputs
outputs
p1 = Prob(one)
0,1,1,0,1,0,1,1,0,1,…
circuit
1,0,0,0,1,0,0,0,0,0,…
p2 = Prob(one)
Current Research
Model defects, variations, uncertainty, etc.:
inputs
outputs
2
circuit
1
5
5
Biochemical Reactions
+
protein count
9
8
6
5
7
9
cell
Biochemical Reactions
slow
+
medium
+
fast
+
Design Scenario
Bacteria are engineered to produce an anti-cancer drug:
triggering
compound
drug
E. Coli
Design Scenario
Bacteria invade the cancerous tissue:
cancerous
tissue
Design Scenario
The trigger
Bacteria
elicits
invade
the bacteria
the cancerous
to produce
tissue:
the drug:
cancerous
tissue
Design Scenario
The trigger
the bacteria
Problem:
patientelicits
receives
too high produce
of a dosethe
of drug:
the drug.
cancerous
tissue
Design Scenario
Conceptual design problem.
Constraints:
• Bacteria are all identical.
• Population density is fixed.
• Exposure to triggering compound is uniform.
Requirement:
• Control quantity of drug that is produced.
Design Scenario
Approach: elicit a fractional response.
cancerous
tissue
Synthesizing Stochasticity
Approach: engineer a probabilistic response in each bacterium.
produce drug
with Prob. 0.3
triggering
compound
E. Coli
don’t produce drug
with Prob. 0.7
Engineering vs. Biology vs. Mathematics
Dilbert
Beaker
Papa
Communicating Ideas
Domains of Expertise
•
•
•
•
Vision
Language
Abstract Reasoning
Farming
Circuit
Human
• Number
Crunching
• Mining Data
• Iterative
Calculations
Astonishing Hypothesis
“A person's mental activities are entirely due to the behavior of
nerve cells, glial cells, and the atoms, ions, and molecules that
make them up and influence them.”
– Francis Crick, 1982
The Astonishing Part
“That the astonishing hypothesis is astonishing.”
– Christophe Koch, 1995
Circuits & Computers as a Window
into our Linguistic Brains
Brain
Circuit
Conceives of
circuits and
computation by
“applying”
language.
?
Lousy at all the tasks
that the brain that
designed it is good at
(including language).
If You Don’t Know the Answer…