Transcript Circuit Engineers Doing Biology - The Circuits and Biology Lab at

Circuit Engineers Doing Biology
A Discourse on the Changing Landscape of Scientific Research
Marc D. Riedel
Assistant Professor, Electrical and Computer Engineering
University of Minnesota
Café Scientifique
Hosted by the Bell Museum of
Natural History
At the Bryant-Lake Bowl
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
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…
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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.
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Marc Riedel, Café Scientifique
15
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.
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Two Made-Up Facts
[well, abstractions, really…]
Logic Gates
x1
g
x2
Biochemical Reactions
+
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Logic Gates
“AND” gate
x1
g
x2
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Marc Riedel, Café Scientifique
x1 x2 g
0 0 0
0 1 0
1 0 0
1 1 1
20
Logic Gates
“XOR” gate
x1
g
x2
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Marc Riedel, Café Scientifique
x1 x2 g
0 0 0
0 1 1
1 0 1
1 1 0
21
Digital Circuit
inputs
outputs
x1
a f1 ( x1 ,  , xm )
x2
a f 2 ( x1 ,  , xm )

a f n ( x1 ,  , xm )

circuit
xm
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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
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gate
circuit
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Digital Circuit
x11
1
x02
NAND
1
x03
OR
0
x14
0
AND
AND
0
x15
NOR
1
x16
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AND
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My PhD Dissertation
[yes, in one slide…]
x3
x2
x1
x1
x1
x1
x2
x3
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
cell
9
8
6
5
7
9
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Biochemical Reactions
slow
+
medium
+
fast
+
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Example: Exponentiation
2  2   2  2
n
n
Riedelian Law of Productivity
“Every task will take twice as long as expected – even if the
Riedelian Law of Productivity is taken into account.”
– That Great Procrastinator Riedel
[midnight last night]
Exponentiation
given
2M
want
M
(m )
(n)
Use working types a, b, n
let a be non-zero
a + 2n
a
let b be zero
m
a+n
sets n to one
med
slow
b +n
b
n
fast
b
v. fast
fast
med.
n
b + 2n
M
sets n to 2
Design Scenario
Bacteria are engineered to produce an anti-cancer drug:
triggering
compound
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drug
E. Coli
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Design Scenario
Bacteria invade the cancerous tissue:
cancerous
tissue
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Design Scenario
The trigger
Bacteria
elicits
invade
the bacteria
the cancerous
to produce
tissue:
the drug:
cancerous
tissue
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Design Scenario
The trigger
the bacteria
Problem:
patientelicits
receives
too high produce
of a dosethe
of drug:
the drug.
cancerous
tissue
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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.
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Design Scenario
Approach: elicit a fractional response.
cancerous
tissue
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Synthesizing Stochasticity
Approach: engineer a probabilistic response in each bacterium.
produce drug
with Prob. 0.3
triggering
compound
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E. Coli
Marc Riedel, Café Scientifique
don’t produce drug
with Prob. 0.7
41
Synthesizing Stochasticity
Generalization: engineer a probability distribution on
logical combinations of different outcomes.
A with Prob. 0.3
B with Prob. 0.2
cell
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C with Prob. 0.5
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42
Synthesizing Stochasticity
Generalization: engineer a probability distribution on
logical combinations of different outcomes.
A with Prob. 0.3
A and B with Prob. 0.3
B with Prob. 0.2
cell
Nov. 20, 2007
B and C with Prob. 0.7
C with Prob. 0.5
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Synthesizing Stochasticity
Generalization: engineer a probability distribution on
logical combinations of different outcomes.
Pr( A)  f1 ( X / Y )
X
A and B with Prob. 0.3
Pr(B)  f2 ( X / Y )
Y
cell
B and C with Prob. 0.7
Pr(C )  f3 ( X / Y )
Further: program probability distribution with (relative)
quantity of input compounds.
Engineering vs. Biology vs. Mathematics
Dilbert
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Beaker
Marc Riedel, Café Scientifique
Papa
45
It’s not a bug, it’s a feature.
Jargon vs.Terminology
“Now this end is called the thagomizer, after the
late Thag Simmons.”
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
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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…