jehoshua (shuki) bruck · mapping and prediction gillespie, 1976; mcadams and arkin, 1997 gibson...
TRANSCRIPT
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Jehoshua (Shuki) Bruck
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From Screws to Systems…
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The Lineage of BMW
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It happens in biological systems!!!
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C. Elegans Lineage
total of 959 cells302 nerve cells131 cells are
destined to die
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C. Elegans Lineage – Simple Questions
total of 959 cells302 nerve cells131 cells are
destined to die
Dealing with identity:How do cells remember what to do?
Dealing with time:How do cells know when? No clock…
Dealing with order:How do cells coordinate their actions?
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Control viaStochastic Chemical Reactions
AB
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Chemical Reactions Networks
1
2
5
4
3
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AGEGDFFEDDCBCBA
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Chemical Reactions Networks
1
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5
4
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AGEGDFFEDDCBCBA
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Chemical Reactions Networks
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AGEGDFFEDDCBCBA
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1 1
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Chemical Reactions Networks
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AGEGDFFEDDCBCBA
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Chemical Reactions Networks
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AGEGDFFEDDCBCBA
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Chemical Reactions Networks
![Page 14: Jehoshua (Shuki) Bruck · Mapping and Prediction Gillespie, 1976; McAdams and Arkin, 1997 Gibson and Bruck, 2000; Riedel and Bruck 2005 Physical chemistry Trajectories 2 _ (1) 10](https://reader035.vdocument.in/reader035/viewer/2022062507/5fd526f18d213317772214f9/html5/thumbnails/14.jpg)
AGEGDFFEDDCBCBA
k
k
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Chemical Reactions Networks
![Page 15: Jehoshua (Shuki) Bruck · Mapping and Prediction Gillespie, 1976; McAdams and Arkin, 1997 Gibson and Bruck, 2000; Riedel and Bruck 2005 Physical chemistry Trajectories 2 _ (1) 10](https://reader035.vdocument.in/reader035/viewer/2022062507/5fd526f18d213317772214f9/html5/thumbnails/15.jpg)
Solving the Puzzle
Mapping and Prediction Principles and Abstractions
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• What are the key players in in a gene regulatory system?
• What are their relevant interactions?
• Success: predictive model
• What are the key computational principles in gene regulations?
• A formal language for design and analysis
• Success: understanding / compressiona calculus for Biology
![Page 16: Jehoshua (Shuki) Bruck · Mapping and Prediction Gillespie, 1976; McAdams and Arkin, 1997 Gibson and Bruck, 2000; Riedel and Bruck 2005 Physical chemistry Trajectories 2 _ (1) 10](https://reader035.vdocument.in/reader035/viewer/2022062507/5fd526f18d213317772214f9/html5/thumbnails/16.jpg)
Mapping and PredictionGillespie, 1976; McAdams and Arkin, 1997
Gibson and Bruck, 2000; Riedel and Bruck 2005
Trajectories Physical chemistry
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Generating trajectories from stochastic chemical equations
We can “see” trajectories and know how compute them faster
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Descriptive Biology: Is It Sufficient?
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Early Work on Abstractions
Computing with neural circuits: a connection between logic and neural networks, 1943
Warren McCulloch1899 - 1969
Walter Pitts1923 - 1969
Neurophysiologist, MD
Warren McCulloch arrived in early 1942 to the University of Chicago, invited Pitts, who was still homeless, to live with his family.
In the evenings McCulloch and Pitts collaborated. Pitts was familiar with the work of Gottfried Leibniz on computing and they considered the question of whether the nervous system could be considered a kind of universal computing device as described by Leibniz.
This led to their 1943 seminal neural networks paper:A Logical Calculus of Ideas Immanent in Nervous Activity.
Logician, Autodidact
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Solving the Biology Puzzle
Mapping and Prediction Principles and Abstractions
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5
4
3
2
1
• What are the key players in in a gene regulatory system?
• What are their relevant interactions?
• Success: predictive model
• What are the key computational principles in gene regulations?
• A formal language for design and analysis
• Success: understanding / compressiona calculus for Biology
![Page 20: Jehoshua (Shuki) Bruck · Mapping and Prediction Gillespie, 1976; McAdams and Arkin, 1997 Gibson and Bruck, 2000; Riedel and Bruck 2005 Physical chemistry Trajectories 2 _ (1) 10](https://reader035.vdocument.in/reader035/viewer/2022062507/5fd526f18d213317772214f9/html5/thumbnails/20.jpg)
Key to the Wonderful Progress in Design:Abstractions in Information Systems
Reasoning to Calculations to Physics
CircuitsBoolean CalculusReasoning
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Key to the Progress in Design:Abstractions in Information Systems
Shannon1916-2001
1938Boolean Algebra to Electrical Circuits
Logic Design
1847Connected Logic
with AlgebraBoolean Algebra
Logical Calculation
Boole1815-1864
Logic to Boolean Calculus to Physical Circuits
S D
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Text to Algebra George Boole, 1854
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The Algebra (Boolean Calculus)Boole, DeMorgan, Jevons, Peirce, Schroder (18xx)
Postulate System: Huntington (1904)
Algebraic system: set of elements B, two binary operations + and B has at least two elements (0 and 1)
•
If the following postulates are true then it is a Boolean Algebra:
(i) identity
(ii) complement
(iii) commutative
(vi) distributive
1; 0a a a a+ = ⋅ =
0 ; 1a = a a = a+ ⋅
;a b b a a b b a+ = + ⋅ = ⋅
( ) ( ); ( )a b c a b a c a b c a b a c+ ⋅ = + ⋅ + ⋅ + = ⋅ + ⋅
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Shannon MSc Thesis, 1938
sum
carry
Who invented the binary representationof numbers?
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Leibniz – Binary System
Gottfried Leibniz1646-1716
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Leibniz – Binary System
Gottfried Leibniz1646-1716
Binary addition algorithm
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The First Digital AdderGeorge Stibitz, 1904-1995
He worked at Bell Labs in New York.
In the fall of 1937 Stibitz used surplus relays, tin can strips, flashlight bulbs, and other common items to construct his "Model K" (K stands for kitchen table).
Model K was designed to display the result of the addition of two bits.
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Key to the Wonderful Progress in Design:Abstractions in Information Systems
Reasoning to Calculations to Physics
CircuitsBoolean CalculusReasoning
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Key Challenge to the Progress in AnalysisAbstractions in Information Systems
Sensory Forms to Calculations to Reasoning
ReasoningCalculusSensoryForms
•Text•Images•Audio•Numbers•Figures•SW•…
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Ask a design question:Is it a feature or a bug?
Sensory Forms to Calculations to Reasoning
+
∧
+
∧∨
xy
z
C
S
Biology Engineering
Key Challenge to the Progress in AnalysisAbstractions in Information Systems
?? ??
Abstractions
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• Cyclic vs. acyclic (feedback)• Stochastic vs. deterministic
A Feature or a Bug?
??
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Bio Circuits vs. Combinational Logic CircuitsJoint work with Marc Riedel
• Cyclic vs. acyclic (feedback)• Stochastic vs. deterministic
+
∧
+
∧∨
xy
z
C
S
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Are Cycles a Feature or a Bug?
Hypothesis ?????
Cycles might help in
• Reducing cost
• Increasing performance
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Circuits With Cycles
a b c
1f 2f 3f
Generally exhibit time-dependent behaviorMay have unstable/unknown outputs
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Generally exhibit time-dependent behaviorMay have unstable/unknown outputs
01 1
? ? ?0: non-controlling for OR1: non-controlling for AND
Circuits With Cycles
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Cyclic Circuits Can be Combinational McCaw’s 1963
Cyclic, 4 AND/OR gates, 5 variables, 2 functions:
ORORAND AND
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Cyclic Circuits Can be Combinational McCaw’s 1963
Cyclic, 4 AND/OR gates, 5 variables, 2 functions:
ORORAND AND
X=0
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Cyclic Circuits Can be Combinational McCaw’s 1963
Cyclic, 4 AND/OR gates, 5 variables, 2 functions:
ORORAND AND
X=1
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Smallest possible equivalent acyclic circuit?5 AND/OR gates; improvement factor is 4/5
x
1f
ba
ORANDOR ORAND
McCaw’s Circuit (1963)
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Cyclic Combinational Circuits
Cyclic circuits can be combinationalShort 1961, McCaw 1963, Kautz 1970, Huffman 1971, Rivest 1977
b c b c
f1 f2 f3 f4 f5 f6
a a
Improvement factor is 2/3 (Rivest 1977)
Improvement factor of ½ (Riedel & Bruck 2003)
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The Role of Cycles in Circuit Design?Best paper award in 2003 Design Automation Conference
• Developed the theory and synthesis techniques for cyclic combinational circuitsSynthesis is based on symbolic analysis
• Caltech Cyclify = a software package for the design of combinational circuits with cycles
• Integrated Caltech Cyclify with the Berkeleydesign tools
• Evaluated benchmark circuits and compared with current design tools
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Cycles in Circuits is a Feature!
Cycles help in
• Reducing cost
• Increasing performance
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Optimization for Cost (Area)
Cost: Number of NAND2/NOR2 gates
7.47%10031084s148811.66%758858styr
20.54%673847duke26.03%483514s5103.91%393409pma4.36%329344cse
15.56%255302bw3.90%222231s3865.73%889943planet
21.65%152194ex610.34%1822035xp1
ImprovementCaltech CYCLIFYBerkeley SISBenchmark
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Optimization for Performance (Delay)and Fixed Cost
15.22%391.01%1079461090s149420.93%342.07%995431016s148810.53%343.50%71638742duke213.89%314.24%54236566s1
14.29%241.77%44428452s51028.57%209.29%25428280bw
4.35%2214.29%180232105xp114.71%295.42%55834590in217.50%331.00%59340599in317.65%144.66%32717343t121.05%154.57%16719175p82
ImprovementDelayImprovementAreaDelayAreabenchmarkCaltech CYCLIFYBerkeley SIS
Cost: number of NAND2/NOR2 gatesDelay: 1 time unit/gate
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Bio Circuits vs. Combinational Logic CircuitsJoint work with Cook, Soloveichik and Winfree
+
∧
+
∧∨
xy
z
C
S
• Cyclic vs. acyclic (feedback)• Stochastic vs. deterministic
??
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Computing with Systems of Chemical Reactions
CBA+
1112
0022 3101
2011
4000
2ADC +
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The Reachability Question
Given a system of chemical reactions, and an initial state A (1112). Also given is a state B (4000).
Starting at A, can the system of chemical reactions reach B?
- This question is decidable
- The state space is finite!!!
- Originally proved by Karp and Miller 1969 in the context of Vector Addition Systems (VAS)
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Stochastic Chemical Reactions
CBA+
1112
0022 3101
2ADC +
The probability for a reaction to happenis a monotonic function in the number of molecules
(#A x #B) or (#C x #D)
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Stochastic Reachability
Given a system of chemical reactions, and an initial state A. Also given is a state B.
Starting at A, is the probability to reach B bigger than 1-small ?
Stochastic chemical reactions are Turing universal – with high probability
- This question is undecidable
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Stochastic Behavior is a Feature
Probability enables general (‘precise’) computationin biochemical systems!!
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Stochastic Behavior is a Feature
Probability enables general (‘precise’) computation in biochemical systems, Proof?
Register Machines (Minsky 1967)
Register A
Register B
Programming Unit
Infinitely large
Idea: Simulate Register Machineswith Chemical Reaction Networks
Register Machinesare universal!!
general computing
Marvin Minsky1927 -
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Register Machines
Programming Unit
Register Machines (Minsky 1967)
Register A
Register C
Inc(A) – increment A and go to the next instruction
Dec(A,k) – if A is not 0, decrement A and go to next instructionotherwise, if A is 0, go to instruction k
Register B
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Register Machines - Example
Inc(A) – increment A and go to the next instruction
Dec(A,k) – if A is not 0, decrement A and go to next instructionotherwise, go to instruction k
1: Dec(A,4)2: Dec(B,5)3: Dec(C,1)4: Inc(C)5: Inc(C)
•Three registers
•a and b are nonnegative integers
•Let A=a , B=b and c = 0
•What is the program computing?
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Register Machines - Example
1: Dec(A,4)2: Dec(B,5)3: Dec(C,1)4: Inc(C)5: Inc(C)
•Three registers
•a and b are nonnegative integers
•Let A=a , B=b and c = 0
•What is the program computing?
Output of program is in C
C=1 a is bigger than b
C=2 a is smaller or equal to b
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Register Machines - Example
1: Dec(A,4)2: Dec(B,5)3: Dec(C,1)4: Inc(C)5: Inc(C)
A B C
0
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Register Machines - Example
1: Dec(A,4)2: Dec(B,5)3: Dec(C,1)4: Inc(C)5: Inc(C)
A B C
0
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Register Machines - Example
1: Dec(A,4)2: Dec(B,5)3: Dec(C,1)4: Inc(C)5: Inc(C)
A B C
000
Output of program is in C
C=1 a is bigger than b
C=2 a is smaller or equal to b
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Register Machines
Programming Unit
Register Machines (Minsky 1967)
Register A
Register C
Inc(A) – increment A and go to the next instruction
Dec(A,k) – if A is not 0, decrement A and go to next instructionotherwise, go to instruction k
Register B
Are Universal
Idea: Simulate Register Machineswith Chemical Reaction Networks
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Simulation of Register Machines with CRNs
i: Inc(R) : Si -> R + Si+1
i: Dec(R,k): R + Si -> Si+1
If R=0 then Si -> Sk
Inc(A) – increment A and go to the next instruction
Dec(A,k) – if A is not 0, decrement A and go to next instructionotherwise, go to instruction k
CompilerRM to CRN
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Simulation of Register Machines with CRNs
i: Inc(R) : Si -> R + Si+1
i: Dec(R,k): R + Si -> Si+1
If R=0 then Si -> Sk
1: Dec(A,4)2: Dec(B,5)3: Dec(C,1)4: Inc(C)5: Inc(C)
A + S1 -> S2S1 -> S4
B + S2 -> S3S2 -> S5
C + S3 -> S4S3 -> S1
S4 -> C + S5
S5 -> C + S6
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Simulation of Register Machines with CRNs
i: Inc(R) : Si -> R + Si+1
i: Dec(R,k): R + Si -> Si+1
If R=0 then Si -> Sk
1: Dec(A,4)2: Dec(B,5)3: Dec(C,1)4: Inc(C)5: Inc(C)
A + S1 -> S2S1 -> S4
B + S2 -> S3S2 -> S5
C + S3 -> S4S3 -> S1
S4 -> C + S5
S5 -> C + S6
A Problem:This reaction can happen even if R is not zero…..
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Simulation of Register Machines with CRNs
i: Inc(R) : Si -> R + Si+1
i: Dec(R,k): R + Si -> Si+1
If R=0 then Si -> Sk
1: Dec(A,4)2: Dec(B,5)3: Dec(C,1)4: Inc(C)5: Inc(C)
A + S1 -> S2S1 -> S4
B + S2 -> S3S2 -> S5
C + S3 -> S4S3 -> S1
S4 -> C + S5
S5 -> C + S6
The solution:Delay thisreaction using a “stochastic clock”
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Simulation of Register Machines with CRNs
i: Inc(R) : Si -> R + Si+1
i: Dec(R,k): R + Si -> Si+1
If R=0 then Si -> Sk
Delay this reaction using a “stochastic clock”
When R>0, it is less likely to happen. DEC (R,i)
Case 1: R=0 Si -> Sk with probability 1
Case 2: R>0
Si -> Sk with small probability
R + Si -> Si+1 with probability close to 1
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Stochastic Behavior is a Feature
Probability enables general (‘precise’) computationin biochemical systems!!
??
![Page 65: Jehoshua (Shuki) Bruck · Mapping and Prediction Gillespie, 1976; McAdams and Arkin, 1997 Gibson and Bruck, 2000; Riedel and Bruck 2005 Physical chemistry Trajectories 2 _ (1) 10](https://reader035.vdocument.in/reader035/viewer/2022062507/5fd526f18d213317772214f9/html5/thumbnails/65.jpg)
Is it a Feature or a Bug?
• Stochastic vs. deterministicProbability enables universal computation in chemical reaction networks (Cook, Soloveichik, Winfree, Bruck, 2005)
• Cyclic vs. acyclic Cycles enable cost savings in real combinational circuits(Riedel & Bruck 2003)
Current / future work:
• Relations vs. functions?
• The logic of computing probability distributions?
??
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Shannon1916-2001
Turing1912-1954
Leibniz1646-1716
Boole1815-1864
• Logic and Binary system
• Calculus
Connected Logicwith AlgebraBoolean AlgebraLogical Calculation
Defined Computingvia universal machinesComputer Science
•Connected Boolean Algebra to Electrical Circuits Logic Design
•Connected probability to Communications Information Theory
Calculus for Biology??
We need to learn / teach about abstract systems forreasoning about information
Emil Post 1897-1954
Compositions of Boolean functionsUniversal Algebra
"The further back you look, the further forward you can see"
Winston Churchill