Molecular Computational Engines of Intelligence The Second Joint Symposium on Computational Intelligence (JSCI) Jan. 19, 2006, KAIST, Korea Byoung-Tak.

Molecular Computational Engines of Intelligence The Second Joint Symposium on Computational Intelligence (JSCI) Jan. 19, 2006, KAIST, Korea Byoung-Tak Zhang Biointelligence Laboratory School of Computer Science and Engineering Brain Science, Cognitive Science, Bioinformatics Programs Seoul National University Seoul , Korea I will talk about evolving DNA-encoded genetic programs in a test tube. We evaluate the potentials of this approach by solving a medical diagnosis problem on a simulated DNA computer. The individual genetic program represents a decision list of variable length and the whole population takes part in making probabilistic decisions.

Da Vinci’s Dream of Flying Machines
© 2006, SNU Biointelligence Lab,

Turing’s Dream of Intelligent Machines
Alan Turing ( ) Computing Machinery and Intelligence (1950) © 2006, SNU Biointelligence Lab,

Computers and Intelligence

Computational Engines of Intelligence
Symbolic Rule-Based Systems Connectionist Neural Networks Evolutionary Genetic Algorithms ? © 2006, SNU Biointelligence Lab,

Brain as a Molecular Computer
Mind Mind Brain Cell memory Molecule 1011 cells 1010 mol. © 2006, SNU Biointelligence Lab,

Molecular Mechanisms of Memory in the Brain

Two Faces of the Brain: Electrical Waves or Chemical Particles?
Brain as a network of neurons and synapses (a) Neuron-oriented cellular view (“electrical” waves) (b) Synapse-oriented molecular view (“chemical” particles) © 2006, SNU Biointelligence Lab, [Zhang, 2005]

Principles of Information Processing in the Brain
The Principle of Uncertainty Precision vs. prediction The Principle of Nonseparability “UN-IBM” Processor vs. memory The Principle of Infinitity Limited matter vs. unbounded memory The Principle of “Big Numbers Count” Hyperinteraction of 1011 neurons (or > 1017 molecules) The Principle of “Matter Matters” Material basis of “consciousness” [Zhang, 2005] © 2006, SNU Biointelligence Lab,

Unconventional Computing
Quantum Computing Atoms Superposition, quantum entanglements Chemical Computing Chemicals Reaction-diffusion computing Molecular Computing Molecules “Self-organizing hardware” © 2006, SNU Biointelligence Lab,

Molecular Computers vs. Silicon Computers
Processing Ballistic Hardwired Medium Liquid (wet) or Gaseous (dry) Solid (dry) Communication 3D collision 2D switching Configuration Amorphous (asynchronous) Fixed (synchronous) Parallelism Massively parallel Sequential Speed Fast (millisec) Ultra-fast (nanosec) Reliability Low High Density Ultrahigh Very high Reproducibility Probabilistic Deterministic © 2006, SNU Biointelligence Lab,

The Quest for the “Right” Molecules
Protein Versatile structures Unpredictable structure Chemically unstable DNA Versatile sequences (synthesizable) Predictable structure (can be designed) Chemically stable and durable RNA Both properties of proteins and DNA Difficult to handle … © 2006, SNU Biointelligence Lab,

DNA as “Programmable Matter”

Molecular Operators Hybridization Ligation Variation Ligation Restriction Mutation (PCR) Selection Gel electrophoresis Affinity separation (beads) Capillary electrophoresis Amplification Polymerase chain reaction (PCR) Rolling circle amplification (RCA) Heat Cool Polymer Repeat © 2006, SNU Biointelligence Lab,

Why Molecular/DNA Computers?
6.022  1023 molecules / mole Massively Parallel Search Desktop: 109 operations / sec Supercomputer: 1012 operations / sec 1 mmol of DNA: 1026 reactions Favorable Energetics: Gibbs Free Energy 1 J for 2  1019 operations Storage Capacity: 1 bit per cubic nanometer The Fastest Supercomputer vs. DNA computer 106 op/sec vs op/sec 109 op/J vs op/J (in ligation step) 1bit per 1012 nm3 vs. 1 bit per 1 nm3 (video tape vs. molecules) © 2006, SNU Biointelligence Lab,

DNA-Linked Nanoparticles
[Mirkin et al.] © 2006, SNU Biointelligence Lab,

Self-assembly Computing by DNA-Linked Nanoparticles
II © 2006, SNU Biointelligence Lab, [Park, J.-Y. et al.]

The Hypernetwork Model: A Molecular Computational Engine of Intelligence

Hypergraphs A hypergraph is a (undirected) graph G whose edges connect a non-null number of vertices, i.e. G = (V, E), where V = {v1, v2, …, vn}, E = {E1, E2, …, En}, and Ei = {vi1, vi2, …, vim} An m-hypergraph consists of a set V of vertices and a subset E of V[m], i.e. G = (V, V[m]) where V[m] is a set of subsets of V whose elements have precisely m members. A hypergraph G is said to be k-uniform if every edge Ei in E has cardinality k. A hypergraph G is k-regular if every vertex has degree k. Rem.: An ordinary graph is a 2-uniform hypergraph. © 2006, SNU Biointelligence Lab,

An Example Hypergraph E1 E3 G = (V, E) V = {v1, v2, v3, …, v7} E = {E1, E2, E3, E4, E5} E1 = {v1, v3, v4} E2 = {v1, v4} E3 = {v2, v3, v6} E4 = {v3, v4, v6, v7} E5 = {v4, v5, v7} v1 v2 E2 E4 v3 v4 v6 v5 v7 E5 © 2006, SNU Biointelligence Lab,

Hypernetworks [Zhang, 2006, in preparation] A hypernetwork is a hypergraph of weighted edges. It is defined as a triple H = (V, E, W), where V = {v1, v2, …, vn}, E = {E1, E2, …, En}, and W = {w1, w2, …, wn}. An m-hypernetwork consists of a set V of vertices and a subset E of V[m], i.e. H = (V, V[m], W) where V[m] is a set of subsets of V whose elements have precisely m members and W is the set of weights associated with the hyperedges. A hypernetwork H is said to be k-uniform if every edge Ei in E has cardinality k. A hypernetwork H is k-regular if every vertex has degree k. Rem.: An ordinary graph is a 2-uniform hypergraph with wi=1. © 2006, SNU Biointelligence Lab,

The Hypernetwork Model of Learning
[Zhang, 2006, in preparation] © 2006, SNU Biointelligence Lab,

Deriving the Learning Rule

Derivation of the Learning Rule

x1 =1 x2 =0 x3 x4 x5 x6 x7 x8 x9 x10 x11 x12 x13 x14 x15 y = 1 1 x1 =0 x2 =1 x3 x4 x5 x6 x7 x8 x9 x10 x11 x12 x13 x14 x15 y = 0 2 4 examples x1 =0 x2 x3 =1 x4 x5 x6 x7 x8 x9 x10 x11 x12 x13 x14 x15 y 3 x1 =0 x2 x3 x4 x5 x6 x7 x8 =1 x9 x10 x11 x12 x13 x14 x15 y 4 x8 x9 x12 x1 x2 x3 x4 x5 x6 x7 x10 x11 x13 x14 x15 Round 3 Round 1 Round 2 x4 x10 y=1 x1 x4 x12 y=1 x1 1 x10 x12 y=1 x4 x3 x9 y=0 x2 x3 x14 y=0 x2 2 x9 x14 y=0 x3 x6 x8 y=1 x3 x6 x13 y=1 x3 3 x8 x13 y=1 x6 x11 x15 y=0 x8 4 © 2006, SNU Biointelligence Lab,

Self-Assemblying Hypernetworks
xi xj y Molecular Encoding Hypernetwork Representation X1 X2 X8 X3 X7 X4 X6 X5 © 2006, SNU Biointelligence Lab,

Encoding a Hypernetwork with DNA
z1 : z2 : z3 : z4 : b) x1 x2 x3 x4 x5 y 1 where z1 : (x1=0, x2=1, x3=0, y=1) z2 : (x1=0, x2=0, x3=1, x4=0, x5=0, y=0) z3 : (x2=1, x4=1, y=1) z4 : (x2=1, x3=0, x4=1, y=0) a) AAAACCAATTGGAAGGCCATGCGG AAAACCAATTCCAAGGGGCCTTCCCCAACCATGCCC AATTGGCCTTGGATGCGG AATTGGAAGGCCCCTTGGATGCCC GG AAAA AATT AAGG CCTT CCAA ATGC CC Collection of (labeled) hyperedges Library of DNA molecules corresponding to (a) For example, a program x sub one equals one and x sub three equals one and x sub five equals one and y equals one in the form of decision lists or its DNA encoding denotes a decision rule saying diagnose the DNA sample as positive for disease y if contains all the three markers x sub one, x sub three and x sub five. © 2006, SNU Biointelligence Lab,

Learning the Hypernetwork (by Evolution)
Next generation Library of combinatorial molecules Library Example + The aim is to build a decision making system f that outputs label Select the library elements matching the example Amplify the matched library elements by PCR Hybridize [Zhang, DNA11] © 2006, SNU Biointelligence Lab,

The Theory of Bayesian Evolution Evolution as a Bayesian inference process Evolutionary computation (EC) is viewed as an iterative process of generating the individuals of ever higher posterior probabilities from the priors and the observed data. generation 0 generation g P(A |D) P(A |D) ... P0(Ai) Pg(Ai |D) Pg(Ai) [Zhang, CEC-99] © 2006, SNU Biointelligence Lab,

Animation for Molecular Evolutionary Learning
MP4.avi © 2006, SNU Biointelligence Lab,

Molecular Programming (MP): The Evolutionary Learning Algorithm
1. Let the library L represent the current distribution P(X,Y). 2. Get a training example (x,y). 3. Classify x using L as follows 3.1 Extract all molecules matching x into M. 3.2 From M separate the molecules into classes: Extract the molecules with label Y=0 into M0 Extract the molecules with label Y=1 into M1 3.3 Compute y*=argmaxY{0,1}| MY |/|M| 4. Update L If y*=y, then Ln ← Ln-1+{c(u, v)} for u=x and v=y for (u, v) Ln-1, If y*≠y, then Ln ← Ln-1{c(u, v)} for u=x and v ≠ y for (u, v) Ln-1 5.Goto step 2 if not terminated. [Zhang, GECCO-2005] © 2006, SNU Biointelligence Lab,

Nano Self-Replication

Benchmark Problem: Digit Images
8x8=64 bit images (made from 64x64 scanned gray images) Training set: 3823 images Test set: 1797 images © 2006, SNU Biointelligence Lab,

Pattern Completion Task II Classes 0-9, Subsampled Features

Pattern Completion Task III Subsampled Features for Two Classes

Biological Application
& 120 samples from 60 leukemia patients Gene expression data Class: ALL/AML Training with 6-fold validation Diagnosis [Cheok et al., Nature Genetics, 2003] © 2006, SNU Biointelligence Lab,

Simulation Results Fitness evolution of the population of wDNF terms

Simulation Results Fitness curves for runs with fixed-size wDNF terms
(fixed-order 1, 4, 7, and 10) © 2006, SNU Biointelligence Lab,

Simulation Results Distribution of the size of wDNF terms
From left to right the epoch number is 0, 5, 10. © 2006, SNU Biointelligence Lab,

Future Technology Enablers
True neural computing Bio-electric computers 1e6-1e7 x lower power for lifetime batteries Quantum computer, molecular electronics Smart lab-on-chip, plastic/printed ICs, self-assembly Full motion mobile video/office Vertical/3D CMOS, Micro-wireless nets, Integrated optics Wearable communications, wireless remote medicine, ‘hardware over internet’ ! Pervasive voice recognition, “smart” transportation Metal gates, Hi-k/metal oxides, Lo-k with Cu, SOI Now +2 +4 +6 +8 +10 +12 Source: Motorola, Inc, 2000 © 2006, SNU Biointelligence Lab,

Da Vinci’s Dream of Flying Machines

Horsepower Per Pound for Flying
Liquid Fuel Rockets Gas Turbines Jet Engines Combustion Engines Steam Engines Gas Piston Engines Steam Engines 1850 1950 2050 © 2006, SNU Biointelligence Lab,

Interaction Horsepower Per Pound for Computing
Molecular Engines Molecular Engines Electronic Engines Mechanical Engines Electronic Engines Electrical Engines Mechanical Engines 1850 1950 2050 © 2006, SNU Biointelligence Lab,

Conclusion Hyperinteraction is a “fundamental information processing principle” underlying the brain functions. Molecular computing is an “unconventional computing paradigm” that can best realize the hyperinteractionistic principle at the moment. DNA molecules are one of the most versatile and reliable “programmable matters” found so far for engineering molecular computers in practice. The hyperinteraction networks are a probabilistic molecular computer that “evolutionarily organize” its random network architecture based on observed data. The capability of learning molecular hypernetworks to perform “hyperinteractionistic, associative, and fault-tolerant” pattern processing seems promising for realizing large-scale computational engines of intelligence. © 2006, SNU Biointelligence Lab,

Acknowledgements Collaborating Labs - Biointelligence Laboratory, Seoul National University - Biochemistry Lab, Seoul National Univ. Medical School - Cell and Microbiology Lab, Seoul National University - Advanced Proteomics Lab, Hanyang University - DigitalGenomics, Inc. - GenoProt, Inc. Supported by - National Research Lab Program of Min. of Sci. & Tech. ( ) - Next Generation Tech. Program of Min. of Ind. & Comm. ( ) More Information at - - © 2006, SNU Biointelligence Lab,

Molecular Computational Engines of Intelligence The Second Joint Symposium on Computational Intelligence (JSCI) Jan. 19, 2006, KAIST, Korea Byoung-Tak.

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Molecular Computational Engines of Intelligence The Second Joint Symposium on Computational Intelligence (JSCI) Jan. 19, 2006, KAIST, Korea Byoung-Tak.

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Presentation on theme: "Molecular Computational Engines of Intelligence The Second Joint Symposium on Computational Intelligence (JSCI) Jan. 19, 2006, KAIST, Korea Byoung-Tak."— Presentation transcript:

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