computer science and biology 07... · 3d bio molecular computing3d bio molecular computing (uci,...
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Computer Science and Biology
Tatsuya SudaInformation and Computer Science
University of California, Irvinesuda@ics uci [email protected]
CCF/CISENational Science Foundation
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Part 1: NSF
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• NSF– Supports Transformative and Interdisciplinary pp p y
Research
National Science FoundationNational Science
BoardOffice of
Inspector General
Administrative OfficesOffice of the Director
BoardInspector General
Directorate for BiologicalSciences
Directorate for Mathematical& Physical Sciences
Directorate for Computer &Information Science & Engineering
Directorate for Social, Behavioral& Economic Sciences
Di f Ed i
CISE
Directorate for Education& Human Resources
Directorate for Engineering
Office Cyberinfrastructure
Office of International Science & Engineering& Engineering
Directorate for Geosciences Office of Polar Programs
CISE: Drivers of Computing7A’s7A s
Anytime Anywhere Affordable
Society
AffordableAccess to Anything by Anyone Society yAuthorized.
Science Technology• What is computable?• P = NP?• (How) can we build complex
t i l ?systems simply?• What is intelligence?• What is information?
J. Wing, “Five Deep Questions in Computing,” CACM January 2008
CISE: Connecting the World
A collection of interconnected,
autonomous devices, which appears to users as a single,
integrated and coherent facility
“You know you have a distributed system, when the h f t h h d f t crash of a computer you have never heard of stops you from getting any work done” Leslie Lamport
CISE: Providing Ubiquitous I f ti A I t lliInformation Access, Intelligence
ClickworkersCollaborative FilteringCollaborative Filtering
Collaborative IntelligenceCollective Intelligenceg
CrowdsourcingHuman-Based Computation
R d S tRecommender SystemsReputation SystemsSocial CommerceSocial CommerceSwarm Intelligence
WikinomicsWisdom of the Crowds
Monitoring Sensors Embedded Medical D iEverywhere Devices
pacemaker
Sonoma Redwood Forest
pacemaker
Hudson River Valleysmart buildings
Kindly donated by Stewart JohnstonyCredit: Arthur Sanderson at RPI
infusion pump
Credit: MO Dept. of Transportation
smart bridges
CISE OrganizationCISE Organization
Office of theAssistant Director
for CISE
Assistant DirectorDr. Jeannette Wing
CCFCNS
Computer andIIS
Information andComputing and
CommunicationsFoundations
NetworkSystems
Di i i Di t
IntelligentSystems
Di i i Di tDivision Director
Dr. Sampath Kannan
Division DirectorDr. Ty ZnatDeputy DD
Rajinder Khosla
Division DirectorDr. Haym Hirsh
Deputy DDMaryLou Maher
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Rajinder Khosla MaryLou Maher
Computing Networking Intelligence
CISE Cross-Cutting ProgramsCISE Cross-Cutting Programs
• Cover areas that – cut across the CISE divisions
– benefit from collaboration of researchers with expertise in a number of fieldsexpertise in a number of fields
• Three focus areas– Data-Intensive Computing– Network Science and Engineeringg g– Trustworthy Computing
CISE Other ProgramsCISE Other Programs
• Expedition Program, CISE– Deadline (last year)( y )
• Preliminary proposal, Sept. 08
• Full proposal, Feb. 09Full proposal, Feb. 09
• Cyber Physical Systems (CPS), CISED dli (l )– Deadline (last year)
• Full proposal, Feb. 09
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NSF: Cyber-Enabled Discovery and Innovation (CDI)
NSF id P• NSF-wide Program• Create revolutionary science and
i i h tengineering research outcomes• Seeks ambitious, transformative, multi-
di i li h idisciplinary research in – From Data to Knowledge
U d t di C l it i N t l B ilt– Understanding Complexity in Natural, Built, and Social Systems
– Building Virtual OrganizationsBuilding Virtual Organizations• Preproposal deadline
• A new keyword
• ContactContact – [email protected] or [email protected]
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Part 2: Shared OrganizingPart 2: Shared Organizing Principlesp
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A New ConversationA New Conversation
• Traditional CS and BIO collaboration
Shared OrganizingShared
Organizing– Techniques and inspiration
• Synergistic CS and BIO
OrganizingPrinciplesOrganizingPrinciples
Synergistic CS and BIO collaboration– Shared organizing principlesg g p p
• Concepts that are fundamental to both CS and BIO Techniques InspirationX XTechniques InspirationX X
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Examples of Shared Organizing Principles
• Networks and their control systems
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Protein Interaction Network Protein Interaction Network in Yeastin YeastInternet Internet
Examples of Shared Organizing P i i lPrinciples
• Learning and Adaptation– Across levels of scale
Biological Biological systemssystems
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Examples of Shared Organizing P i i lPrinciples
• Learning and Adaptation– Across levels of scale
Neural Net Model
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Examples of Shared Organizing P i i lPrinciples
• Learning and Adaptation– Across levels of scale
Neural Net Model
Biological Biological systemssystems
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Examples of Shared Organizing Principles
• Information Representation and Processing– Both biological and computer systems exploit g p y p
structure of information to represent and process informationp
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Same issues are faced by nano-electronics today
• Stochasticity– Same input, different outputs
– Noise
– May not need to remove these characteristicsy
– Not performance, but other criteria (“adaptability, etc”)
• Component unreliability• Component unreliability
• Energy efficiency
• Environmental lability
• Evolvability/adaptability• Evolvability/adaptability
• Transport limitations 21
• NSF would like to see more proposals in – Shared Organizing Principlesg g p
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Part 3: Some Thoughts:Part 3: Some Thoughts:3-1 Biological Systemsg y
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Computer Science and BiologyComputer Science and Biology
T f h• Target of research– Biological systemsBiological systems– Nonbiological systems (or bio-inspired
systems)systems)
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Computer Science and BiologyComputer Science and Biology
T f h• Target of research– Biological systemsBiological systems– Nonbiological systems (or bio-inspired
systems)systems)
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• We know (to some extent)– How bio entities compute/communicatep
– How to manipulate/create bio entities
How to experiment with model and– How to experiment with, model and understand bio entities
• We know (to some extent)– How to make simple bio componentsp p
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C t T h l• Component Technology – Computing units
C ti t f bi l i l t i l• Creating gates from biological materials• Molecular computing (Computing with DNA transcription)
– Prof. Ned C. Seeman, New York University– Prof. Ron Weiss, Princeton University
– Communication units• Communication Propagation• Communication Propagation
– Molecular shuttle (Prof. Henry Hess, University of Florida)
• Addressing– DNA addressing (Docomo / Tokyo University)
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• Computing with enzymes– Function as logic gates
• If both substrate and effector exist, product produced
• If no effector or no substrate, substrate remains unchanged
ANDC
SP
C
ProductSubstrate
Enzyme
Effector
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Computing with DNA transcription Molecular ShuttleComputing with DNA transcription - Prof. Ron Weiss, Princeton University
Molecular Shuttle- Prof. Henry Hess, University of Florida
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• We do not know– How to artificially create a system of bio y y
entities• To compute/communicate/coordinateTo compute/communicate/coordinate
• May be, we can create a system by applyingapplying– “shared organizing principles” concept
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• Spatial correlation
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3D Bio Molecular Computing3D Bio Molecular Computing(UCI, Egashira, Ye, Enomoto, Watanabe, Nakano, Suda)
Information I operation information I’ operation Information I’’
3D Bio Molecular Computing3D Bio Molecular Computing(UCI, Egashira, Ye, Enomoto, Watanabe, Nakano, Suda)
Information I
3D Bio Molecular Computing3D Bio Molecular Computing(UCI, Egashira, Ye, Enomoto, Watanabe, Nakano, Suda)
• Operation– Add some bio
materials
Information I operation
Bio materials
3D Bio Molecular Computing3D Bio Molecular Computing(UCI, Egashira, Ye, Enomoto, Watanabe, Nakano, Suda)
Self organization
Information I operation information I’
Bio materials
3D Bio Molecular Computing3D Bio Molecular Computing(UCI, Egashira, Ye, Enomoto, Watanabe, Nakano, Suda)
Self organization
Information I operation information I’ operation
Bio materials Bio materials
3D Bio Molecular Computing3D Bio Molecular Computing(UCI, Egashira, Ye, Enomoto, Watanabe, Nakano, Suda)
Self organization Self organization
Information I operation information I’ operation Information I’’
Bio materials Bio materials
3D Bio Molecular Computing3D Bio Molecular Computing(UCI, Egashira, Ye, Enomoto, Watanabe, Nakano, Suda)
Self organization Self organization
Information I operation information I’ operation Information I’’
Program
Bio materials Bio materials
3D Bio Molecular Computing3D Bio Molecular Computing(UCI, Egashira, Ye, Enomoto, Watanabe, Nakano, Suda)
Self organizationSelf organization
Information I operation information I’ operation Information I’’
Program
Part 3: Some Thoughts:Part 3: Some Thoughts:3-2 Non-biological Systemsg y
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Computer Science and BiologyComputer Science and Biology
T f h• Target of research– Biological systemsBiological systems– Nonbiological systems (or bio-inspired
systems)systems)
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Part 3 1Part 3-1:Bio-Net:
An Evolvable Architecture for Adaptive Network ServicesAdaptive Network Services
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MotivationMotivation
• Network services/applications need to be– scalable, adaptable, survivable/available, , p , ,
simple to design/maintain
• Observation:• Observation: – large scale biological systems have desirable
f tfeatures
• So, apply biological concepts/mechanisms, pp y g p
Emergent BehaviorEmergent Behavior
• Biological systems– (useful) group behavior emerges from local ( ) g p g
interaction of individuals with simple behaviors
• In Bio NetA li ti f l l i t ti f– Application emerges from local interaction of cyber-entities with simple behaviors
Emergent Behavior in Bio NetEmergent Behavior in Bio-Net
i di id l b titi• individuals = cyber-entities (agents/objects) in Bio-Net– abstraction of various system components
• service components (e.g., program code, flight reservation service component), resource, user
– autonomous with simple behaviors• replication, reproduction, migration, death, etc.• makes its own decision, according to its own
behavioral policybehavioral policy
• CE behavior: energy exchange– gain energy from a cyber-entity (e.g., a user) in
exchange for performing a service
– expend energy to receive service from other cyber-titi ( t t k/ ti )entities (e.g., to use network/computing resources)
– can be used as a natural selection mechanismd th f t ti• death from energy starvation
• tendency to replicate/reproduce from energy abundance
Evolution and AdaptationEvolution and Adaptation
• Biological systems– individuals adjust their behaviors to j
environmental changes
– key componentskey components• diversity from mutations and crossovers during
replication/reproductionreplication/reproduction
• natural selection keeps entities with beneficial features alive and increase reproduction pprobability
Evolution and Adaptation in Bio NetEvolution and Adaptation in Bio-Net
• Bio Net– cyber-entities (CEs) adjust their behaviors to y ( ) j
environmental changes
– Key components• diversity
– A CE behavior is implemented by different policies
» human designers can introduce diversity in CE behaviorsbehaviors
» CEs replicate/reproduce with mutation/crossover in behavior policies
• natural selection (using energy)– death from energy starvation
– tendency to replicate/reproduce from energy abundance
Adaptation at CE LevelAdaptation at CE Level
• Cyber-entity behaviors implemented– Replication
• If current energy level > threshold, then create a new entity of same type
Death– Death• if current energy level = 0, then, die
– Migration– Migration• migrate towards source of energy (user requesting service)
• avoid coexisting on a node with same entity g y
Energy Seeking Entity (Simulation 1)
1 2 3 2 1
2 3 4 3 3
3 4 5 4 3
4 5 6 5 4
Source2 4 3 3
3 4 5 4 4 5 6 7 6 5
2 3 4 3 3
1 3 4 3 1
4 5 6 5 4
3 5 6 5 3
3 4 5 4 3 5 6 7 6 5
74 5 6 5 5
5 6 7 6 6
6 7 8 7 6
7 8 9 8 7
4 5 6 5 5
3 4 5 4 3
7 8 7 6
5 6 7 6 5
Source6
Entity 1: w1 = .5, w2 = .5, aggress = 4
Entity 2: w1 = .425, w2 = .575, aggress = 2.25
Entity 3: w1 = .575, w2 = .45, aggress = 4.5
Energy Seeking Entity (Simulation 1)
1 2 3 2 1
2 3 4 3 3
3 4 5 4 3
4 5 6 5 4
Source2 4 3 3
3 4 5 4 4 5 6 7 6 5
2 3 4 3 3
1 3 4 3 1
4 5 6 5 4
3 5 6 5 3
3 4 5 4 3 5 6 7 6 5
74 5 6 5 5
5 6 7 6 6
6 7 8 7 6
7 8 9 8 7
4 5 6 5 5
3 4 5 4 3
7 8 7 6
5 6 7 6 5
Source6
Entity 1: w1 = .5, w2 = .5, aggress = 4
Entity 2: w1 = .425, w2 = .575, aggress = 2.25
Entity 3: w1 = .575, w2 = .45, aggress = 4.5
Energy Seeking Entity (Simulation 1)
1 2 3 2 1
2 3 4 3 3
3 4 5 4 3
4 5 6 5 4
Source2 4 3 3
3 4 5 4 4 5 6 7 6 5
2 3 4 3 3
1 3 4 3 1
4 5 6 5 4
3 5 6 5 3
3 4 5 4 3 5 6 7 6 5
74 5 6 5 5
5 6 7 6 6
6 7 8 7 6
7 8 9 8 7
4 5 6 5 5
3 4 5 4 3
7 8 7 6
5 6 7 6 5
Source6
Entity 1: w1 = .5, w2 = .5, aggress = 4
Entity 2: w1 = .425, w2 = .575, aggress = 2.25
Entity 3: w1 = .575, w2 = .45, aggress = 4.5
Energy Seeking Entity (Simulation 1)
1 2 3 2 1
2 3 4 3 3
3 4 5 4 3
4 5 6 5 4
Source2 4 3 3
3 4 5 4 4 5 6 7 6 5
2 3 4 3 3
1 3 4 3 1
4 5 6 5 4
3 5 6 5 3
3 4 5 4 3 5 6 7 6 5
74 5 6 5 5
5 6 7 6 6
6 7 8 7 6
7 8 9 8 7
4 5 6 5 5
3 4 5 4 3
7 8 7 6
5 6 7 6 5
Source6
Entity 1: w1 = .5, w2 = .5, aggress = 4
Entity 2: w1 = .425, w2 = .575, aggress = 2.25
Entity 3: w1 = .575, w2 = .45, aggress = 4.5
Energy Seeking Entity (Simulation 1)
1 2 3 2 1
2 3 4 3 3
3 4 5 4 3
4 5 6 5 4
Source2 4 3 3
3 4 5 4 4 5 6 7 6 5
2 3 4 3 3
1 3 4 3 1
4 5 6 5 4
3 5 6 5 3
3 4 5 4 3 5 6 7 6 5
74 5 6 5 5
5 6 7 6 6
6 7 8 7 6
7 8 9 8 7
4 5 6 5 5
3 4 5 4 3
7 8 7 6
5 6 7 6 5
Source6
Entity 1: w1 = .5, w2 = .5, aggress = 4
Entity 2: w1 = .425, w2 = .575, aggress = 2.25
Entity 3: w1 = .575, w2 = .45, aggress = 4.5
VisionVision
N t l di ti tit i t• No central or coordinating entity exists.• A large number of CEs (created by millions of
illi f I t t ) t lmillions of Internet users), autonomously moving/replicating,CE ki l ti hi ith th CE• CEs making relationships with other CEs providing related services, di b h i li i tti t d d• diverse behavior policies getting created, good behaviors survive, bad ones die, making system flexible adaptable and evolvableflexible, adaptable and evolvable
• Let the Internet live its own life.
Some Thoughts on Bio Inspired Nets
• A large number of bio inspired network research– Ant routing
• Ants find a route following strength of pheromone• Ants find a route following strength of pheromone
– Immune system based intruder detectionI t fi d h th t t i il t• Immune system finds shapes that are not similar to self
Etc etc– Etc, etc
• “Bio inspired nets” at this point seems to be just an analogy between bio world and j gynets
• No systematic approach to decide at level analogy should be made– Molecular level
– Protein level
– Single cell organism level
– Multi-cell organism level
– Insect level
– Human level
– Human society level
• No systematic approach to decide at level analogy should be made– Molecular level
– Protein level
– Single cell organism level (immune system)
– Multi-cell organism level
– Insect level (ant routing)
– Human level
– Human society level (bio net)
• No systematic approach to decide how accurate analogy need to begy– Ants emit different types of pheromone
Queen ants regular ants; being ignored– Queen ants, regular ants; being ignored
– Bio systems are usually more complex than l th t h b li d i t kanalogy that has been applied in networks
• Existing approaches seem to be ad hocg pp
• We need to be clear on– what our “target” system is
• A network?
• A router?
?• ?
– what features we want a “target” system to have?• Robustness?• Robustness?
• Scalability?
• ?
• We need to consider multilevel analogy– Human society ------ ???
– Individuals ----------- ???
– Organs ---------------- ???
– Cells ------------------- ???
– Proteins --------------- ???
– Atoms ----------------- ???
• Bio inspired mechanism at one level will lead to psome behavior at a higher level
• We need to consider multilevel analogy– Human society ------ network applications (bio net)
– Individuals ----------- cyber entities (bio net)
– Organs ---------------- ???
– Cells ------------------- ???
– Proteins --------------- ???
– Atoms ----------------- ???
• Bio inspired mechanism at one level will lead to psome behavior at a higher level
• We need to consider multilevel analogy– Human society ------ ???
– Individuals ----------- ant routing
– Organs ---------------- ???
– Cells ------------------- ???
– Proteins --------------- ???
– Atoms ----------------- ???
• Bio inspired mechanism at one level will lead to psome behavior at a higher level
• We need to consider multilevel analogy– Human society ------ ???
– Individuals ----------- ???
– Organs ---------------- ???
– Cells -------------- immune sys based intruder detection
– Proteins --------------- ???
– Atoms ----------------- ???
• Bio inspired mechanism at one level will lead to psome behavior at a higher level
• We need to consider multilevel analogy– Human society ------ ???
– Individuals ----------- ???
– Organs ---------------- ???
– Cells ------------------- ???
– Proteins --------------- ???
– Atoms ----------------- ???
• Bio inspired mechanism at one level will lead to psome behavior at a higher level
• Existing approaches– Just making an analogyg gy
• May be, we can create a scientific approach by applyingapproach by applying– “shared organizing principles” concept
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Thanks!
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