Computer Science and Biology

Tatsuya SudaInformation and Computer Science

University of California, Irvinesuda@ics uci edusuda@ics.uci.edu

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 – suda@ics.uci.edu or suda@nsf.gov

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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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