ai for synthetic biology · mazzoldi, e. groban autodesk research, usa 4:20 mdp-based planning for...
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AI for Synthetic Biology @ IJCAI 2016
Aaron Adler Fusun Yaman
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ThisdocumentdoesnotcontaintechnologyorTechnicalDatacontrolledundereithertheU.S.Interna9onalTrafficinArmsRegula9onsortheU.S.ExportAdministra9onRegula9ons.
[email protected] Workshop Goals
• Expose AI researchers to a new domain • Bring new tools and techniques to Synthetic
Biologists to help address hard problems • Cross pollenate the AI and SynBio communities • Develop collaborations between the
communities • Discuss next steps
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[email protected] Schedule
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Time Title Authors Affilia0ons
1:30 Welcome A.Adler,F.Yaman
1:40 Introduc9ontoSynthe9cBiology A.Adler,F.Yaman BBN,USA
2:00 AIforSynthe9cBiology
F.Yaman,A.Adler
BBN,USA
2:25 Automatedreading,assemblyandexplana9ontoguidebiologicaldesign
N.Miskov-Zivanov
UniversityofPiTsburgh,USA
2:50 DebuggingGene9cProgramswithBayesianNetworks
G.Karlebach,L.Woodruff,C.VoigtandB.Gordon
MITBroadFoundry,USA
3:10 MolecularRobotsObeyingAsimov'sThreeLawsofRobo9cs
G.Kaminka,R.Spokoini-Stern,Y.Amir,N.AgmonandI.Bachelet
BarIlanUniversity&Augmanity,Israel
3:30 CoffeeBreak
4:00 ACombinatorialDesignWorkflowforSearchandPriori9za9oninLarge-ScaleSynthe9cBiologyConstructAssembly
J.Ng,A.Berliner,J.Lachoff,F.Mazzoldi,E.Groban
AutodeskResearch,USA
4:20 MDP-basedPlanningforDesignofGene-RepressionofCircuits
T.AmimeurandE.Klavins UniversityofWashington,USA
4:40 UsingMachineLearningtoInterpretUntargetedMetabolomicsintheContextofBiologicalSamples
A.Tong,N.Alden,V.Porokhin,N.Hassanpour,K.LeeandS.Hassoun
TucsUniversity,USA
5:00 Discussionandclosingremarks
5:30 Workshopends
Introduction to Synthetic Biology
Aaron Adler Fusun Yaman
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ThisdocumentdoesnotcontaintechnologyorTechnicalDatacontrolledundereithertheU.S.Interna9onalTrafficinArmsRegula9onsortheU.S.ExportAdministra9onRegula9ons.
Workpar9allysponsoredbyDARPAundercontractHR0011-10-C-0168.TheviewsandconclusionscontainedinthisdocumentarethoseoftheauthorsandnotDARPAortheU.S.Government.
[email protected] What is Synthetic Biology?
• “… a maturing scientific discipline that combines science and engineering in order to design and build novel biological functions and systems” [SynBERC]
• Synthetic biologists are working on diverse applications: – New medical diagnostics and therapies – Extract harmful pollutants from the ground – Chemical production or detection
• Synthetic Biology is at a crossroads: AI can help!
Program CellsExecu9ngProgram
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[email protected] Synthetic Biology vs. Genetic Engineering
• Genetic Engineering is the ability to read, copy, and edit DNA so that controlled changes can be made to organisms
• Engineering is the application of scientific, economic, social, and practical knowledge in order to invent, design, build, maintain, and improve structures, machines, devices, systems, materials, and processes. (Wikipedia)
• Understand the design enough to make a prediction about how it will behave
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Synthetic Biology as an Engineering Discipline
• Goal: Design sophisticated biological systems in a reliable, efficient, and predictable manner
• Useful engineering practices: – Libraries of “parts”, Component testing, Standards &
interfaces, Decoupling, Modularity, Computer aided design • Issues in engineering biological systems:
– Device characterization, Impedance matching, Rules of composition, Noise, Cellular context, Environmental conditions, Rational design vs. directed evolution, Persistence, Mutations, Crosstalk, Cell death, Chemical diffusion, Motility, Incomplete models
• A discipline that needs new engineering rule – The rules don’t have to be identical to natural evolution
[Weiss] 8
[email protected] Why is this Important?
• Breaking the complexity barrier:
• Multiplication of research impact • Reduction of barriers to entry
*Samplingofsystemsinpublica9onswithexperimentalcircuits
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2,100 2,7007,500 14,600
32,000
583,0001,080,000
100
1,000
10,000
100,000
1,000,000
1975 1980 1985 1990 1995 2000 2005 2010
Lengthinbasepa
irs
Year
DNA synthesis Circuit size ?
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[Purnick&Weiss,‘09]
Systemintegra0on
Gene0cparts
Modules
Applica0ons
HierarchicalOrganiza9oninSynthe9cBiology
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[email protected] DNA, RNA, Proteins • DNA (Deoxyribonucleic acid)
is a double helix encoding genetic instructions – Composed of nucleotides:
adenine (A), cytosine (C), guanine (G), or thymine (T)
• RNA (Ribonucleic acid) is usually single stranded – Composed of nucleotides:
adenine (A), cytosine (C), guanine (G), or uracil (U)
• RNA can encode an amino acid sequence that can in turn produce a protein
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[Wikimedia]
• Expression dependent on cellular platform, e.g., animal, yeast, bacteria, and cellular context, e.g., heart vs. skin cell
Common Machinery: Transcriptional Logic
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Transcrip)onisthecopyingofaregionofDNA,themoleculeinwhichgene9cinforma9onisencodedasasequenceofnucleo9des,intoastrandofRNA.
Transla)onisthedecodingoftheaminoacidsequenceofanRNAsequencetoproduceaprotein,therebyincreasingtheconcentra9onofthatprotein.Proteinsarethemain“machinery”ofacell.Amongotherthings,theyactassensors,asactuators,andasregulatorsofotherbiologicalprocesses.
Degrada)onandDilu)onaretheprocessesbywhichtheconcentra9onofproteinsandRNAtranscriptsdecrease.Degrada9onisthechemicalbreakdownofamoleculebycellularprocessesorbyitsowninstability.Dilu9onisthesideeffectofcellsgrowinganddividing:theeffec9veconcentra9onofanymoleculedropspropor9onaltotheamountthatthevolumeofthecellincreases.
Regula)onistheinterac9onofaproteinwiththepromoterregionofaDNAsequence,therebymodula9ngtherateatwhichtranscrip9onactsontheregionofDNAcontrolledbythepromoter.Theproteinmayrepressthepromoter,inhibi9ngtranscrip9on,orismayac)vatethepromoter,enhancingtranscrip9on.
Timeconstantsoftheseprocessesareocenquiteslow,ac9ngontheorderofminutes,hours,or(insomecases)days.
Structural
Chemical
Informa0onal
Proteins
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2
3
RNADNA
promoter
Degrada0on&Dilu0on
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RNApolymerase
ribosome
Focus on Information / Control
Most complex applications will require all three
Informa9onal:Representprocessesasdigitallogic,dataflowsChemical:Cellularreac9onsarefundamentallyprobabilis9candchemicalStructural:Reac9onsdependonthephysicalstructureofDNA/RNA/Proteins/Cells
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Cellularcontext
System Design
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SENSORS PROCESSING ACTUATION
Environment
Synthe0ccircuit
• Number?• Types?
• Sophis9ca9on?§ Timing§ States§ Lookuptables§ …
• Number?• Types?
Highlevelgoal:developanengineeringdisciplineforbiology
temperature,pH,light,chemicalsignals,mechanicalforce
fluorescence,movement,electricalac9vity,chemicalproducts
[email protected] Building Blocks
• Features (Parts) are previously identified DNA sequences that perform a specific biological function – promoter initiates transcription – coding sequence for a protein – terminator that halts transcription
• Parts used as basis for engineering • Fluorescent proteins can be observed and used
to help understand what is going on in a cell
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Promoter CDS Terminator
+
[email protected] Genetic Regulatory Networks • A collection of DNA regions and their regulatory
interactions is called a genetic regulatory network (GRN) – Takes advantage of the modularity of the DNA molecule – Design of a desired computation
• The GRN may be designed as a single DNA sequence or as multiple separate sequences – Can operate as an insertion into the organism’s existing DNA, as
a virus, or as independent free-floating DNA loops (known as plasmids)
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Key
Promoter
Protein
Repress
Ac9vate
pTrepHef1a pTrertTA CFP LacI EYFP
Dox
pHef1a-LacO1Oid
IfthereisDoxThenglowCyanElseglowYellow
[email protected] Biological Circuits
• Various forms of interaction can be used as computational building blocks for building more complex biological circuits – Deliberately analogous to electronic circuits
• Loops in the regulatory network can be used for feedback control or to create state memory
• Allows an extremely wide variety of computational and control systems to be implemented as genetic regulatory networks
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Input Concentration
Out
put C
once
ntra
tion
Digital Logic in an Analog World
• Biological processes can support digital logic devices!
ideal (step fn)
reality (sigmoidal)
“0” “1”
“1”
“0”
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Genetic Building Block – Digital Inverter
0 1
CDS P
Transcription / Translation
output protein input protein (repressor)
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Gene9cBuildingBlock–DigitalInverter
1 0
CDS P
Transcription / Translation
output protein input protein (repressor)
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[email protected] Interac9ngwithCells–IMPLIESGate
CDS P
Transcription / Translation
output protein input protein (repressor)
Repressor Inducer Output
Repressor Inducer Output0 0 10 1 11 0 01 1 1
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[email protected] Interac9ngwithCells–IMPLIESGate
CDS P
Transcription / Translation
output protein inactive repressor
Repressor Inducer Output
Repressor Inducer Output0 0 10 1 11 0 01 1 1
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[email protected] Abstract Genetic Regulatory Network (A-GRN)
• Defines logical relationship between abstract parts • The GRN above
– Y induces and Z represses the transcription of X
• The overall behavior depends on chemical properties – degradation (γ ), dissociation (D), fold activation (K), basal
expression (α), cooperativity (H)
Y
Z
X
Kz Hz Dz αz γz
Kx Hx Dx αx γx
Ky Hy Dy αy γy
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[email protected] Simulating System Behavior
• Change in concentration of the chemicals approximated using differential equations
• The input/output relationship between X&Y and X&Z
( Kz , Hz , Dz )
(αx , γx ) ( Ky , Hy , Dy )
X
Y Z 26
[email protected] DNA Assembly Techniques
• BioBricks • Magnetic Beads • Gateway-Gibson • Golden Gate • Enzymes break DNA
apart allowing parts to join together
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[igem.org]
[email protected] Getting the New DNA into Cells
• How do you get new DNA into the cell? – Transfection
• Chemical and non-chemical methods – Lipofection – Virus delivery
• DNA can be: – chromosomally integrated OR – transiently transfected OR – on a separate plasmid
• How do you measure the results? – fluorescence – mass spectrometry – anti-body assays – cells emit other chemicals – RNA/DNA assays, …
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GFP
“Fluoresce green when doxycycline is present”
Currently, even something this simple isn’t easy…
rtTA
Dox
Simple Circuit Example
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[email protected] Sense/Actuate Example
NoArabinose HighDoseArabinose
Ara
AraC pBAD GFP TetR pTet RFPpBAD
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OutlookforApplica9ons
Microbialbiochemical
synthesis• artemisinin• otherpharmaceu9cals
Environmentalapplica0ons• environmentalremedia9on• toxinsensing• explosivesensing
Bioenergyproduc0on• biodiesel• hydrogen• methane• …
Biomedicalapplica0ons• cancertherapeu9cagents• ar9ficial9ssuehomeostasis• programmed9ssueregenera9on• ar9ficialimmunesystem
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