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Designing Biological Devices in GEC

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1 Designing Biological Devices in GEC
Cells are highly sophisticated organic machines. We can program these machines using DNA, which is, in essence the software of cells. James Brown, Neil Dalchau, Michael Pedersen, Andrew Phillips ATGCTTACCGGTACGTTTACGACTACGTAGCTAGCATGCTTACCGGTACGTTTACGAC Image courtesy of James Brown, Haseloff Lab, University of Cambridge

2 DNA: Software for Cells
4-letter code that tells a cell what proteins to make From the software perspective, DNA is a 4-letter digital code that tells a cell what proteins to make. The proteins perform a range of information processing functions for the cell. DNA transcription in real time RNA polymerase II: base/second mRNA translation Videos by Drew Berry

3 Executing DNA Machine Code
A simplified view of DNA instructions (parts) Here we take a highly simplified view of DNA. A bit like computer machine code. Instead of 0s and 1s there are 4 letters, G,C,A,T. Just like machine code, divided into instructions or parts. We give these instructions types and names. Here we illustrate how a cell can executes a given sequence of DNA instructions Promoter=start, ribosome binding site = speed, protein coding region = procedure, terminator = stop. ATGCTTACCGGTACGTTTACGACTACGTAGCTAGCATGCTTACCGGTACGTTTACGAC TACGTAGCTAGCATGCTTACCGGTACGTTTACGACTACGTAGCTAGCATGCTTACT

4 Compiling DNA Machine Code
Given a design, automatically determine the DNA We have shown how a cell can execute a given DNA sequence. In our case we actually want to do the reverse process. Define the program and then determine what sequence is needed to implement this program. Start with the behaviour and then deduce the DNA sequence automatically.

5 Genetic Engineering of Cells (GEC)
Designing biological devices in GEC Step 1: Program device design Step 2: Compile design to parts Step 3: Compile parts to reactions predator [ r0051:prom; rbs; pcr<codes(Q2b)> ; rbs; pcr<codes(Q1a)>; ter ; prom<pos(Q2b-H2)>; rbs; pcr<prot(A)>; ter ; r0051:prom; rbs; pcr<prot(ccdB)>; ter | Q1a ˜ -> H1 | Q2b + H2 <-> Q2b-H2 | A ˜ ccdB -> | ccdB ˜ Q1a *->{10.0} | H1 *->{10.0} | H2 *->{10.0} ] || prey [ prom<pos(H1-Q1b)>; rbs; pcr<prot(ccdB)>; ter ; r0051:prom; rbs; pcr<codes(Q1b)> ; rbs; pcr<codes(Q2a)>; ter | Q2a ˜ -> H2 | H1 + Q1b <-> H1-Q1b | ccdB ˜ Q2a *->{10.0} c1[H1] -> H1 | H1 -> c2[H1] | c2[H2] -> H2 | H2 -> c1[H2] c2 [ mrna148 ->{RMRNADeg} | init g147 1 | g147 ->{1e-6} g147 + mrna148 | g147 + m3OC12HSL-lasR ->{1} g147-m3OC12HSL-lasR | g147-m3OC12HSL-lasR ->{0.8} g147 + m3OC12HSL-lasR | g147-m3OC12HSL-lasR ->{0.1} g147-m3OC12HSL-lasR + mrna148 | mrna148 ->{0.1} mrna148 + ccdB | m3OC12HSL + lasR ->{0.5} m3OC12HSL-lasR | m3OC12HSL-lasR ->{1} m3OC12HSL + lasR | m3OC6HSL ->{10} | luxI ~ ->{1} m3OC6HSL | m3OC12HSL ->{10} | ccdB + luxI ->{10} ccdB | init g174 1 | g174 ->{0.12} g174 + mrna175 | mrna175 ->{RMRNADeg} | mrna175 ->{0.1} mrna175 + luxI | mrna175 ->{0.1} mrna175 + lasR ] | c1[m3OC12HSL] ->{0.5} m3OC12HSL | m3OC12HSL->{0.5} c2[m3OC12HSL] | c2[m3OC6HSL] ->{0.5} m3OC6HSL | m3OC6HSL->{0.5} c1[m3OC6HSL] | ccdA ->{0.1} | ccdB ->{0.005} | lasI ->{0.001} | lasR ->{0.001} | luxI ->{0.001} | luxR ->{0.001} ]| predator prey rate RMRNADeg = 0.001; c1 [ init g47 1 | mrna48 ->{RMRNADeg} | g47 ->{0.12} g47 + mrna48 | mrna48 ->{0.1} mrna48 + luxR | mrna48 ->{0.1} mrna48 + lasI | lasI ~ ->{1} m3OC12HSL | m3OC12HSL ->{10} | luxR + m3OC6HSL ->{0.5} luxR-m3OC6HSL | m3OC6HSL ->{10} | luxR-m3OC6HSL ->{1} luxR + m3OC6HSL | init g92 1 | mrna93 ->{RMRNADeg} | g92 + luxR-m3OC6HSL ->{1} g92-luxR-m3OC6HSL | g92 ->{1e-6} g92 + mrna93 | g92-luxR-m3OC6HSL ->{0.8} g92 + luxR-m3OC6HSL | g92-luxR-m3OC6HSL ->{0.1} g92-luxR-m3OC6HSL + mrna93 | init g115 1 | mrna93 ->{0.1} mrna93 + ccdA | mrna116 ->{RMRNADeg} | g115 ->{0.12} g115 + mrna116 | mrna116 ->{0.1} mrna116 + ccdB | ccdB + lasI ->{10} ccdB ccdA ~ ccdB ->{1} | ] | ccdB ->{0.005} | ccdA ->{0.1} | lasI ->{0.001} | lasR ->{0.001} | luxI ->{0.001} | ]| luxR ->{0.001} Specify the design of a biological device as a set of parts and relationships between these parts. Step 4: Simulate reactions Step 5: Insert parts into cells predator prey Pedersen & Phillips, Royal Society Interface, 2009

6 GEC Development Cycle

7 GEC Language: Parts and Properties
Representation X:prom X:rbs X:pcr X:ter Part Property Representation X:prom<con(RT)> X:prom<pos(P,RB,RUB,RTB)> X:prom<neg(P,RB,RUB,RTB)> X:rbs<rate(R)> X:pcr<codes(P,RD)> Part Property Representation X:prom<pos(P)> X:prom<neg(P)> X:pcr<codes(P)>

8 GEC Parts Database ID Type Properties i723017 pcr codes(xylR, 0.001)
codes(phzM, 0.001) i723025 codes(phzS, 0.001) i723028 codes(pca, 0.001) c0051 codes(cI, 0.001) c0040 codes(tetR, 0.001) c0080 codes(araC, 0.001) c0012 codes(lacI,0.001) cunknown2 codes(unknown2, 0.001) c0061 codes(luxI,0.001) c0062 codes(luxR,0.001) c0079 codes(lasR,0.001) c0078 codes(lasI,0.001) cunknown3 codes(ccdB,0.005) cunknown4 codes(ccdA, 0.1) i723020 prom pos(toluene-xylR, 0.001, 0.001, 1.0), con(0.0001) r0051 neg(cI, 1.0, 0.5, ), con(0.12) r0040 neg(tetR, 1.0, 0.5, ), con(0.09) runknown1 neg(unknown1, 1.0, 0.005, 0.001), con(0.04) i0500 neg(araC, 1.0, , ), con(0.1) r0011 neg(lacI, 1.0, 0.5, ), con(0.1) runknown2 pos(lasR-m3OC12HSL, 1.0, 0.8, 0.1), pos(luxR-m3OC6HSL, 1.0, 0.8, 0.1), con( ) b0034 rbs rate(0.1) b0015 ter cunknown5 codes(ccdA2, 10.0) runknown5 con(10.0)

9 Compiling GEC Design to Parts
Specific set of parts: [r0040; b0034; c0040; b0015] r0040:prom; b0034:rbs; c0040:pcr; b0015:ter tetR negative feedback [r0040; b0034; c0040; b0015] X1:prom<neg(tetR)>; X2:rbs; X3:pcr<codes(tetR)>; X4:ter If no variable names are given, assume they are all the same. [r0051; b0034; c0051; b0015] clR [r0040; b0034; c0040; b0015] tetR [i0500; b0034; c0080; b0015] araC [r0011; b0034; c0012; b0015] lacI Any negative feedback: [r0051; b0034; c0051; b0015] [r0040; b0034; c0040; b0015] [i0500; b0034; c0080; b0015] [r0011; b0034; c0012; b0015] prom<neg(Y)>; rbs; pcr<codes(Y)>; ter

10 GEC Language: Reactions

11 Compiling GEC Parts to Reactions
rate mrnaDeg = 0.001; init gcI 1 | gcI ->{0.12} gcI + mcI | gcI + cI ->{1} gcI_cI | gcI_cI ->{0.5} gcI + cI | gcI_cI ->{5e-5} gcI_cI + mcI | mcI ->{0.1} mcI + cI | mcI ->{mrnaDeg} | cI ->{0.001} r0040:prom; b0034:rbs; c0040:pcr; b0015:ter Repeats what the cell would actually do. Lookup the part properties in the database.

12 Example: Repressilator
Repressilator Design 24 possible solutions, many of them defective, e.g. [ r0051; b0034; c0040; b0015 ; r0040; b0034; c0080; b0015 ; i0500; b0034; c0051; b0015] prom<neg(C)>; rbs; pcr<codes(A)>; ter; prom<neg(A)>; rbs; pcr<codes(B)>; ter; prom<neg(B)>; rbs; pcr<codes(C)>; ter [("A", "tetR"); ("B", "araC"); ("C", "clR")] [["r0051"; "b0034"; "c0040"; "b0015"; "r0040"; "b0034"; "c0080"; "b0015"; "i0500"; "b0034"; "c0051"; "b0015"]] Engineered by Elowitz et al., Nature (2000)

13 Case Study: Repressilator
Add constraints on rates, e.g. promoter strength Selects functional Repressilator [ r0040; b0034; c0051; b0015 ; r0051; b0034; c0012; b0015 ; r0011; b0034; c0040; b0015] | 0.4 < RubB | RubB < 0.6 | 0.4 < RubC | RubC < 0.6 | 0.4 < RubA | RubA < 0.6 prom<con(RtA),neg(C,RbA,RubA,RtbA)>; rbs<rate(RA)>; pcr<codes(A,RdA)>; ter; prom<con(RtB),neg(A,RbB,RubB,RtbB)>; rbs<rate(RB)>; pcr<codes(B,RdB)>; ter; prom<con(RtC),neg(B,RbC,RubC,RtbC)>;rbs<rate(RC)>; pcr<codes(C,RdC)>; ter Constraint: repressors of similar strength.

14 Case Study: Repressilator
Definition of modules module gate(i,o) { prom<neg(i)>; rbs; pcr<codes(o)>; ter }; gate(A,B) | gate(B,C) | gate(C,A) Constraint: repressors of similar strength. module gate(i,o) { new RB. new RUB. new RTB. new RT. new R. new RD. prom<con(RT), neg(i, RB, RUB, RTB)>;rbs<rate(R)>; pcr<codes(o,RD)>; ter | 0.4 < RUB | RUB < 0.6 }; gate(A,B) | gate(B,C) | gate(C,A)

15 Case Study: Predator Prey
Specified in GEC as follows predator [ r0051:prom; rbs; pcr<codes(Q2b)> ; rbs; pcr<codes(Q1a)>; ter ; prom<pos(Q2b-H2)>; rbs; pcr<codes(A)>; ter ; r0051:prom; rbs; pcr<codes(ccdB)>; ter | Q1a ~-> H1 | Q2b + H2 <-> Q2b-H2 | A ~ccdB -> ] || prey [ prom<pos(H1-Q1b)>; rbs; pcr<codes(ccdB)>; ter ; r0051:prom; rbs; pcr<codes(Q1b)> ; rbs; pcr<codes(Q2a)>; ter | Q2a ~-> H2 | H1 + Q1b <-> H1-Q1b ||predator[H1] -> H1 | H1 -> prey[H1] | prey[H2] -> H2 | H2 -> predator[H2] Engineered by Balagadde et al., MSB (2008)

16 Case Study: Predator Prey
Compile to parts to reactions and simulate rate RMRNADeg = 0.001; c1 [ init g47 1 | mrna48 ->{RMRNADeg} | g47 ->{0.12} g47 + mrna48 | mrna48 ->{0.1} mrna48 + lasI | mrna48 ->{0.1} mrna48 + luxR | lasI ~ ->{1} m3OC12HSL | m3OC12HSL ->{10} | m3OC6HSL ->{10} | luxR-m3OC6HSL ->{1} luxR + m3OC6HSL | luxR + m3OC6HSL ->{0.5} luxR-m3OC6HSL | init g92 1 | mrna93 ->{RMRNADeg} | g92 ->{1e-6} g92 + mrna93 | g92-luxR-m3OC6HSL ->{0.8} g92 + luxR-m3OC6HSL | g92 + luxR-m3OC6HSL ->{1} g92-luxR-m3OC6HSL | g92-luxR-m3OC6HSL ->{0.1} g92-luxR-m3OC6HSL + mrna93 | mrna93 ->{0.1} mrna93 + ccdA | mrna116 ->{RMRNADeg} | init g115 1 | g115 ->{0.12} g115 + mrna116 | mrna116 ->{0.1} mrna116 + ccdB | ccdA ~ ccdB ->{1} | ] | ccdB + lasI ->{10} ccdB ccdA ->{0.1} | lasI ->{0.001} | ccdB ->{0.005} | lasR ->{0.001} | luxI ->{0.001} | luxR ->{0.001} ]| r0051; b0034; c0062; b0034; c0078; b0015; runknown2; b0034; cunknown4; b0015; r0051; b0034; cunknown3; b0015 || runknown2; b0034; cunknown3; b0015; r0051;b0034;c0061;b0034;c0079;b0015 c2 [ init g147 1 | g147 ->{1e-6} g147 + mrna148 | mrna148 ->{RMRNADeg} | g147 + m3OC12HSL-lasR ->{1} g147-m3OC12HSL-lasR | g147-m3OC12HSL-lasR ->{0.8} g147 + m3OC12HSL-lasR | g147-m3OC12HSL-lasR ->{0.1} g147-m3OC12HSL-lasR + mrna148 | mrna148 ->{0.1} mrna148 + ccdB | m3OC12HSL + lasR ->{0.5} m3OC12HSL-lasR | m3OC12HSL-lasR ->{1} m3OC12HSL + lasR | luxI ~ ->{1} m3OC6HSL | m3OC12HSL ->{10} | m3OC6HSL ->{10} | ccdB + luxI ->{10} ccdB | init g174 1 | mrna175 ->{RMRNADeg} | g174 ->{0.12} g174 + mrna175 | mrna175 ->{0.1} mrna175 + luxI | mrna175 ->{0.1} mrna175 + lasR ] | c1[m3OC12HSL] ->{0.5} m3OC12HSL | c2[m3OC6HSL] ->{0.5} m3OC6HSL | m3OC12HSL->{0.5} c2[m3OC12HSL] | m3OC6HSL->{0.5} c1[m3OC6HSL] | ccdA ->{0.1} | ccdB ->{0.005} | lasI ->{0.001} | lasR ->{0.001} | luxI ->{0.001} | ]| luxR ->{0.001}

17 GEC Demo: Databases

18 GEC Demo: Models

19 GEC Demo: Reactions

20 GEC Demo: Simulations New repository of parts for Gram +ve bacteria
Future work: New repository of parts for Gram +ve bacteria Collaboration with Cambridge and Newcastle For engineering colonies of synchronising cells GEC to be developed as part of the repository Engineering predictable spatio-temporal dynamics James Brown, Plant Sciences, developing a set of well-characterised parts. Modelling spatio-temporal dynamics (Matthew Smith, MSR)

21 Engineering Turing Patterns
Determine conditions for Turing instability Simulate model that satisfies these conditions Use conditions as GEC constraints Simulated the system in spatially inhomogeneous setup in two spatial dimension. An example of what the system looks like. We end up with constraints on the parameters. Constrain the GEC model to find the appropriate parts. Illustrates future potential, when the design of the biological system is non-trivial.

22 GEC Development Cycle Add things to do with the implementation step.

23 Future Work Parts User Interface Analysis Implementation
Better part characterisation More realistic part properties User Interface Visual editor Web-based tool Analysis Integrated ODE analysis Implementation Optimise translation to DNA sequences

24 Acknowledgements Microsoft Research University of Cambridge
Neil Dalchau, Matthew Smith, Matthew Lakin, Filippo Polo. University of Cambridge Jim Haseloff, Jim Ajioka, James Brown, Tim Rudge (PhD scholar), Michael Pedersen (EPSRC fellow). University of Newcastle Neil Wipat, Jennifer Hallinan.

25 Thanks

26 GEC Calculus

27 GEC Compilation Algorithm

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