Practical Synthetic Biology

1. Practical Synthetic Biology

2. Practical Synthetic Biology • Plasmids - Hosting and transmitting • Restriction Enzymes - Cutting • Ligation - Joining • PCR - Fine resolution changes • Electrophoresis – Separating

3. Plasmids Plasmids are natural small circular pieces of DNA that live autonomously inside cells (often in bacteria). They are useful because they can be easily isolated and manipulated. They can vary in size from 1 to over 400 kilobase pairs and may exist in many copies in a single cell. Plasmids often contain genes that confer a selective advantage, such as the ability to be antibiotic resistant. Plasmids are also easily transferred to other bacteria. http://universe-review.ca/I10-71-plasmid.jpg

4. Why Plasmids are Useful In recombinant DNA, plasmid are called vectors and are used to transfer genes from one organism to another. Typically plasmids are constructed to contain a genetic marker that allows them to be identified and selected for, many different kinds can be purchased. http://universe-review.ca/I10-71-plasmid.jpg

5. Getting Plasmids into Cells Chilling cells in the presence of divalent cations such as Ca2+ (in CaCl2) prepares the cell walls to become permeable to plasmid DNA. Cells are incubated with the DNA and then briefly heat shocked (42C for 30-120 seconds), which causes the DNA to enter the cell. Electroporation is another way to make holes in cells, by briefly shocking them with an electric field of 100-200V/cm http://universe-review.ca/I10-71-plasmid.jpg

6. Restriction Sites (Cutting) Restriction endonuclease are enzymes that will recognize, bind to and hydrolyze specific nucleic acid sequences in double-stranded DNA. Such sequences are often palindromic. http://www-math.mit.edu/~lippert/18.417/lectures/02_PartialDigest/

7. Restriction Sites (Cutting) Restriction endonuclease are enzymes that will recognize, bind to and hydrolyze specific nucleic acid sequences in double-stranded DNA. Such sequences are often palindromic. Restriction enzymes evolved as a defense against viral infection. GAGGATACCACCAGGGTTACAGGATAGGAGTCAGAATTCAGAGGACCTAGGATACCTC CTCCTATGGTGGTCCCAATGTCCTATCCTCAGTCTTAAGTCTCCTGGATCCTATGGAG EcoR1 GAGGATACCACCAGGGTTACAGGATAGGAGTCAG AATTCAGAGGACCTAGGATACCTC CTCCTATGGTGGTCCCAATGTCCTATCCTCAGTCTTAA GTCTCCTGGATCCTATGGAG Sticky Ends

8. DNA Ligation Ligation (DNA ligase) – ‘sealing’ two sticky ends together http://openwetware.org/

9. Plasmids - pBR322 • Selectable Markers: Ampicillin Resistance (β-lactamase gene) and Tetracycline Resistance (tet gene). • pBR322 has many restriction sites making it a versatile plasmid. • If we add EcoR1 and HindII to a solution of pBR322 it will disrupt the • tet gene.

10. Plasmids http://www.mrothery.co.uk/genetech/genetechnotes.htm

11. PCR PCR is a method to amplify DNA fragments. By denaturing DNA and adding primers, new copies can be made. In addition, by designing primers that extend beyond the end it is possible to add new sequences to the DNA. See Wikipedia for Detailed article.

12. Gel Electrophoresis Gel electrophoresis is a method that separates (based on size, electrical charge and other physical properties) macromolecules such as nucleic acids or proteins. In synthetic biology it can be used to separate restriction fragments which can then be sequenced to confirm that the cloning was Successful. http://www.biologyreference.com/Dn-Ep/Electrophoresis.html

13. NdeI restricition site (CATATG) Sal I digestion Sal I restriction site (GTCGAC) - GTCGAC - - GTCGAC - lac promoter - G TCGAC - - GTCGA C - pBR322 plasmid with lac promoter Double digest with Nde I and Sal I Nde I digestion -CATATG- -GTATAC- lac promoter NdeI overhang -CA TATG- -GTAT AC- Sal I overhang

14. 6 bp extension NdeI restricition site (CATATG) GFP gene 6 bp extension Sal I restriction site (GTCGAC) PCR Amplification GFP gene with new restriction sites Double digest with Nde I and Sal I

15. Ligation reaction GFP lac promoter repeat process for lac I

16. Standard Assembly BioBricks have been designed to be assembled using normal cloning techniques. Two BioBrick parts, for example, one blue and one green, can be assembled into a blue-green system by a process called BioBrick Standard Assembly http://parts.mit.edu/registry/index.php/Assembly:Standard_assembly

17. Standard Assembly http://biobricks.ai.mit.edu/Assembly/BB_Assembly.htm

18. Network Readout Aequorea victoria Green fluorescent protein • Advantages: • Doesn’t require any other molecules to fluoresce. • Relatively small, 238 amino acids (27 kDa)

19. Network Readout Green fluorescent protein (GFP) Red fluorescent protein Cyan and Yellow

20. Network Readout http://en.wikipedia.org/wiki/Image:FPbeachTsien.jpg

21. Network Readout http://www.jacobsschool.ucsd.edu/news/news_releases/release.sfe?id=518

22. Network Readout

23. Stochastic Dynamics http://www.jacobsschool.ucsd.edu/news/news_releases/release.sfe?id=518

24. Stochastic Dynamics

25. Stochastic Dynamics Experimental design for live-cell observations of gene expression. Tsr-Venus is expressed under the control of lac repressor, which binds tightly to the lac operator on DNA. Transcription of one mRNA by an RNA polymerase results from an infrequent and transient dissociation event of repressor from DNA. Multiple copies of protein molecules are translated from the mRNA by ribosomes. Upon being assembled into E. coli's inner membrane, Tsr-Venus protein molecules can be detected individually by a fluorescence microscope. Venus is the name for the yellow fluorescent protein.

26. Stochastic Dynamics Living E. coli cells were monitored for YFP fluorescence, Probing Gene Expression in Live Cells, One Protein Molecule at a Time Ji Yu, Jie Xiao, Xiaojia Ren Kaiqin Lao X. Sunney Xie Science 17 March 2006:Vol. 311. no. 5767, pp. 1600 - 1603

27. Stochastic Dynamics Dennis Bray, Cambridge

28. Stochastic Models Simulating a stochastic model is quite different from an ODE model. In a stochastic model we take account of individual reactions as they convert one molecule into another. Solving a stochastic model is a two stage process. At each time point we must answer the following two questions: • Determine when the next reaction will occur. • Determine which reaction will occur. The most well know implementation of this approach is the Gillespie method (Gillespie, 1977).

29. Simulating a Simple System Consider the following simple system:

30. Simulating a Simple System • Set t = 0, initialize concentrations (molecule numbers) • A = 50; B = 0; k1 = 0.1; k2 = 0.2; • 2. Compute reaction probabilities for all reactions and compute the total • reaction probability, rtot

31. Simulating a Simple System • 3. Generate two random numbers, p1 and p2 - urnd() • 4. Compute the time of next reaction: • Tau is the time the next reaction will occur (units are time per molecule). • Determine when the next reaction will occur.

32. Simulating a Simple System 5. Compute the relative probability rates:

33. Simulating a Simple System Determine which reaction will occur. 6. Compute which reaction will ‘fire’: 7. Update the current time: 8. Go back to step 2

34. Stochastic Algorithm(old notes) • The Direct Method of Gillespie: • Which Reaction occurs next? • When does the reaction occur?

35. Stochastic Algorithm(old notes) • Which Reaction occurs next? • Calculate all the rates of reaction, ri • Sum the rates to yield: H = sum (ri) (Note the units of H are molecules per unit time) • Normalize each ri with H, rin = ri/H • Obtain a random number from a • uniform distribution (0 to 1.0) – urnd () in Jarnac • 5. Use the random number to select • which reaction fires. r1n r2n r3n r4n 0 1.0

36. Stochastic Algorithm When does the reaction occur? p(t) = exp (k t) t = -ln (p)/k Probability of it occurring (p) Most events occur soon, a few taken a long time to occur, the likelihood of an event exponentially decaying.

37. Stochastic Algorithm(old notes) • When does the reaction occur? • 1. Obtain a random number from a • uniform distribution (0 to 1.0) r = urnd () in Jarnac • Calculate delta t = - ln (r) / H (exponential distribution) • (Note that the units for this are time per molecule) • 3. Update molecule numbers for the chosen reaction.

38. Stochastic Algorithm(old notes) A <-> B Let A = 10; B = 2 k1 = 0.1; k2 = 0.2;

39. Stochastic Algorithm(old notes) A <-> B Let A = 10; B = 2 k1 = 0.1; k2 = 0.2; r1 = 1; r2 = 0.4; H = 1.4; rn1 = 0.625; rn2 = 0.28;

40. Stochastic Algorithm(old notes) A <-> B Let A = 10; B = 2 k1 = 0.1; k2 = 0.2; r1 = 1; r2 = 0.4; H = 1.4; rn1 = 0.625; rn2 = 0.28; p1 = 0.4;

41. Stochastic Algorithm(old notes) A <-> B Let A = 10; B = 2 k1 = 0.1; k2 = 0.2; r1 = 1; r2 = 0.4; H = 1.4; rn1 = 0.625; rn2 = 0.28; p1 = 0.4; Therefore the first reaction will occur (A -> B) p2 = 0.7

42. Stochastic Algorithm(old notes) A <-> B Let A = 10; B = 2 k1 = 0.1; k2 = 0.2; r1 = 1; r2 = 0.4; H = 1.4; rn1 = 0.625; rn2 = 0.28; p1 = 0.4; Therefore the first reaction will occur (A -> B) p2 = 0.7 dt = -ln (0.7)/1.4 = 0.25 secs

43. Stochastic Algorithm(old notes) A <-> B Let A = 10; B = 2 k1 = 0.1; k2 = 0.2; r1 = 1; r2 = 0.4; H = 1.4; rn1 = 0.625; rn2 = 0.28; p1 = 0.4; Therefore the first reaction will occur (A -> B) p2 = 0.7 dt = -ln (0.7)/1.4 = 0.25 secs per molecule A = A – 1; B = B + 1; t = t + dt