Practical Synthetic Biology
Practical Synthetic Biology
1. Plasmids - Hosting and transmitting
2. Restriction Enzymes - Cutting
3. Ligation - Joining
4. PCR - Fine resolution changes
5. Electrophoresis – Separating
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.
http://universe-review.ca/I10-71-plasmid.jpg
Plasmids often containgenes that confer aselective advantage,such as the abilityto be antibiotic resistant.
Plasmids are also easilytransferred to other bacteria.
Why Plasmids are Useful
In recombinant DNA, plasmid are called vectors and are used to transfergenes from one organism to another.
Typically plasmids are constructed to contain a genetic marker that allowsthem to be identified and selected for, many different kinds can be purchased.
http://universe-review.ca/I10-71-plasmid.jpg
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
Restriction Sites (Cutting)
http://www-math.mit.edu/~lippert/18.417/lectures/02_PartialDigest/
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 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.
GAGGATACCACCAGGGTTACAGGATAGGAGTCAGAATTCAGAGGACCTAGGATACCTC CTCCTATGGTGGTCCCAATGTCCTATCCTCAGTCTTAAGTCTCCTGGATCCTATGGAG
GAGGATACCACCAGGGTTACAGGATAGGAGTCAG AATTCAGAGGACCTAGGATACCTC CTCCTATGGTGGTCCCAATGTCCTATCCTCAGTCTTAA GTCTCCTGGATCCTATGGAG
Sticky Ends
Restriction enzymes evolved asa defense against viral infection.
EcoR1
DNA Ligation
Ligation (DNA ligase) – ‘sealing’ two sticky ends together
http://openwetware.org/
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 thetet gene.
Plasmids
http://www.mrothery.co.uk/genetech/genetechnotes.htm
PCR
PCR is a method toamplify DNA fragments.
By denaturing DNA and adding primers, new copiescan be made. In addition,by designing primersthat extend beyond the endit is possible to add newsequences to the DNA.
See Wikipedia for Detailed article.
Gel Electrophoresis
http://www.biologyreference.com/Dn-Ep/Electrophoresis.html
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 toseparate restriction fragmentswhich can then be sequenced toconfirm that the cloning wasSuccessful.
lac promoter
NdeI restricition site (CATATG)
Sal I restriction site (GTCGAC)
pBR322 plasmid with lac promoter
Double digest withNde I and Sal I
lac promoter
NdeI overhangSal I overhang
Nde I digestion
-CATATG--GTATAC-
-CA TATG--GTAT AC-
Sal I digestion
- GTCGAC -- GTCGAC -
- G TCGAC -- GTCGA C -
GFP gene
NdeI restricition site (CATATG)
Sal I restriction site (GTCGAC)
6 bp extension
6 bp extension
PCR Amplification
GFP gene with new restriction sites
Double digest withNde I and Sal I
lac promoter GFP
Ligation reaction
repeat process for lac I
Standard Assembly
http://parts.mit.edu/registry/index.php/Assembly: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
Standard Assembly
http://biobricks.ai.mit.edu/Assembly/BB_Assembly.htm
Network Readout
Aequorea victoriaGreen fluorescent protein
Advantages:
1. Doesn’t require any other molecules to fluoresce.2. Relatively small, 238 amino acids (27 kDa)
Network Readout
Green fluorescent protein(GFP)
Red fluorescent protein
Cyan and Yellow
Network Readout
http://en.wikipedia.org/wiki/Image:FPbeachTsien.jpg
Network Readout
http://www.jacobsschool.ucsd.edu/news/news_releases/release.sfe?id=518
Network Readout
Stochastic Dynamics
http://www.jacobsschool.ucsd.edu/news/news_releases/release.sfe?id=518
Stochastic Dynamics
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 yellowfluorescent protein.
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
Stochastic Dynamics
Dennis Bray, Cambridge
Stochastic Models
1. Determine when the next reaction will occur.
2. Determine which reaction will occur.
Simulating a stochastic model is quite different from anODE 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:
The most well know implementation of this approach isthe Gillespie method (Gillespie, 1977).
Simulating a Simple System
Consider the following simple system:
Simulating a Simple System
1. 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 totalreaction probability, rtot
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).
1. Determine when the next reaction will occur.
Simulating a Simple System
5. Compute the relative probability rates:
Simulating a Simple System
6. Compute which reaction will ‘fire’:
7. Update the current time:
8. Go back to step 2
0
20
40
60
80
100
120
0 2 4 6 8 10 12 14 16
Determine which reaction will occur.
Stochastic Algorithm(old notes)
The Direct Method of Gillespie:
1. Which Reaction occurs next?
2. When does the reaction occur?
Stochastic Algorithm(old notes)
Which Reaction occurs next?
1. Calculate all the rates of reaction, ri
2. Sum the rates to yield: H = sum (ri) (Note the units of H are molecules per unit time)
3. Normalize each ri with H, rin = ri/H
4. 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
Stochastic Algorithm
When does the reaction occur?P
roba
bilit
y of
it o
ccur
ring
(p)
Most events occur soon, a few taken a long timeto occur, the likelihood of an event exponentially
decaying.
p(t) = exp (k t)
t = -ln (p)/k
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
2. 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.
Stochastic Algorithm(old notes)
A <-> B
Let A = 10; B = 2k1 = 0.1; k2 = 0.2;
Stochastic Algorithm(old notes)
A <-> B
Let A = 10; B = 2k1 = 0.1; k2 = 0.2;
r1 = 1; r2 = 0.4;
H = 1.4; rn1 = 0.625; rn2 = 0.28;
Stochastic Algorithm(old notes)
A <-> B
Let A = 10; B = 2k1 = 0.1; k2 = 0.2;
r1 = 1; r2 = 0.4;
H = 1.4; rn1 = 0.625; rn2 = 0.28;
p1 = 0.4;
Stochastic Algorithm(old notes)
A <-> B
Let A = 10; B = 2k1 = 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
Stochastic Algorithm(old notes)
A <-> B
Let A = 10; B = 2k1 = 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
Stochastic Algorithm(old notes)
A <-> B
Let A = 10; B = 2k1 = 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
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