Transcript Glossary

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.
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
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
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)
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/
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
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 the
tet gene.
Plasmids
http://www.mrothery.co.uk/genetech/genetechnotes.htm
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.
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
NdeI restricition site (CATATG)
Sal I restriction site (GTCGAC)
Sal I digestion
- GTCGAC - GTCGAC -
lac promoter
pBR322 plasmid with lac promoter
-G
- GTCGA
TCGAC C-
Double digest with
Nde I and Sal I
Nde I digestion
lac promoter
NdeI overhang
Sal I overhang
-CATATG-GTATAC-
-CA
-GTAT
TATGAC-
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
Ligation reaction
lac promoter
GFP
repeat process for lac I
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 bluegreen system by a process
called BioBrick Standard
Assembly
http://parts.mit.edu/registry/index.php/Assembly:Standard_assembly
Standard Assembly
http://biobricks.ai.mit.edu/Assembly/BB_Assembly.htm
Network Readout
Aequorea victoria
Green 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/new
s/news_releases/release.sfe?id=518
Network Readout
Stochastic Dynamics
http://www.jacobsschool.ucsd.edu/new
s/news_releases/release.sfe?id=518
Stochastic Dynamics
Stochastic Dynamics
Experimental design for live-cell
observations of gene expression. TsrVenus 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, TsrVenus protein molecules can be
detected individually by a
fluorescence microscope.
Venus is the name for the yellow
fluorescent 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
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:
1.
Determine when the next reaction will occur.
2.
Determine which reaction will occur.
The most well know implementation of this approach is
the 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 total
reaction 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
Determine which reaction will occur.
6. Compute which reaction will ‘fire’:
7. Update the current time:
120
100
80
60
8. Go back to step 2
40
20
0
0
2
4
6
8
10
12
14
16
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
r1n
5. Use the random number to select
which reaction fires.
0
r2n
r3n
r4n
1.0
Stochastic Algorithm
Probability of it occurring (p)
When does the reaction occur?
p(t) = exp (k t)
t = -ln (p)/k
Most events occur soon, a few taken a long time
to occur, the likelihood of an event exponentially
decaying.
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 = 2
k1 = 0.1; k2 = 0.2;
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;
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;
p1 = 0.4;
rn1 = 0.625; rn2 = 0.28;
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
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
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