Synbio course
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==2010 Fall LMB904== | ==2010 Fall LMB904== | ||
- | *[[Synthetic_Biology|Introduction to Synthetic Biology]] | + | *What is Synthetic Biology? - [[Synthetic_Biology|Introduction to Synthetic Biology]] |
- | **check readings in above link | + | **check readings (recent special issue of various journals) in above link |
- | *[[R|R package]] | + | |
- | ** | + | *We will use [[R|R package]] for simulating several biological processes |
- | + | **check [http://bioconductor.org bioconductor] for more applications in bioinformatics | |
+ | **tutorials and course materials in CSBL's [[R|R page]] (some collections) | ||
==Practices in R== | ==Practices in R== |
Revision as of 12:34, 29 October 2010
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2010 Fall LMB904
- What is Synthetic Biology? - Introduction to Synthetic Biology
- check readings (recent special issue of various journals) in above link
- We will use R package for simulating several biological processes
- check bioconductor for more applications in bioinformatics
- tutorials and course materials in CSBL's R page (some collections)
Practices in R
- We will practice the materials in MIT open courseware
Practice #1
- Simulating Michaelis–Menten kinetics in R
- assumption: quasi-equilibrium state (pseudosteady state)
- OCW Matlab code ported in R
- using R
- install a specific package (e.g. 'odesolve') in R (there are several ways to do this)
# execute 'R' & run a command # you should have a internet connection >install.package("odesolve")
- R-code
library(odesolve) # Michaelis-menten equation # E + S <-> ES -> E + P # k1,k2 k3 # rate equations # dE = -k1*E*S+k2*ES, dS = -k1*E*S+k2*ES+k3*ES, dES = k1*E*S-k2*ES-k3*ES # quasi-equilibrium; E0 = E + ES; dES = 0 # dE = -k1*E0*S+(k1*S+k2)*ES, dES = k1*E0*S-(k1*S+k2+k3)*ES, dP = k2*ES # ode functions michaelis = function(t,y,p) { # S = y[1]; ES = y[2]; P = y[3] # k1 = p[1]; k2 = p[2]; k3=p[3]; E0=p[4] dS = -p[1]*p[4]*y[1]+(p[1]*y[1]+p[2])*y[2] dES = p[1]*p[4]*y[1]-(p[1]*y[1]+p[2]+p[3])*y[2] dP = p[2]*y[2] list(c(dS,dES,dP)) } #### initial parameter k1 = 1000; k2 = 1; k3 = 0.05; E0 = 0.0005 p = c(k1,k2,k3,E0) t = seq(0,100,1) # time scale #t = 1 y = c(0.001,0,0) # initial values of S, ES and P #### solving equations res = lsoda(y,t,michaelis,p) #### plotting results plot(res[,1],res[,2],type="l",col="red",ylim=c(0,0.001)) points(res[,1],res[,3],type="l",col="blue") E = p[4]-res[,3] points(res[,1],E,type="l",col="cyan") points(res[,1],res[,4],type="l",col="green")
- Result (above script is working but the result is not correct; you may find an error in the code)
Practice #2
- A Genetic Switch in Lamba Phage in R
Error fetching PMID 10681449:
- Error fetching PMID 10681449:
Practice #3
- A Genetic Toggle Switch in R
Error fetching PMID 10659857:
- Error fetching PMID 10659857: