cDNA synthesis

Background Info:

http://dwb4.unl.edu/Chem/CHEM869N/CHEM869NLinks/www.dur.ac.uk/~dbl0www/Staff/Croy/cDNAfigs.htm

mix:

water
reaction mix
RNA
enzyme (keep on ice)

add RNA
tap it

PCR

Protein Extraction

http://www.piercenet.com/browse.cfm?fldID=FA97D803-6953-48E4-A7BD-6947D35FE83B

Basic Protocol:

Mix:
Lysis Buffer
Protease Inhibitor (comes in pellet --> vortex with buffer)
Phosphorase Inhibitor

add to pulverized tumor samples
homogenize while keeping tube on ice
spin down using vortex

Protein Assay- for measuring concentration

http://en.wikipedia.org/wiki/Bradford_protein_assay

FGFR2 - alternative pathway for angiogenesis

http://www.ncbi.nlm.nih.gov/omim/176943

http://www.fasebj.org/cgi/content/full/18/2/326

Taqman

http://www.diabetesgenome.org/docs/Patti_Lab_Real-Time_qPCR_pr.pdf

Oral Dosing

http://www.iacuc.ucsf.edu/Policies/awSPOralGavageMice.asp

BLAST Algorithm and Substitution Matrix

http://en.wikipedia.org/wiki/BLAST#Algorithm
http://en.wikipedia.org/wiki/Substitution_matrix

Sequence Alignment

http://en.wikipedia.org/wiki/Sequence_alignment

*know global vs local alignment, various programs: FASTA, genebank, dot matrix-hits on the main diagonal determines sequence similarity

Poisson Distribution

http://en.wikipedia.org/wiki/Poisson_distribution


http://bioinfo.mbb.yale.edu/course/classes/c4/c4-p1.html

"Having introduced the idea of approximating the binomial distribution with two distributions, each of which is applicable in a different regime of the value of p, lets consider the case where p is small (p0.1). First, let us perform the substitution l =np. The binomial distribution then becomes,
(7)

Now, consider the case where n grows to infinity and p shrinks to zero. Hopefully you appreciate the utility of the substitution that we made above, since we can force n to grow and p to shrink such that l =np remains constant. This is nice since nothing in the above expression will "blow up" for large n and/or small p. In this limit we get,
(8) for k=0,1,2,3,...

This expression is the Poisson distribution, and is useful in the situations where the probability of an occurrence is small and the number of "trials" (n) is large. For example, we might consider the probability of k adverse reactions to a test drug in a given sample of the population or the probability of registering k complaints about a particular product in a 1-hour period or the probability of finding k point mutations in a given stretch of nucleotides. Though the Poisson distribution is essential to application and you will doubtless see it again, we will leave it now to discuss the other binomial-approximating continuous distribution. "

bioinformatics informational slides

http://bioinfo.mbb.yale.edu/mbb452a/intro/