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2012 Oct 29. Monday
Dhami 11 paper, Ella and Teneisha, 2012 Oct 29.
Gasch 2000 paper, Khayla and Asha, 2012 Oct 29
Clustering exercise
CV calculation
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2012 Oct 24 Wed.
inclass self-paced moodle quiz on qin's research proposal.
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2012 Oct 22 Monday
review exam
Read DNA microarray wikipedia entry:
http://en.wikipedia.org/wiki/DNA_microarray
read Qin's proposal
read Gasch paper, presentation
what is robustness? how to measure them?
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2012 Oct 17 Wed, midterm exam, collaborative part
em: permutaiton on gIN
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3 days, intro to cellular aging and research
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2012 Oct 10.
yeast aging presentation to m0 and G formula.
take-home exam, due Oct 17 Wed before class. 1) permutation on genetic network 2) compare yeast gNet and PIN: are there correlation between genetic interactions and protein interactions?
review genetic network assignment
student presentations (Dhami paper, Newman paper)
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2012 Oct 8.
intro to yeast aging.
(aging, gompertz model, dS/dt = slope, non-aging)
RLS, CLS
figure 3B.
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2012 Oct 3, Wed
match slides
Figure 2
partial regression demo, age, shoesize, readingability
Permutation
homework on figure 3B.
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quiz on Wed to see whether Fraser paper data can be loaded to R and first few lines can be ran.
set up dropbox accounts:
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2012 Sep 26 Wed
summary key points
Ka, Ks, poistive, negative, neutral selection
pairwise interaction -> network
yeast ORF names
competive growth fitness measures (using bar codes)
use table to counter protein interactions
data.frames
match
For review and quiz, load genetic interaction data into R.
convert ORF to letters.
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2012 Sep 24, Mon
discussion of Fraser Science paper
Some key ponts:
Protein interaction network
For Fig 1:
connectivity = number of interactions per gene
evolution rate =K
linear regression
For Fig 2: causal relationships (daycare example), multiple regression
For Fig 3: null distribution and p-value; permutation
Key messages:
science paper is not a big deal
how to read a scientific paper
how to interpret figures
p-value and null distribution
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2012 Sep 19, Wed
***collaborative exam, student demo and exercise on screen. This method works well!
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2012 Sep 17, Mon
exercises and reviews
go over exam1
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2012 Sep 12 Wed
2.exericse
function on make solution
give home work
input out,
summary, leave homework, quiz on Monday.
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=>2012 Sep 10, Mon
quiz on daycare score again
basic programming concepts: loops, conditions, functions.
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=>2012 Sep 5, Wed
Quiz on salary.R
Homework on fraser paper
salary.R
review new learnings. regression, t.test(), p-value.
homework is requested.
//
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=>2012 August 29, Wed
quiz: a list of values (0.1, 0.01, 0.001), take log
irb signature: ella
salary example. socratice method, working on it, the ask questions.
give review guide.
skills and functions to cover:
str, read.table(), pick columns, pick rows, pick rows and columns,
hist
table() #how many female ‘Arts’ faculty
#how many female ‘Assistant’, ‘Associate’, and ‘Full’ professors
//
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=>2012 August 27, Mon
welcome new comers
simple.R takes 2 hours
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=> 2012 Aug 22, Wed
x irb signature
x go over syllabus, writing milestones, previous grades,
x pre-survey
* foucs on why computing for biology majors (four students just come to class for no reasons without computing backgrounds)
googleDoc to work on list of reasons for biology majors to learn programming and computing concepts and skills
using cards to explain computational thinking and search space
Reasons why biology major should learn programming and computing
Learning programming skills and computational thinking offers a key set of problem solving skills.
genomics medicine
highlights on resumes when applying for medical school or graduate schools
more competitive for jobs
Another useful language
Computing holds the key to understand complicated biology phenomena.
Further understanding of genomic dynamics of emerging pathogens and diseases
To use computer storage and other programs to study the biological data and maps of the human genome sequence
Updated on new technology
Understanding programming and computing can aid in drug discovery
Aids in conducting research projects
How to find out a missing card from a stack? (A key concept is search space)
Plan A: count card one by one. Search space is 52 possibilities.
Plan B: separate into sets and colors
Question: What are the key differences between these two search plans?
Answers: Random data versus organized (structured) data
* I should ask students to introduce themselves.
