Saturday, September 1, 2012

bio386 fall 2012 log

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

2012 Oct 24 Wed.
inclass self-paced moodle quiz on qin's research proposal.
2012 Oct 22 Monday
 review exam
Read DNA microarray wikipedia entry:

read Qin's proposal
read Gasch paper, presentation
what is robustness? how to measure them?

2012 Oct 17 Wed, midterm exam, collaborative part
em: permutaiton on gIN

3 days, intro to cellular aging and research
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)

2012 Oct 8.
 intro to yeast aging.
    (aging, gompertz model, dS/dt = slope, non-aging)
    RLS, CLS
 figure 3B.

2012 Oct 3, Wed
  match slides

  Figure 2
  partial regression demo, age, shoesize, readingability

  homework on figure 3B.

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:

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


For review and quiz, load genetic interaction data into R.
convert ORF to letters.

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

2012 Sep 19, Wed
 ***collaborative exam, student demo and exercise on screen. This method works well!
2012 Sep 17, Mon
 exercises and reviews
 go over exam1
2012 Sep 12 Wed
  function on make solution
  give home work
  input out,
 summary, leave homework, quiz on Monday.

=>2012 Sep 10, Mon
  quiz on daycare score again
  basic programming concepts: loops, conditions, functions.
=>2012 Sep 5, Wed
  Quiz on salary.R
  Homework on fraser paper
  review new learnings. regression, t.test(), p-value.
  homework is requested.
=>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,
table() #how many female ‘Arts’ faculty
#how many female ‘Assistant’, ‘Associate’, and ‘Full’ professors

=>2012 August 27, Mon
  welcome new comers
  simple.R takes 2 hours

=> 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.