CURE reference

http://www.ncbi.nlm.nih.gov/pubmed/24591501

*** Calibur, gating for wildtype and vector for bio125 MSH2 yeast strains

__ AGY 124 (vector control), AGY125(MSH2 control), grown in SD-His-Trp-Ura for 2 days
__ take 100ul to 2ml water
__ point sonication, level 2, 4 times

Calibur, E03 FSC, 952 SSC, choose gate R1 for yeast cells.

template file: tempalte20150320-R1R2-v2

bio125, March 26, Thu 2015. MCAT problem, More exercises

section 1: 8-10am. 
go over problems sets.
40 minutes on a passage with 6 questions
30 minutes on 4 independent questions.
30 minutes on a RE agarose gel question.

section 2:
Use I85 traffic to explain pathway mutation and buildup of metabolite.


todo:  Write explanations for choices on midterm practices at Moodle.


Codeschool try R for bonus points.

Forward and reverse primer

Draw a concept map of central dogma

bio125, March 24, Tue, 2015, transcription and translation

_ summary of midterm grades to students
_ take picture of transformation results
_ dbSNP
_ ApE
_ central dogma review
_ tRNA questions
_MCAT question, using search to find the questions.

Section 1:
8-8:10am, play central dogma video and wait for students tickling in.
https://youtu.be/yLQe138HY3s

Problems:
A student asked a question on reading plasmid RE agarose gel picture. She was confused the ruler with the size of the DNA ladder.
AGY124 and 125 are on plate for 2 months now, and probably lost their plasmids, which lead to growth problems.

Section 2:

Spent ~30 minutes on explaining midterm grades.

Problems:
Many student struggled to write down reverse complementary sequences.
Some students did not know mRNA are synthesized from template strand, mistook complementary strand of the coding sequences as mRNA.

References
transcription and translation, cartoon
https://www.youtube.com/watch?v=6YqPLgNjR4Q

http://hongqinlab.blogspot.com/2015/03/march-19-thu-bio125-transformation.html
http://hongqinlab.blogspot.com/2014/03/bio125-central-dogma-review.html

Growth of AGY124 and AGY125, FSC SSC setting in Calibur

Grow in SD-Trp-His-Ura media. Pick colonies from 20130114 plates. After four days, no growth. I suspect the old colonies on plat has lost their plasmids.

20140323: restreak plates of AGY124 and AGY125 from frozen stocks, Box 10, G4 and G5.
 After two days, the colonies are healthy looking.

20140325. Grow cells in SD-His-Trp-Ura media. 30C shaker.

20140326.  Cloudy culture. Take 100ul to 2ml water. Check with flow cytometer. Good signals for cells.
Use E03 for FSC, 952 for SSC.


References:
http://hongqinlab.blogspot.com/2015/01/bio125-strains-from-gammie-lab-media.html

March 19, Thu, bio125, transformation

Before class:

• Reporter strain growth,
• PLATE media (PEG cannot be autoclaved and has to be filtered. A painful process. pH 8. PLATE is for PEG, Lithium Acetate, Tris and EDTA)
• Autoclave glass beads
• Prepare collection flasks
• Denatured carrier DNA on ice. 
• DTT: Dithiothreitol

Section 1
By 8:14, only students are here.
Group 1 explained lab 8.1
Group 2 draw a diagram on the transformation protocol.

20 minutes lecture on review MSH2 project, transformation, selection media.

9:15, experiment started.
Changes from the protocol: Kioko gave 2ml of cells in 1 falcon tube to students. He asked students to spin down cells,  add 2mL water, resuspend the cells and split them to 2 eppendorf tubes.

by 10:15am, some groups started 30C incubation. I asked students cut incubation to 10 minutes.

by 10:30, I asked to cut heat shock time to 10 minutes as well. 

Kioko: DTT is a reducing agent and can break disulfide bonds on glycoprotein in cell wall and membrane, which will loosen the cell barrier for transformation.



Problems:
One group of students could identify their concentrated plasmid DNA from diluted PCR template.
Some students complained about the lack of effort on team mates.
Many students keep

Section 1 recording,  https://youtu.be/AgX3odjM1vs


Section 2,
Ask group 1 to draw diagram on board.

by 1:30pm, students started diluting plasmid to 0.1ug/ul for 10 uL

Problems:
Some students insert micropipette shaft into sterile water.
Some students forgot carrier DNAs or mistook DTT as carrier DNA
Some student pour sterile glass beads into their hands and then put onto plates.

TODO: Forward and backward PCR primers

Review concepts
Genetic pathway selection

Program coordinator and administrative assistant

Program coordinator and administrative assistant are different categories in Spelman HR.

Hydroxyurea treatment of yeast cells with WT or deletion of MSH2 gene.

Materials

• 2M Hydroxy Urea (HU) stock solution in water: 5mL x 2M x FW 76.06/1000 = 0.7606 gram. 

HU are salt-like, and can dissolve in water in about 10 minutes at 30C with shaking. 

• 1 ug/ul Propidum Iodine(PrI) stock
• AGY124, yeast strain with pRS413 and pSH44 (This is the plasmid control)
• AGY125, yeast strain with the wild type pMSH2 and pSH44 (This is the wildtype MSH2 control)

These strains were shipped from Gammie lab in January 2015,
see http://hongqinlab.blogspot.com/2015/01/bio125-strains-from-gammie-lab-media.html

Grown in SD-HIS-TRP-URA media by Dr. Kioko on Monday March 16, 2015

• Microscope and smart-phone stages. Students can take pictures of cell morphology. 

Experimental procedure and results. 

Under microscope, WT and Vector control yeast cells are about 50% in budding phases (big cells with small buds). 

Vector control cells, about 50% in budding phases. 

WT in about 50% budding phases.

WT and Vec cells were treated with 200mM HU for 1 hour. Under microscope, it can be seen that HU-treated cells are most round and arrested at G1 phase. (Big balls in a string). 

For students:
Day1:
450ul log-culture +/- 50ul HU, monitor shape change under microscope.
After 1.5 hours, HU-arrested cells should be like watermelons.
Then wash with water once, resuspend with 500ul water, left at 4C.
(20160405. Water arrested cells just like HU, so cells ). 

Day 2.
spin down cells, add 500ul SD growth media,
shake at 30C
after 1.5 hours, monitor shape changes under microscope.
spin down, add 500ul 70% enthanol, shake at room temp

Day 2.5 Faculty do PI stain and flow cytomer run

Day 3. R exercise on flow data










References:
http://hongqinlab.blogspot.com/2013/12/sce-cell-cycle-and-morphology.html
http://hongqinlab.blogspot.com/2015/01/cell-cycle-and-msh2-project.html

bio125, March 17, review midterm, basic R program

Section 1:
Students worked on midterm as a huge group. It was a very active discussion.

#3 nucleotide
#7 nucleotide
#16 RE
#18. Ecoli and NER.
#38 PCR
#24 MSH2

Bad questions in the midterm exam
#17 agarose gel, bad question

#20, a terribly madeup question on MSH2. ambiguous.

Announcing CURE and GaTech REU experiences
BIO386 in the fall

Ask students to leave anonymous notes on paper.

Section 2:
Ask students to leave anonymous comments and write down questions that they want to go over.
In 30 minutes, 4 students finished the closed book exam.
Students in Section 2 basically sat at their desk and worked only with their own partners. Most of them still get 100% eventually.


Common mistakes in ApE
Chose linear for plasmids or chose circular for PCR fragments.
Mixed up protein, DNA, or RNA sequences



Skipped:
Basic R programming, loop usage

Moodle, question modifications

It is much easy to duplicate questions in "Question bank"->"Questions".
Just duplicate the questions and modify one of them.

This seems to solve the question modification headache in the new version of Moodle.

Spelman boilerplate text

From: 

http://faculty.spelman.edu/osp/about-osp/boilerplate/#.VQiet2TF_0p

Boilerplate Text

The information below is offered as boilerplate text for Spelman PIs to use in the institutional context section required for many grant proposals.

If you need additional information for your proposal, or if you have suggestions for data to include on this list, please email Dacia Myree.

Spelman College

Spelman College is a private, independent, historically Black college for women which is committed to academic rigor, career development, leadership, community involvement and positive social action. The College is committed to providing students with the benefits of a liberal arts education: intellectual and skill flexibility, intercultural experiences and competencies,  writing and communication skills, and the ability to think critically. Curricular cornerstones include the First Year Experience (FYE), Sophomore Year Experience and the Major Capstone.

Spelman is located in an southwest Atlanta, Georgia. It is adjacent to Morehouse College, Clark Atlanta University and Morehouse School of Medicine, which together form the Atlanta University Center.  The consortium is served by the AUC Woodruff Library.

Spelman was founded in 1881 as Atlanta Baptist Female Seminary.  Its name was changed to Spelman College in 1924.  Spelman College is accredited by the Southern Association of Colleges and Schools.

Mission Statement

Spelman College, a historically Black college and a global leader in the education of women of African descent, is dedicated to academic excellence in the liberal arts and sciences and the intellectual, creative, ethical, and leadership development of its students. Spelman empowers the whole person to engage the many cultures of the world and inspires a commitment to positive social change.

“Firsts” and other distinctions

• In 1995, Spelman was selected as a National Science Foundation NSF/NASA Model Institution for Excellence.
• First HBCU to offer a women’s studies major
• First HBCU to establish a Spelman’s Women’s Research and Resource Center (1981)
• One of only four HBCUs to be awarded a chapter of the Phi Beta Kappa National Honor Society

Recent rankings

• U.S. News and World Report ranks Spelman first among HBCUs and in the top 100 of liberal arts colleges in the country.
• Spelman ranks second among HBCUs for the proportion of its graduates that go on to earn doctoral degrees in STEM fields. (NCSES 2013)
• Spelman is ranked fourth in the country in the baccalaureate origin of African-American doctoral recipients in STEM fields.  In the social sciences, it is ranked second. (NCSES 2013)

Factbook Highlights

• First-generation college students make up between 15% and 18% of each entering class.  (2013-14 Factbook pp. 34-5)
• About 46% of Spelman students are eligible for Pell grants. (2013-14 Factbook pp. 45, 47)

Institutional Datasheets

• Spelman Factbook
• Leadership Profile
• U.S. News profile: Spelman College
• Carnegie Classification of Spelman College
• National Center for Education Statistics
• “College Factual” Datasheet for Spelman College
• National Science Foundation institutional profile: Spelman College
• Spelman’s audited financial reports
• Quickfacts about Spelman (DUNS number, Authorized officials, etc.)

Sources of Higher Education Data

• National Center for Science & Engineering Statistics (NCSES)
• National Science Foundation WebCASPAR (also includes IPEDS data)
• Higher Education Research & Development Survey (HERD)

Institutional Memberships

• Atlanta University Center
• Associated Colleges of the South
• Council of Independent Colleges
• Council on Undergraduate Research

Docx midterm closed book to Moodle, DOCX formating bugs

The original DOCX files has some inconsistent formating issues. Some questions were not recognized by Respondus.

I reformatted DOCX file to enable Respondus recognize all the questions.

R code, midterm grading, for sections, with letter grade assignment

file = "gradebio125,20150310.R"

#bio125 grades
# Mid-Semester Exam   25%
# Final Exam 25%
# Assignments 25%
# Project Report 10%
# Presentation    10%
# Class Participation     5%

rm(list=ls())
list.files()
flag = 2

#The - signs have to be replaced with zeros in textwrangler
infile = "201501-61953-01 Grades.csv"
if( flag == 2){ infile = "201501-61954-02 Grades.csv" }

tb = read.csv(infile)

empty.columns= NULL
for (j in 7:length(tb[1,])){
 tb[,j] = as.numeric( tb[,j])
 tb[is.na(tb[,j]),j] = 0
 if( max(tb[,j])==0 ) { empty.columns = c(empty.columns, j)}
}
str(tb)
tb2 = tb[, - empty.columns]
summary(tb)

names(tb2)[ grep("total", names(tb2)) ]
tb2 = tb2[, -grep("total", names(tb2)) ]
tb2 = tb2[, -grep("assessment", names(tb2)) ]
names(tb2);
sort(names(tb2))
tb3 = tb2[, sort(names(tb2))]

####
#pick highest scores from regular and makeup ones
makeups = grep("make", names(tb3))
regulars = makeups - 1
names(tb3)[c(makeups, regulars)]
for (i in makeups){
  tb3[,i-1]= apply(tb3[,c(i-1,i)], 1, max)
}
tb3[, c(makeups[2]-1, makeups[2])] #passed.
# now, remove makeups
tb4 = tb3[, -makeups]
###end of pick highest grades

##here is the output report
out= tb4[,c("First.name","Last.name")]

#Midterm 25%
names(tb4)[ grep( 'mid', names(tb4)) ]
#section 1
Midterm = c(  "Quiz.Spring.2015..section.1..midterm..open.part", "X2015midterm.closed.book"  )
#section 2  
if( flag==2){ Midterm = c(  "Quiz.Sp15..Section2..midterm..open.book.part", "X2015.midterm.exam.closed.book" )  }
out$Exam = apply( tb4[,Midterm], 1, sum)
out$Exam = out$Exam + 10  #adjust midterm exam

#Final 25%
# ....

# Project Report  10%
names(tb4)[ grep( 'port', names(tb4)) ]
Reports = c("Assignment.GoogleDoc.report.of.RE.digestion.lab..group.submission.",    
  "Assignment.Set.up.GoogleDoc.for.final.project.and.report.of.miniprep.lab"
)

# Class Participation     5%
#names(tb4)[ grep( 'note', names(tb4)) ]
names(tb4)[ grep( 'Quiz.lab', names(tb4)) ]
Participation = names(tb4)[ grep( 'Quiz', names(tb4)) ]
Participation = Participation [-grep("lab", Participation )]
#Participation = Participation[-grep("Quiz.Spring.2015..section.1..midterm..open.part", Participation)]
Participation = Participation[-grep(Midterm, Participation)]

out$Participation = apply(tb4[,Participation], 1, sum)
out$Participation = 5 * out$Participation / max(out$Participation)

#Assignments  #25%
Assignments = names(tb4)[ grep("Quiz", names(tb4)) ]
out$Assignments = apply( tb4[,Assignments], 1, sum)
out$Assignments = 25 * out$Assignments / max( out$Assignments )

# Presentation    10%
# ...


#out$exam = tb4[,Midterm]

head(out)
out$total = apply( out[,3:5], 1, sum)
out$FinalGrade =  100*out$total/ (50+25+5)
hist(out$FinalGrade, br=20)
summary(out$Exam*2)

grade2letter = function(x){
  if(x>94){    ret='A'
  }else if (x >90) {    ret='A-'
  }else if (x >87 ){    ret = 'B+'
  }else if (x > 84){    ret = 'B'
  }else if (x >80){    ret = 'B-'
  }else if (x > 76){  ret = 'C+'
  }else if (x > 70){ ret = 'C'
  }else if (x > 67){ ret = 'C-'
  }else if (x > 64){ ret = 'D+'
  }else if (x > 60){ ret = 'D'
  }else {   ret = 'F'
  }
  return (ret)
}
grade2letter(70); grade2letter(88)

out$letter = unlist(lapply(out$FinalGrade,  grade2letter))

outfile = paste( "out", infile, sep="." )
write.csv(out, outfile )

#generate a sorted report
out.sorted = out[order(out$FinalGrade),]
write.csv(out.sorted, file=paste("sorted.out",infile, sep="."))

#q("no")
#################

bio233 todo for fall 2015

For syllabus 

Change presentation to paper presentation, two trials.

Add R exercises to the learning objectives

From excel to R,

https://districtdatalabs.silvrback.com/intro-to-r-for-microsoft-excel-users

R code, bio125 midterm grade analysis

#bio125 grades
# Mid-Semester Exam   25%
# Final Exam 25%
# Assignments 25%
# Project Report 10%
# Presentation    10%
# Class Participation     5%

rm(list=ls())

list.files()
flag = 2
tb = read.csv("201501-61954-02 Grades.csv")
#tb = read.csv("201501-61953-01 Grades.csv")
#The - signs have to be replaced with zeros in textwrangler

empty.columns= NULL
for (j in 7:length(tb[1,])){
 tb[,j] = as.numeric( tb[,j])
 tb[is.na(tb[,j]),j] = 0
 if( max(tb[,j])==0 ) { empty.columns = c(empty.columns, j)}
}
str(tb)
tb2 = tb[, - empty.columns]
summary(tb)

names(tb2)[ grep("total", names(tb2)) ]
tb2 = tb2[, -grep("total", names(tb2)) ]
tb2 = tb2[, -grep("assessment", names(tb2)) ]
names(tb2);
sort(names(tb2))
tb3 = tb2[, sort(names(tb2))]

####
#pick highest scores from regular and makeup ones
makeups = grep("make", names(tb3))
regulars = makeups - 1
names(tb3)[c(makeups, regulars)]
for (i in makeups){
  tb3[,i-1]= apply(tb3[,c(i-1,i)], 1, max)
}
tb3[, c(makeups[2]-1, makeups[2])] #passed.
# now, remove makeups
tb4 = tb3[, -makeups]
###end of pick highest grades

#Midterm 25%
names(tb4)[ grep( 'mid', names(tb4)) ]
Midterm = c(  "Quiz.Spring.2015..section.1..midterm..open.part"  ) #section 1
if( flag==2) {
 Midterm = c(  "Quiz.Sp15..Section2..midterm..open.book.part"  )   #section 2
}

#Final 25%
# ....

# Project Report  10%
names(tb4)[ grep( 'port', names(tb4)) ]
Reports = c("Assignment.GoogleDoc.report.of.RE.digestion.lab..group.submission.",    
  "Assignment.Set.up.GoogleDoc.for.final.project.and.report.of.miniprep.lab"
)

# Class Participation     5%
#names(tb4)[ grep( 'note', names(tb4)) ]
names(tb4)[ grep( 'Quiz.lab', names(tb4)) ]
Participation = names(tb4)[ grep( 'Quiz', names(tb4)) ]
Participation = Participation [-grep("lab", Participation )]
#Participation = Participation[-grep("Quiz.Spring.2015..section.1..midterm..open.part", Participation)]
Participation = Participation[-grep(Midterm, Participation)]

#Assignments  #25%
Assignments = names(tb4)[ grep("Quiz", names(tb4)) ]

# Presentation    10%
out= tb4[,c("First.name","Last.name")]

out$Assignments = apply( tb4[,Assignments], 1, sum)
out$Assignments = 25 * out$Assignments / max( out$Assignments )

out$Participation = apply(tb4[,Participation], 1, sum)

out$Participation = 5 * out$Participation / max(out$Participation)

out$exam = tb4[,Midterm]
head(out)
out$total = apply( out[,3:5], 1, sum)
hist(out$total, br=10)

yeast cell cycle flow cytometry notes

=> Change, Boone, PNAS 2002, genome-wide screen for MMS sensitive mutants
http://www.pnas.org/content/99/26/16934.full.pdf+html?with-ds=yes
MMS 0.035% vol/vol in YPD plates
HU in 0.2M in YPD
Flow cytometry: cells fixed in 70% ethanol, resuspend in 0.5ml of 0.1 mg/ml RNase A in 50mM sodium citrate, incubated at 30C O/N.  Cell were stained with 2 uM SYTOX gree (Molecular Probes) in 50mM citrate.

HydroxyUrea in yeast cells protocol

http://fg.cns.utexas.edu/fg/protocol__cell_synchrony.html (images and detailed notes)

http://torreslab.biology.gatech.edu/wp-content/uploads/2013/02/Budding-Yeast-Cell-Cycle-Arrest-Procedures.htm

Note of caution: 
We have made lab stock (4°C) of hydroxyurea in a 50 mL conical tube.  Per the instructions on the tube, use 5 mL of the stock for a 35 mL culture.  Please be sure to vortex the stock of hydroxyurea before use - it often comes out of solution in storage.  This type of thing should be considered for any chilled solution of this nature

NSF ERC european

http://www.nsf.gov/pubs/2015/nsf15036/nsf15036.jsp

Exam preparation

random seating assignments

label the paper exam and assign to seat
scantrons and penciles (erasers )

extra papers

calculators

video recordings, (google hangout recordings)

Print exam

For online exam,
 timing,
 Check picture quality

During the exam,
Phone must be silenced.
Paper exam should named, scantron should belabeled A or B version of the exam.
Extra paper for sketching

I ran two YouTube live-events using two channels on two computers to monitor the room.

learning in context

learning data analysis, computing and modeling in the context of biology

merge the silos


http://www.astc.org/resource/education/learning_martin.htm

http://en.wikipedia.org/wiki/Context-based_learning

training courses on systems biology

=> Q-bio school
http://q-bio.org/wiki/The_Ninth_q-bio_Summer_School

=> CSHL computational cell biology (not exist in 2015 courses? stopped? )
http://meetings.cshl.edu/courses/2014/c-comp14.shtml
3 weeks of well-planned training, lectures and hands-on labs.
Biology-oriented participants often attend.

=> Systems biology website announcemnt
http://systems-biology.org/conference/announcement/2015-calendar-1/

=> Modeling course at center for complex biological systems (UC Irvine)
Short course.

=> training courses at UTK NimbioS
Usually one or two day short courses.


=> MBI at Ohio state University
More math-oriented.

=> U of Connecticut, V-cell based course

AAMC guideline on medical school recommendation letter

https://www.aamc.org/download/349990/data/lettersguidelinesbrochure.pdf