1. Theme: Transcriptional Program in the Response of Human
Fibroblasts to Serum.
Lab #2
Etienne Z. Gnimpieba
BRIN WS 2013
Mount Marty College – June 24th 2013
Etienne.gnimpieba@usd.edu

2. Resolution Process
Context
Specification & Aims
Lab #2
 Preprocessing
 Viewing
 Clustering
 Differential expression
 Classification
 Data mining
2
Statement of problem / Case study:
The temporal program of gene expression during a model physiological response of human cells, the response of fibroblasts to serum, was explored with a
complementary DNA microarray representing about 8600 different human genes. Genes could be clustered into groups on the basis of their temporal patterns of expression in
this program. Many features of the transcriptional program appeared to be related to the physiology of wound repair, suggesting that fibroblasts play a larger and richer role in
this complex multicellular response than had previously been appreciated.
Gene Expression Data Analysis
16 Vishwanath R. Iyer, Scince, 1999
Conclusion: ?
Aim:
The purpose of this lab is to initiate on gene expression data analysis process.
We simulated the application on “Transcriptional Program in the Response of
Human Fibroblasts to Serum” . Now we can understand how a researcher can
come to identify a significant expressed gene from microarray dataset.
T1. Gene expression overview
T2. Excel used in Genomics
Objective: used of basic excel functionalities to solve some gene
expression data analysis needs
Acquired skills
- Gene expression data overview
- Excel Used for genomics
- Microarray data analysis using GEPAS
T1.1. Review of genomics place in OMIC- world
T1.2. Microarray data technics and process
T1.3. Data analysis cycle and tools
T2.1. Colum manipulation, functions used, anchor, copy with
function, sort data, search and replace
T2.2. Experiment comparison: Data pre-treatment
T1.3. Differential expressed gene from replicate experiments (SAM)
T2. GEPAS: Gene expression analysis pattern suite
Objective: used of the GEPAS suite to apply the whole microarray data
analyzing process on fibroblast data.
http://www.transcriptome.ens.fr/gepas/index.html
Expression Profile Clustering:
Slide Scanning:
Target Preparation:
Hybridization:

3. Data manipulation Gene expression data analysis
OMIC World
DNA
E
DNA
mRNA
E
Degradation
Degradation
Translation
Transcription
Gene
Repression
S P
Catalyse
Genomics
Functional
Genomics
Transcriptomics
Proteomics
Metabolomics
Etienne Z. Gnimpieba
BRIN WS 2013
Mount Marty College – June 24th 2013

4. Data manipulation Gene expression data analysis
Epidemiology model
Etienne Z. Gnimpieba
BRIN WS 2013
Mount Marty College – June 24th 2013
## WHAT IS IT?
This model is an extension of the basic model of epiDEM (a curricular unit which stands for
Epidemiology: Understanding Disease Dynamics and Emergence through Modeling). It simulates
the spread of an infectious disease in a semi-closed population, but with additional features
such as travel, isolation, quarantine, inoculation, and links between individuals. However, we still
assume that the virus does not mutate, and that upon recovery, an individual will have perfect
immunity.
Overall, this model helps users:
1) understand the emergent disease spread dynamics in relation to the changes in control
measures, travel, and mobility
2) understand how the reproduction number, R_0, represents the threshold for an epidemic
3) understand the relationship between derivatives and integrals, represented simply as rates
and cumulative number of cases, and
4) provide opportunities to extend or change the model to include some properties of a disease
that interest users the most.

5. Data manipulation Gene expression data analysis
Epidemiology model
Etienne Z. Gnimpieba
BRIN WS 2013
Mount Marty College – June 24th 2013
## HOW IT WORKS
Individuals wander around the world in random motion. There are two groups of individuals, represented as either
squares or circles, and are geographically divided by the yellow border. Upon coming into contact with an infected
person, he or she has a chance of contracting the illness. Depending on their tendencies, which are set by the user, sick
individuals will either isolate themselves at "home," go to a hospital, be force-quarantined into a hospital by health
officials, or just move about. An infected individual has a chance of recovery after the given recovery time has elapsed.
The presence of the virus in the population is represented by the colors of individuals. Four colors are used: white
individuals are uninfected, red individuals are infected, green individuals are recovered, and blue individuals are
inoculated. Once recovered, the individual is permanently immune to the virus. The yellow person symbolizes the health
official or ambulance, who patrols the world in search of ill people. Once coming in contact with an infected individual,
the ambulance immediately delivers the infected to the hospital within the region of residence.
The graph INFECTION AND RECOVERY RATES shows the rate of change of the cumulative infected and recovered in the
population. It tracks the average number of secondary infections and recoveries per tick. The reproduction number is
calculated under different assumptions than those of the KM model, as we allow for more than one infected individual in
the population, and introduce aforementioned variables.
At the end of the simulation, the R_0 reflects the estimate of the reproduction number, the final size relation that
indicates whether there will be (or there was, in the model sense) an epidemic. This again closely follows the
mathematical derivation that R_0 = beta*S(0)/ gamma = N*ln(S(0) / S(t)) / (N - S(t)), where N is the total population, S(0)
is the initial number of susceptibles, and S(t) is the total number of susceptibles at time t. In this model, the R_0
estimate is the number of secondary infections that arise for an average infected individual over the course of the
person's infected period.

6. Data manipulation Gene expression data analysis
Epidemiology model
Etienne Z. Gnimpieba
BRIN WS 2013
Mount Marty College – June 24th 2013
## HOW TO USE IT
The SETUP button creates individuals according to the parameter values chosen by the user. Each individual has a 5% chance of being
initialized as infected. Once the simulation has been setup, push the GO button to run the model. GO starts the simulation and runs it
continuously until GO is pushed again.
Each time-step can be considered to be in hours, although any suitable time unit will do.
What follows is a summary of the sliders in the model.
INITIAL-PEOPLE (initialized to vary between 50 - 400): The total number of individuals the simulation begins with.
INFECTION-CHANCE (10 - 50): Probability of disease transmission from one individual to another.
RECOVERY-CHANCE (10 - 100): Probability of an individual's recovery, after the average recovery tie has elapsed.
AVERAGE-RECOVERY-TIME (50 - 300): Time it takes for an individual to recover, on average. The actual individual's recovery time is pulled
from a normal distribution centered around the AVERAGE-RECOVERY-TIME at its mean, with a standard deviation of a quarter of the
AVERAGE-RECOVERY-TIME. Each time-step can be considered to be in hours, although any suitable time unit will do.
AVERAGE-ISOLATION-TENDENCY (0 - 50): Average tendency of individuals to isolate themselves and will not spread the disease. Once an
infected person is identified as an "isolator," the individual will isolate himself in the current location (as indicated by the grey patch) and will
stay there until full recovery.
AVERAGE-HOSPITAL-GOING-TENDENCY (0 - 50): Average tendency of individuals to go to a hospital when sick. If an infected person is
identified as a "hospital goer," then he or she will go to the hospital, and will recover in half the time of an average recovery period, due to
better medication and rest.
INITIAL-AMBULANCE (0 - 4): Number of health officials or ambulances that move about at random, and force-quarantine sick individuals
upon contact. The health officials are immune to the disease, and they themselves do not physically accompany the patient to the hospital.
They move at a speed 5 times as fast as other individuals in the world and are not bounded by geographic region.
INOCULATION-CHANCE (0 - 50): Probability of an individual getting vaccinated, and hence immune from the virus.
INTRA-MOBILITY (0 - 1): This indicates how "mobile" an individual is. Usually, an individual at each time step moves by a distance 1. In this
model, the person will move at a distance indicated by the INTRA-MOBILITY at each time-step. Thus, the lower the intra-mobility level, the
less the movement in the individuals. Individuals move randomly by this assigned value; ambulances always move 5 times faster than this
assigned value.

7. • Gene Expression Measurement
• Microarray Process
• Gene Expression Data Stores
• Data Mining / Querying
• Data Analysis
• Example: ATP13A2 Profile in Stress
Conditions