Theme: Transcriptional Program in the Response of HumanFibroblasts to Serum.Lab #2Etienne Z. GnimpiebaBRIN WS 2013Mount Marty College – June 24th 2013Etienne.email@example.com
Resolution ProcessContextSpecification & AimsLab #2 Preprocessing Viewing Clustering Differential expression Classification Data mining2Statement 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 acomplementary DNA microarray representing about 8600 different human genes. Genes could be clustered into groups on the basis of their temporal patterns of expression inthis 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 inthis complex multicellular response than had previously been appreciated.Gene Expression Data Analysis16 Vishwanath R. Iyer, Scince, 1999Conclusion: ?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 ofHuman Fibroblasts to Serum” . Now we can understand how a researcher cancome to identify a significant expressed gene from microarray dataset.T1. Gene expression overviewT2. Excel used in GenomicsObjective: used of basic excel functionalities to solve some geneexpression data analysis needsAcquired skills- Gene expression data overview- Excel Used for genomics- Microarray data analysis using GEPAST1.1. Review of genomics place in OMIC- worldT1.2. Microarray data technics and processT1.3. Data analysis cycle and toolsT2.1. Colum manipulation, functions used, anchor, copy withfunction, sort data, search and replaceT2.2. Experiment comparison: Data pre-treatmentT1.3. Differential expressed gene from replicate experiments (SAM)T2. GEPAS: Gene expression analysis pattern suiteObjective: used of the GEPAS suite to apply the whole microarray dataanalyzing process on fibroblast data.http://www.transcriptome.ens.fr/gepas/index.htmlExpression Profile Clustering:Slide Scanning:Target Preparation:Hybridization:
Data manipulation Gene expression data analysisOMIC WorldDNAEDNAmRNAEDegradationDegradationTranslationTranscriptionGeneRepressionS PCatalyseGenomicsFunctionalGenomicsTranscriptomicsProteomicsMetabolomicsEtienne Z. GnimpiebaBRIN WS 2013Mount Marty College – June 24th 2013
Data manipulation Gene expression data analysisEpidemiology modelEtienne Z. GnimpiebaBRIN WS 2013Mount Marty College – June 24th 2013## WHAT IS IT?This model is an extension of the basic model of epiDEM (a curricular unit which stands forEpidemiology: Understanding Disease Dynamics and Emergence through Modeling). It simulatesthe spread of an infectious disease in a semi-closed population, but with additional featuressuch as travel, isolation, quarantine, inoculation, and links between individuals. However, we stillassume that the virus does not mutate, and that upon recovery, an individual will have perfectimmunity.Overall, this model helps users:1) understand the emergent disease spread dynamics in relation to the changes in controlmeasures, travel, and mobility2) understand how the reproduction number, R_0, represents the threshold for an epidemic3) understand the relationship between derivatives and integrals, represented simply as ratesand cumulative number of cases, and4) provide opportunities to extend or change the model to include some properties of a diseasethat interest users the most.
Data manipulation Gene expression data analysisEpidemiology modelEtienne Z. GnimpiebaBRIN WS 2013Mount Marty College – June 24th 2013## HOW IT WORKSIndividuals wander around the world in random motion. There are two groups of individuals, represented as eithersquares or circles, and are geographically divided by the yellow border. Upon coming into contact with an infectedperson, he or she has a chance of contracting the illness. Depending on their tendencies, which are set by the user, sickindividuals will either isolate themselves at "home," go to a hospital, be force-quarantined into a hospital by healthofficials, 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: whiteindividuals are uninfected, red individuals are infected, green individuals are recovered, and blue individuals areinoculated. Once recovered, the individual is permanently immune to the virus. The yellow person symbolizes the healthofficial 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 thepopulation. It tracks the average number of secondary infections and recoveries per tick. The reproduction number iscalculated under different assumptions than those of the KM model, as we allow for more than one infected individual inthe 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 thatindicates whether there will be (or there was, in the model sense) an epidemic. This again closely follows themathematical 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_0estimate is the number of secondary infections that arise for an average infected individual over the course of thepersons infected period.
Data manipulation Gene expression data analysisEpidemiology modelEtienne Z. GnimpiebaBRIN WS 2013Mount Marty College – June 24th 2013## HOW TO USE ITThe SETUP button creates individuals according to the parameter values chosen by the user. Each individual has a 5% chance of beinginitialized as infected. Once the simulation has been setup, push the GO button to run the model. GO starts the simulation and runs itcontinuously 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 individuals 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 individuals recovery time is pulledfrom a normal distribution centered around the AVERAGE-RECOVERY-TIME at its mean, with a standard deviation of a quarter of theAVERAGE-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 aninfected person is identified as an "isolator," the individual will isolate himself in the current location (as indicated by the grey patch) and willstay 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 isidentified 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 tobetter medication and rest.INITIAL-AMBULANCE (0 - 4): Number of health officials or ambulances that move about at random, and force-quarantine sick individualsupon 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 thismodel, the person will move at a distance indicated by the INTRA-MOBILITY at each time-step. Thus, the lower the intra-mobility level, theless the movement in the individuals. Individuals move randomly by this assigned value; ambulances always move 5 times faster than thisassigned value.
• Gene Expression Measurement• Microarray Process• Gene Expression Data Stores• Data Mining / Querying• Data Analysis• Example: ATP13A2 Profile in StressConditions