Presentation on theme: "Computational Methods in Systems Biology"— Presentation transcript:
1 Computational Methods in Systems Biology Nir Friedman Maya Schuldiner.
2 What is Biology?“A branch of knowledge that deals with living organisms and vital processes”The hottest scientific frontier of our timesMany great processes have been figured outMuch is still unknownTremendous impact on MedicineBoth diagnosis, prognosis, and treatmentI didn’t add pictures to anything, but we should once we decide what the slides will look like.NF: I agree
3 Bakers Yeast Saccharomyces Cereviciae Used to make bread and beerThe simplest cell that still resembles human cells
4 Biological Systems are Complex The System is NOT just a sum of its parts
5 What is Systems Biology? “Systems biology is the study of the interactions between the components of a biological system, and how these interactions give rise to the function and behavior of that system”The last decades lead to revolution on how we can examine and understand biological systemsCharacterized byHigh-throughput assaysIntegration of multiple forms of experiments & knowledgeMathematical modeling
6 The Age of Genomes404 Complete Microbial Genomes (Thousands in progress)31 Complete Eukaryotic Genomes (315 in progress!)3 Complete Plant Genomes (6 in progress)Bacteria 1.6Mb 1600 genesEukaryote 13Mb ~6000 genesAnimal 100Mb ~20,000 genesHuman 3Gb ~30,000 genes?Individual Genomes?Bacteria Haemophilus influenzaeEukaryote S. CerevisiaeAnimal C. elegans95969798990001020304050607080910
9 Why Biology for NIPS Crowd? QuantityData-intense discipline: Too vast for manual interpretationSystematicCollection of data on all genes/proteins/…Multi-facetedMeasurements of complementary aspects of cellular function, development and disease statesChallenge of integration and fusion of multiple dataHas the potential to be medically applicative!This is way too much writing for one slide but we can think how to make it more interestingShitloads of interesting data, chance to make an impact. Chance to publish in good journals?
10 Flow of Information in Biology DNARNAProteinPhenotypeRecipe(in safe)WorkingcopyThe resulting dishThe Review
11 The “Post-Genomic Era” Systematic is Not Just More AssaysDNAGenomic sequencesVariations within a population…RNAQuantityStructureDegradation rate…ProteinQuantityLocationModificationsInteractions…PhenotypeGenetic interventionsEnvironmental interventions…
12 Outline Protein DNA RNA Phenotype Stores genetically inherited informationSequence of four nucleotide types (A, C, G, T)Two complementary strands creating base pairs (bp)105 bp in bacteria, 3x109 in humans 6 X1013 in wheat
15 Open Reading Frame ATGCTCAGCGTGACCTCA . . . CAGCGTTAA M L S V T S Q R STPProtein-encoding DNA sequence consists of a sequence of 3 letter codonsStarts with the START codon (ATG)Ends with a STOP codon (TAA, TAG, or TGA)
16 Finding Open Reading Frames ATGCTCAGCGTGACCTCA CAGCGTTAAM L S V T S Q R STPTry all possible starting points3 possible offsets2 possible strandsSimple algorithm finds all ORFs in a genomeMany of these are spurious (are not real genes)How do we focus on the real ones?
17 Using Additional Genomes Basic premise“What is important is conserved”Evolution = Variation + SelectionVariation is randomSelection reflects functionIdea:Instead of studying a single genome, compare related genomesA real open reading frame will be conserved
18 Phylogentic Tree of Yeasts S. cerevisiaeS. paradoxusS. mikataeS. bayanusC. glabrataS. castelliiK. lactisA. gossypiiK. waltiiD. hanseniiC. albicansY. lipolyticaN. crassaM. graminearumM. griseaA. nidulansS. pombe~10M yearsKellis et al, Nature 2003
19 Evolution of Open Reading Frame S. cerevisiaeS. paradoxusS. mikataeS. bayanusATGCTCAGCGTGACCTCA . . .ATGCTCAGCGTGACATCA . . .ATGCTCAGGGTGACA--A . . .ATGCTCAGG---ACA--A . . .ConservedpositionsFrame shiftchanges interpretation of downstream seqVariablepositionsA deletion
20 Examples Spurious ORF Confirmed ORF Frame shift ATG not conserved VariableFrame shiftSpurious ORFATG not conservedConfirmed ORFGreedy algorithm to find conserved ORFs surprisingly effective (> 99% accuracy) on verified yeast dataSequencing error[Kellis et al, Nature 2003]
21 Defining Conservation ConservedVariableNaïve approachConsensus between all speciesProblem:Rough grainedIgnores distances between speciesIgnores the tree topologyGoal:More sensitive and robust methodsAACGTACCA% conserv100335555
22 Probabilistic Model of Evolution AardvarkBisonChimpDogElephantRandom variables – sequence at current day taxa or at ancestorsPotentials/Conditional distribution – represent the probability of evolutionary changes along each branch
23 Parameterization of Phylogenies Assumptions:Positions (columns) are independent of each otherEach branch is a reversible continuous time discrete state Markov processgoverned by a rate matrix Q
24 Conserved vs. unconserved Two hypotheses:Use23412341ConservedShort branches(fewer mutations)UnconservedLong branches (more mutations)[Boffelli et al, Science 2003]
25 Genes Are Better Conserved log Fast/Slow% conserved[Boffelli et al, Science 2003]
26 Evolution: Beyond Vanilla Flavor Rate variation:Different positions evolve at different rateDependencies between rates of related positionsRate does not have to be constant (change in evolutionary selection)These variants can be naturally captured by graphical models
27 Challenges Other types of genomic elements Small polypeptides (peptohormones, neuropeptides)RNA coding genesrRNA, tRNA, snoRNA…miRNARegulatory regions
29 Transcription Factor Binding Sites Relatively short words (6-20bp)Recognition is not perfectBinding sites allow variationsOften conserved
30 Challenges Other types of genomic elements Small polypeptides (peptohormones, neuropeptides)RNA coding genesrRNA, tRNA, snoRNA…miRNARegulatory regionsRecognition of elements without comparisonsClearly sequence contains enough information to “parse” it within the living cell
31 Outline Protein DNA RNA Phenotype Copied from DNA template Conveys information (mRNA)Can also perform function (tRNA, rRNA, …)Single stranded, four nucleotide types (A,C, G, U)For each expressed gene there can be as few as 1 molecule and up to 10,000 molecules per cell.
33 Gene Expression Same DNA content Very different phenotype Difference is in regulation of expression of genes
34 High Throughput Gene Expression TranscriptionTranslationExtractMicroarrayRNA expression levels of 10,000s of genes in one experiment
35 Dynamic Measurements Time courses ConditionsTime coursesDifferent perturbations (genetic & environmental)Biopsies from different patient populations…GenesGasch et al. Mol. Cell 2001
36 Expression: Supervised Approaches Labeled samplesClassifier confidenceP-value =< 0.027Feature selection+ClassificationPotential diagnosis/prognosis toolCharacterizes the disease state insights about underlying processesMaybe switch to a red/green example from another paperSegman et al, Mol. Psych. 2005
37 Expression: Unsupervised PCAClusterEisen et al. PNAS 1998; Alter et al, PNAS 2000
38 CompendiaFrom “small” directed experiments to compendia of gene expression measurementsYeastCancerDevelopmentChange of emphasis in questionsSlide about network reconstruction?
39 Papers Compendia 26 datasets from Whitehead and Stanford Breast cancerFibroblastEWS/FLIinfectionserumGliomasHeLa cell cycleLeukemiaLiver cancerLung cancerNCI60Neuro tumorsProstatecancerStimulatedimmunePBMCVarious tumorsViral infectionB lymphoma26 datasets from Whitehead and StanfordSegal et al Nat. Gen. 2004
40 Cancer span wide range of phenomena Tumor type specific Cancer types& foldingTranslation, degradationCell cycleApoptosisImmuneIF & keratinsCytoskeleton & ECMCell linesChromatinnucleotide metabolismDNA damage /MMPsgrowth regulationSignaling &SignalingMuscleAdhesion & signalingSynapse & signalingMetabolismBreastCytoskeleton (IF & MT)developmentProtein biosynthesisNucleotide metabolism& oxidative phos.Signaling, developmentimmuneMetabolism, detox &ECMTissuesSignaling & CNSCancer span wide range of phenomenaTumor type specificTissue specificGeneric across many tumorsModules>0.4CNSImmuneCell linesHematoLeukemiaLung/ADBreast\liverAD/CNSLiverLung /HematiHemaoSegal et al Nat. Gen. 2004
42 First Attempt: Bayesian networks Gene AGene CGene DGene EGene BOne gene One variableAn instance: microarray sampleUse standard approaches for learning networksFriedman et al, JCB 2000
43 Second Attempt: Module Networks MAPK of cell wall integrity pathwaySLT2RLM1CRH1YPS3PTP2One common regulation functionRegulation Function 1Regulation Function 2Regulation Function 3Regulation Function 4Idea: enforce common regulatory programStatistical robustness: Regulation programs are estimated from m*k samplesOrganization of genes into regulatory modules: Concise biological descriptionSegal et al, Nature Genetics 2003
45 Validation How do we evaluate ourselves? Statistical validation Ability to generalize (cross validation test)Test Data Log-Likelihood (gain per instance)Number of modules-150-100-5050100150200300400500Bayesian network performance
46 Validation How do we evaluate ourselves? Statistical validation Biological interpretationAnnotation databaseLiterature reportsOther experiments, potentially different experiment types
48 Hap4 Msn4 Gene set coherence (GO, MIPS, KEGG) HAP4 Motif29/55; p<2x10-13Hap4STRE (Msn2/4)32/55; p<103Oxid. Phosphorylation (26, 5x10-35)Mitochondrion (31, 7x10-32)Aerobic Respiration (12, 2x10-13)HAP4+STRE17/29; p<7x10-10Msn4-500-400-300-200-100Gene set coherence (GO, MIPS, KEGG)Match between regulator and targetsMatch between regulator and cis-reg motifMatch between regulator and condition/logicRemember to mention that coherence underestimatedp-values using hypergeometric dist; corrected for multiple hypotheses
49 Segal et al, Nature Genetics 2003 ValidationHow do we evaluate ourselves?Statistical validationBiological interpretationExperimentsTest causal predictions in the real systemLead to additional understanding beyond the predictionExperimental validation of three regulators3/3 successful resultsSegal et al, Nature Genetics 2003
50 ChallengesNew methodologies for the huge amount of existing RNA profilesMeta analysisBetter mechanistic modelsContrasting new profiles with existing databasesVisualizationOther measurementsDegradation ratesLocalization
51 Outline Proteins are the main executers of cellular function DNARNAPhenotypeProteins are the main executers of cellular functionBuilding blocks are 20 different amino-acidSynthesized from mRNA templateAcquires a sequence dependent 3-D conformationProteomics: Systematic Study of Proteins
52 Why Measure Proteins?RNA Level ≠ Protein level Protein quantity is not a direct function of RNA levelsProtein Level ≠ Activity level Activity of proteins is regulated by many additional mechanismsCellular localizationPost-translational modificationsCo-factors (protein, RNA, …)
53 Challenges in Proteomics Problematic recognition: No generic mechanism to detect different protein formsThousands of different proteins in the typical cellProtein abundances vary over several orders of magnitude
54 Making a Protein Generic TAGTags make a protein genericUnderlying assumption is that the tag does not change the proteinAll proteins have the same tagInability to pool strainsEach experiment is done on a “different” strain
55 TAP-Tag Libraries for Abundance ~4500 Yeast strains have been TAP taggedHow much is each protein expressed?What is the proteome under different conditions?
56 Why Study Protein Complexes? # Most proteins in the cell work in protein complexes or through protein/protein interactions# To understand how proteins function we must know:- who they interact with - when do they interact - where do they interact - what is the outcome of that interaction
58 Large Scale Pull Downs Provide Information on Protein Complexes Both labs used the same proteins as baitEach lab got slightly different resultsThe results depended dramatically on analysis method*Gavin et al. Nature 2006*Krogan et al. Nature 2006.
59 *Gavin et al. Nature 2006*Krogan et al. Nature 2006
60 We can now define a yeast “interactome” Isnt full use of dataStatic picture.
61 Making a Protein Generic Fluorescent proteins allow us to visualize the proteins within the cell.Allow us to measure individual cells and the variation/ noise within a population
62 Cellular Localization Using GFP Tags What can it teach us? A library of yeast GFP fusion strains has been used to localize nearly all yeast proteinsA collection of cloned C. elegans promoters is being created for similar purposesHuh et al Nature 2003Genome Research 14: , 2004
63 Challenges in Fluorescence-based Approaches Better Vision processing will allow to do this in High-Throughput and answer questions like:Changes in localization in response to cellular cuesChanges in localization in response to environment cuesChanges in localization in various genetic backgroundsDynamics of localization changes
65 Single Cell Measurements: Flow Cytometry Cells pass through a flow cell one at a timeLasers focused on the flow cell excite fluorescent protein fusionsAllows multiple measurements (cell size, shape, DNA content)Applications:Protein abundanceProtein-protein interactionsSingle-cell measurements
66 High Throughput Flow Cytometer Describe software samples per 10 hours is much more rapid than western blotting (especially when one considers that data analysis is automated for cytometry, but not for western blotting. Compares favorably with the time required for a mass spec. run.7 seconds/sample~50,000 counts per sample
67 Comparison of mRNA to Protein Levels Allows Identification of Post-transcriptional Regulation CompareRich mediaPoor mediaObserved behaviorsNo change in bothCoordinated changeChange in protein, but not mRNALog2 Poor/Rich proteinLog2 Poor/Rich mRNANewman et al Nature 2006
68 Noise in Biological Systems Measurement of 10,000 individual cells allows measurement of variation (noise) in a biological contextfactors that affect levels of noise in gene expression:Abundance, mode of transcriptional regulation, sub-cellular localizationNature 441, (15 June 2006)
69 Challenges Proteomics is in its infancy - easier to make an impact Integrating this data with other proteomic/genomic data to better predict protein functionHigher Throughput methods such as flow cytometry will allow generation of varied data: Different growth conditions, Cell cycle, Stress, MatingTagging is mammalian cells becoming more feasable - near future should bring proteomic data on human cells
70 Outline Protein DNA RNA Phenotype Traits that selection can apply to, the observable characteristicsMutations in the DNA can cause a change in a phenotype.Shape and sizeGrowth rateHow many years your liver can survive alcohol damage….
71 Single Gene KOPhenotypic ScreenGiaver et ., 2002
72 Starting to Probe the Cellular Network Genetic InteractionThe effect of a mutation in one gene on the phenotype of a mutation in a second geneDifferent type of interaction - not physicalDIFFERENT TYPE OF INTERACTION - NOT PHYSICAL
73 w.t. Genetic Interactions ∆X ∆Y ∆X Y<∆X*∆Y ∆X Y=∆X*∆Y The way to do so is to measure the growth rate of the wild type strain, and then measuring the effect of knocking out X on the growth rate, and the effect of knocking out Y on the growth rate. Now, if there is no functional relation between the genes, we expect the growth rate of the double mutant to be similar to the cumulative effect of both single knockouts. However if they belong to two alternative pathways that lead to the same product we would expect that the effect of the double mutant will be much graver then the cumulative effect of both single KO. we call this an aggravating interaction. But, if the activity of the genes in the pathway depend on wach other, then after knocking out X, the KO of Y will have almost no effect - hence we expect the doulbe KO growth rate to be faster than what we would expect from the cumulative effect of both single KO. This case is called an alleviating genetic interaction.Aggravating interactionNo interactionAlleviating interaction
74 What is a genetic interaction (Epistasis)? The effect of a mutation in one gene on the phenotype of a mutation in a second gene.Genotype Growth RateWT 1D geneA x (x <= 1)D geneB y (y <= 1)geneA D geneB xy (Product)DIFFERENT TYPE OF INTERACTION - NOT PHYSICALDIFFERENT TYPE OF INTERACTION - NOT PHYSICAL
75 What is a genetic interaction? Genetic Interaction Growth RateNone xyAggravating less than xyAlleviating greater than xyWhat is a genetic interaction?ABCXYABCXYROY KISHONI - PRODUCT OF TWOACTIN - LYSINE TRANSPORTATP PRODUCTIONUSED EXTENSIVELY IN GENETICS TO INFER GENE FUNCTION -WE DO COMBINATION BTWEEN CLASSIC GENETICS AND HIGH THROUGH PUTLARGE SCALE NOT ONLY FASTER BUT ALSO DEEPER UNDERSTANDING
76 Systematic Method of Analyzing Double Mutants X∆X:NAT∆Y:KAN∆X:NAT∆Y:KANMENTION NEVAN∆X:NAT∆Y:KANWT∆X:NAT∆Y:KANDouble deletion mutants are made systematicallyColony sizes are measured in high throughputTong et al., 2001
77 Epistasis Mini Array Profiles E-MAPSEpistasis Mini Array ProfilesBY SOLVING ALL THESE PROBLEMS WE COULD GET FOR THE FIRST TIME E-MAPS…..AggravatingAlleviatingSchuldiner et al., Cell 2005
78 Defining Protein Complexes OnOffBCACo-complex proteins havesimilar interaction patternsalleviating interactionsAggravatingAlleviating
79 Challenges for the future Only a small fraction of the information has been utilized in E-MAPS made so farE-MAPS to cover all yeast cellular processes to come out until the end of 2007Extending this to human cells is now feasible using gene silencing techniquesAmount of data scales exponentially - Higher organisms - more genes.
80 Outline Model-free approach Model-based approach Protein DNA RNA PhenotypeCombined InsightsModel-free approachModel-based approach
81 Why Integrate Data? High-throughput assays: attttgggccagtgaatttttttctaagctaatatagttatttggacttt tgacatgactttgtgtttaattaaaacaaaaaaagaaattgcagaagtgt tgtaagcttgtaaaaaaattcaaacaatgcagacaaatgtgtctcgcagt cttccactcagtatcatttttgtttgtaccttatcagaaatgtttctatg tacaagtctttaaaatcatttcgaacttgctttgtccactgagtatatta tggacatcttttcatggcaggacatatagatgtgttaatggcattaaaaa taaaacaaaaaactgattcggccgggtacggtggctcacgcctgtaatcc aattgtgctctgcaaattatgatagtgatctgtatttactacgtgcatatHigh-throughput assays:Observations about one aspect of the systemOften noisy and less reliable than traditional assaysProvide partial account of the system
82 Model-Free ApproachKanYAR041CYAR040WYAL003WYAL002WYAL001CGCN4HSF1RAP1SaltPoorRichMitoCytoNucGeneLocationExpressionPhenotypeBinding sitesTreat different observations about elements as multivariate dataClusteringStatistical tests
83 Model-Free ApproachFinding bi-clusters in large compendium of functional dataTanay et al PNAS 2004
84 Model-Free Approach Pros: No assumptions about data Unbiased Can be applied to many data typesCan use existing tools to analyze combined dataCons:Interpretation is post-analysisNo sanity checkCannot deal with data from different modalities (interactions, other types of genetic elements)
85 Model-Based Approach What is a model? “A description of a process that could have generated the observed data”Idealized, simplified, cartoonishDescribes the system & how it generates observationsattttgggccagtgaatttttttctaagctaatatagttatttggacttt tgacatgactttgtgtttaattaaaacaaaaaaagaaattgcagaagtgt tgtaagcttgtaaaaaaattcaaacaatgcagacaaatgtgtctcgcagt cttccactcagtatcatttttgtttgtaccttatcagaaatgtttctatg tacaagtctttaaaatcatttcgaacttgctttgtccactgagtatatta tggacatcttttcatggcaggacatatagatgtgttaatggcattaaaaa taaaacaaaaaactgattcggccgggtacggtggctcacgcctgtaatcc aattgtgctctgcaaattatgatagtgatctgtatttactacgtgcatat
86 Explaining Expression DNA binding proteinsNon-coding regionGeneRepressorActivatorBinding sitesCoding regionRNA transcriptKey Question:Can we explain changes in expression?General concept:Transcription factor binding sites in promoter region should “explain” changes in transcription
87 Explaining Expression Relevant data:Expression under environmental perturbationsExpression under transcription factors KOsPredicted binding sites of transcription factorsProtein-DNA interactions of transcription factorsProtein levels/location of transcription factors…
88 A Stab at Model-Based Analysis TCGACTGCGATACCCAATGCAGTTMotifsACGATGCTAGTGTAGCTGATGCTGATCGATCGTACGTGCTAGCTAGCTAGCTAGCTAGCTAGCTAGCAGCTAGCTCGACTGCTTTGTGGGGCCTTGTGTGCTCAAACACACACAACACCAAATGTGCTTTGTGGTACTGATGATCGTAGTAACCACTGTCGATGATGCTGTGGGGGGTATCGATGCATACCACCCCCCGCTCGATCGATCGTAGCTAGCTAGCTGACTGATCAAAAACACCATACGCCCCCCGTCGCTGCTCGTAGCATGCTAGCTAGCTGATCGATCAGCTACGATCGACTGATCGTAGCTAGCTACTTTTTTTTTTTTGCTAGCACCCAACTGACTGATCGTAGTCAGTACGTACGATCGTGACTGATCGCTCGTCGTCGATGCATCGTACGTAGCTACGTAGCATGCTAGCTGCTCGCAAAAAAAAAACGTCGTCGATCGTAGCTGCTCGCCCCCCCCCCCCGACTGATCGTAGCTAGCTGATCGATCGATCGATCGTAGCTGAATTATATATATATATATACGGCGGenesSequenceTCGACTGCGATAC+CCAATGCAGTTMotif ProfilesExpression Profiles
89 Unified Probabilistic Model SequenceExperimentGeneExpressionSequenceS4S1S2S3R2R1R3MotifsMotif ProfilesExpression ProfilesSegal et al, RECOMB 2002, ISMB 2003
90 Unified Probabilistic Model SequenceSequenceS1S2S3S4MotifsR1R2R3ModuleExperimentMotif ProfilesGeneExpression ProfilesExpressionSegal et al, RECOMB 2002, ISMB 2003
91 Unified Probabilistic Model SequenceSequenceObservedS1S2S3S4MotifsR1R2R3ExperimentModuleMotif ProfilesIDLevelGeneObservedExpression ProfilesExpressionSegal et al, RECOMB 2002, ISMB 2003
92 Probabilistic Model Regulatory Modules Sequence Sequence Motifs genesMotif profileExpression profileRegulatory ModulesSequenceSequenceS1S2S3S4MotifsR1R2R3ExperimentModuleMotif ProfilesIDLevelGeneExpression ProfilesExpressionSegal et al, RECOMB 2002, ISMB 2003
93 Model-Based Approach Pros: Incorporates biological principles Suggests mechanismsIncorporate diverse data modalitiesDeclarative semantics -- easy to extendCons:Reconstruction depends on the modelBiological principlesBias
95 Physical Interactions Interaction between two proteins makes it more probable that theyshare a functionreside in the same cellular localizationtheir expression is coordinatedhave similar genetic interactions…Can we exploit this to make better inference of properties of proteins?
96 Relational Markov Network Probabilistic patterns hold for all groups of objectsRepresent local probabilistic dependencies-11P2.MP1.NProteinNucleusCytoplasmMitochndriaInteractionExists21-1I.EP2.NP1.NProteinNucleusMitochndriaCytoplasm
97 Relational Markov Network Compact modelAllows to infer protein attributes by combiningInteraction network topology (observed)Observations about neighboring proteins
98 Adding Noisy Observations Add class for experimental assayView assay result as stochastic function (CPD) of underlying biologyGFP imageProteinCytoplasmNucleusNucleusCytoplasmMitochndriaMitochndriaInteractionExistsDirected CPDProteinNucleusMitochndriaCytoplasm
99 Uncertainty About Interactions Add interaction assays as noisy sensors for interactionsGFP imageProteinCytoplasmNucleusNucleusCytoplasmMitochndriaMitochndriaInteractionExistsProteinNucleusMitochndriaCytoplasmAssayInteract
104 DiscussionEvery day papers are published with high-throughput data that is not analyzed completely or not used in all ways possibleThe bottlenecks right now are the time and ideas to analyze the data
105 The Need for Computational Methods Experiment design and facilitation: Control, vision, and signal processing; Natural Language RetrievalLow-level analysis: Informatics, Ontologies and Knowledge bases.Higher order analysis (discovering principles)Integration of multiple datasetsModeling: “in silico” simulation of biological systems (e-cell)This might not come across well. I thought about digaram here showing the experimental process and how they connect
106 The Need for Computational Methods ExperimentLow-level analysisModeling & SimulationHigh-level analysis
107 What are the Options? Analyze published data Abundant, easy to obtainMethod orientedDon’t have to bump into biologistsTwo million other groups have that data tooCollaborate with an experimental groupBe involved in all stages of projectUnderstand the system and the data betterHave priority on the dataInvolved in generating & testing biological hypothesesGoal orientedStart your own experimental group…(yeah, sure)
108 Questions to Keep in Mind Crucial questions to ask about biological problemsWhat quantities are measured? Which aspects of the biological systems are probedHow are they measured? How this measurement represents the underlying system? Bias and noise characteristics of the dataWhy are these measurements interesting?Which conclusions will make the biggest impact?
109 Acknowledgements Slides: Special thanks: Gal Elidan, Ariel Jaimovich The Computational BunchYoseph BarashAriel JaimovichTommy KaplanDaphne KollerNoa NovershternDana Pe’erItsik Pe’erAviv RegevEran SegalThe Biologist CrowdDavid BreslowSean CollinsJan IhmelsNevan KroganJonathan Weissman
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