computational biology · computational biology, the department’s unique computational approach to...

“Ourdepartment,thefirstsuchdepartmenttobecreatedwithinaSchoolofComputerScience,emphasizesdevelopingrigorousandtheoreticallysoundcomputationalapproachestobuildingcomprehensivemodelsthataddressthefundamentalproblemofunderstandinghowbiologicalsystemsfunction.Computationalmethodswillalsobeessentialtotranslatethisunderstandingintoimprovedhealthcare,andwehavesignificantinterestindevelopingclinicalapplicationsofthemachinelearningandanalysistoolswearedeveloping.Ourapproachestakeadvantageofthegrowingavailabilityofgenome-scaledatasetstobuildcomprehensivemodels,buttheyarealsocriticallyneededtodecidewhatadditionalexperimentsshouldbedoneinordertooptimallyimprovethemodelsandleadasrapidlyaspossibleto

invaluableinsightsintopossiblemeansfortreatingorpreventingdisease.”

Robert F. Murphy, Ph.D.

Head, Computational Biology Department

Ray and Stephanie Lane Professor of Computational Biology and Professor of Biological Sciences, Biomedical Engineering and Machine Learning

Computational Biology

Ph.D. Program

• JointPh.D.in ComputationalBiology

Master’s Programs

• JointM.S.in ComputationalBiology

• JointM.S.inBiotechnology InnovationandComputation

Undergraduate Programs

• JointB.S.inComputational Biology

• MinorinComputational Biology

Faculty by the Numbers:

Core

24Affiliated and Adjunct

17

Efficient Algorithms for Genome Sequence Analyses

Nucleicacidsequencinghasbecomeaninexpensive,commonplacetoolforbiologistsandclinicians;however,analysisremainscomputationallyslow,limitingitsusability.Wearedevelopingfast,morememory-efficientalgorithmsforusingsequencingforgeneexpressionquantification,genomeassembly,genomicvariantdetection,andotheranalysessothattheycanbecarriedoutatclinicalorpopulationscales.Currentareasoffocusincludescalingsequencesearchuptopetabyte-scalecollections,fasterandmoreaccuratedetectionofgenomicvariants.Wearealsodevelopingmethodstolearnhowvariantsofmanygenescombinetoaffectthechancesofacquiringcomplexdiseases.Thisworkwillenableresearchers,hospitalsandsequencingcenterstoperformtheanalysesrequiredtoinformclinicaldecisionsandtobuildbettermodelsofbiologicalsystemswithoutenormouscomputationalresources.

Spatiotemporal Network Learning

Networksofinteractingmoleculesunderlieallbiologicalsystems.Creationofcomputationalmodelsthatcanbecombinedtorepresentandsimulatecomplexinteractingnetworksiscriticaltounderstandinghowcells,tissuesandorganismsfunctionandhowthingscangowrongleadingtodisease.Thisareaisaparticularfocusinthedepartment,rangingfromdynamicnetworkmodelsatthemolecularleveltospatiotemporalmodelsofmorphologicalchangesatthecellandtissuelevel.

The strongest negative genetic interactions appear at the top of the sorted table, but strong positive genetic interactions at the bottom of the table may also be of interest (see Notes 18 and 19).

8. Assess the functional enrichment of the SDREM pathways. Obtain a list of all proteins on the predicted signaling pathways by opening topPathNodes_itr<N>.noa with spreadsheet software

Fig. 3 SDREM EGF response signaling pathways. There are three types of nodes along the signaling pathways. Red nodes (top) are the sources given as input. Green nodes (bottom) are the active TFs on the regulatory paths (which are also assigned by SDREM as shown in Fig. 2). Blue nodes (middle) are intermediate proteins that are used to connect the sources and target TFs. Diamond shaped nodes (ELK1, GRB2, HRAS, JUN, MAP2K1, and MAPK8) were assigned a large node prior making them more likely to be included on the predicted pathway. Circles received the default prior. All edges among these nodes are displayed. Solid edges are PPI whose ori-entation was inferred by SDREM. Dashed edges are interactions with a previously known orientation

SDREM for Reconstructing Response Networks

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Research Themes

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Gregory Johnson Ph.D.studentinComputationalBiology

GregoryJohnson’sgoalistousemachinelearningandstatisticalmodelingmethodstolearnhowtoputacelltogether.Hetakeshundredsorthousandsofimagesofcellsinwhichparticularcomponentshavebeenvisualizedbyfluorescentlabelingandconstructsamodelthatcapturestheessenceofthepatternstheimagescontain.Thishasenabledthefirst-evermodelsofhowthepatternsofdifferentorganellesarerelatedtoeachother.

Computational Biology Department | 5000ForbesAvenue | Gates&HillmanCenters8203 | PittsburghPA15213-3890 | 412-268-1299

Sponsored Research Funding Sources*

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Students Enrolled [as of Fall 2014]

Ph.D. 17

Join us on our journey scs.cmu.edu/give Ouralumniandfriendsarecriticaltoenablingtheexperiencesthatchangethelivesofourfacultyandstudents.YOUcanhelpusbyengagingwithourfaculty,advocatingforourprogramsandsupportingoursharedvisionthatwilltransformthefuture.

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Endowed Chairs and Professorships

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Male

82%

Female

18%

We invite you to learn more about how you can help by contacting:

HHS 42%NSF 29%State of PA 20%Foundations 2%Other 7%

Gender Breakdown

Giving* [all sources]

$203K

BrianT.PeckAssociateDirectorSchoolofComputerScience412.268.1739peckb@cs.cmu.edu

PhilipL.LehmanAssociateDeanSchoolofComputerScience412.268.9962philip.lehman@cs.cmu.edu

*3-yr. averages, FY12–FY14

Hao Wang Ph.D.studentinComputationalBiology

Theproductionofproteinsisfundamentaltoalllife.Proteinsareproducedfromlineartemplatetranscriptsbymolecularmachinescalledribosomesthatwalkdownthesetemplates.Haoisstudyingthemotionoftheseribosomes.Sheisdevelopingnovelcomputationalmodelingandoptimizationtechniquestodetectribosometrafficjamsandstallingandtodeterminethe“speedlimit”ateachpointalongatranscriptfromnext-generationsequencingdata.Thesearebeingusedtocreatemoresophisticatedandmoreaccuratemodelsofproteinproductionthatwillenableustobetterpredicthowacellrespondstovaryingconditionsandstresses

bycontrollinglevelsofproteinabundance.

“BeginningwithitsfoundingastheLaneCenterforComputationalBiology,thedepartment’suniquecomputationalapproachtobiologicalquestionsisadefinitiveadvantagetoourcampuscommunityandpartnersaswesolvethemostdifficultbiologicalquestionsthatfaceourworld.“

Andrew W. Moore Dean, School of Computer Science

Student Spotlights

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