Gene Regulatory Networks: Methods and Protocols

433 Pages·2019·11.347 MB·English

by  Guido Sanguinetti, Vân Anh Huynh-Thu

Methods in
Molecular Biology 1883
Guido Sanguinetti
Vân Anh Huynh-Thu Editors
Gene
Regulatory
Networks
Methods and Protocols
M M B
E T H O D S I N O L E C U L A R I O L O G Y
SeriesEditor
JohnM.Walker
SchoolofLifeandMedicalSciences
UniversityofHertfordshire
Hatfield,Hertfordshire,AL109AB,UK
Forfurthervolumes:
http://www.springer.com/series/7651
Gene Regulatory Networks
Methods and Protocols
Editedby
Guido Sanguinetti
SchoolofInformatics,UniversityofEdinburgh,Edinburgh,UK
Vân Anh Huynh-Thu
DepartmentofElectricalEngineeringandComputerScience,UniversityofLiège,Liège,Belgium
Editors
GuidoSanguinetti VânAnhHuynh-Thu
SchoolofInformatics DepartmentofElectricalEngineering
UniversityofEdinburgh andComputerScience
Edinburgh,UK UniversityofLiège
Liège,Belgium
ISSN1064-3745 ISSN1940-6029 (electronic)
MethodsinMolecularBiology
ISBN978-1-4939-8881-5 ISBN978-1-4939-8882-2 (eBook)
https://doi.org/10.1007/978-1-4939-8882-2
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Preface
High-throughput technologies have brought about a revolution in molecular biology.
Over the last two decades, the research paradigm has moved from a characterization of
individualgenesandtheirfunctionincreasinglytowardasystems-levelappreciationofhow
the complex interactions between multiple genes shape the dynamics and functions of
biological systems. At the same time, the computational and statistical challenges posed
by the interpretation of such data have motivated an exciting cross-fertilization between
the disciplines of biology and the mathematical and computational sciences, leading to the
birthoftheinterdisciplinaryfieldofsystemsbiology.
Acrucialcomputationaltaskinsystemsbiologyistheso-calledreverseengineeringtask:
givenobservationsofmultiplebiologicalfeatures(e.g.,proteinlevels)acrossdifferenttime
points/conditions, determine computationally the interaction structure (the network) that
best explains the data. Within the context of modeling gene expression, this is the task of
inferringgeneregulatorynetworks(GRNs)fromdata.
GRN inference has been a major challenge in systems biology for nearly two decades,
and, while challenges still abound, it is rapidly reaching maturity both in terms of the
concepts involved, and in terms of the software tools available. In this book, we aim to
take stock of the situation, providing an overview of methods that cover the majority of
recentdevelopments,aswellasindicatingthepathforwardforfuturedevelopments.
The book opens with a tutorial overview of the main biological and mathematical
concepts and a survey of the current software landscape. This is meant to be an entry
level chapter, which the interested, graduate-level practitioner (either computational or
biological) can consult as a rough guide to the concepts described more in detail in
the more technical chapters in the book. The next two chapters then focus on Bayesian
methods to infer networks from time varying data, while Chapters 4 and 5 describe
how to attempt to extract causal information (as opposed to purely correlative) from
biological data. Chapters 6 and 7 describe network inference techniques in the presence
of multiple heterogeneous data sets, while Chapters 8 and 9 focus on nonparametric and
hybrid statistical methods for network inference. The following five Chapters 10–14 focus
on the idea of inference of different (but related) networks, arising either from intrinsic
heterogeneity(suchasinsingle-celldata)orduetomultipleconditionsbeingassayed,and
furtherexploreconceptsofdifferentialnetworksandnetworkstability.Finally, thelasttwo
chapters focus more on a mechanistic view of the biological process, covering methods for
exploringnetworkswithinlarge,mechanisticmodelsofbiologicaldynamics.
As most books, this volume presents an incomplete snapshot of an evolving field, and,
given the considerable research activity in this area, it is clear that we can look forward
to considerable progress within the next decades. Our hope is that this collection will
be instrumental in assessing the current state of the art and in focusing research on the
commonchallengesfacedbythefield.
Edinburgh,UK GuidoSanguinetti
Liège,Belgium VânAnhHuynh-Thu
v
Contents
Preface.................................................................................... v
Contributors.............................................................................. ix
1 GeneRegulatoryNetworkInference:AnIntroductorySurvey................... 1
VânAnhHuynh-ThuandGuidoSanguinetti
2 StatisticalNetworkInferenceforTime-VaryingMolecularData
withDynamicBayesianNetworks.................................................. 25
FrankDondelingerandSachMukherjee
3 OverviewandEvaluationofRecentMethodsforStatisticalInference
ofGeneRegulatoryNetworksfromTimeSeriesData............................ 49
MarcoGrzegorczyk,AndrejAderholdandDirkHusmeier
4 Whole-TranscriptomeCausalNetworkInferencewithGenomic
andTranscriptomicData........................................................... 95
LingfeiWangandTomMichoel
5 CausalQueriesfromObservationalDatainBiologicalSystemsviaBayesian
Networks:AnEmpiricalStudyinSmallNetworks ................................ 111
AlexWhiteandMatthieuVignes
6 A Multiattribute Gaussian Graphical Model for Inferring Multiscale
RegulatoryNetworks:AnApplicationinBreastCancer .......................... 143
JulienChiquet,GuillemRigaill,andMartinaSundqvist
7 Integrative Approaches for Inference of Genome-Scale Gene Regulatory
Networks............................................................................ 161
AlirezaFotuhiSiahpirani,DeborahChasman,andSushmitaRoy
8 Unsupervised Gene Network Inference with Decision Trees and Random
Forests .............................................................................. 195
VânAnhHuynh-ThuandPierreGeurts
9 Tree-Based Learning of Regulatory Network Topologies and Dynamics
withJump3 ......................................................................... 217
VânAnhHuynh-ThuandGuidoSanguinetti
10 NetworkInferencefromSingle-CellTranscriptomicData........................ 235
HelenaTodorov,RobrechtCannoodt,WouterSaelens,andYvanSaeys
11 InferringGeneRegulatoryNetworksfromMultipleDatasets.................... 251
ChristopherA.Penfold,IuliaGherman,AnastasiyaSybirna,
andDavidL.Wild
12 UnsupervisedGRNEnsemble ..................................................... 283
PauBellot,PhilippeSalembier,NgocC.Pham,andPatrickE.Meyer
13 Learning Differential Module Networks Across Multiple Experimental
Conditions.......................................................................... 303
PauErola,EricBonnet,andTomMichoel
vii
viii Contents
14 StabilityinGRNInference......................................................... 323
GiuseppeJurman,MicheleFilosi,RobertoVisintainer,
SamanthaRiccadonna,andCesareFurlanello
15 GeneRegulatoryNetworks:APrimerinBiologicalProcessesandStatistical
Modelling........................................................................... 347
OliviaAngelin-Bonnet,PatrickJ.BiggsandMatthieuVignes
16 ScalableInferenceofOrdinaryDifferentialEquationModels
ofBiochemicalProcesses........................................................... 385
FabianFröhlich,CarolinLoos,andJanHasenauer
Index ..................................................................................... 423
Contributors
ANDREJADERHOLD (cid:129) SchoolofMathematicsandStatistics,UniversityofGlasgow,Glasgow,
UK
OLIVIA ANGELIN-BONNET (cid:129) Institute of Fundamental Sciences, Palmerston North, New
Zealand
PAUBELLOT (cid:129) CentreforResearchinAgriculturalGenomics(CRAG),CSIC-IRTA-UAB-
UBConsortium,Bellaterra,Barcelona,Spain
PATRICK J. BIGGS (cid:129) Institute of Fundamental Sciences, Palmerston North, New Zealand;
SchoolofVeterinaryScience,MasseyUniversity,PalmerstonNorth,NewZealand
ERIC BONNET (cid:129) Centre Nationale de Recherche en Genomique Humaine, Institut de
BiologieFrancoisJacob,DirectiondelaRechercheFondamentale,CEA,Evry,France
ROBRECHT CANNOODT (cid:129) Data Mining and Modelling for Biomedicine, VIB Center for
Inflammation Research, Ghent, Belgium; Center for Medical Genetics, Ghent University
Hospital,Ghent,Belgium
DEBORAH CHASMAN (cid:129) WisconsinInstituteforDiscovery,UniversityofWisconsin-Madison,
Madison,WI,USA
JULIEN CHIQUET (cid:129) UMR MIA-Paris, AgroParisTech, INRA, Université Paris-Saclay,
Paris,France
FRANK DONDELINGER (cid:129) LancasterMedicalSchool,LancasterUniversity,Lancaster,UK
PAU EROLA (cid:129) Division of Genetics and Genomics, The Roslin Institute, The University of
Edinburgh,Midlothian,Scotland,UK
MICHELE FILOSI (cid:129) CIBIO,UniversityofTrento,Trento,Italy
FABIAN FRÖHLICH (cid:129) Institute of Computational Biology, Helmholtz Zentrum München,
Neuherberg, Germany; Center for Mathematics, Technische Universität München, Garch-
ing,Germany
CESARE FURLANELLO (cid:129) FondazioneBrunoKessler,Trento,Italy
PIERREGEURTS (cid:129) DepartmentofElectricalEngineeringandComputerScience,University
ofLiège,Liège,Belgium
IULIA GHERMAN (cid:129) Warwick Integrative Synthetic Biology Centre, School of Engineering,
UniversityofWarwick,Coventry,UK
MARCOGRZEGORCZYK (cid:129) JohannBernoulliInstitute,UniversityofGroningen,Groningen,
TheNetherlands
JAN HASENAUER (cid:129) Institute of Computational Biology, Helmholtz Zentrum Muenchen,
Neuherberg, Germany; Center for Mathematics, Technische Universität München, Garch-
ing,Germany
DIRK HUSMEIER (cid:129) School of Mathematics and Statistics, University of Glasgow, Glasgow,
UK
VÂN ANH HUYNH-THU (cid:129) Department of Electrical Engineering and Computer Science,
UniversityofLiège,Liège,Belgium
GIUSEPPE JURMAN (cid:129) FondazioneBrunoKessler,Trento,Italy
CAROLIN LOOS (cid:129) Institute of Computational Biology, Helmholtz Zentrum München,
Neuherberg, Germany; Center for Mathematics, Technische Universität München,
Garching,Germany
ix
x Contributors
PATRICKE.MEYER (cid:129) BioinformaticsandSystemsBiology(BioSys)Unit,UniversitédeLiège,
Liège,Belgium
TOM MICHOEL (cid:129) Division of Genetics and Genomics, The Roslin Institute, The University
of Edinburgh, Midlothian, Scotland, UK; Computational Biology Unit, Department of
Informatics,UniversityofBergen,Bergen,Norway
SACH MUKHERJEE (cid:129) German Center for Neurodegenerative Diseases (DZNE), Bonn,
Germany
CHRISTOPHER A. PENFOLD (cid:129) Wellcome/CRUK Gurdon Institute, University of Cam-
bridge,Cambridge,UK
NGOC C. PHAM (cid:129) Bioinformatics and Systems Biology (BioSys) Unit, Universite de Liège,
Liège,Belgium
SAMANTHA RICCADONNA (cid:129) FondazioneEdmundMach,SanMicheleall’Adige,Italy
GUILLEM RIGAILL (cid:129) Institute of Plant Sciences Paris-Saclay, UMR 9213/UMR1403,
CNRS, INRA, Université Paris -Sud, Université d’Evry, Université Paris-Diderot,
SorbonneParis-Cité,Paris,France;LaboratoiredeMathématiquesetModélisationd’Evry
(LaMME), Université d’Evry, Val d’Essonne, UMR CNRS 8071, ENSIIE, USC INRA,
Paris,France
SUSHMITA ROY (cid:129) Wisconsin Institute for Discovery, University of Wisconsin-Madison,
Madison, WI, USA; Department of Biostatistics and Medical Informatics, University
ofWisconsin-Madison,Madison,WI,USA
WOUTERSAELENS (cid:129) DataMiningandModellingforBiomedicine,VIBCenterforInflam-
mationResearch,Ghent,Belgium;DepartmentofAppliedMathematics,ComputerScience
andStatistics,GhentUniversity,Ghent,Belgium
YVAN SAEYS (cid:129) DataMiningandModellingforBiomedicine,VIBCenterforInflammation
Research, Ghent, Belgium; Department of Applied Mathematics, Computer Science and
Statistics,GhentUniversity,Ghent,Belgium
PHILIPPE SALEMBIER (cid:129) UniversitatPolitecnicadeCatalunya,Barcelona,Spain
GUIDO SANGUINETTI (cid:129) SchoolofInformatics,UniversityofEdinburgh,Edinburgh,UK
ALIREZAFOTUHISIAHPIRANI (cid:129) WisconsinInstituteforDiscovery,UniversityofWisconsin-
Madison,Madison,WI,USA;DepartmentofComputerSciences,UniversityofWisconsin-
Madison,Madison,WI,USA
MARTINA SUNDQVIST (cid:129) UMR MIA-Paris, AgroTechParis, INRA, Université Paris-
Saclay,Paris,France
ANASTASIYA SYBIRNA (cid:129) Wellcome/CRUK Gurdon Institute, University of Cambridge,
Cambridge, UK; Wellcome/MRC Cambridge Stem Cell Institute, University of Cam-
bridge,Cambridge,UK;Physiology,DevelopmentandNeuroscienceDepartment,Univer-
sityofCambridge,Cambridge,UK
HELENA TODOROV (cid:129) Data Mining and Modelling for Biomedicine, VIB Center for
Inflammation Research, Ghent, Belgium; Department of Applied Mathematics, Com-
puter Science and Statistics, Ghent University, Ghent, Belgium; Centre International de
Recherche en Infectiologie, Inserm, U1111, Université Claude Bernard Lyon 1, CNRS,
UMR5308,ÉcoleNormaleSupérieuredeLyon,UnivLyon,Lyon,France
MATTHIEU VIGNES (cid:129) Institute of Fundamental Sciences, Massey University, Palmerston
North,NewZealand
ROBERTO VISINTAINER (cid:129) CIBIO,UniversityofTrento,Trento,Italy
LINGFEI WANG (cid:129) Division of Genetics and Genomics, The Roslin Institute, The University
ofEdinburgh,Midlothian,Scotland,UK
Contributors xi
ALEX WHITE (cid:129) Institute of Fundamental Sciences, Massey University, Palmerston North,
NewZealand
DAVID L. WILD (cid:129) Department of Statistics and Systems Biology Centre, University of
Warwick,Coventry,UK