Towards structure and dynamics of metabolic networks [Elektronische Ressource] / eingereicht von Sergio Grimbs

Sergio Grimbs

Sujets

• Biologie

Informations

• Publié par: universitat_potsdam
• Publié le: 01 janvier 2009
• Nombre de lectures: 13
• Langue: English
• Poids de l'ouvrage: 2 Mo

Extrait

Towards structure and dynamics of
metabolic networks
Dissertation
zur Erlangung des akademischen Grades eines
Doktors der Naturwissenschaften (Dr. rer. nat.)
der Mathematisch-Naturwissenschaftlichen Fakulta¨t
der Universita¨t Potsdam
eingereicht von
SERGIO GRIMBS
Arbeitsgruppe Bioinformatik
Max-Planck-Institut fu¨r molekulare Pflanzenphysiologie
Potsdam, im Januar 2009This work is licensed under a Creative Commons License:
Attribution - Noncommercial - Share Alike 3.0 Germany
To view a copy of this license visit
http://creativecommons.org/licenses/by-nc-sa/3.0/de/deed.en










































Published online at the
Institutional Repository of the University of Potsdam:
URL http://opus.kobv.de/ubp/volltexte/2009/3239/
URN urn:nbn:de:kobv:517-opus-32397
[http://nbn-resolving.org/urn:nbn:de:kobv:517-opus-32397] Abstract
This work proposes solutions for several issues pertaining to metabolic network modelling, rang-
ingfromnetworkreconstructiontomultistabilityanalysistonewmodellingstrategiescopingwith
unreliable kinetic parameters. The emphasis is on the close connection between structure and
dynamicalbehaviourofmetabolicnetworks.
High-throughput data fromvarious “omics” and sequencing techniques haverendered the au-
tomated metabolic network reconstruction a highly relevant problem. It is provably hard to find
a suitable and fully automated algorithm to solve a mathematical abstraction of a reconstruction
problem, that accounts for the uncertain, ambiguous and hence inherently probabilistic relations
betweengenes,enzymes,reactionsandmetabolites.
The biosynthetic capabilities of given genome-scale metabolic networks, i.e. the metabolites
that can be produced after providing some seed compounds, reflect prominent aspects of their
functionality. The reverse problem of determining a minimal set of metabolites that has to be
providedinordertoobtainsomedesiredtargetcompounds,isalsoofimportance,especiallywith
respect to identification of drug targets and biotechnological applications. This problem is shown
tobecomputationallyhard,evenafterrelaxationforapproximationresults.
A relevant property of metabolic networks viewed as dynamic systems is their capability to
support multistability, as it enables switching between different modes of operation as a response
to changing conditions. Chemical reaction network theory (CRNT) and its extensions provide
a powerful and mathematically sound framework to obtain multistability results derived directly
from the structure of a given network. CRNT is applied to compare and discriminate against
severalmodelsoftheCalvincycle.
Thedevelopmentofdetailedkineticmodelsisoftenhamperedbythelackofknowledgeabout
the kinetic properties of the involved enzymes and membrane transporters. This can be partly
overcomebyreformulatingtheJacobianmatrixintermsofsaturationparameters,whichdescribe
thenormalizedinfluenceofeachmetaboliteoneveryreactionatsteadystate. Subsequentsampling
ofsaturationparametersisusedtoevaluatethefunctionalroleofallostericfeedbackregulationin
thestabilizationofthemetabolicnetwork. Furthermore,statisticalmeasuresfortherelativeimpact
ofenzymaticreactionsonlocalstabilityofthesteadystatearederived.
Severalmodellingapproachesderivedfromassumingdifferentsimplistickineticmechanisms
(mass-action, Michaelis-Menten, power-law, LinLog) are compared to a well established refer-
ence model of the human red blood cell. The quality of such simple models can be increased
significantly by choosing a small subset of reactions, for which detailed rate equations, including
allosterical effects, are established consecutively. The appropriate reactions are found by ranking
thereactionsaccordingtotheabove-mentionedmeasurefortheirrespectiveinfluenceonstability.Acknowledgement
First of all I would like to thank my supervisor, Joachim Selbig, for putting faith in me and sup-
porting my work in all possible ways. He provided me with many helpful suggestions, important
adviceandconstantencouragementduringthecourseofthiswork.
I thank Hermann-Georg Holzhu¨tter for cordially inviting me to visit his lab. This resulted in
fruitfulcollaboration,forwhichIespeciallyhavetothankSaschaBulik.
Furthermore, I like to thank Ralf Steuer, who introduced me to metabolic modelling and helped
mewithmyproject,especiallyinthebeginning.
Special thanks go to Zoran Nikoloski for inspiring discussions and raising new and interesting
questions. Hiscriticalcommentsonthisprojectweremostvaluable.
This work could not have been completed without the help of Marco Ende, who took care of all
technicalproblemsandbackedmeupwhenevernecessary.
Manythankstoallpresentandformermembersofthebioinformaticsgroup,whichgavemeawon-
derful time at the Max-Planck-Institute and were always willing to answer my questions: Georg
Basler, Liam Childs, Jan-Ole Christian, Pawel Durek, Tanja Ga¨rtner, Detlef Groth, Stefanie Hart-
mann, Manuela Hische, Jan Hummel, Kathrin Ju¨rchott, Sabrina Kleeßen, Sebastian Klie, Peter
Kru¨ger, Abdelhalim Larhlimi, Jan Lisec, Patrick May, Ram Narang, Alexander Platzer, Henning
Redestig,DirkRepsilber,ChristianSchudoma,WolframStacklies,MatthiasSteinfath,XiaoLiang
Sun,HirokiTakahashi,DirkWaltherundDanielWeicht.
IsincerelythankKatharinaRachforallhersupportandherpatience.Contents
Abstract i
Acknowledgement iii
1 Introduction 1
1.1 Systemsbiology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Biologicalnetworks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2.1 Generegulatorynetworks . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2.2 Protein-proteininteractionnetworks . . . . . . . . . . . . . . . . . . . . 3
1.2.3 Furtherexamples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.3 Metabolicnetworks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.3.1 Experimentalmethodsanddatageneration . . . . . . . . . . . . . . . . 4
1.3.2 Modellingandanalysisofmetabolicnetworks . . . . . . . . . . . . . . 5
1.3.3 Databases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.4 Networkanalysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.5 Thesisoutline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2 MetabolicnetworksareNP-hardtoreconstruct 11
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.2 Problemdefinition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.3 ReconstructionofsucrosebiosynthesispathwayinChlamydomonasreinhardtii . 17
2.4 Complexityofautomatedmetabolicnetworkreconstruction . . . . . . . . . . . . 18
2.5 Polynomial-timealgorithmfor(edge-weighted)trees . . . . . . . . . . . . . . . 21
2.6 Approximationresults . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
2.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
3 Inversescopeproblem 27
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
3.2 Problemdefinition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3.3 Hardnessresults . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
3.4 Approximationresults . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
3.5 Instancesof IS and ISFS inP . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
3.6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
4 Structureandbistability 39
4.1 ChemicalReactionNetworkTheory . . . . . . . . . . . . . . . . . . . . . . . . 39
4.1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
4.1.2 Overviewofexistingtheoremsandfindings . . . . . . . . . . . . . . . . 43
4.1.3 Advancementsandextensions . . . . . . . . . . . . . . . . . . . . . . . 46
4.2 BistabilityintheCalvincycle . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48vi CONTENTS
4.3 HierarchyofCalvincyclemodels . . . . . . . . . . . . . . . . . . . . . . . . . 48
4.3.1 ModelofZhu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
4.3.2 ModelsofPetterssonandPoolman . . . . . . . . . . . . . . . . . . . . . 54
4.3.3 ExtendedmodeloftheCalvincycle . . . . . . . . . . . . . . . . . . . . 56
4.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
5 Stabilityandrobustnessofmetabolicstates 61
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
5.2 Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
5.2.1 Theparametrizationofmetabolicstates . . . . . . . . . . . . . . . . . . 62
5.2.2 Theroleofregulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
5.2.3 Therankingofparameters . . . . . . . . . . . . . . . . . . . . . . . . . 65
5.2.4 Comparisonwiththeexplicitmodel . . . . . . . . . . . . . . . . . . . . 67
5.2.5 Robustnessofmetabolicstates . . . . . . . . . . . . . . . . . . . . . . . 68
5.3 MaterialsandMethods . . . . . . . . . . . . . . . . . . . . . . . .