Information visualization techniques for metabolic engineering [Elektronische Ressource] / vorgelegt von Ermir Qeli

Ermir Qeli

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• Publié par: philipps-universitat_marburg
• Publié le: 01 janvier 2007
• Nombre de lectures: 74
• Langue: English
• Poids de l'ouvrage: 5 Mo

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Information Visualization
Techniques for Metabolic
Engineering
Dissertation
zur Erlangung des akademischen Grades
Doktor der Naturwissenschaften (Dr. rer. nat)
dem
Fachbereich Mathematik und Informatik der
Philipps-Universit¨at Marburg
vorgelegt von
Ermir Qeli
aus Berat, Albanien
Marburg/Lahn 2007Vom Fachbereich Mathematik und Informatik der
Philipps-Universit¨at Marburg als Dissertation am
29. Januar 2007 angenommen.
Erstgutachter: Prof. Dr. Bernd Freisleben, Philipps-Universit¨at Marburg
Zweitgutachter: Prof. Dr. Wolfgang Wiechert, Universit¨at Siegen
Tag der mu¨ndlichen Pru¨fung: 5. Februar 2007Acknowledgements
The accomplishment of this dissertation would not have been possible with-
out the contribution of many individuals, to whom I want to express my
appreciation and gratitude.
Firstofall,Iwanttogratefullyacknowledgemysupervisor,Prof.Dr.Bernd
Freisleben, forhispersistentsupportandencouragement. Hisacademicguid-
ance and thorough insights helped me to find my own way in the world of
research.
I wish to express my gratitude to Prof. Dr. Wolfgang Wiechert for the
fruitful technical discussions we had and for the insights he provided to me
in the field of metabolic engineering and systems biology.
MyacknowledgementalsogoestoProf.Dr.BernhardSeegerandProf.Dr.
Eyke Hu¨llermeier for serving in my dissertation committee.
The research work presented in this thesis was financially supported by
the Deutsche Forschungsgemeinschaft (SPP 1063, Teilprojekt FR 791/8-1)
and by Deutscher Akademischer Austauschdienst (DAAD, Stability Pact for
Southeastern Europe). In this context, I would like to thank Prof. Dr. Mira
Mezini and Dr. Jochen W. Mu¨nch for their support.
For the fruitful cooperation between the University of Marburg, the Uni-
versity of Siegen and the Research Center Ju¨lich, I want to thank my col-
leagues Aljoscha Wahl and Stephan Noack for the discussions we had and
their ideas and comments with respect to the visualization tools MetVis and
MatVis. MarcHaunschildisacknowledgedforhisworkonbuildingthesimu-
lationtoolMMT2. Dr. RalfTakorsandhissuccessor,Dr. MarcoOldiges,are
acknowledged for their support in the broader context of the DFG project.
I want to thank all the people at the Department of Mathematics and
Computer Science of the University of Marburg for their support during my
timehere. Inparticular, IwanttomentionDr. AxelSchr¨oderandAlexander
Markowetz. I want to thank Mrs. Mechthild Keßler for being there when Ineeded her.
Special thanks go to my family, my parents Ismail and Feride, my sister
Nilda and my brother Albi, for their extensive support. They provided me
with the necessary backing and kept me in high spirits when it was needed.
Last but not least I want to thank Julinda Gllavata for her precious help
and wide-ranging support. She had the patience to listen to my viewpoints,
discuss issues with me and convince me when I was on the wrong track.Abstract
The main purpose of metabolic engineering is the modification of biological
systems towards specific goals using genetic manipulations. For this purpose,
models are built that describe the stationary and dynamic behaviour of bio-
chemical reaction networks inside a biological cell. Based on these models,
simulations are carried out with the intention to understand the cell’s be-
haviour. The modeling process leads to the generation of large amounts of
data, both during the modeling itself and after the simulation of the created
models. The manual interpretation is almost impossible; consequently, ap-
propriate techniques for supporting the analysis and visualization of these
data are needed.
The purpose of this thesis is to investigate visualization and data mining
techniques to support the metabolic modeling process. The work presented
in this thesis is divided into several tracks:
• Visualization of metabolic networks and the associated simulation data.
Novel visualization techniques will be presented, which allow the visual
exploration of metabolic network dynamics, beyond static snapshots of
the simulated data plots. Node-link representations of the metabolic
network are animated using the time series of metabolite concentra-
tions and reaction rates. In this way, bottlenecks and active parts of
metabolicnetworkscanbedistinguished. Additionally,3Dvisualization
techniquesformetabolicnetworksareexploredforcross-freedrawingof
the networks in 3D visualization space. Steerable drawing of metabolic
networks is also investigated. In contrast to other approaches for draw-
ing metabolic networks, user guided drawing of the networks allows
the creation of high quality drawings by including user feedback in the
drawing process.• Comparison of XML/SBML files.
SBML (Systems Biology Markup Language) has become ubiquitous
in metabolic modeling, serving the storage and exchange of models
in XML format. Generally, the modeling process is an iterative task
where the next generation model is a further development of the cur-
rent model, resulting in a family of models stored in SBML format.
The SBML format, however, includes a great deal of information, from
the structure of the biochemical network to parameters of the model or
measureddata. Consequently,theCustX-Diffalgorithmforacustomiz-
able comparison of XML files will be introduced. By customizing the
comparison process through the specification of XPath expressions, an
adaptable change detection process is enabled. Thus, the comparison
process can be focused on specific parts of a XML/SBML document,
e.g. on the structure of a metabolic network.
• Visual exploration of time-varying sensitivity matrices.
Sensitivity analysis is a special method used in simulation to analyze
the sensitivity of a model with respect to its parameters. The results
of sensitivity analysis of a metabolic network are large time-varying
matrices, which need to be properly visualized. However, the visual-
ization of time-varying high-dimensional data is a challenging problem.
For this purpose, an extensible framework is proposed, consisting of
existing and novel visualization methods, which allow the visual ex-
ploration of time-varying sensitivity matrices. Tabular visualization
techniques, such as the reorderable matrix, are developed further, and
algorithms for their reordering are discussed. Existing and novel tech-
niques for exploring proximity data, both in matrix form and projected
using multi-dimensional scaling (MDS), are also discussed. Informa-
tion visualization paradigms such as focus+context based distortion
and overview+details are proposed to enhance such techniques.
• Cluster ensembles for analyzing time-varying sensitivity matrices.
A novel relationship-based cluster ensemble, which relies on the accu-
mulation of the evolving pairwise similarities of objects (i.e. parame-
ters) will be proposed, as a robust and efficient method for clustering
time-varying high-dimensional data. The time-dependent similarities,
obtained from the fuzzy partitions created during the fuzzy clustering
process, are aggregated, and the final clustering result is derived from
this aggregation.Zusammenfassung
Metabolic Engineering beabsichtigt die zielgerichtete Modifikation biologis-
cher Systeme mittels genetischer Manipulationen. In diesem Kontext werden
Modellegebaut,diesowohldasstation¨are,alsauchdasdynamischeVerhalten
derbiochemischenReaktioneninnerhalbeinerbiologischenZellebeschreiben.
Auf dieser Basis werden Simulationen durchgefu¨hrt, mit dem Ziel, das Ver-
halten einer Zelle zu verstehen. Der Modellierungsprozess ist mit der Erzeu-
gung großer Datenmengen, sowohl w¨ahrend der Modellierung, als auch nach
der Simulation der erstellten Modelle, verknu¨pft. Da eine manuelle Auswer-
tungnahezuunm¨oglichist,werdenTechnikenzurAnalyseundVisualisierung
solcher Daten ben¨otigt.
DievorliegendeDissertationbehandeltgeeigneteAns¨atzederVisualisierung
unddesDataMiningszurUnterstu¨tzungdesmetabolischenModellierungspro-
zesses. Die Arbeit liefert Beitr¨age zu folgenden Problemstellungen:
• Visualisierung metabolischer Netzwerke sowie der zugeh¨origen Simula-
tionsdaten.
Neuartige Visualisierungstechniken, die die visuelle Exploration des
Simulationsverlaufs metabolischer Netzwerke jenseits von statischen
Schnappschu¨ssenerlauben,werdenvorgestellt. Knoten-KantenDarstel-
lungen von metabolischen Netzwerken werden durch Benutzung der
jeweiligen Zeitreihen der Metabolitenkonzentrationen und Reaktion-
sraten animiert. Engp¨asse und aktive Teile eines Netzwerks werden
leichter identifizierbar. 3D Visualisierungsmethoden, die das Zeichnen
¨von Netzwerken ohne Uberschneidungen von Kanten erm¨oglichen, wer-
den untersucht. Das Konzept des gesteuerten Zeichnens metabolischer
Netzwerke wird eingefu¨hrt. Im Gegensatz zu anderen Ans¨atzen wird
ein vom Benutzer steuerbarer Ansatz zum Zeichnen metabolischer Net-zwerke laut biochemischer Anforderungen mittels Einbindung von Be-
nutzerru¨ckmeldungen vorgestellt.
• Vergleich von XML/SBML Dateien.
SBML(SystemsBiologyMarkupLanguage),einXML-FormatzurSpe-
icherung und zum Austausch von biochemischen Modellen, ist allge-
genw¨artigindermetabolischenModellierunggeworden. Di