Development and applications of neutral models for evolution of gene [Elektronische Ressource] / vorgelegt von Michael Roßkopf

Michael Rosskopf

Sujets

• Biologie

Informations

• Publié par: heinrich-heine-universitat_dusseldorf
• Publié le: 01 janvier 2007
• Nombre de lectures: 15
• Langue: English
• Poids de l'ouvrage: 2 Mo

Extrait

Development and Applications of Neutral
Models for Evolution of Gene Expression
Inaugural – Dissertation
zur
Erlangung des Doktorgrades der
Mathematisch-Naturwissenschaftlichen Fakult at
der Heinrich-Heine-Universit at Dusseldorf
vorgelegt von
Michael Ro kopf
aus Bochum
Mai 2007Aus dem Institut fur Bioinformatik
der Heinrich-Heine-Universit at Dusseldorf
Gedruckt mit der Genehmigung der Mathematisch-Naturwissenschaftlichen
Fakult at der Heinrich-Heine-Universitat Dusse ldorf
Referent: Prof. Dr. Arndt von Haeseler
Korreferent: Prof. Dr. Michael Leuschel
Tag der mundlic hen Prufung: 22.06.2007Acknowledgments
First and foremost, I wish to thank my supervisor Arndt von Haeseler for his excellent
advise, collaborations, and his friendly behaviour. I want to thank Gunter Weiss for the
idea for this thesis and the mentoring in the rst year of my PhD studies. Also I want
tothankMichaelLeuschelforacceptingthetasktoreadthisthesisasasecondreviewer.
I thank Ralf Kronenwett from the University Hospital Dusse ldorf for the close collabora-
tion and the medical data sets. I want to thank Philipp Khaitovich, Michael Lachmann,
Wolfgang Enard, Ines Hellmann, and Svante P aa bo for the primate data sets, fruitful
discussions,andnewimpulsesduringmyvisitsattheMax-PlanckInstituteforEvolution-
ary Anthropology in Leipzig. Furthermore, I thank Chris Voolstra from the University
of Cologne for discussions and his mice data sets.
Special thanks to Heiko Schmidt for help on several stu and to Lutz Voigt for keeping
the computers running. Finally, I would like to thank Thomas Laubach, Simone Linz,
Jochen Kohl, Stefan Zanger, Gabriel Gelius-Dietrich, Ste en Kl are, Claudia Kiometzis,
Anja Walge, Le Sy Vinh, Bui Quang Minh, Ricardo de Matos Simoes, Nicole Scherer,
Thomas Schlegel, Tanja Gesell, Andrea Fuhrer, Sascha Strauss, Jutta Buschbom, Ingo
Ebersberger,andallothercolleaguesandformermembersoftheBioinformaticsInstitute
in Duss eldorf and the Center for Integrative Bioinformatics Vienna (CIBIV) in Vienna.
Ultimately, I am grateful to my family, my friends, and Christin.
Financial support from the rectorate of Duss eldorf University, from the Wiener Wissen-
schafts-,Forschungs-undTechnologiefonds(WWTF),andfromtheDeutscheForschungs-
gemeinschaft (DFG) is gratefully acknowledged.
iiiiv
Parts of this thesis have been published in the following articles and conference proceed-
ings:
1. M. Rosskopf, A. von Haeseler (2006) Testing the neutral evolution hypothesis for
gene expression data, Proc. Mathematical and Statistical Aspects of Molecular
Biology (MASAMB 2006).
2. M. Rosskopf, A. von Haeseler (2007) A gene expression evolution model with mu-
tational and non-mutational e ects, submitted to Genetics.
3. M. Rosskopf, G. Weiss, A. von Haeseler (2007) A neutral model for evolution of
gene expression with gamma-distributed mutation e ects, in preparation.
4. M. Rosskopf,A.vonHaeseler(2007)ATajima-typetesttodetectselectioningene
expression data, in preparation.
The EMOGEE software package presented in this thesis is freely available from
http://www.cibiv.at/software/emogee.
Other publications:
1. U.-P.Rohr,A.Rohrbeck,H.Geddert,S.Kliszewski,M. Rosskopf,A.vonHaeseler,
A. Schwalen, U. Steidl, R. Fenk, R. Haas, R. Kronenwett(2005) Primary human
lung cancer cells of di erent histological subtypes can be distinguished by speci c
gene expression pro les, Onkologie 2005, 28(suppl 3):127.
2. I.Bruns,U.Steidl,J.-C.Fischer,S.Raschke,G.KobbeG,R.Fenk,M. Rosskopf,S.
Pechtel,U.-P.Rohr,A.vonHaeseler,P.Wernet,D.Tenen,R.Haas,R.Kronenwett
(2006) Pegylated G-CSF mobilizes CD34+cells with di erent stem and progenitor
cell subsets and distinct functional properties in comparison with unconjugated
G-CSF (2006) Blood, 108, 965A-966A 3382 Part 1.
3. E. Diaz-Blanco, I. Bruns, F. Neumann, J.-C. Fischer, T. Graef, M. Rosskopf, B.
Brors, S. Pechtel, S. Bork, A. Koch, A. Baer, U.-P. Rohr, G. Kobbe, A. von
Haeseler, N. Gattermann, R. Haas, R. Kronenwett (2007) Molecular signature of
CD34+ hematopoietic stem and progenitor cells of patients with CML in chronic
phase, Leukemia, 21, 494-504.Abstract
Recent studies describe that the level of gene expression between species is positively
correlated with the time that has passed since the species split from a common ancestor
(Ranz and Machado, 2006). Moreover, Khaitovich et al. (2004) found a linear relation-
shipbetweendivergencetimeandexpressiondi erences. Thislinearitycanbeexplained
by the neutral theory (Kimura, 1983). Consequently, a neutral model for gene expres-
sion evolution was suggested (Khaitovich et al., 2005b). The model describes mutations
in the regulatory region of a gene by a compound Poisson process. The strength of
changes in the expression level is described by a continuous distribution, the so-called
mutation e ect distribution. That is, whenever a mutation occurs, the gene expression
level changes according to the mutation e ect distribution.
In this thesis the model by Khaitovich et al. (2005b) is extended in two ways. In a rst
extensionagammadistributionisusedtodescribemutatione ectswhichismore exible
thanthedistributionsusedintheoriginalmodel. Inasecondextension, non-mutational
e ectsaretakenintoaccount. Thesee ects(e.g.,metabolismandenvironmentale ects)
overlay mutational changes of gene expression. To describe them a new parameter is
introduced which provides a better t to evolutionary data. This makes it possible to
estimate in uences of mutational and non-mutational changes of the gene expression
level. According to this, a Bayesian method to detect genes with mutations in their
regulatory regions is suggested. Furthermore, a non-neutrality test is presented which
can be applied to gene expression data sampled from individuals of a population. Based
on this test one can detect those genes that show a signi cant deviation from expression
levels under neutrality. The test is an adaptation of the widely used Tajima’s D test
(Tajima, 1989). Finally, a medical application is applied in which carcinogenesis is
considered as an evolutionary process. All models and methods described in this thesis
are evaluated with synthetic data and applied to biological data.
vContents
Acknowledgments iii
Abstract v
1. Introduction 1
1.1. Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2. Organisation of the thesis . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2. Background 6
2.1. The Neutral Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.1.1. De nition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.1.2. Formation of the synthetic theory . . . . . . . . . . . . . . . . . . 6
2.1.3. The emergence of the neutral theory . . . . . . . . . . . . . . . . 8
2.1.4. Further cases for the neutral theory . . . . . . . . . . . . . . . . . 9
2.1.5. Modes of selection . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.2. Gene expression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.2.1. The process of gene expression . . . . . . . . . . . . . . . . . . . . 12
2.2.2. Measuring the level of gene expression with microarrays . . . . . . 14
2.2.3. Analysis of microarray data . . . . . . . . . . . . . . . . . . . . . 15
2.3. Stochastic models for evolutionary processes . . . . . . . . . . . . . . . . 20
2.3.1. Mathematical background of models and parameter estimation . . 20
2.3.2. The Poisson process and the compound Poisson process . . . . . . 22
2.3.3. The Wright-Fisher model and the coalescent process. . . . . . . . 23
2.3.4. Mutation models . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
2.3.5. Models for continuous traits . . . . . . . . . . . . . . . . . . . . . 28
2.4. Optimisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
2.4.1. Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
viContents vii
2.4.2. Bracketing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
2.4.3. The Brent’s method . . . . . . . . . . . . . . . . . . . . . . . . . 29
2.4.4. The Downhill Simplex Method. . . . . . . . . . . . . . . . . . . . 31
3. A model with gamma-distributed mutation e ects 34
3.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
3.2. Materials and methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
3.2.1. The M-gamma model . . . . . . . . . . . . . . . . . . . . . . . . . 35
3.2.2. Parameter estimation . . . . . . . . . . . . . . . . . . . . . . . . . 36
3.3. Experiments and results . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
3.3.1. Evaluation of the parameter estimation method . . . . . . . . . . 43
3.3.2. Analysis of primate data . . . . . . . . . . . . . . . . . . . . . . . 45
3.3.3. of mice data . . . . . . . . . . . . . . . . . . . . . . . . . 50
3.4. Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
3.5. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
4. A model with mutational and non-mutational e ects 57
4.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
4.2. Materials and methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
4.2.1. The M&E model . . . . . . . . . . . . . . . . . . . . . . . . . . . 58