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Last update: February 2010 |
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I am broadly interested in the area of computational systems
biology (*) and more specifically in the analysis
and modeling of large-scale cellular interaction networks. See also
the Systems
Biology Team page. Current research projects include:
Integrative modeling of physical interaction networks and functional data
It is a widely held hypothesis that
complex cellular processes such as disease arise from local
perturbations in interaction networks. To understand such dynamical
processes, we need to develop predictive mathematical models for the
dynamical behavior of interacting mRNAs, proteins and metabolites. We
approach this problem by integrating (static) physical interaction
networks with perturbational and/or dynamical functional data. In a
first project on this topic, we have introduced the notion of
regulatory path motifs, short significantly enriched paths in physical
networks which connect perturbed cause-effect protein pairs
in perturbational expression data.
Identification of functional modules in integrated interaction
networks
Cells are organized in overlapping
functional modules which carry out discrete functions. Functional
modules can be identified in interaction networks as densely connected
components. Traditional network clustering methods consider one
network at a time, usually assuming it is undirected. However,
networks corresponding to different interaction types
(transcriptional, protein-protein, phosphorylation, genetic, etc.)
strongly influence each other. We use topological motifs (small 3- or
4-node subgraphs) to represent the functional relationships between
heterogeneous data sources and have developed a motif-based clustering
algorithm to identify overlapping functional modules in integrated
interaction networks.
Inference and modeling of regulatory networks using expression
data
Large-scale compendia of gene expression
data measure the genome-wide transcriptional state of a cell in a
variety of external or internal perturbations, cell types or
individuals. We have developed a probabilistic method, called LeMoNe,
for inferring regulatory modules and predict their condition-dependent
regulators. LeMoNe
has been benchmarked and compared to state-of-the-art methods using
data for S. cerevisiae and E. coli. We have used LeMoNe
to infer developmental regulatory modules in C. elegans,
microRNA regulatory modules in human cancer cells, regulatory variants
underlying heterosis in A. thaliana using SNP and diallel
expression data, stress and cell cycle dependent regulatory modules in
A. thaliana and posttranscriptional regulatory modules in S.
cerevisiae.
Modeling network evolution following whole genome duplications
The modular organization observed in all
biological interaction networks has arisen during evolution by gene
and genome duplications followed by interaction gain and loss. While
simple duplication-divergence models have been able to explain some
large-scale properties of biological networks, such as the appearance
of a scale-free-like topology, very little is known about how specific
modular subparts have evolved. We have recently started to develop
models for the evolution of regulatory and protein interaction
networks following whole genome duplication and we are also working on
methods for identifying conserved modules between multiple species.
"Systems biology is the study of dynamic networks of interacting biological elements."
R. Aebersold, Molecular Systems Biology: a new journal for a new
biology?, Molecular
Systems Biology 1:2005.0005 (2005).
"We expect to encounter fascinating and, I believe, very
fundamental questions at each stage in fitting together less
complicated pieces into the more complicated system and understanding
the basically new types of behavior which can result."
P.W. Anderson, More is
Different, Science
177:393 (1972).
"Mathematics is biology's next microscope, only better; biology is mathematics' next physics, only better."
J.E. Cohen, PLoS Biol 2:e439 (2004).
The links below lead to an abstract of the paper and a choice of
download formats. [arXiv] points to the arXiv Preprint server
and [journal] to the publisher of the
paper; you can also download a [PDF] file directly.
- Enrichment and aggregation of topological motifs are
independent organizational principles of integrated interaction
networks (2009) (submitted).
(T. Michoel,
B. Nachtergaele,
Y. Van de Peer).
- Characterizing regulatory path motifs in integrated networks
using perturbational data (2009) (submitted).
(A. Joshi,
T. Van Parys,
Y. Van de Peer,
T. Michoel).
- Network inference from a cancer gene expression data set
identifies microRNA regulatory modules (2009) (submitted).
(E. Bonnet,
M. Tatari,
A. Joshi,
T. Michoel,
K. Marchal,
G. Berx,
Y. Van de Peer).
- Transcription regulatory networks in Caenorhabditis
elegans inferred through reverse-engineering of gene expression
profiles constitute biological hypotheses for metazoan
development, Mol. BioSyst. 5, 1817 - 1830 (2009).
(V. Vermeirssen,
A. Joshi,
T. Michoel,
E. Bonnet,
T. Casneuf,
Y. Van de Peer)
[journal]
[PDF]
[Supplementary information]
- Comparative analysis of module-based versus direct methods
for reverse-engineering transcriptional regulatory networks, BMC
Systems Biology 3, 49 (2009).

(T. Michoel,
R. De Smet,
A. Joshi,
Y. Van de Peer,
K. Marchal)
[arXiv]
[journal]
[PDF]
[Supplementary information]
- Implementing quantum gates using the ferromagnetic spin-J XXZ
chain with kink boundary conditions, New. J. Phys. (accepted)
(2009).
(T. Michoel,
J. Mulherkar,
B. Nachtergaele)
[arXiv]
[PDF]
- Module networks revisited: computational assessment and
prioritization of model predictions, Bioinformatics 25,
490 - 496 (2009).

(A. Joshi,
R. De Smet, K. Marchal,
Y. Van de Peer,
T. Michoel)
[arXiv]
[journal]
[PDF]
[Supplementary
information]
- Reverse-engineering transcriptional modules from gene
expression data, Ann. N. Y. Acad. of Sci. 1158, 36 - 43
(2009).
(T. Michoel,
R. De Smet,
A. Joshi,
K. Marchal,
Y. Van de Peer).
[arXiv]
[journal]
[PDF]
- Analysis of a Gibbs sampler method for model based clustering
of gene expression data, Bioinformatics 24, 176 - 183
(2008).
(A. Joshi,
Y. Van de Peer,
T. Michoel).
[arXiv]
[journal]
[PDF]
[Supplementary
information]
- Transport of interface states in the Heisenberg chain,
J. Phys. A: Math. Theor. 41, 492001 (2008). FastTrack
(T. Michoel,
B. Nachtergaele,
W. Spitzer).
[arXiv]
[journal]
[PDF]
- Validating module networks learning algorithms using
simulated data, BMC Bioinformatics 8, S5 (2007).
(T. Michoel,
S. Maere,
E. Bonnet,
A. Joshi,
Y. Saeys,
T. Van den Bulcke, K. van Leemput, P. van Remortel, M. Kuiper, K. Marchal,
Y. Van de Peer).
[arXiv]
[journal]
[PDF]
[Supplementary
Information]
- A helicoidal transfer matrix model for inhomogeneous DNA
melting, Phys. Rev. E 73, 011908 (2006).
(T. Michoel,
Y. Van de Peer).
[arXiv]
[journal]
[PDF]
- The large-spin asymptotics of the ferromagnetic XXZ
chain, Markov Proc. Rel. Fields 11, 237 - 266
(2005).
(T. Michoel,
B. Nachtergaele).
[arXiv]
[PDF]
- Central limit theorems for the large-spin asymptotics of
quantum spins, Prob. Th. Rel. Fields 130, 493 - 517
(2004).
(T. Michoel,
B. Nachtergaele).
[arXiv]
[journal]
[PDF]
- The Goldstone Boson, PhD Thesis, Katholieke Universiteit
Leuven, April 2001.
[PDF]
[PS]
- Goldstone boson normal coordinates,
Comm. Math. Phys. 216, 461 - 490 (2001).
(T. Michoel, A. Verbeure).
[arXiv]
[mp_arc]
[journal]
[PDF]
- Interferencing in coupled Bose-Einstein condensates,
J. Stat. Phys. 102, 1383 - 1405 (2001).
(T. Michoel, A. Verbeure).
[arXiv]
[mp_arc]
[journal]
[PDF]
- Mathematical structure of magnons in quantum
ferromagnets, J. Phys. A: Math. Gen. 32, 5875 - 5883
(1999).
(T. Michoel, A. Verbeure).
[arXiv]
[mp_arc]
[journal]
[PDF]
- Goldstone boson normal coordinates in interacting Bose
gases, J. Stat. Phys. 96, 1125 - 1162 (1999).
(T. Michoel, A. Verbeure).
[arXiv]
[mp_arc]
[journal]
[PDF]
- Nonextensive Bose-Einstein condensation model,
J. Math. Phys. 40, 1268 - 1279 (1999).
(T. Michoel,
A. Verbeure).
[arXiv]
[journal]
[PDF]
- CCR-algebra structure of normal k-mode fluctuations,
Rep. Math. Phys. 41, 361 - 395 (1998).
(T. Michoel, B. Momont, A. Verbeure).
[mp_arc]
[journal]
[PDF]
The links below lead to a [download] location of the
software package and a list of [paper]'s which have
used it.
-
MINT - a Cytoscape plugin for Module Identification in
Integrated NeTworks. MINT is a graphical user interface for the
Network Motif Clustering Toolbox.
-
Network Motif Clustering Toolbox - a Matlab toolbox for
clustering topological motifs in integrated networks.
[download]
-
Pathicular - a Cytoscape plugin for identifying regulatory path
motifs in integrated networks.
[download]
-
MatrixClust - a Matlab toolbox for fuzzy clustering of a
symmetric matrix, typically the weighted adjacency matrix of an
undirected network.
[download]
[paper]
[paper]
-
LeMoNe - a Java package for learning module networks from gene
expression data.
[download]
[paper]
[paper]
[paper]
[paper]
[paper]
-
GaneSh - a Java package for 2-way clustering of gene expression
data using a Gibbs sampling method. Ganesh is also part of the LeMoNe package.
[download]
[paper]
-
D-MiRaGe - a Matlab toolbox for performing ground state and
time-dependent Density Matrix Renormalization Group computations for
one-dimensional quantum spin systems which need not be translation
invariant.
[download]
[paper]
[paper]
-
DNAmelt - a Matlab toolbox to compute DNA melting properties
using a helicoidal transfer matrix model.
[download]
[paper]
-
Towards system level modeling of functional modules and
context-specific pathways using genome-scale data, BioFrame User Meeting (5 February
2010). [PDF]
-
Enrichment and aggregation of topological motifs in integrated
interaction networks, RECOMB Regulatory Genomics /
RECOMB Systems Biology / DREAM4 (2 - 6 December 2009)
(poster). [PDF]
-
Network motifs, modules and hierarchical organization of the
post-transcriptional regulatory network in yeast, RECOMB Regulatory Genomics /
RECOMB Systems Biology / DREAM4 (2 - 6 December 2009) (poster).
[PDF]
-
Knowledge discovery in genome-scale data, Department of Computer Science, K.U.Leuven (30 November 2009).
[PDF]
-
Enrichment and aggregation of topological motifs in integrated
interaction networks, CWI-NISB Life Sciences Seminar (25
September 2009).
[PDF]
-
Identification of functional modules in integrated biological networks,
VIB Seminar (12 March 2009).
[PDF]
-
Motif based module identification in integrated networks,
RECOMB Regulatory Genomics / RECOMB Systems Biology / DREAM3
(29 October - 2 November 2008) (poster).
[PDF]
-
Module identification in biological networks,
Mathematical biology seminar, Department of Mathematics,
University of California, Davis (23 October 2008).
[PDF]
-
Module networks revisited: assessment and prioritization of model predictions,
BioMAGNet workshop (28 May 2008).
[PDF]
-
Ensemble method for reverse-engineering transcriptional modules,
BioFrame User Meeting (25 January 2008).
[PDF]
-
Introduction to systems biology and reverse engineering biological networks,
Institute for Theoretical Physics, KULeuven
(9 January 2008).
[PDF]
-
Reverse-engineering transcriptional modules from gene expression data,
DREAM2
(3-4 December 2007) (poster).
[PDF]
-
Module networks ensembles for reverse engineering transcription regulatory networks,
ISMB/ECCB 2007
(21-25 July 2007) (poster).
[PDF]
-
Bioframe kick-off: algorithms and modeling,
[PDF]
-
Validating module networks learning algorithms using simulated data,
The 3rd
EMBL Biennial Symposium: From Functional Genomics to Systems Biology
(14 October 2006) (poster).
[PDF]
- Inferring regulatory networks from transcriptome data,
PSB Lab Meeting (14 September 2006).
[PDF]
- Transport of one-dimensional interfaces in the Heisenberg model,
Mathematical Physics Seminar,
Department of Mathematics,
University of California, Davis
(25 May 2006).
[PDF]
Service as reviewer
Genome Biology,
PLoS Computational Biology,
Bioinformatics,
BMC Systems Biology,
BMC Bioinformatics,
Journal of Mathematical Physics,
EURASIP Journal on Bioinformatics and Systems Biology,
Current Proteomics
Before moving to systems biology, I was active in the areas of
statistical mechanics and mathematical physics. Some topics I have
worked on:
The computation of the thermal stability and statistical physics of
nucleic acids is a classical problem going back to the 1960's, with
recent results relating the physics of denaturation (DNA strand
separation) to the biology of genomes. Other experimental
developments, which can also be modeled accurately by statistical
physics, have made it possible to manipulate single polymeric
molecules directly and offer access to a whole new range of DNA
properties. Coming from physics, this topic was a nice introduction
into the world of biology. I developed a Matlab
toolbox for analyzing the melting properties of a non-linear
helicoidal DNA model [paper].
This is the area where I worked for my PhD
(at the ITF in Leuven) and
first postdoc (at UCDavis).
I still have a pleasant collaboration with Bruno Nachtergaele and
Wolfgang Spitzer
on this topic, nowadays mostly limited to writing
code for numerical analysis, leaving the difficult mathematics to
them. In our latest project we studied the transport of domain walls
in quantum spin systems by moving external fields [paper]. For this
study we developed a Matlab
toolbox for performing ground state and time-dependent Density
Matrix Renormalization Group computations for one-dimensional quantum
spin systems which need not be translation invariant.