home::research Last update: February 2010

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Systems biology research

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.

(*) What is systems biology?

"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).

Publications

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.

  1. Enrichment and aggregation of topological motifs are independent organizational principles of integrated interaction networks (2009) (submitted).
    (T. Michoel, B. Nachtergaele, Y. Van de Peer).
  2. Characterizing regulatory path motifs in integrated networks using perturbational data (2009) (submitted).
    (A. Joshi, T. Van Parys, Y. Van de Peer, T. Michoel).
  3. 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).
  4. 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]
  5. 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]
  6. 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]
  7. 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]
  8. 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]
  9. 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]
  10. 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]
  11. 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]
  12. A helicoidal transfer matrix model for inhomogeneous DNA melting, Phys. Rev. E 73, 011908 (2006).
    (T. Michoel, Y. Van de Peer).
    [arXiv] [journal] [PDF]
  13. The large-spin asymptotics of the ferromagnetic XXZ chain, Markov Proc. Rel. Fields 11, 237 - 266 (2005).
    (T. Michoel, B. Nachtergaele).
    [arXiv] [PDF]
  14. 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]
  15. The Goldstone Boson, PhD Thesis, Katholieke Universiteit Leuven, April 2001.
    [PDF] [PS]
  16. Goldstone boson normal coordinates, Comm. Math. Phys. 216, 461 - 490 (2001).
    (T. Michoel, A. Verbeure).
    [arXiv] [mp_arc] [journal] [PDF]
  17. Interferencing in coupled Bose-Einstein condensates, J. Stat. Phys. 102, 1383 - 1405 (2001).
    (T. Michoel, A. Verbeure).
    [arXiv] [mp_arc] [journal] [PDF]
  18. 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]
  19. Goldstone boson normal coordinates in interacting Bose gases, J. Stat. Phys. 96, 1125 - 1162 (1999).
    (T. Michoel, A. Verbeure).
    [arXiv] [mp_arc] [journal] [PDF]
  20. Nonextensive Bose-Einstein condensation model, J. Math. Phys. 40, 1268 - 1279 (1999).
    (T. Michoel, A. Verbeure).
    [arXiv] [journal] [PDF]
  21. 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]

Software

The links below lead to a [download] location of the software package and a list of [paper]'s which have used it.

Recent presentations

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

Lecture notes

Previous research

Before moving to systems biology, I was active in the areas of statistical mechanics and mathematical physics. Some topics I have worked on:

Statistical physics of DNA

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].

Quantum statistical mechanics

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.