Systems biology: Structure and function of modules in biological networks
In a great paper, Hartwell & co beautifully explain the principle that cellular functions are carried out by modules. Modules are composed of many types of molecules and have discrete functions that arise from the interactions among their components. Modules can be:
My current research focuses on the first and most basic question, more precisely on developing computational methods for the automatic identification of modules from various kinds of biological networks. In our group, we have developed a software package for learning module networks from gene expression data [paper 1, paper 2]. Recently, I've started working on matrix and tensor methods for identifying modules in integrated networks [poster]. An initial matrix algorithm is already available. For more information, have a look at this recent presentation.
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.
Papers(27) Claeys, M., Storms, V., Sun, H., Michoel, T., Marchal, K. (2012) MotifSuite: workflow for probabilistic motif detection and assessment. Bioinformatics 28, 1931-1932.
(26) Michoel, T., Joshi, A., Nachtergaele, B., Van de Peer, Y. (2011) Enrichment and aggregation of topological motifs are independent organizational principles of integrated interaction networks. Mol. BioSyst. 7(10):2769-78.
(25) Joshi, A., Van de Peer, Y., Michoel, T. (2011) Structural and functional organization of RNA regulons in the post-transcriptional regulatory network of yeast. Nucleic Acids Res. 39(21):9108-17.
(24) * Audenaert, P., * Van Parys, T., Brondel, F., Pickavet, M., Demeester, P., Van de Peer, Y., Michoel, T. (2011) CyClus3D: a Cytoscape plugin for clustering network motifs in integrated networks. Bioinformatics 27(11):1587-8. *contributed equally
(23) Bonnet, E., Michoel, T., Van de Peer, Y. (2010) Prediction of a regulatory network linked to prostate cancer from gene expression, microRNA and clinical data. Bioinformatics 26(18):i638-44.
(22) Michoel, T., Joshi, A., Bonnet, E., Vermeirssen, V., Van de Peer, Y. (2010) Towards system level modeling of functional modules and regulatory pathways using genome-scale data. Proceedings of the Seventh International Workshop on Computational Systems Biology (WCSB 2010) 71-74. Luxembourg, Luxembourg.
(21) Bonnet, E., Tatari, M., Joshi, A., Michoel, T., Marchal, K., Berx, G., Van de Peer, Y. (2010) Module network inference from a cancer gene expression data set identifies microRNA regulated modules. PLoS One 5(4):e10162.
(20) Joshi, A., Van Parys, T., Van de Peer, Y., Michoel, T. (2010) Characterizing regulatory path motifs in integrated networks using perturbational data. Genome Biol. 11(3):R32.
(19) Michoel, T., Mulherkar, J., Nachtergaele, B. (2010) Implementing quantum gates using the ferromagnetic spin-J XXZ chain with kink boundary conditions. New J. Phys. 12:025003.
(18) Vermeirssen, V., Joshi, A., Michoel, T., Bonnet, E., Casneuf, T., Van de Peer, Y. (2009) Transcription regulatory networks in Caenorhabditis elegans inferred through reverse-engineering of gene expression profiles constitute biological hypotheses for metazoan development. Molecular BioSystems 5(12):1817-30.
(17) Michoel, T., De Smet, R., Joshi, A., Van de Peer, Y., Marchal, K. (2009) Comparative analysis of module-based versus direct methods for reverse-engineering transcriptional regulatory networks. BMC Syst. Biol. 3:49.
(16) Michoel, T., De Smet, R., Joshi, A., Marchal, K., Van de Peer, Y. (2009) Reverse-engineering transcriptional modules from gene expression data. Ann. NY. Acad. Sci. 1158:36-43.
(15) Joshi, A., De Smet, R., Marchal, K., Van de Peer, Y., Michoel, T. (2009) Module networks revisited: computational assessment and prioritization of model predictions. Bioinformatics 25(4):490-6.
(14) Michoel, T., Nachtergaele, B., Spitzer, W. (2008) Transport of interface states in the Heisenberg chain. J. Phys. A: Math. Theor. 41:492001.
(13) Joshi, A., Van de Peer, Y., Michoel, T. (2008) Analysis of a Gibbs sampler method for model based clustering of gene expression data. Bioinformatics 24(2):176-83.
(12) Michoel, T., Maere, S., Bonnet, E., Joshi, A., Saeys, Y., Van den Bulcke, T., Van Leemput, K., van Remortel, P., Kuiper, M., Marchal, K., Van de Peer, Y. (2007) Validating module network learning algorithms using simulated data. BMC Bioinformatics 8, 860-871.
(11) * Michoel, T., * Maere, S., Bonnet, E., Joshi, A., Saeys, Y., Van den Bulcke, T., Van Leemput, K., van Remortel, P., Kuiper, M., Marchal, K., Van de Peer, Y. (2007) Validating module networks learning algorithms using simulated data. BMC Bioinformatics 8 Suppl 2:S5. *contributed equally
(10) Michoel, T., Van de Peer, Y. (2006) A helicoidal transfer matrix model for inhomogeneous DNA melting. Physical Review E. 73(1 Pt 1):011908.
(9) Michoel, T., Nachtergaele, B. (2005) The large-spin asymptotics of the ferromagnetic XXZ chain. Markov Processes and Related Fields 11, 237 - 266.
(8) Michoel, T., Nachtergaele, B. (2004) Central limit theorems for the large-spin asymptotics of quantum spins. Probability Theory and Related Fields 130, No. 4, 493 - 517.
(7) Michoel, T., Verbeure, A. (2001) Goldstone boson normal coordinates. Commun. Math. Phys. 216, 461 - 490.
(6) Michoel, T., Verbeure, A. (2001) Interferencing in coupled Bose-Einstein condensates. J. Stat. Phys. 102, No. 5/6, 1383 - 1405.
(5) Michoel, T. (2001) The Goldstone Boson. PhD Thesis, Katholieke Universiteit Leuven.
(4) Michoel, T., Verbeure, A. (1999) Mathematical structure of magnons in quantum ferromagnets. J. Phys. A: Math. Gen. 32, 5875 - 5883.
(3) Michoel, T., Verbeure, A. (1999) Goldstone boson normal coordinates in interacting Bose gases. J. Stat. Phys. 96, No. 5/6, 1125 - 1162.
(2) Michoel, T., Verbeure, A. (1999) Nonextensive Bose-Einstein condensation model. J. Math. Phys. 40, No.3, 1268 - 1279.
(1) Michoel, T., Momont, B., Verbeure, A. (1998) CCR-algebra structure of normal k-mode fluctuations. Reports on Mathematical Physics 41, No. 3, 361 - 395.
VIB / UGent
Bioinformatics & Evolutionary Genomics
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