Dynamical systems
For many biological interactions, only qualitative information like "A activates B" or "A inhibits B" is available. Such information can readily be converted to sets of Boolean equations which, despite their simplicity, still capture most dynamical properties of the system.
From discrete to continuous models
- We developed a method for the automatic transformation of Boolean to fully quantitative models based on multivariate polynomial interpolation and optional application of sigmoidal Hill function. Odefy
- The method has been successfully applied for the prediction of T-cell receptor affinities from downstream signaling profiles. Link
- We generalized random Boolean networks by softening the hard binary discretization into multiple discrete states and analytically determined the critical connectivity that separates the biologically unfavorable frozen and chaotic regimes for different update rules.
Specification of the mid-hindbrain boundary
- During vertebrate development, the differentiation between mid- and hindbrain is determined by several transcription factors (e.g. Otx2, Gbx2) and secreted factors (e.g. Fgf8, Wnt1). These genes are stably expressed in a well-defined spatial pattern around the boundary between prospective mid- and hindbrain, the so-called mid-hindbrain boundary.
- We used our Boolean-to-continuous network transformation technique to predict a maintaining, rather than inducing, effect of Fgf8 on Wnt1 expression; an issue that remained unclear from published data. Using mouse anterior neural plate/tube explant cultures, we provide experimental evidence that Fgf8 in fact only maintains but does not induce ectopic Wnt1 expression in these explants. Link
References
- D. Wittmann and F. Theis. Dynamic regimes of random fuzzy logic networks. New Journal of Physics, 13(013041), 2011. 10.1088/1367-2630/13/1/013041. [ DOI ]
- D. Wittmann, C. Marr and F. Theis. Biologically meaningful update rules increase the critical connectivity of generalized Kauffman networks. Journal of Theoretical Biology, 266:436-448, 2010. 10.1016/j.jtbi.2010.07.007. [ DOI | PubMed | .pdf ]
- D. Wittmann, F. Blöchl, N. Prakash, D. Trümbach, W. Wurst and F. Theis. Spatial analysis of expression patterns predicts genetic interactions at the mid-hindbrain boundary. PLoS Computational Biology, 5(11):e1000569, 2009. 10.1371/journal.pcbi.1000569. [ DOI | PubMed | .pdf ]
- J. Krumsiek, S. Poelsterl, D. Wittmann and F. Theis. Odefy - From discrete to continuous models. BMC Bioinformatics, 11(233), 2010. 10.1186/1471-2105-11-233. [ DOI | PubMed | .pdf ]
- D. Wittmann, J. Krumsiek, J. Saez-Rodriguez, D. Lauffenburger, S. Klamt and F. Theis. Transforming Boolean Models to Continuous Models: Methodology and Application to T-Cell Receptor Signaling. BMC Systems Biology, 3(98), 2009. 10.1186/1752-0509-3-98. [ DOI | PubMed | .pdf ]
