%0 Book Section %1 statphys23_1033 %A Balcan, D. %A Kabakcioglu, A. %A Mungan, M. %A Erzan, A. %B Abstract Book of the XXIII IUPAP International Conference on Statistical Physics %C Genova, Italy %D 2007 %E Pietronero, Luciano %E Loreto, Vittorio %E Zapperi, Stefano %K genetic networks properties regulatory statphys23 topic-10 topological %T Modeling the topological properties of the transcriptional regulatory network of yeast %U http://st23.statphys23.org/webservices/abstract/preview_pop.php?ID_PAPER=1033 %X Transcriptional regulation of gene expression relies on recognition and binding mechanism between DNA binding proteins, namely the transcription factors (TFs), and the regulatory sequences (RSs), which are common in promoter regions (PRs) of DNA. Topological properties of genetic regulatory networks need investigation and explanation. The recent growth in gene expression data has made it possible to test the validity of abstract models. Here we present a model, which employs a sequence-matching rule mimicking the recognition and binding process between TFs and RSs. In our network model, each node corresponds to a gene and a directed link signifies a possible regulatory interaction between a pair of nodes (genes). With each node we associate a random binary code representing its PR. A small percentage of the genes are assumed to be regulatory genes (TF-coding), each of which is assigned another random binary sequence representing the binding motifs (regulatory sequences) that the corresponding TF may recognize in PRs. The directed link from a regulatory gene $i$ to a gene $j$ is established if and only if the RS associated with node $i$ is repeated as an uninterrupted subsequence in the PR of node $j$. The length distributions of these linear codes are the most important biological inputs of our model and determine the topological properties of model networks. We have modeled the transcriptional regulatory network of yeast, Saccharomyces cerevisiae, on the basis of the available data regarding the distribution of the information content of the RSs of the organism, from which we deduce their effective length distribution. The length distribution of PRs has been assumed to be of power-law form, whose exponent has been tuned to optimize the quantitative agreement between the model networks and that of yeast. We have computed the degree distributions, the degree-degree correlation of nearest neighbors, the clustering coefficient as well as the rich-club coefficient and the structure of $k$-cores. Our model is able to reproduce all these topological features of the yeast TRN. Thus we have the possibility to explain the origin of the observed topological properties of the TRN and make predictions regarding the form of the PRs as well as the essential topological features, given the distribution of the information content of the RSs.

@incollection{statphys23_1033, abstract = {Transcriptional regulation of gene expression relies on recognition and binding mechanism between DNA binding proteins, namely the transcription factors (TFs), and the regulatory sequences (RSs), which are common in promoter regions (PRs) of DNA. Topological properties of genetic regulatory networks need investigation and explanation. The recent growth in gene expression data has made it possible to test the validity of abstract models. Here we present a model, which employs a sequence-matching rule mimicking the recognition and binding process between TFs and RSs. In our network model, each node corresponds to a gene and a directed link signifies a possible regulatory interaction between a pair of nodes (genes). With each node we associate a random binary code representing its PR. A small percentage of the genes are assumed to be regulatory genes (TF-coding), each of which is assigned another random binary sequence representing the binding motifs (regulatory sequences) that the corresponding TF may recognize in PRs. The directed link from a regulatory gene $i$ to a gene $j$ is established if and only if the RS associated with node $i$ is repeated as an uninterrupted subsequence in the PR of node $j$. The length distributions of these linear codes are the most important biological inputs of our model and determine the topological properties of model networks. We have modeled the transcriptional regulatory network of yeast, {\it Saccharomyces cerevisiae}, on the basis of the available data regarding the distribution of the information content of the RSs of the organism, from which we deduce their effective length distribution. The length distribution of PRs has been assumed to be of power-law form, whose exponent has been tuned to optimize the quantitative agreement between the model networks and that of yeast. We have computed the degree distributions, the degree-degree correlation of nearest neighbors, the clustering coefficient as well as the rich-club coefficient and the structure of $k$-cores. Our model is able to reproduce all these topological features of the yeast TRN. Thus we have the possibility to explain the origin of the observed topological properties of the TRN and make predictions regarding the form of the PRs as well as the essential topological features, given the distribution of the information content of the RSs.}, added-at = {2007-06-20T10:16:09.000+0200}, address = {Genova, Italy}, author = {Balcan, D. and Kabakcioglu, A. and Mungan, M. and Erzan, A.}, biburl = {https://www.bibsonomy.org/bibtex/243af746478a81b842287ca850fe25f76/statphys23}, booktitle = {Abstract Book of the XXIII IUPAP International Conference on Statistical Physics}, editor = {Pietronero, Luciano and Loreto, Vittorio and Zapperi, Stefano}, interhash = {b60cf20be5fa22138386feef81adc991}, intrahash = {43af746478a81b842287ca850fe25f76}, keywords = {genetic networks properties regulatory statphys23 topic-10 topological}, month = {9-13 July}, timestamp = {2007-06-20T10:16:37.000+0200}, title = {Modeling the topological properties of the transcriptional regulatory network of yeast}, url = {http://st23.statphys23.org/webservices/abstract/preview_pop.php?ID_PAPER=1033}, year = 2007 }