Gene regulatory networks: a new conceptual framework to analyse breast cancer behaviour
ABSTRACT The study of complex systems has clearly evidenced that a few overall behavioural properties cannot be inferred from the properties of their single parts and are rather determined by their architecture. Such an approach has been recently proposed in biology to understand genome functioning and in oncology to endeavour a more consistent explanation of the variegated cancer behaviours. In the present perspective, we summarise the basic concepts of the proposed global approach and then we reconsider, in this new context, tumour dormancy and primary tumour removal effects, which recently emerged as critical points for breast cancer understanding.
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ABSTRACT: In this paper, we develop a comprehensive framework for optimal perturbation control of dynamic networks. The aim of the perturbation is to drive the network away from an undesirable steady-state distribution and to force it to converge towards a desired steady-state distribution. The proposed framework does not make any assumptions about the topology of the initial network, and is thus applicable to general-topology networks. We define the optimal perturbation control as the minimum-energy perturbation measured in terms of the Frobenius-norm between the initial and perturbed probability transition matrices of the dynamic network. We subsequently demonstrate that there exists at most one optimal perturbation that forces the network into the desirable steady-state distribution. In the event where the optimal perturbation does not exist, we construct a family of suboptimal perturbations, and show that the suboptimal perturbation can be used to approximate the optimal limiting distribution arbitrarily closely. Moreover, we investigate the robustness of the optimal perturbation control to errors in the probability transition matrix, and demonstrate that the proposed optimal perturbation control is robust to data and inference errors in the probability transition matrix of the initial network. Finally, we apply the proposed optimal perturbation control method to the Human melanoma gene regulatory network in order to force the network from an initial steady-state distribution associated with melanoma and into a desirable steady-state distribution corresponding to a benign cell.IEEE Transactions on Signal Processing 04/2013; 61(7):1733-1742. DOI:10.1109/TSP.2013.2241054 · 3.20 Impact Factor
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ABSTRACT: Tandem 3' UTRs produced by alternative polyadenylation (APA) play an important role in gene expression by impacting mRNA stability, translation, and translocation in cells. Several studies have investigated APA site switching in various physiological states; nevertheless, they only focused on either the genes with two known APA sites or several candidate genes. Here, we developed a strategy to study APA sites in a genome-wide fashion with second-generation sequencing technology which could not only identify new polyadenylation sites but also analyze the APA site switching of all genes, especially those with more than two APA sites. We used this strategy to explore the profiling of APA sites in two human breast cancer cell lines, MCF7 and MB231, and one cultured mammary epithelial cell line, MCF10A. More than half of the identified polyadenylation sites are not included in human poly(A) databases. While MCF7 showed shortening 3' UTRs, more genes in MB231 switched to distal poly(A) sites. Several gene ontology (GO) terms and pathways were enriched in the list of genes with switched APA sites, including cell cycle, apoptosis, and metabolism. These results suggest a more complex regulation of APA sites in cancer cells than previously thought. In short, our novel unbiased method can be a powerful approach to cost-effectively investigate the complex mechanism of 3' UTR switching in a genome-wide fashion among various physiological processes and diseases.Genome Research 05/2011; 21(5):741-7. DOI:10.1101/gr.115295.110 · 13.85 Impact Factor
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ABSTRACT: Conan is a C++ library created for the accurate and efficient modelling, inference and analysis of complex networks. It implements the generation and modification of graphs according to several published models, as well as the unexpensive computation of global and local network properties. Other features include network inference and community detection. Furthermore, Conan provides a Python interface to facilitate the use of the library and its integration in currently existing applications. Conan is available at http://github.com/rhz/conan/. Comment: 5 pages and 1 figure