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: The cancer cell attractors theory provides a next-generation understanding of carcinogenesis and natural explanation of punctuated clonal expansions of tumor progression. The impressive notion of atavism of cancer is now updated but more evidence is awaited. Besides, the mechanisms that the ectopic expression of some germline genes result in somatic tumors such as melanoma and brain tumors are emerging but are not well understood. Cancer could be triggered by cells undergoing abnormal cell attractor transitions, and may be reversible with "cyto-education". From mammals to model organisms like Caenorhabditis elegans and Drosophila melanogaster, the versatile Mi-2β/nucleosome remodeling and histone deacetylation complexes along with their functionally related chromatin remodeling complexes (CRCs), i.e., the dREAM/Myb-MuvB complex and Polycomb group complex are likely master regulators of cell attractors. The trajectory that benign cells switch to cancerous could be the reverse of navigation of embryonic cells converging from a series of intermediate transcriptional states to a final adult state, which is supported by gene expression dynamics inspector assays and some cross-species genetic evidence. The involvement of CRCs in locking cancer attractors may help find the recipes of perturbing genes to achieve successful reprogramming such that the reprogrammed cancer cell function in the same way as the normal cells.Cellular and Molecular Life Sciences CMLS 09/2011; 68(21):3557-71. · 5.62 Impact Factor
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ABSTRACT: The majority of deaths owing to cancer are ultimately caused by metastatic disease. However, most research, to date, has focused on the molecular features of cancers at their primary sites rather than on understanding disseminated malignancy in its systemic form. The dynamic nature of metastatic malignancy and its behavior as a co-ordinated systemic disease require a cancer progression paradigm that is integrative and can incorporate both the proximate causes of cancer and the broader ultimate causes in an evolutionary and developmental context. The study of robust cellular attractor states that arise directly from the architectural patterns contained within gene regulatory networks is proposed as a conceptual framework through which many of the other disparate models of cancer metastasis can be more clearly viewed and, ultimately, unified, thus providing a new conceptual framework in which to understand cancer progression and metastasis.Future Oncology 04/2014; 10(5):735-48. · 3.20 Impact Factor
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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. · 2.81 Impact Factor