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Tumors as attractor states. Use of the global signatures of tumor expression as diagnostic tools.

Ganiraju Manyam, Alessandro Giuliani, Ancha Baranova

This is a collaborative project between

Molecular and Microbiology Department, College of Science, George Mason University, Fairfax,   Virginia USA

Istituto Superiore di Sanità, Viale Regina Elena 299  00161 - Roma

To date, most of the high-throughput studies of the gene expression studies were focused on elucidation of the discriminatory gene signatures reflecting key regulatory processes defining the physiology and the behavior of individual cell. On the other hand, a change in a cell phenotype requires coordinated interaction of a variety of genes that determine the functional identity of the cell (Bar-Yam et al.). This notion implies an understanding that a given cell type could be represented as a dynamic system occupying a specific position in the multidimensional phase space spanned by the different genes (Tsuchiya et al, Giuliani et al., Chuang et al.). In terms of dynamics, this specific position is called an ‘attractor’, i.e. a ‘stable” position characterized by a specific pattern of gene expression levels that determines the particular type of the cell differentiation.
Some studies (Chuang et al., Marziali et al.) have indicated that the differentiation destinies of the progenitor cells could be defined as high dimensional attractor states of the underlying molecular networks. Particularly, a study of the differentiation trajectories of blood stem cells demonstrated that specific cell types behaves as attractors, while a genome-wide self-sustained transcription cycles behave as a timekeeping oscillator that can be tuned in order to shift the transcription pattern toward a particular attractor state. Therefore, cell population is considered as a dynamical system that could be attracted to one or another “stable” state by transition that implies extensive mutual regulation of all elements of cell’ genome. This is in striking contrast with the traditional idea of a division of the mRNA transcripts into those generated by ‘house keeping’ and ‘tissue-specific genes’, while a set of the master genes is responsible for the switch between different phenotypes. In their seminal paper Bar-Yam and colleagues describe this dichotomy as they differentiate an ‘autocratic’ (few master genes drive the differentiation process) and a ‘democratic’ (no master genes, all genes act as mutual regulators going toward a global attractor state) regulatory landscape.
A possible middle ground may be described as a general attractor-like behavior of the regulatory machinery with some local ‘vantage points’ representing genes most sensitive to dynamical changes of the system. Recent study of Tsuchiya et. al demonstrated biphasic nature of the cellular response to innate immune stimuli involving an acute-stochastic mode consisting of small number of sharply induced genes and a collective mode where a large number of weakly induced genes adjust their expression levels to novel “stable” state.
We performed a quantitative estimation of the relative importance of global and local features of gene expression regulation landscape in the process of tumor development. The remarkable behavioral invariance we observed in eighteen independent tumor data sets gives a robust proof of the dynamical picture of cell populations.