2015
DOI: 10.1371/journal.pone.0140172
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Precritical State Transition Dynamics in the Attractor Landscape of a Molecular Interaction Network Underlying Colorectal Tumorigenesis

Abstract: From the perspective of systems science, tumorigenesis can be hypothesized as a critical transition (an abrupt shift from one state to another) between proliferative and apoptotic attractors on the state space of a molecular interaction network, for which an attractor is defined as a stable state to which all initial states ultimately converge, and the region of convergence is called the basin of attraction. Before the critical transition, a cellular state might transit between the basin of attraction for an a… Show more

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Cited by 14 publications
(14 citation statements)
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“…1b ) 1 . Dysregulation of the signaling network by a certain perturbation can lead to a fatal disease such as cancer since the altered signal flow might provide an incorrect information on the cell fate determination 2 4 . Hence, understanding of signal flow in complex signaling networks is critical to uncover the underlying mechanisms of the related disease and to identify promising drug targets.…”
Section: Introductionmentioning
confidence: 99%
“…1b ) 1 . Dysregulation of the signaling network by a certain perturbation can lead to a fatal disease such as cancer since the altered signal flow might provide an incorrect information on the cell fate determination 2 4 . Hence, understanding of signal flow in complex signaling networks is critical to uncover the underlying mechanisms of the related disease and to identify promising drug targets.…”
Section: Introductionmentioning
confidence: 99%
“…As chemical concentration increases or their immediate effects (such as mutations) accumulate in cells/tissues, driving the gene network close to the tipping point, based on the theory of critical slowing down, the gene transcripts and proteins, which are constantly perturbed by stochastic gene expression noise ( 106 ), would be sluggish to return to their deterministic steady-state levels and thus become more variable (Figure 2D ). As a result, the expression pattern of these network genes will exhibit certain statistical features near the tipping point ( 102 , 103 , 107 ), despite that their mean expression levels can remain largely indistinguishable from the normal, healthy attractor state: it is expected that (i) gene expression variance will increase dramatically across time and between cells; (ii) expression correlation between genes in the same network will increase dramatically due to mutual feedback regulation; (iii) similarity between individual cells, defined by the gene expression vector, will decrease. So the pre-tipping point can be predicted by a composite index derived from the above statistics of network genes.…”
Section: Computational Approaches For Dose-response and Extrapolationmentioning
confidence: 99%
“…Using such complex network biomarkers, the onset of influenza could be predicted hours to days before the actual symptoms were presented . At the cell level, changes in the dynamics of attractor states have been found to indicate a transition of a normal cell into a cancerous cell …”
Section: Health Awarenessmentioning
confidence: 99%
“…64 At the cell level, changes in the dynamics of attractor states have been found to indicate a transition of a normal cell into a cancerous cell. 65 Experience sampling methods are being used to collect time series of data of people in a daily life context. For example, data on mood states have been used to make subjects aware of upcoming depressive episodes and migraine attacks.…”
Section: Healthmentioning
confidence: 99%