A Systems Biology Approach to Cancer: Fractals, Attractors, and Nonlinear Dynamics

Abstract

Cancer begins to be recognized as a highly complex disease, and advanced knowledge of the carcinogenic process claims to be acquired by means of supragenomic strategies. Experimental data evidence that tumor emerges from disruption of tissue architecture, and it is therefore consequential that the tissue level should be considered the proper level of observation for carcinogenic studies. This paradigm shift imposes to move from a reductionistic to a systems biology approach. Indeed, cell phenotypes are emergent modes arising through collective nonlinear interactions among different cellular and microenvironmental components, generally described by a phase space diagram, where stable states (attractors) are embedded into a landscape model. Within this framework cell states and cell transitions are generally conceived as mainly specified by the gene-regulatory network. However, the system's dynamics cannot be reduced to only the integrated functioning of the genome-proteome network, and the cell-stroma interacting system must be taken into consideration in order to give a more reliable picture. As cell form represents the spatial geometric configuration shaped by an integrated set of cellular and environmental cues participating in biological functions control, it is conceivable that fractal-shape parameters could be considered as "omics" descriptors of the cell-stroma system. Within this framework it seems that function follows form, and not the other way around.

A linear-encoding model explains the variability of the target morphology in regeneration

“…A linear encoding mechanism during development and regeneration directly contains spatial information of the target morphology, which can reprogram the cancerous cells according to their location to become part of the encoded target morphology. Linear encoding models thus suggest alternative approaches to cancer normalization (in line with the views of cancer as a problem of tissue organization [157,158]) focused on activating cellular responses to fields of non-local patterning information [21,34,159,160].…”

A new bioinformatics insight into human cancer-associated proteins

“…Many studies were carried out to find cancer biomarkers in various cancer types through comparing the status of the disease (21). The obtained data offered the opportunities for the diagnosis and effective therapy of cancer.…”

If Systems Approach is the Way Forward, What can the Ayurvedic Theory of <i>Tridosha</i> Teach Us?

“…This indicates the relevance of dryness in biology. Interestingly, many of these properties such as dryness, viscosity and adhesion used in ayurveda are increasingly being studied for their physiological roles and clinical relevance [19][20][21][22][23][24][25][26][27][28][29][30] . Just as the functions associated with VPK, these parameters (V1-V7, P1-P7, K1-K7) also exist at all levels of biological organization.…”

“…Dryness at cellular level causes changes in cell motility, shape, adhesion, and osmosis, all of which have physiological and clinical impications [20][21][22][23][24] . Recent studies have shown that change in cell shape is associated with malignant transformation of normal cells 24 . Pathologic dryness at system level is referred as 'medical dryness' in conventional medicine, e.g.…”