In my article Cancer’s Intelligence I conjectured that cancer is able to compute survival solutions to future environmental changes that it has not yet encountered. That is, that cancer can predict the future and ready itself to adapt to new, yet unseen environmental conditions, whether a change in oxygen tension or a chemotherapeutic drug.
A recent, exceedingly relevant paper entitled Trade-offs between cost and information in cellular prediction by Tjalma et al analyzes cellular adaptation and lays out the conditions for prediction of a survival response to an adverse environmental change. The cellular response is an intrinsically computed solution to future external events using the cell’s internal computational machinery. Historically, it has been thought that only higher organisms “can predict the future environment and initiate a response ahead of time”, but this is not the case.
At bottom, energy and time are two key computational limits. Energy (free energy) for computation, storing information, and the protein synthesis needed for future actions limits computation of the optimal solution for future survival moves. Time is a factor in computing solutions and a in sensing and sampling the current and past environments sufficiently finely for accurate predictions. Optimal time averaging for the cell is key, which also has the advantage of filtering noise.
The Tjalma article elucidates the informational bounds a cell can possibly have about the future. The costs of a bit of informational sampling and processing are computed and compared. As information-processing humans, we extract only the most useful information from the past environment as we plan future actions – the cell does the same. How and under what conditions a cell approaches the information bound for its internal computation is examined in detail by exploring Markovian and non-Markovian models and by examining the necessary fineness of temporal sampling of the environment and the cell’s computational speed. The information bottleneck theory is key in this analysis, as it dictates information compression for storage and its costs.
The article is a challenging, yet rewarding read and one that is intimately related to the vast computation ability of the cancer cell and more importantly, cancer cell networks. In a way, this article is the missing link between cancer’s computation from instantaneous, present environmental conditions – the constrained regime held by most cancer biologists – to the tracking and selective sampling of the past and the present to the response to yet-unseen future events – just as human and higher animal computation occurs
One can imagine several next steps extending from the consideration of the computational bounds for cell networks in which cells sense and transfer information among the network nodes and simultaneously sense and respond to the environment. In cancer, the combinatorial space of the vast tumor genotypic and phenotypic heterogeneity increases cancer’s computational state space enormously – accounting for cancer’s ability to escape the majority of lethal moves against it. Cancer’s choice to move or grow (epithelial-mesenchymal transition) and the advance construction of the metastatic niche well prior to the movement of the first metastatic cell are paramount unsolved problems of cancer’s lethal march. How many solutions to uncertain future environments can a cancer cell and its intercellular network ultimately store? Humans, of course, store multiple future scenarios and actions with probabilities and bet hedging within a game theoretical framework.
Next, if the molecular mediators of cancer’s tremendous computational potential could be elucidated, then they could be targeted and possibly degraded. This could, in turn, render cancer less able to out-compute the lethal moves by the oncologist. What is actually the computational backbone of cancer: the whole cell and interaction network or a substructure more amenable for targeting? The enhanced broken symmetry of cancer in its computational network is another relevant factor. See the first article in my cancer physics trilogy: Symmetry and symmetry breaking in cancer: a foundational approach to the cancer problem.
This exciting and extraordinarily detailed paper could provide the theory and practical algorithms for a new stage of understanding and manipulating intrinsic cellular computation, with far reaching implications for solving the cancer problem and providing benefit to our patients. This is the work that I have been seeking for some time.
For more information on the molecular imaging aspects of cancer characterization, treatment, and monitoring, please visit BioMolecular Imaging, LLC http://biomolecularimaging.com
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