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International Journal of Cancer Research & Therapy

Does the Logic Underlying Micro and Macrostate Statistics Reflect Different Aspects of Cancer Cell Biology?

Published Oct 30, 2020• 0 citations
Publisher

Abstract

Do elements of microstate logic apply to microstate events? What is the relation between macrostate two-valued logic and microstate quantum logic as they reflect deterministic or random events in biology and medicine or even in other fields. The relations between different classifications of probability and logic are not often discussed in the popular literature. It could be especially interesting if random and “chaotic” events in these two realms exhibited identifiable differences affecting outcomes which if more fully understood could have implications for biologic, medical or other fields. Do the two forms of logic reflect identifiable differences in cancer biology?

DOI
10.33140/ijcrt.05.04.04

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Do elements of microstate logic apply to microstate events? What is the relation between macrostate two-valued logic and microstate quantum logic as they reflect deterministic or random events in biology and medicine or even in other fields. The relations between different classifications of probability and logic are not often discussed in the popular literature. It could be especially interesting if random and “chaotic” events in these two realms exhibited identifiable differences affecting outcomes which if more fully understood could have implications for biologic, medical or other fields. Do the two forms of logic reflect identifiable differences in cancer biology?
Physicists have, hitherto, mostly adopted a frequentist conception of probability, according to which probability statements apply only to ensembles. It is argued that we should, instead, adopt an epistemic, or Bayesian conception, in which probabilities are conceived as logical constructs rather than physical realities, and in which probability statements do apply directly to individual events. The question is closely related to the disagreement between the orthodox school of statistical thought and the Bayesian school. It has important technical implications (it makes a difference, what statistical methodology one adopts). It may also have important implications for the interpretation of the quantum state. 1 1.
There is no doubt that quantum mechanics has become one of the building blocks of our physical world today. It is one of the most rapidly growing fields of science that can potentially change every aspect of our life. Quantum biology is one of the most essential parts of this era which can be considered as a game-changer in medicine especially in the field of cancer. Despite quantum biology having gained more attention during the last decades, there are still so many unanswered questions concerning cancer biology and so many unpaved roads in this regard. This review paper is an effort to answer the question of how biological phenomena such as cancer can be described through the quantum mechanical framework. In other words, is there a correlation between cancer biology and quantum mechanics, and how? This literature review paper reports on the recently published researches based on the principles of quantum physics with focus on cancer biology and metabolism.
Pure quantum evolution is deterministic, ψ → exp (—iHt/ħ)ψ , but as for classical mechanics probability enters because a given macroscopic initial condition contains microscopic states that lead to different outcomes; the relative probability of those outcomes equals the relative abundance of the microscopic states for each outcome. This is the postulated basis for the recovery of the usual quantum probabilities, as discussed in Chapter 6. In this chapter we take up the question of whether the allowable microstates (the ‘special’ states) do indeed come with the correct abundance. To recap: ‘special’ states are microstates not leading to superpositions of macroscopically different states (‘grotesque’ states). For a given experiment and for each macroscopically distinct outcome of that experiment these states form a subspace. We wish to show that the dimension of that subspace is the relative probability of that outcome. This is an ambitious goal, especially considering the effort needed to establish that there are any special states—the subject of Chapter 7. As remarked there, the special states exhibited are likely to be only a small and atypical fraction of all special states in the physical apparatus being modeled (e.g., the cloud chamber). In one example (the decay model) there is a remarkable matching of dimension and conventional probability, but I would not make too much of that. What is especially challenging about the present task is that we seek a universal distribution.