Cancer is the most intensively studied biological phenomenon, yet it remains poorly understood. Mortality and morbidity rates for many major cancer types have scarcely changed in decades. We posit that this lack of progress stems from a flawed conceptual model for the nature of cancer. A novel NCI physical science and cancer initiative encouraged us to re-consider the conceptual foundations of the current cancer paradigm, and we present an outline of an alternative view here.
Although cancer is one of the most intensively studied phenomena in biology and occurs in almost all multicellular species (1, 2), an explanation for its existence and properties within the context of evolutionary history has received comparatively little attention. However, it is widely recognized that progress in treatment and prevention depends on a deeper understanding of the biology of cancer (3).
It is well known that life on Earth alters its environment over evolutionary and geological timescales. An important open question is whether this is a result of evolutionary optimization or a universal feature of life. In the latter case, the origin of life would be coincident with a shift in environmental conditions. Here we present a model for the emergence of life in which replicators are explicitly coupled to their environment through the recycling of a finite supply of resources.
Open-ended evolution (OEE) is relevant to a variety of biological, artificial and technological systems, but has been challenging to reproduce in silico. Most theoretical efforts focus on key aspects of open-ended evolution as it appears in biology. We recast the problem as a more general one in dynamical systems theory, providing simple criteria for open-ended evolution based on two hallmark features: unbounded evolution and innovation.
A prominent feature of life on Earth is the evolution of biological complexity: over evolutionary history the biosphere has displayed continual adaptation and innovation, giving rise to an apparent open-ended increase in complexity. The capacity for open-ended evolution has been cited as a hallmark feature of life and also characterizes human and technological systems. Yet, the underlying drivers of open-ended evolution remain poorly understood. League of Legends (League) is an online team-based strategy game that has become immensely popular over the last 6 years.
The origins of life stands among the great open scientific questions of our time. While a number of proposals exist for possible starting points in the pathway from non-living to living matter, these have so far not achieved states of complexity that are anywhere near that of even the simplest living systems. A key challenge is identifying the properties of living matter that might distinguish living and non-living physical systems such that we might build new life in the lab.
A major conceptual step forward in understanding the logical architecture of living systems was advanced by von Neumann with his universal constructor, a physical device capable of self-reproduction. A necessary condition for a universal constructor to exist is that the laws of physics permit physical universality, such that any transformation (consistent with the laws of physics and availability of resources) can be caused to occur.
Cancer is sometimes depicted as a reversion to single cell behavior in cells adapted to live in a multicellular assembly. If this is the case, one would expect that mutation in cancer disrupts functional mechanisms that suppress cell-level traits detrimental to multicellularity. Such mechanisms should have evolved with or after the emergence of multicellularity.
The current paradigm views cancer as arising clonally from a degradation of genetic information in single cells. A complementary perspective, originating at the dawn of modern developmental biology, is that cancer is the result of a system disorder of algorithms that normally orchestrate individual cell activities toward specific anatomical structures and away from tumorigenesis. A view of cancer as a disease of geometry focuses on the pathways that allow cells to cooperate to build and maintain large-scale anatomical patterning.
We propose a novel, information-based classification of elementary cellular automata. The classification scheme proposed circumvents the problems associated with isolating whether complexity is in fact intrinsic to a dynamical rule, or if it arises merely as a product of a complex initial state. Transfer entropy variations processed by cellular automata split the 256 elementary rules into three information classes, based on sensitivity to initial conditions.