Defense of the ansatz for dynamical hierarchies

Towards the First Principles in Biology and Cancer: New Vistas in Computational Systems Biology of Cancer

These days many leading scientists argue for a new paradigm for cancer research and propose a complex systems-view of cancer supported by empirical evidence. As an example, Thea Newman (2021) has applied "the lessons learned from physical systems to a critique of reductionism in medical research, with an emphasis on cancer". It is the understanding of this author that the mesoscale constructs that combine the bottom-up as well as top-down approaches, are very close to the concept of emergence. The mesoscale constructs can be said to be those effective components through which the system allows itself to be understood. A short list of basic concepts related to life/biology fundamentals are first introduced to demonstrate a lack of emphasis on these matters in literature. It is imperative that physical and chemical approaches are introduced and incorporated in biology to make it more conceptually sound, quantitative, and based on the first principles. Non-equilibrium thermodynamics is the only tool currently available for making progress in this direction. A brief outline of systems biology, the discovery of emergent properties, and metabolic modeling are introduced in the second part. Then, different cancer initiation concepts are reviewed, followed by application of non-equilibrium thermodynamics in the metabolic and genomic analysis of initiation and development of cancer, stressing the endogenous network hypothesis (ENH). Finally, extension of the ENH is suggested to include a cancer niche (exogenous network hypothesis). It is expected that this will lead to a unifying systems-biology approach for a future combination of the analytical and synthetic arms of two major hypotheses of cancer models (SMT and TOFT).

Open-Endedness for the Sake of Open-Endedness

Natural evolution keeps inventing new complex and intricate forms and behaviors. Digital evolution and genetic algorithms fail to create the same kind of complexity, not just because we still lack the computational resources to rival nature, but because (it has been argued) we have not understood in principle how to create open-ended evolving systems. Much effort has been made to define such open-endedness so as to create forms of increasing complexity indefinitely. Here, however, a simple evolving computational system that satisfies all such requirements is presented. Doing so reveals a shortcoming in the definitions for open-ended evolution. The goal to create models that rival biological complexity remains. This work suggests that our current definitions allow for even simple models to pass as open-ended, and that our definitions of complexity and diversity are more important for the quest of open-ended evolution than the fact that something runs indefinitely.

Generating minimal living systems from non-living materials and increasing their evolutionary abilities

We review lessons learned about evolutionary transitions from a bottom-up construction of minimal life. We use a particular systemic protocell design process as a starting point for exploring two fundamental questions: (i) how may minimal living systems emerge from non-living materials? and (ii) how may minimal living systems support increasingly more evolutionary richness? Under (i), we present what has been accomplished so far and discuss the remaining open challenges and their possible solutions. Under (ii), we present a design principle we have used successfully both for our computational and experimental protocellular investigations, and we conjecture how this design principle can be extended for enhancing the evolutionary potential for a wide range of systems.This article is part of the themed issue 'The major synthetic evolutionary transitions'.

Levels of description: a novel approach to dynamical hierarchies

We present a novel formal interpretation of dynamical hierarchies based on information theory, in which each level is a near-state-determined system, and levels are related to one another in a partial ordering. This reformulation moves away from previous definitions, which have considered unique hierarchies of structures or objects arranged in aggregates. Instead, we consider hierarchies of dynamical systems: these are more suited to describing living systems, which are not mere aggregates, but organizations. Transformations from lower to higher levels in a hierarchy are redescriptions that lose information. There are two criteria for partial ordering. One is a state-dependence criterion enforcing predictability within a level. The second is a distinctness criterion enforcing the idea that the higher-level description must do more than just throw information away. We hope this will be a useful tool for empirical studies of both computational and physical dynamical hierarchies.

Thirty years of computational autopoiesis: a review

Computational autopoiesis--the realization of autopoietic entities in computational media--holds an important and distinctive role within the field of artificial life. Its earliest formulation by Francisco Varela, Humberto Maturana, and Ricardo Uribe was seminal in demonstrating the use of an artificial, computational medium to explore the most basic question of the abstract nature of living systems--over a decade in advance of the first Santa Fe Workshop on Artificial Life. The research program it originated has generated substantive demonstrations of progressively richer, lifelike phenomena. It has also sharply illuminated both conceptual and methodological problems in the field. This article provides an integrative overview of the sometimes disparate work in this area, and argues that computational autopoiesis continues to provide an effective framework for addressing key open problems in artificial life.

Toward a formalization of emergence

Emergence is a concept widely used in the sciences, the arts, and engineering. Some effort has been made to formalize it, but it is used in various contexts with different meanings, and a unified theory of emergence is still distant. The ultimate goal of a theory of emergence should include using emergence to model, design, or predict the behavior of multiagent systems. The author proposes a formal definition of a basic type of emergence using a language-theoretic and grammar systems approach. It is shown which types of phenomena can be modeled in this sense and what the consequences are for other more complex phenomena.