Ansatz for dynamical hierarchies

Provenance of life: Chemical autonomous agents surviving through associative learning

We present a benchmark study of autonomous, chemical agents exhibiting associative learning of an environmental feature. Associative learning systems have been widely studied in cognitive science and artificial intelligence but are most commonly implemented in highly complex or carefully engineered systems, such as animal brains, artificial neural networks, DNA computing systems, and gene regulatory networks, among others. The ability to encode environmental information and use it to make simple predictions is a benchmark of biological resilience and underpins a plethora of adaptive responses in the living hierarchy, spanning prey animal species anticipating the arrival of predators to epigenetic systems in microorganisms learning environmental correlations. Given the ubiquitous and essential presence of learning behaviors in the biosphere, we aimed to explore whether simple, nonliving dissipative structures could also exhibit associative learning. Inspired by previous modeling of associative learning in chemical networks, we simulated simple systems composed of long- and short-term memory chemical species that could encode the presence or absence of temporal correlations between two external species. The ability to learn this association was implemented in Gray-Scott reaction-diffusion spots, emergent chemical patterns that exhibit self-replication and homeostasis. With the novel ability of associative learning, we demonstrate that simple chemical patterns can exhibit a broad repertoire of lifelike behavior, paving the way for in vitro studies of autonomous chemical learning systems, with potential relevance to artificial life, origins of life, and systems chemistry. The experimental realization of these learning behaviors in protocell or coacervate systems could advance a new research direction in astrobiology, since our system significantly reduces the lower bound on the required complexity for autonomous chemical learning.

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.

An Overview of Open-Ended Evolution: Editorial Introduction to the Open-Ended Evolution II Special Issue

Nature's spectacular inventiveness, reflected in the enormous diversity of form and function displayed by the biosphere, is a feature of life that distinguishes living most strongly from nonliving. It is, therefore, not surprising that this aspect of life should become a central focus of artificial life. We have known since Darwin that the diversity is produced dynamically, through the process of evolution; this has led life's creative productivity to be called Open-Ended Evolution (OEE) in the field. This article introduces the second of two special issues on current research in OEE and provides an overview of the contents of both special issues. Most of the work was presented at a workshop on open-ended evolution that was held as a part of the 2018 Conference on Artificial Life in Tokyo, and much of it had antecedents in two previous workshops on open-ended evolution at artificial life conferences in Cancun and York. We present a simplified categorization of OEE and summarize progress in the field as represented by the articles in this special issue.

On the Potential for Open-Endedness in Neural Networks

Natural evolution gives the impression of leading to an open-ended process of increasing diversity and complexity. If our goal is to produce such open-endedness artificially, this suggests an approach driven by evolutionary metaphor. On the other hand, techniques from machine learning and artificial intelligence are often considered too narrow to provide the sort of exploratory dynamics associated with evolution. In this article, we hope to bridge that gap by reviewing common barriers to open-endedness in the evolution-inspired approach and how they are dealt with in the evolutionary case-collapse of diversity, saturation of complexity, and failure to form new kinds of individuality. We then show how these problems map onto similar ones in the machine learning approach, and discuss how the same insights and solutions that alleviated those barriers in evolutionary approaches can be ported over. At the same time, the form these issues take in the machine learning formulation suggests new ways to analyze and resolve barriers to open-endedness. Ultimately, we hope to inspire researchers to be able to interchangeably use evolutionary and gradient-descent-based machine learning methods to approach the design and creation of open-ended systems.

Two Modes of Evolution: Optimization and Expansion

We document and discuss two different modes of evolution across multiple systems, optimization and expansion. The former suffices in systems whose size and interactions do not change substantially over time, while the latter is a key property of open-ended evolution, where new players and interaction types enter the game. We first investigate systems from physics, biology, and engineering and argue that their evolutionary optimization dynamics is the cumulative effect of multiple independent events, or quakes, which are uniformly distributed on a logarithmic time scale and produce a decelerating fitness improvement when using the appropriate independent variable. The appropriate independent variable can be physical time for a disordered magnetic system, the number of generations for a bacterial system, or the number of produced units for a particular technological product. We then derive and discuss a simple microscopic theory that explains the nature of the involved optimization processes, and provide simulation results as illustration. Finally, we explore the evolution of human culture and technology, using empirical economic data as a proxy for human fitness. Assuming the overall dynamics is a combined optimization and expansion process, the two processes can be separated and quantified by superimposing the mathematical form of an optimization process on the empirical data and thereby transforming the independent variable. This variable turns out to increase faster than any exponential function of time, a property likely due to strong historical changes in the web of human interactions and to the associated increase in the amount of available knowledge. A microscopic theory for this time dependence remains, however, a challenging open problem.

Developing a complex systems perspective for medical education to facilitate the integration of basic science and clinical medicine

RATIONALE, AIMS AND OBJECTIVES

The purpose of medical education is to produce competent and capable professional practitioners who can combine the art and science of medicine. Moreover, this process must prepare individuals to practise in a field in which knowledge is increasing and the contexts in which that knowledge is applied are changing in unpredictable ways. The 'basic sciences' are important in the training of a physician. The goal of basic science training is to learn it in a way that the material can be applied in practice. Much effort has been expended to integrate basic science and clinical training, while adding many other topics to the medical curriculum. This effort has been challenging. The aims of the paper are (1) to propose a unifying conceptual framework that facilitates knowledge integration among all levels of living systems from cell to society and (2) illustrate the organizing principles with two examples of the framework in action - cybernetic systems (with feedback) and distributed robustness.

METHODS

Literature related to hierarchical and holarchical frameworks was reviewed.

RESULTS

An organizing framework derived from living systems theory and spanning the range from molecular biology to health systems management was developed. The application of cybernetic systems to three levels (regulation of pancreatic beta cell production of insulin, physician adjustment of medication for glycaemic control and development and action of performance measures for diabetes care) was illustrated. Similarly distributed robustness was illustrated by the DNA damage response system and principles underlying patient safety.

CONCLUSIONS

Each of the illustrated organizing principles offers a means to facilitate the weaving of basic science and clinical medicine throughout the course of study. The use of such an approach may promote systems thinking, which is a core competency for effective and capable medical practice.

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'.

Open-Ended Evolution: Perspectives from the OEE Workshop in York

We describe the content and outcomes of the First Workshop on Open-Ended Evolution: Recent Progress and Future Milestones (OEE1), held during the ECAL 2015 conference at the University of York, UK, in July 2015. We briefly summarize the content of the workshop's talks, and identify the main themes that emerged from the open discussions. Two important conclusions from the discussions are: (1) the idea of pluralism about OEE-it seems clear that there is more than one interesting and important kind of OEE; and (2) the importance of distinguishing observable behavioral hallmarks of systems undergoing OEE from hypothesized underlying mechanisms that explain why a system exhibits those hallmarks. We summarize the different hallmarks and mechanisms discussed during the workshop, and list the specific systems that were highlighted with respect to particular hallmarks and mechanisms. We conclude by identifying some of the most important open research questions about OEE that are apparent in light of the discussions. The York workshop provides a foundation for a follow-up OEE2 workshop taking place at the ALIFE XV conference in Cancún, Mexico, in July 2016. Additional materials from the York workshop, including talk abstracts, presentation slides, and videos of each talk, are available at http://alife.org/ws/oee1 .

Cyclic growth of hierarchical structures in the aluminum-silicate system

Background

Biological structures grow spontaneously from a seed, using materials supplied by the environment. These structures are hierarchical, with the ‘building blocks’ on each level constructed from those on the lower level. To understand and model the processes that occur on many levels, and later construct them, is a difficult task. However interest in this subject is growing. It is now possible to study the spontaneous growth of hierarchical structures in simple, two component chemical systems.

Results

Aluminum-silicate systems have been observed to grow into structures that are approximately conical. These structures are composed of multiple smaller cones with several hierarchical levels of complexity. On the highest level the system resembles a metropolis, with a horizontal resource distribution network connecting vertical, conical structures. The cones are made from many smaller cones that are connected together forming a whole with unusual behavior. The growth is observed to switch periodically between the vertical and horizontal directions.

Conclusion

A structure grown in a dish is observed to have many similarities to other hierarchical systems such as biological organisms or cities. This system may provide a simple model system to search for universal laws governing the growth of complex hierarchical structures.

Graphical Abstract

Interaction networks for identifying coupled molecular processes in microbial communities

BACKGROUND

Microbial communities adapt to environmental conditions for optimizing metabolic flux. Such adaption may include cooperative mechanisms eventually resulting in phenotypic observables as emergent properties that cannot be attributed to an individual species alone. Understanding the molecular basis of cross-species cooperation adds to utilization of microbial communities in industrial applications including metal bioleaching and bioremediation processes. With significant advancements in metagenomics the composition of microbial communities became amenable for integrative analysis on the level of entangled molecular processes involving more than one species, in turn offering a data matrix for analyzing the molecular basis of cooperative phenomena.

METHODS

We present an analysis framework aligned with a dynamical hierarchies concept for unraveling emergent properties in microbial communities, and exemplify this approach for a co-culture setting of At. ferrooxidans and At. thiooxidans. This minimum microbial community demonstrates a significant increase in bioleaching efficiency compared to the activity of individual species, involving mechanisms of the thiosulfate, the polysulfide and the iron oxidation pathway.

RESULTS

Populating gene-centric data structures holding rich functional annotation and interaction information allows deriving network models at the functional level coupling energy production and transport processes of both microbial species. Applying a network segmentation approach on the interaction network of ortholog genes covering energy production and transport proposes a set of specific molecular processes of relevance in bioleaching. The resulting molecular process model essentially involves functionalities such as iron oxidation, nitrogen metabolism and proton transport, complemented by sulfur oxidation and nitrogen metabolism, as well as a set of ion transporter functionalities. At. ferrooxidans-specific genes embedded in the molecular model representation hold gene functions supportive for ammonia utilization as well as for biofilm formation, resembling key elements for effective chalcopyrite bioleaching as emergent property in the co-culture situation.

CONCLUSIONS

Analyzing the entangled molecular processes of a microbial community on the level of segmented, gene-centric interaction networks allows identification of core molecular processes and functionalities adding to our mechanistic understanding of emergent properties of microbial consortia.

Transient and sustained elementary flux mode networks on a catalytic string-based chemical evolution model

Theoretical models designed to test the metabolism-first hypothesis for prebiotic evolution have yield strong indications about the hypothesis validity but could sometimes use a more extensive identification between model objects and real objects towards a more meaningful interpretation of results. In an attempt to go in that direction, the string-based model SSE ("steady state evolution") was developed, where abstract molecules (strings) and catalytic interaction rules are based on some of the most important features of carbon compounds in biological chemistry. The system is open with a random inflow and outflow of strings but also with a permanent string food source. Although specific catalysis is a key aspect of the model, used to define reaction rules, the focus is on energetics rather than kinetics. Standard energy change tables were constructed and used with standard formation reactions to track energy flows through the interpretation of equilibrium constant values. Detection of metabolic networks on the reaction system was done with elementary flux mode (EFM) analysis. The combination of these model design and analysis options enabled obtaining metabolic and catalytic networks showing several central features of biological metabolism, some more clearly than in previous models: metabolic networks with stepwise synthesis, energy coupling, catalysts regulation, SN2 coupling, redox coupling, intermediate cycling, coupled inverse pathways (metabolic cycling), autocatalytic cycles and catalytic cascades. The results strongly suggest that the main biological metabolism features, including the genotype-phenotype interpretation, are caused by the principles of catalytic systems and are prior to modern genetic systems principles. It also gives further theoretical support to the thesis that the basic features of biologic metabolism are a consequence of the time evolution of a random catalyst search working on an open system with a permanent food source. The importance of the food source characteristics and evolutionary possibilities are discussed.

Energy landscapes for shells assembled from pentagonal and hexagonal pyramids

We present new rigid body potentials that should favour efficient self-assembly of pentagonal and hexagonal pyramids into icosahedral shells over a wide range of temperature. By adding an extra repulsive site opposite the existing apex sites of the pyramids considered in a previously published model, frustrated energy landscapes are transformed into systems identified with self-assembling properties. The extra interaction may be considered analogous to a hydrophobic-hydrophilic repulsion, as in micelle formation.

A category theoretical argument against the possibility of artificial life: Robert Rosen's central proof revisited

One of Robert Rosen's main contributions to the scientific community is summarized in his book Life itself. There Rosen presents a theoretical framework to define living systems; given this definition, he goes on to show that living systems are not realizable in computational universes. Despite being well known and often cited, Rosen's central proof has so far not been evaluated by the scientific community. In this article we review the essence of Rosen's ideas leading up to his rejection of the possibility of real artificial life in silico. We also evaluate his arguments and point out that some of Rosen's central notions are ill defined. The conclusion of this article is that Rosen's central proof is wrong.

Self-organizing hierarchies in sensor and communication networks

We consider a hierarchical multicellular sensing and communication network, embedded in an ageless aerospace vehicle that is expected to detect and react to multiple impacts and damage over a wide range of impact energies. In particular, we investigate self-organization of impact boundaries enclosing critically damaged areas, and impact networks connecting remote cells that have detected noncritical impacts. Each level of the hierarchy is shown to have distinct higher-order emergent properties, desirable in self-monitoring and self-repairing vehicles. In addition, cells and communication messages are shown to need memory (hysteresis) in order to retain desirable emergent behavior within and between various hierarchical levels. Spatiotemporal robustness of self-organizing hierarchies is quantitatively measured with graph-theoretic and information-theoretic techniques, such as the Shannon entropy. This allows us to clearly identify phase transitions separating chaotic dynamics from ordered and robust patterns.

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.

Hierarchical organization in smooth dynamical systems

This article is concerned with defining and characterizing hierarchical structures in smooth dynamical systems. We define transitions between levels in a dynamical hierarchy by smooth projective maps from a phase space on a lower level, with high dimensionality, to a phase space on a higher level, with lower dimensionality. It is required that each level describe a self-contained deterministic dynamical system. We show that a necessary and sufficient condition for a projective map to be a transition between levels in the hierarchy is that the kernel of the differential of the map is tangent to an invariant manifold with respect to the flow. The implications of this condition are discussed in detail. We demonstrate two different causal dependences between degrees of freedom, and how these relations are revealed when the dynamical system is transformed into global Jordan form. Finally these results are used to define functional components on different levels, interaction networks, and dynamical hierarchies.

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.

Bridging nonliving and living matter

Assembling non-biological materials (geomaterials) into a proto-organism constitutes a bridge between nonliving and living matter. In this article we present a simple step-by-step route to assemble a proto-organism. Many pictures have been proposed to describe this transition within the origins-of-life and artificial life communities, and more recently alternative pictures have been emerging from advances in nanoscience and biotechnology. The proposed proto-organism lends itself to both traditions and defines a new picture based on a simple idea: Given a set of required functionalities, minimize the physicochemical structures that support these functionalities, and make sure that all structures self-assemble and mutually enhance each other's existence. The result is the first concrete, rational design of a simple physicochemical system that integrates the key functionalities in a thermodynamically favorable manner as a lipid aggregate integrates proto-genes and a proto-metabolism. Under external pumping of free energy, the metabolic processes produce the required building blocks, and only specific gene sequences enhance the metabolic kinetics sufficiently for the whole system to survive. We propose an experimental implementation of the proto-organism with a discussion of our experimental results, together with relevant results produced by other experimental groups, and we specify what is still missing experimentally. Identifying the missing steps is just as important as providing the road map for the transition. We derive the kinetic and thermodynamic conditions of each of the proto-organism subsystems together with relevant theoretical and computational results about these subsystems. We present and discuss detailed 3D simulations of the lipid aggregation processes. From the reaction kinetics we derive analytical aggregate size distributions, and derive key properties of the metabolic efficiency and stability. Thermodynamics and kinetics of the ligation directed self-replication of the proto-genes is discussed, and we summarize the full life cycle of the proto-organism by comparing size, replication time, and energy with the biomass efficiency of contemporary unicells. Finally, we also compare our proto-organism picture with existing origins-of-life and protocell pictures. By assembling one possible bridge between nonliving and living matter we hope to provide a piece in the ancient puzzle about who we are and where we come from.

Evolvable self-replicating molecules in an artificial chemistry

This paper gives details of Squirm3, a new artificial environment based on a simple physics and chemistry that supports self-replicating molecules somewhat similar to DNA. The self-replicators emerge spontaneously from a random soup given the right conditions. Interactions between the replicators can result in mutated versions that can outperform their parents. We show how artificial chemistries such as this one can be implemented as a cellular automaton. We concur with Dittrich, Ziegler, and Banzhaf that artificial chemistries are a good medium in which to study early evolution.

Is it the right ansatz?

This article is a response to Rasmussen et al. [Artificial Life, 7, 329-350], in which the authors suggest that, within a particular simulation "framework," there is a tight correspondence between the complexity of the primitive objects and the emergence of dynamical hierarchies. As an example they report a two-dimensional artificial chemistry that supports the spontaneous emergence of micellar structures, which they classify as third-order structures. We report in this article that essentially comparable phenomena can be produced with relatively simpler primitive objects. We also question the order classification of the micellar structures.

Defense of the ansatz for dynamical hierarchies

Gross and McMullin [Artificial Life, 7, 355-365] criticize the conclusions of our article on dynamical hierarchies [Artificial Life, 7, 329-353]. In this note we respond to their criticisms. After clarifying our ansatz, we argue that the simulations presented by Gross and McMullin present no evidence against the ansatz, in part because their simulations use a different simulation framework, and in part because their simulations are no less complex than ours. We also clarify why the micelles in our simulations are third-order emergent structures, and why we emphasize realism in our simulation.