Smart behavior of true slime mold in a labyrinth

Memory Formation in Adaptive Networks

The continuous adaptation of networks like our vasculature ensures optimal network performance when challenged with changing loads. Here, we show that adaptation dynamics allow a network to memorize the position of an applied load within its network morphology. We identify that the irreversible dynamics of vanishing network links encode memory. Our analytical theory successfully predicts the role of all system parameters during memory formation, including parameter values which prevent memory formation. We thus provide analytical insight on the theory of memory formation in disordered systems.

Fungi anaesthesia

Electrical activity of fungus Pleurotus ostreatus is characterised by slow (h) irregular waves of baseline potential drift and fast (min) action potential likes spikes of the electrical potential. An exposure of the myceliated substrate to a chloroform vapour lead to several fold decrease of the baseline potential waves and increase of their duration. The chloroform vapour also causes either complete cessation of spiking activity or substantial reduction of the spiking frequency. Removal of the chloroform vapour from the growth containers leads to a gradual restoration of the mycelium electrical activity.

Behaviorist approaches to investigating memory and learning: A primer for synthetic biology and bioengineering

The fields of developmental biology, biomedicine, and artificial life are being revolutionized by advances in synthetic morphology. The next phase of synthetic biology and bioengineering is resulting in the construction of novel organisms (biobots), which exhibit not only morphogenesis and physiology but functional behavior. It is now essential to begin to characterize the behavioral capacity of novel living constructs in terms of their ability to make decisions, form memories, learn from experience, and anticipate future stimuli. These synthetic organisms are highly diverse, and often do not resemble familiar model systems used in behavioral science. Thus, they represent an important context in which to begin to unify and standardize vocabulary and techniques across developmental biology, behavioral ecology, and neuroscience. To facilitate the study of behavior in novel living systems, we present a primer on techniques from the behaviorist tradition that can be used to probe the functions of any organism – natural, chimeric, or synthetic – regardless of the details of their construction or origin. These techniques provide a rich toolkit for advancing the fields of synthetic bioengineering, evolutionary developmental biology, basal cognition, exobiology, and robotics.

Correspondence insights into the role of genes in cell functionality. Comments on "The gene: An appraisal" by K. Baverstock

One of the most important goals of the post-genomic era is to understand the different sources of molecular information that regulate the functional and structural architecture of cells. In this regard, Prof. K. Baverstock underscores in his recent article "The gene: An appraisal" (Baverstock, 2021) that genes are not the leading elements in cellular functionality, inheritance and evolution. As a consequence, the theory of evolution based on the Neo-Darwinian synthesis, is inadequate for today's scientific evidence. Conversely, the author contends that life processes viewed on the basis of thermodynamics, complex system dynamics and self-organization provide a new framework for the foundations of Biology. I consider it necessary to comment on some essential aspects of this relevant work, and here I present a short overview of the main non-genetic sources of biomolecular order and complexity that underline the molecular dynamics and functionality of cells. These sources generate different processes of complexity, which encompasses from the most elementary levels of molecular activity to the emergence of systemic behaviors, and the information necessary to sustain them is not contained in the genome.

Self-Organization and Information Processing: From Basic Enzymatic Activities to Complex Adaptive Cellular Behavior

One of the main aims of current biology is to understand the origin of the molecular organization that underlies the complex dynamic architecture of cellular life. Here, we present an overview of the main sources of biomolecular order and complexity spanning from the most elementary levels of molecular activity to the emergence of cellular systemic behaviors. First, we have addressed the dissipative self-organization, the principal source of molecular order in the cell. Intensive studies over the last four decades have demonstrated that self-organization is central to understand enzyme activity under cellular conditions, functional coordination between enzymatic reactions, the emergence of dissipative metabolic networks (DMN), and molecular rhythms. The second fundamental source of order is molecular information processing. Studies on effective connectivity based on transfer entropy (TE) have made possible the quantification in bits of biomolecular information flows in DMN. This information processing enables efficient self-regulatory control of metabolism. As a consequence of both main sources of order, systemic functional structures emerge in the cell; in fact, quantitative analyses with DMN have revealed that the basic units of life display a global enzymatic structure that seems to be an essential characteristic of the systemic functional metabolism. This global metabolic structure has been verified experimentally in both prokaryotic and eukaryotic cells. Here, we also discuss how the study of systemic DMN, using Artificial Intelligence and advanced tools of Statistic Mechanics, has shown the emergence of Hopfield-like dynamics characterized by exhibiting associative memory. We have recently confirmed this thesis by testing associative conditioning behavior in individual amoeba cells. In these Pavlovian-like experiments, several hundreds of cells could learn new systemic migratory behaviors and remember them over long periods relative to their cell cycle, forgetting them later. Such associative process seems to correspond to an epigenetic memory. The cellular capacity of learning new adaptive systemic behaviors represents a fundamental evolutionary mechanism for cell adaptation.

An Argument for an Ecosystemic AI: Articulating Connections across Prehuman and Posthuman Intelligences

As an art collective Cesar & Lois develops projects that examine sociotechnical systems, attempting to challenge anthropocentric technological pathways while linking to intelligences sourced in biological circuitry. As artists we imagine new configurations for what we understand as (social, economic, technological) networks and intelligences. With this ecosystemic approach we consider the possibility of an artificial intelligence (AI) that supports well-being in a broad sense, accommodating relationships across different layers of living worlds and involving local and global communities of all kinds. This thinking is grounded in research by theorists across disciplines, including communications and media theory, microbiology, anthropology, decolonial studies, social ecology, sociology and environmental psychology. At a time when human beings and their ecosystems face grave threats due to climate change and a global pandemic, we are rethinking the basis for our AIs, and for the resulting decision-making on behalf of societies and ecosystems. Creative projects by Cesar & Lois provide alternative conceptual models for thinking across networks, reframing the artists’ and potentially viewers’ understanding of what motivates and shapes societies. Referencing a series of artworks and the theories that underpin them, this article envisages a sociotechnical framework that takes into account ecosystems and challenges the philosophical orientations that guide society. Degenerative Cultures is an artwork in which the artists overlap microbiological organisms, AI and human systems as a speculative restructuring of networks across human and nonhuman entities. The push for ecosystemic technologies and intelligences is linked to the expansion of community to include planetary constituents, such as nonhuman beings and environments. The artists posit that such ecosystemic networks would be capable of taking into account the planet’s human societies as well as nonhuman species and their environments, broadening the concept of community well-being and shifting the technological architecture to meet the complex needs of the planet and its constituent parts. The experimental series, [ECO]nomic Revolution, layers Physarum polycephalum, or slime mold, over the mapped demographics of human cities. The species polycephalum references multi-brains, and implies a decentralized logic, which for the non-neurological microbiological network translates to the sharing of nutrients and regulated growth across a culture. Assuming a perspective based in the arts, this proposition imagines a shift from the dominant conceptions of AI as an individual intelligence and frames it as part of a network that necessarily includes ecosystems. We envision the creation of sociotechnical systems that could be modeled on networked lifeforms that have optimized themselves across millions of years, like the organism Physarum polycephalum, which occurs globally in moist environments, or like those microbial populations within and outside of human bodies, whose percussive biological processing interacts with and alters many layers of lifeforms. We argue that an environmentally responsive intelligence based on relationships across living systems potentially serves a broad community composed of diverse human populations, nonhuman beings and ecosystems.

Homeostasis as a fundamental principle for a coherent theory of brains

'Brains' may be considered to be computation engines, with neurons and synapses analogized to electronic components wired into networks that process information, learn and evolve. Alternatively, 'brains' are cognitive systems, which contain elements of intentionality, purposefulness and creativity that do not fit comfortably into a brain-as-computer metaphor. I address the question of how we may think most constructively about brains in their various forms-solid, liquid or fluid-and whether there is a coherent theory that unites them all. In this essay, I explore cognitive systems in the context of new understanding of life's distinctive nature, in particular the core concept of homeostasis, and how this new understanding lays a sound conceptual foundation for an expansive theory of brains. This article is part of the theme issue 'Liquid brains, solid brains: How distributed cognitive architectures process information'.

Trail-mediated self-interaction

A number of microorganisms leave persistent trails while moving along surfaces. For single-cell organisms, the trail-mediated self-interaction will influence the dynamics. It has been discussed recently [Kranz et al., Phys. Rev. Lett. 117, 038101 (2016)] that the self-interaction may localize the organism above a critical coupling χ_c to the trail. Here, we will derive a generalized active particle model capturing the key features of the self-interaction and analyze its behavior for smaller couplings χ < χ_c. We find that fluctuations in propulsion speed shift the localization transition to stronger couplings.

Towards fungal computer

We propose that fungi Basidiomycetes can be used as computing devices: information is represented by spikes of electrical activity, a computation is implemented in a mycelium network and an interface is realized via fruit bodies. In a series of scoping experiments, we demonstrate that electrical activity recorded on fruits might act as a reliable indicator of the fungi's response to thermal and chemical stimulation. A stimulation of a fruit is reflected in changes of electrical activity of other fruits of a cluster, i.e. there is distant information transfer between fungal fruit bodies. In an automaton model of a fungal computer, we show how to implement computation with fungi and demonstrate that a structure of logical functions computed is determined by mycelium geometry.

Collective behaviour and swarm intelligence in slime moulds

The study of collective behaviour aims to understand how individual-level behaviours can lead to complex group-level patterns. Collective behaviour has primarily been studied in animal groups such as colonies of insects, flocks of birds and schools of fish. Although less studied, collective behaviour also occurs in microorganisms. Here, we argue that slime moulds are powerful model systems for solving several outstanding questions in collective behaviour. In particular, slime mould may hold the key to linking individual-level mechanisms to colony-level behaviours. Using well-established principles of collective animal behaviour as a framework, we discuss the extent to which slime mould collectives are comparable to animal groups, and we highlight some potentially fruitful areas for future research.

The Integration of Electrical Signals Originating in the Root of Vascular Plants

Plants have developed different signaling systems allowing for the integration of environmental cues to coordinate molecular processes associated to both early development and the physiology of the adult plant. Research on systemic signaling in plants has traditionally focused on the role of phytohormones as long-distance signaling molecules, and more recently the importance of peptides and miRNAs in building up this communication process has also been described. However, it is well-known that plants have the ability to generate different types of long-range electrical signals in response to different stimuli such as light, temperature variations, wounding, salt stress, or gravitropic stimulation. Presently, it is unclear whether short or long-distance electrical communication in plants is linked to nutrient uptake. This review deals with aspects of sensory input in plant roots and the propagation of discrete signals to the plant body. We discuss the physiological role of electrical signaling in nutrient uptake and how nutrient variations may become an electrical signal propagating along the plant.

Physarum machines imitating a Roman road network: the 3D approach

Physarum Polycephalum is a single cell visible by unaided eye. This is a plasmodial, vegetative stage of acellular slime mould. This single cell has myriad of nuclei which contribute to a network of bio-chemical oscillators responsible for the slime mould’s distributed sensing, concurrent information processing and decision making, and parallel actuation. When presented with a spatial configuration of sources of nutrients, the slime mould spans the sources with networks of its protoplasmic tube. These networks belong to a family of planar proximity graphs. The protoplasmic networks also show a degree of similarity to vehicular transport networks. Previously, we have shown that the foraging behaviour of the slime mould can be applied in archaeological research to complement and enhance conventional geographic information system tools. The results produced suffered from limitation of a flat substrate: transport routes imitated by the slime mould did not reflect patterns of elevations. To overcome the limitation of the ‘flat world’ we constructed a three-dimensional model of Balkans. In laboratory experiments and computer modelling we uncovered patterns of the foraging behaviour that might shed a light onto development of Roman roads in the Balkans during the imperial period (1st century BC – 4th century AD).

On the role of the plasmodial cytoskeleton in facilitating intelligent behavior in slime mold Physarum polycephalum

The plasmodium of slime mold Physarum polycephalum behaves as an amorphous reaction-diffusion computing substrate and is capable of apparently ‘intelligent’ behavior. But how does intelligence emerge in an acellular organism? Through a range of laboratory experiments, we visualize the plasmodial cytoskeleton—a ubiquitous cellular protein scaffold whose functions are manifold and essential to life—and discuss its putative role as a network for transducing, transmitting and structuring data streams within the plasmodium. Through a range of computer modeling techniques, we demonstrate how emergent behavior, and hence computational intelligence, may occur in cytoskeletal communications networks. Specifically, we model the topology of both the actin and tubulin cytoskeletal networks and discuss how computation may occur therein. Furthermore, we present bespoke cellular automata and particle swarm models for the computational process within the cytoskeleton and observe the incidence of emergent patterns in both. Our work grants unique insight into the origins of natural intelligence; the results presented here are therefore readily transferable to the fields of natural computation, cell biology and biomedical science. We conclude by discussing how our results may alter our biological, computational and philosophical understanding of intelligence and consciousness.

Plant hairy root cultures as plasmodium modulators of the slime mold emergent computing substrate Physarum polycephalum

Roots of the medicinal plant Valeriana officinalis are well-studied for their various biological activities. We applied genetically transformed V. officinalis root biomass to exert control of Physarum polycephalum, an amoeba-based emergent computing substrate. The plasmodial stage of the P. polycephalum life cycle constitutes a single, multinucleate cell visible by unaided eye. The plasmodium modifies its network of oscillating protoplasm in response to spatial configurations of attractants and repellents, a behavior that is interpreted as biological computation. To program the computing behavior of P. polycephalum, a diverse and sustainable library of plasmodium modulators is required. Hairy roots produced by genetic transformation with Agrobacterium rhizogenes are a metabolically stable source of bioactive compounds. Adventitious roots were induced on in vitro V. officinalis plants following infection with A. rhizogenes. A single hairy root clone was selected for massive propagation and the biomass was characterized in P. polycephalum chemotaxis, maze-solving, and electrical activity assays. The Agrobacterium-derived roots of V. officinalis elicited a positive chemotactic response and augmented maze-solving behavior. In a simple plasmodium circuit, introduction of hairy root biomass stimulated the oscillation patterns of slime mold's surface electrical activity. We propose that manipulation of P. polycephalum with the plant root culture platform can be applied to the development of slime mold microfluidic devices as well as future models for engineering the plant rhizosphere.

Elements of the cellular metabolic structure

A large number of studies have demonstrated the existence of metabolic covalent modifications in different molecular structures, which are able to store biochemical information that is not encoded by DNA. Some of these covalent mark patterns can be transmitted across generations (epigenetic changes). Recently, the emergence of Hopfield-like attractor dynamics has been observed in self-organized enzymatic networks, which have the capacity to store functional catalytic patterns that can be correctly recovered by specific input stimuli. Hopfield-like metabolic dynamics are stable and can be maintained as a long-term biochemical memory. In addition, specific molecular information can be transferred from the functional dynamics of the metabolic networks to the enzymatic activity involved in covalent post-translational modulation, so that determined functional memory can be embedded in multiple stable molecular marks. The metabolic dynamics governed by Hopfield-type attractors (functional processes), as well as the enzymatic covalent modifications of specific molecules (structural dynamic processes) seem to represent the two stages of the dynamical memory of cellular metabolism (metabolic memory). Epigenetic processes appear to be the structural manifestation of this cellular metabolic memory. Here, a new framework for molecular information storage in the cell is presented, which is characterized by two functionally and molecularly interrelated systems: a dynamic, flexible and adaptive system (metabolic memory) and an essentially conservative system (genetic memory). The molecular information of both systems seems to coordinate the physiological development of the whole cell.

Toward Hybrid Nanostructure-Slime Mould Devices

The plasmodium of slime mould Physarum polycephalum has recently received significant attention for its value as a highly malleable amorphous computing substrate. In laboratory-based experiments, nanoscale artificial circuit components were introduced into the P. polycephalum plasmdodium to investigate the electrical properties and computational abilities of hybridized slime mould. It was found through a combination of imaging techniques and electrophysiological measurements that P. polycephalum is able to internalize a range of electrically active nanoparticles (NPs), assemble them in vivo and distribute them around the plasmodium. Hybridized plasmodium is able to form biomorphic mineralized networks inside the living plasmodium and the empty trails left following its migration, both of which facilitate the transmission of electricity. Hybridization also alters the bioelectrical activity of the plasmodium and likely influences its information processing capabilities. It was concluded that hybridized slime mould is a suitable substrate for producing functional unconventional computing devices.

On a model of pattern regeneration based on cell memory

We present here a new model of the cellular dynamics that enable regeneration of complex biological morphologies. Biological cell structures are considered as an ensemble of mathematical points on the plane. Each cell produces a signal which propagates in space and is received by other cells. The total signal received by each cell forms a signal distribution defined on the cell structure. This distribution characterizes the geometry of the cell structure. If a part of this structure is removed, the remaining cells have two signals. They keep the value of the signal which they had before the amputation (memory), and they receive a new signal produced after the amputation. Regeneration of the cell structure is stimulated by the difference between the old and the new signals. It is stopped when the two signals coincide. The algorithm of regeneration contains certain rules which are essential for its functioning, being the first quantitative model of cellular memory that implements regeneration of complex patterns to a specific target morphology. Correct regeneration depends on the form and the size of the cell structure, as well as on some parameters of regeneration.

A bio-inspired method for the constrained shortest path problem

The constrained shortest path (CSP) problem has been widely used in transportation optimization, crew scheduling, network routing and so on. It is an open issue since it is a NP-hard problem. In this paper, we propose an innovative method which is based on the internal mechanism of the adaptive amoeba algorithm. The proposed method is divided into two parts. In the first part, we employ the original amoeba algorithm to solve the shortest path problem in directed networks. In the second part, we combine the Physarum algorithm with a bio-inspired rule to deal with the CSP. Finally, by comparing the results with other method using an examples in DCLC problem, we demonstrate the accuracy of the proposed method.

Kanizsa illusory contours appearing in the plasmodium pattern of Physarum polycephalum

The plasmodium of Physarum polycephalum is often used in the implementation of non-linear computation to solve optimization problems, and this organismal feature was not used in this analysis to compute perception and/or sensation in humans. In this paper, we focused on the Kanizsa illusion, which is a well-known visual illusion resulting from the differentiation-integration of the visual field, and compared the illusion with the adaptive network in the plasmodium of P. polycephalum. We demonstrated that the network pattern mimicking the Kanizsa illusion can be produced by an asynchronous automata-fashioned model of the foraging slime mold and by the real plasmodia of P. polycephalum. Because the protoplasm of the plasmodium is transported depending on both local and global computation, it may contain differentiation-integration processes. In this sense, we can extend the idea of perception and computation.

Pathogen intelligence

Different species inhabit different sensory worlds and thus have evolved diverse means of processing information, learning and memory. In the escalated arms race with host defense, each pathogenic bacterium not only has evolved its individual cellular sensing and behavior, but also collective sensing, interbacterial communication, distributed information processing, joint decision making, dissociative behavior, and the phenotypic and genotypic heterogeneity necessary for epidemiologic success. Moreover, pathogenic populations take advantage of dormancy strategies and rapid evolutionary speed, which allow them to save co-generated intelligent traits in a collective genomic memory. This review discusses how these mechanisms add further levels of complexity to bacterial pathogenicity and transmission, and how mining for these mechanisms could help to develop new anti-infective strategies.

Route 20, Autobahn 7, and Slime Mold: Approximating the Longest Roads in USA and Germany With Slime Mold on 3-D Terrains

A cellular slime mould Physarum polycephalum is a monstrously large single cell visible by an unaided eye. The slime mold explores space in parallel, is guided by gradients of chemoattractants, and propagates toward sources of nutrients along nearly shortest paths. The slime mold is a living prototype of amorphous biological computers and robotic devices capable of solving a range of tasks of graph optimization and computational geometry. When presented with a distribution of nutrients, the slime mold spans the sources of nutrients with a network of protoplasmic tubes. This protoplasmic network matches a network of major transport routes of a country when configuration of major urban areas is represented by nutrients. A transport route connecting two cities should ideally be a shortest path, and this is usually the case in computer simulations and laboratory experiments with flat substrates. What searching strategies does the slime mold adopt when exploring 3-D terrains? How are optimal and transport routes approximated by protoplasmic tubes? Do the routes built by the slime mold on 3-D terrain match real-world transport routes? To answer these questions, we conducted pioneer laboratory experiments with Nylon terrains of USA and Germany. We used the slime mold to approximate route 20, the longest road in USA, and autobahn 7, the longest national motorway in Europe. We found that slime mold builds longer transport routes on 3-D terrains, compared to flat substrates yet sufficiently approximates man-made transport routes studied. We demonstrate that nutrients placed in destination sites affect performance of slime mold, and show how the mold navigates around elevations. In cellular automaton models of the slime mold, we have shown variability of the protoplasmic routes might depends on physiological states of the slime mold. Results presented will contribute toward development of novel algorithms for sensorial fusion, information processing, and decision making, and will provide inspirations in design of bioinspired amorphous robotic devices.

Creating living cellular machines

Development of increasingly complex integrated cellular systems will be a major challenge for the next decade and beyond, as we apply the knowledge gained from the sub-disciplines of regenerative medicine, synthetic biology, micro-fabrication and nanotechnology, systems biology, and developmental biology. In this prospective, we describe the current state-of-the-art in the assembly of source cells, derived from pluripotent cells, into populations of a single cell type to produce the components or building blocks of higher order systems and finally, combining multiple cell types, possibly in combination with scaffolds possessing specific physical or chemical properties, to produce higher level functionality. We also introduce the issue, questions and ample research opportunities to be explored by others in the field. As these "living machines" increase in capabilities, exhibit emergent behavior and potentially reveal the ability for self-assembly, self-repair, and even self-replication, questions arise regarding the ethical implications of this work. Future prospects as well as ways of addressing these complex ethical questions will be discussed.

Attractor metabolic networks

BACKGROUND

The experimental observations and numerical studies with dissipative metabolic networks have shown that cellular enzymatic activity self-organizes spontaneously leading to the emergence of a Systemic Metabolic Structure in the cell, characterized by a set of different enzymatic reactions always locked into active states (metabolic core) while the rest of the catalytic processes are only intermittently active. This global metabolic structure was verified for Escherichia coli, Helicobacter pylori and Saccharomyces cerevisiae, and it seems to be a common key feature to all cellular organisms. In concordance with these observations, the cell can be considered a complex metabolic network which mainly integrates a large ensemble of self-organized multienzymatic complexes interconnected by substrate fluxes and regulatory signals, where multiple autonomous oscillatory and quasi-stationary catalytic patterns simultaneously emerge. The network adjusts the internal metabolic activities to the external change by means of flux plasticity and structural plasticity.

METHODOLOGY/PRINCIPAL FINDINGS

In order to research the systemic mechanisms involved in the regulation of the cellular enzymatic activity we have studied different catalytic activities of a dissipative metabolic network under different external stimuli. The emergent biochemical data have been analysed using statistical mechanic tools, studying some macroscopic properties such as the global information and the energy of the system. We have also obtained an equivalent Hopfield network using a Boltzmann machine. Our main result shows that the dissipative metabolic network can behave as an attractor metabolic network.

CONCLUSIONS/SIGNIFICANCE

We have found that the systemic enzymatic activities are governed by attractors with capacity to store functional metabolic patterns which can be correctly recovered from specific input stimuli. The network attractors regulate the catalytic patterns, modify the efficiency in the connection between the multienzymatic complexes, and stably retain these modifications. Here for the first time, we have introduced the general concept of attractor metabolic network, in which this dynamic behavior is observed.

Adaptive collective decision-making in limited robot swarms without communication

In this work we investigate spatial collective decision-making in a swarm of microrobots, inspired by the thermotactic aggregation behavior of honeybees. The sensing and navigation capabilities of these robots are intentionally limited; no digital sensor data processing and no direct communication are allowed. In this way, we can approximate the features of smaller mesoscopic-scale systems and demonstrate that even such a limited swarm is nonetheless able to exhibit simple forms of intelligent and adaptive collective behavior.

Strasburger's legacy to mitosis and cytokinesis and its relevance for the Cell Theory

Eduard Strasburger was one of the most prominent biologists contributing to the development of the Cell Theory during the nineteenth century. His major contribution related to the characterization of mitosis and cytokinesis and especially to the discovery of the discrete stages of mitosis, which he termed prophase, metaphase and anaphase. Besides his observations on uninucleate plant and animal cells, he also investigated division processes in multinucleate cells. Here, he emphasised the independent nature of mitosis and cytokinesis. We discuss these issues from the perspective of new discoveries in the field of cell division and conclude that Strasburger's legacy will in the future lead to a reformulation of the Cell Theory and that this will accommodate the independent and primary nature of the nucleus, together with its complement of perinuclear microtubules, for the organisation of the eukaryotic cell.

DEVELOPING PROXIMITY GRAPHS BY PHYSARUM POLYCEPHALUM: DOES THE PLASMODIUM FOLLOW THE TOUSSAINT HIERARCHY?

Plasmodium of Physarum polycephalum spans sources of nutrients and constructs varieties of protoplasmic networks during its foraging behavior. When the plasmodium is placed on a substrate populated with sources of nutrients, it spans the sources with protoplasmic network. The plasmodium optimizes the network to deliver efficiently the nutrients to all parts of its body. How exactly does the protoplasmic network unfold during the plasmodium's foraging behavior? What types of proximity graphs are approximated by the network? Does the plasmodium construct a minimal spanning tree first and then add additional protoplasmic veins to increase reliability and through-capacity of the network? We analyze a possibility that the plasmodium constructs a series of proximity graphs: nearest-neighbour graph (NNG), minimum spanning tree (MST), relative neighborhood graph (RNG), Gabriel graph (GG) and Delaunay triangulation (DT). The graphs can be arranged in the inclusion hierarchy (Toussaint hierarchy): NNG ⊆ MST ⊆ RNG ⊆ GG ⊆ DT . We aim to verify if graphs, where nodes are sources of nutrients and edges are protoplasmic tubes, appear in the development of the plasmodium in the order NNG → MST → RNG → GG → DT , corresponding to inclusion of the proximity graphs.

PHYSARUM MACHINE: IMPLEMENTATION OF A KOLMOGOROV-USPENSKY MACHINE ON A BIOLOGICAL SUBSTRATE

We implement a Kolmogorov-Uspensky machine on the Plasmodium of the slime mold Physarum polycephalum. We provide experimental findings on realization of the machine instructions, illustrate basic operations, and elements of programming.

PHYSARUM SPATIAL LOGIC

Plasmodium of Physarum polycephalum is a large single cell capable for distributed sensing, information processing, decentralized decision-making and collective action. In the paper, we interpret basic features of the plasmodium foraging behavior in terms of process calculus and spatial logic and show that this behavior could be regarded as one of the natural implementations of spatial logic without modal operators.

ON ELECTRICAL CORRELATES OF PHYSARUM POLYCEPHALUM SPATIAL ACTIVITY: CAN WE SEE PHYSARUM MACHINE IN THE DARK?

Plasmodium of Physarum polycephalum is a single cell visible by unaided eye, which spans sources of nutrients with its protoplasmic network. In a very simple experimental setup we recorded electric potential of the propagating plasmodium. We discovered a complex interplay of short range oscillatory behavior combined with long range, low frequency oscillations which serve to communicate information between different parts of the plasmodium. The plasmodium's response to changing environmental conditions forms basis patterns of electric activity, which are unique indicators of the following events: plasmodium occupies a site, plasmodium functions normally, plasmodium becomes "agitated" due to drying substrate, plasmodium departs a site, and plasmodium forms sclerotium. Using a collective particle approximation of Physarum polycephalum we found matching correlates of electrical potential in computational simulations by measuring local population flux at the node positions, generating trains of high and low frequency oscillatory behavior. Motifs present in these measurements matched the response "grammar" of the plasmodium when encountering new nodes, simulated consumption of nutrients, exposure to simulated hazardous illumination and sclerotium formation. The distributed computation of the particle collective was able to calculate beneficial network structures and sclerotium position by shifting the active growth zone of the simulated plasmodium. The results show future promise for the non-invasive study of the complex dynamical behavior within — and health status of — living systems.

Deep evolutionary origins of neurobiology: Turning the essence of 'neural' upside-down

It is generally assumed, both in common-sense argumentations and scientific concepts, that brains and neurons represent late evolutionary achievements which are present only in more advanced animals. Here we overview recently published data clearly revealing that our understanding of bacteria, unicellular eukaryotic organisms, plants, brains and neurons, rooted in the Aristotelian philosophy is flawed. Neural aspects of biological systems are obvious already in bacteria and unicellular biological units such as sexual gametes and diverse unicellular eukaryotic organisms. Altogether, processes and activities thought to represent evolutionary 'recent' specializations of the nervous system emerge rather to represent ancient and fundamental cell survival processes.

Flow-induced channel formation in the cytoplasm of motile cells

A model is presented to explain the development of flow channels within the cytoplasm of the plasmodium of the giant amoeba Physarum polycephalum. The formation of channels is related to the development of a self-organizing tubular network in large cells. Experiments indicate that the flow of cytoplasm is involved in the development and organization of these networks, and the mathematical model proposed here is motivated by recent experiments involving the observation of development of flow channel in small cells. A model of pressure-driven flow through a polymer network is presented in which the rate of flow increases the rate of depolymerization. Numerical solutions and asymptotic analysis of the model in one spatial dimension show that under very general assumptions this model predicts the formation of channels in response to flow.

Speed-accuracy trade-offs during foraging decisions in the acellular slime mould Physarum polycephalum

Speed-accuracy trade-offs (SATs) are thought to be a fundamental feature of biological information processing, yet most evidence of SATs comes from animals. Here, we examine SATs in the foraging decisions of an acellular, amoeboid organism: the slime mould Physarum polycephalum. Slime moulds were given a simple discrimination task: selecting the highest-quality food item from a set of three options. We investigated the effect of two stressors, light exposure and hunger, on the speed and accuracy of decision-making. We also examined the effect of task difficulty. When given a difficult discrimination task, stressed individuals tend to make faster decisions than non-stressed individuals. This effect was reversed in plasmodia given easy discrimination tasks, where stressed individuals made slower decisions than non-stressed individuals. We found evidence of SATs, such that individuals who made fast decisions were more likely to make costly errors by selecting the worst possible food option. Our results suggest that SATs occur in a wider range of taxa than previously considered.

Routing Physarum with repellents

Plasmodium of Physarum polycephalum is a single cell with many nuclei. Plasmodium is an easy-to-experiment-with biological substrate, a multi-functional bio-material used to implement novel and future computing architectures. The plasmodium exhibits typical features of excitable chemical systems and capable for distributed sensing, parallel information processing and decentralized actuation. Plasmodium of P. polycephalum is proved to be a universal storage modification machine. Actively growing zones of the plasmodium are considered to be elementary processors of the growing computing machine, as well as messages traveling in the spatially extended non-linear medium. Controlling propagation of the messages and computing processes is a prerequisite for a successful implementation of working prototypes of plasmodium machines. In laboratory experiments and computer simulation we show that active growing zones of plasmodium can be precisely routed using repelling diffusion gradients generated by crystals of sodium chloride. We demonstrate how to achieve controllable reflection, splitting/multiplication and merging of plasmodium's active zones.

Fungal network responses to grazing

Mycelial networks operate on scales from microscopic to many m(2) and naturally persist for extended periods. As fungi exhibit highly adaptive development, it is important to test behavioural responses on natural substrata with realistic nutrient levels across a range of spatial scales and extended time periods. Here we quantified network responses over 7.5 months in large (57 x 57cm) microcosms to test whether grazing shifts the network to a more resilient architecture. Resource limitation constrained any ability to respond at all, with both grazed and ungrazed networks gradually thinning out over time. Added resources sustained further exploratory growth, but only transiently increased cross-connectivity and network resilience, when tested by simulated damage in silico. Grazed networks were initially weaker and emergence of new exploratory growth was curtailed. However, increased interstitial proliferation led to new cross-links, consolidating the existing mycelial network and increasing the resilience of the network to further attack.

Cell-cell channels, viruses, and evolution: via infection, parasitism, and symbiosis toward higher levels of biological complexity

Between prokaryotic cells and eukaryotic cells there is dramatic difference in complexity which represents a problem for the current version of the cell theory, as well as for the current version of evolution theory. In the past few decades, the serial endosymbiotic theory of Lynn Margulis has been confirmed. This results in a radical departure from our understanding of living systems: the eukaryotic cell represents de facto"cells-within-cell." Higher order "cells-within-cell" situations are obvious at the eukaryotic cell level in the form of secondary and tertiary endosymbiosis, or in the male and female gametophytes of higher plants. The next challenge of the current version of the cell theory is represented by the fact that the multicellular fungi and plants are, in fact, supracellular assemblies as their cells are not physically separated from each other. Moreover, there are also examples of alliances and mergings between multicellular organisms. Infection, especially the viral one, but also bacterial and fungal infections, followed by symbiosis, is proposed to act as the major force that drives the biological evolution toward higher complexity.

Implications of the Turing completeness of reaction-diffusion models, informed by GPGPU simulations on an XBox 360: cardiac arrhythmias, re-entry and the Halting problem

In the arsenal of tools that a computational modeller can bring to bare on the study of cardiac arrhythmias, the most widely used and arguably the most successful is that of an excitable medium, a special case of a reaction-diffusion model. These are used to simulate the internal chemical reactions of a cardiac cell and the diffusion of their membrane voltages. Via a number of different methodologies it has previously been shown that reaction-diffusion systems are at multiple levels Turing complete. That is, they are capable of computation in the same manner as a universal Turing machine. However, all such computational systems are subject to a limitation known as the Halting problem. By constructing a universal logic gate using a cardiac cell model, we highlight how the Halting problem therefore could limit what it is possible to predict about cardiac tissue, arrhythmias and re-entry. All simulations for this work were carried out on the GPU of an XBox 360 development console, and we also highlight the great gains in computational power and efficiency produced by such general purpose processing on a GPU for cardiac simulations.