Social infrastructure networks, essential for daily life and economic activities, encompass utilities such as water, electricity, roads, and telecommunications. Dynamic remodeling of these systems is crucial for responding to continuous changes, unexpected events, and increased demand. This study proposes a new dynamic remodeling model inspired by biological mechanisms, focusing on a model based on the chemotaxis of slime molds. Slime molds adapt spontaneously to environmental changes by remodeling through the growth and degeneration of tubes. This capability can be applied to optimizing and dynamic remodeling social infrastructure networks. This study elucidated the chemotactic response characteristics of slime molds using biological experiments. The mold\'s response was observed by considering changes in the concentration of chemicals as environmental changes, confirming that slime molds adapt to environmental changes by shortening their periodic cycles. Subsequently, based on this dynamic response, we propose a new dynamic model (oscillated Physarum solver, O-PS) that extends the existing Physarum solver (PS). Numerical simulations demonstrated that the O-PS possesses rapid and efficient path-remodeling capabilities. In particular, within a simplified maze network, the O-PS was confirmed to have the same shortest-path searching ability as the PS, while being capable of faster remodeling. This study offers a new approach for optimizing and dynamically remodeling social infrastructure networks by mimicking biological mechanisms, enabling the rapid identification of solutions considering multiple objectives under complex constraints. Furthermore, the variation in convergence speed with oscillation frequency in the O-PS suggests flexibility in responding to environmental changes. Further research is required to develop more effective remodeling strategies.

The acellular slime mold Physarum polycephalum is a large, unicellular amoeba, which, due to its huge size, is well suited to investigate chemotaxis and cellular locomotion. The myxomycete has an astonishing behavioral repertoire and is highly responsive to changes in its environment, which map to changes in its tubular network, internal cytoplasm flow, and cytoskeleton. The behavioral repertoire includes problem-solving, decision-making, and memory. P. polycephalum\'s chemo- and phototaxis are especially well studied. This chapter describes how to cultivate different morphotypes of P. polycephalum (micro-, meso-, and macroplasmodia). Furthermore, the setup of a chemotaxis experiment and the acquisition and analysis of chemotaxis data is described.

Modern medicine has produced large genetic datasets of high dimensions through advanced gene sequencing technology, and processing these data is of great significance for clinical decision-making. Gene selection (GS) is an important data preprocessing technique that aims to select a subset of feature information to improve performance and reduce data dimensionality. This study proposes an improved wrapper GS method based on forensic-based investigation (FBI). The method introduces the search mechanism of the slime mould algorithm in the FBI to improve the original FBI; the newly proposed algorithm is named SMA_FBI; then GS is performed by converting the continuous optimizer to a binary version of the optimizer through a transfer function. In order to verify the superiority of SMA_FBI, experiments are first executed on the 30-function test set of CEC2017 and compared with 10 original algorithms and 10 state-of-the-art algorithms. The experimental results show that SMA_FBI is better than other algorithms in terms of finding the optimal solution, convergence speed, and robustness. In addition, BSMA_FBI (binary version of SMA_FBI) is compared with 8 binary algorithms on 18 high-dimensional genetic data from the UCI repository. The results indicate that BSMA_FBI is able to obtain high classification accuracy with fewer features selected in GS applications. Therefore, SMA_FBI is considered an optimization tool with great potential for dealing with global optimization problems, and its binary version, BSMA_FBI, can be used for GS tasks.

Many cells face search problems, such as finding food, mates, or shelter, where their success depends on their search strategy. In contrast to other unicellular organisms, the slime mold Physarum polycephalum forms a giant network-shaped plasmodium while foraging for food. What is the advantage of the giant cell on the verge of multicellularity? We experimentally study and quantify the migration behavior of P. polycephalum plasmodia on the time scale of days in the absence and presence of food. We develop a model which successfully describes its migration in terms of ten data-derived parameters. Using the mechanistic insights provided by our data-driven model, we find that regardless of the absence or presence of food, P. polycephalum achieves superdiffusive migration by performing a self-avoiding run-and-tumble movement. In the presence of food, the run duration statistics change, only controlling the short-term migration dynamics. However, varying organism size, we find that the long-term superdiffusion arises from self-avoidance determined by cell size, highlighting the potential evolutionary advantage that this macroscopically large cell may have.

The question of whether a single-celled organism without a brain could have functions such as learning and memory has been the subject of much debate in recent years. The plasmodium of the true slime mold, Physarum polycephalum, is an ideal model organism for such a question. The plasmodium exhibits behaviors that resemble intelligence, including solving mazes, mimicking optimal rail transportation networks, predicting the weather, and solving traveling salesman problems. In addition, the plasmodium has recently been shown to have the simplest form of learning: habituation. In the experiments in which plasmodia were repeatedly allowed to cross bridges containing aversive chemicals, the habituation behavior has been confirmed. It has been shown that the habituation process involves chemicals that are stored internally. However, it is not clear how these chemicals result in change in the behavior of plasmodium during habituation learning. This study focused on the transport tube network formed in plasmodium during the above experiments. Then, the role of the network morphology in the habituation learning process was investigated. The results showed that the network morphology changes from tree to mesh type during habituation learning, and disrupting the learned network reduces habituation behavior. In addition, it was shown that the thickness oscillation frequency depends on the network morphology. The study found that in the plasmodium of P. polycephalum, a primitive organism without a brain, transport tube networks, instead of neuronal networks, play an important role in habituation learning and the resulting decision making.

The tubular network-forming slime moldPhysarum polycephalumis able to maintain long-scale contraction patterns driven by an actomyosin cortex. The resulting shuttle streaming in the network is crucial for the organism to respond to external stimuli and reorganize its body mass giving rise to complex behaviors. However, the chemical basis of the self-organized flow pattern is not fully understood. Here, we present ratiometric measurements of free intracellular calcium in simple morphologies ofPhysarumnetworks. The spatiotemporal patterns of the free calcium concentration reveal a nearly anti-correlated relation to the tube radius, suggesting that calcium is indeed a key regulator of the actomyosin activity. We compare the experimentally observed phase relation between the radius and the calcium concentration to the predictions of a theoretical model including calcium as an inhibitor. Numerical simulations of the model suggest that calcium indeed inhibits the contractions inPhysarum, although a quantitative difference to the experimentally measured phase relation remains. Unraveling the mechanism underlying the contraction patterns is a key step in gaining further insight into the principles ofPhysarum\'s complex behavior.

Desiccation is a severe survival problem for organisms. We have been studying the desiccation tolerance mechanisms in the true slime mold Physarum polycephalum. We measured the trehalose content of P. polycephalum vegetative cells (plasmodia) and drought cells (sclerotia). Surprisingly, we found that the content in sclerotia was about 473-fold greater than in the plasmodia. We then examined trehalose metabolism-related genes via RNAseq, and consequently found that trehalose 6-phosphate phosphorylase (T6pp) expression levels increased following desiccation. Next, we cloned and expressed the genes for T6pp, trehalose 6-phosphate synthase/phosphatase (Tps/Tpp), maltooligosyltrehalose trehalohydrolase (TreZ), and maltooligosyltrehalose synthase (TreY) in E. coli. Incidentally, TreY and TreZ clones have been reported in several prokaryotes, but not in eukaryotes. This report in P. polycephalum is the first evidence of their presence in a eukaryote species. Recombinant T6pp, TreY, and TreZ were purified and confirmed to be active. Our results showed that these enzymes catalyze reactions related to trehalose production, and their reaction kinetics follow the Michaelis-Menten equation. The t6pp mRNA levels of the sclerotia were about 15-fold higher than in the plasmodia. In contrast, the expression levels of TreZ and TreY showed no significant change between the sclerotia and plasmodia. Thus, T6pp is probably related to desiccation tolerance, whereas the contribution of TreY and TreZ is insufficient to account for the considerable accumulation of trehalose in sclerotia.

Trait expression in metazoans is strongly influenced by the balance of macronutrients (i.e. protein, carbohydrate and fat) in the diet. At the same time, an individual\'s genetic background seems to regulate the magnitude of phenotypic response to a particular diet. It needs to be better understood whether interactions between diet, genetic background and trait expression are found in unicellular eukaryotes. A protist-the slime mould, Physarum polycephalum can choose diets based on protein-to-carbohydrate (P:C) content to support optimal growth rate. Yet, the role of genetic background (variation in the mitochondrial and nuclear DNAs) in mediating growth rate response to dietary P:C ratios in the slime mould is unknown. Here, we studied the effects of interactions between mitochondrial and nuclear DNA haplotypes and diet (i.e. G × G × E interactions) on the growth rate of P. polycephalum. A genetic panel of six distinct strains of P. polycephalum that differ in their mitochondrial and nuclear DNA haplotypes was used to measure growth rate across five diets that varied in their P:C ratio and total calories. We first determined the strains\' growth rate (total biomass and surface area) when grown on a set menu with access to a particular diet. We then assessed whether the growth rate of strains increased on a buffet menu with access to all diets. Our findings show that the growth rate of P. polycephalum is generally higher on diets containing more carbohydrates than protein and that total calories negatively affect the growth rate. Three-way interactions between mitochondrial, nuclear haplotypes and dietary P:C ratios affected the strains\' surface area of growth but not biomass. Intriguingly, strains did not increase their surface area and biomass when they had access to all diets on the buffet menu. Our findings have broad implications for our understanding of the effect of mitonuclear interactions on trait expression across diverse eukaryotic lineages.

The network-shaped body plan distinguishes the unicellular slime mouldPhysarum polycephalumin body architecture from other unicellular organisms. Yet, network-shaped body plans dominate branches of multi-cellular life such as in fungi. What survival advantage does a network structure provide when facing a dynamic environment with adverse conditions? Here, we probe how network topology impactsP. polycephalum\'s avoidance response to an adverse blue light. We stimulate either an elongated, I-shaped amoeboid or a Y-shaped networked specimen and subsequently quantify the evacuation process of the light-exposed body part. The result shows that Y-shaped specimen complete the avoidance retraction in a comparable time frame, even slightly faster than I-shaped organisms, yet, at a lower almost negligible increase in migration velocity. Contraction amplitude driving mass motion is further only locally increased in Y-shaped specimen compared to I-shaped-providing further evidence that Y-shaped\'s avoidance reaction is energetically more efficient than in I-shaped amoeboid organisms. The difference in the retraction behaviour suggests that the complexity of network topology provides a key advantage when encountering adverse environments. Our findings could lead to a better understanding of the transition from unicellular to multicellularity.

Sensing, communication, navigation, decision-making, memory and learning are key components in a standard cognitive tool-kit that enhance an animal\'s ability to successfully survive and reproduce. However, these tools are not only useful for, or accessible to, animals-they evolved long ago in simpler organisms using mechanisms which may be either unique or widely conserved across diverse taxa. In this article, I review the recent research that demonstrates these key cognitive abilities in the plasmodial slime mould Physarum polycephalum, which has emerged as a model for non-animal cognition. I discuss the benefits and limitations of comparisons drawn between neural and non-neural systems, and the implications of common mechanisms across wide taxonomic divisions. I conclude by discussing future avenues of research that will draw the most benefit from a closer integration of Physarum and animal cognition research.