The Physarum polycephalum Genome Reveals Extensive Use of Prokaryotic Two-Component and Metazoan-Type Tyrosine Kinase Signaling.

Physarum polycephalum is a well-studied microbial eukaryote with unique experimental attributes relative to other experimental model organisms. It has a sophisticated life cycle with several distinct stages including amoebal, flagellated, and plasmodial cells. It is unusual in switching between open and closed mitosis according to specific life-cycle stages. Here we present the analysis of the genome of this enigmatic and important model organism and compare it with closely related species. The genome is littered with simple and complex repeats and the coding regions are frequently interrupted by introns with a mean size of 100 bases. Complemented with extensive transcriptome data, we define approximately 31,000 gene loci, providing unexpected insights into early eukaryote evolution. We describe extensive use of histidine kinase-based two-component systems and tyrosine kinase signaling, the presence of bacterial and plant type photoreceptors (phytochromes, cryptochrome, and phototropin) and of plant-type pentatricopeptide repeat proteins, as well as metabolic pathways, and a cell cycle control system typically found in more complex eukaryotes. Our analysis characterizes P. polycephalum as a prototypical eukaryote with features attributed to the last common ancestor of Amorphea, that is, the Amoebozoa and Opisthokonts. Specifically, the presence of tyrosine kinases in Acanthamoeba and Physarum as representatives of two distantly related subdivisions of Amoebozoa argues against the later emergence of tyrosine kinase signaling in the opisthokont lineage and also against the acquisition by horizontal gene transfer.

A new model to imitate the foraging behavior of Physarum polycephalum on a nutrient-poor substrate

Researches on Physarum polycephalum show that methods inspired by the primitive unicellular organism can construct an efficient network and solve some complex problems in graph theory. Current models simulating the intelligent behavior of Physarum are mainly based on Hagen-Poiseuille Law and Kirchhoff Law, reaction-diffusion, Cellular Automaton and multi-agent approach. In this paper, based on an assumption that the plasmodium of Physarum forages for food along the gradient of chemo-attractants on a nutrient-poor substrate, a new model is proposed to imitate its intelligent foraging behavior. The key point of the model is that the growth of Physarum is determined by the simple particle concentration field relating the distance to food source and the shape of food source on a nutrient-poor substrate. To verify this model, numerical experiments are conducted according to Adamatzky[U+05F3]s experiment. Results in spanning tree construction by this model are almost the same as those of Physarum and Oregonator model. The proposed model can also imitate Physarum to avoid repellents. Furthermore, the Euclidean Spanning tree built by this model is similar to its corresponding Minimal Euclidean Spanning tree.

A physarum network evolution model based on IBTM

The traditional Cellular Automation-based Physarum model reveals the process of amoebic self-organized movement and self-adaptive network formation based on bubble transportation. However, a bubble in the traditional Physarum model often transports within active zones and has little change to explore newareas.And the efficiency of evolution is very low because there is only one bubble in the system. This paper proposes an improved model, named as Improved Bubble Transportation Model (IBTM). Our model adds a time label for each grid of environment in order to drive bubbles to explore newareas, and deploysmultiple bubbles in order to improve the evolving efficiency of Physarum network.We first evaluate the morphological characteristics of IBTM with the real Physarum, and then compare the evolving time between the traditional model and IBTM. The results show that IBTM can obtain higher efficiency and stability in the process of forming an adaptive network.

Rapid Physarum Algorithm for shortest path problem

As shortest path (SP) problem has been one of the most fundamental network optimization problems for a long time, technologies for this problem are still being studied. In this paper, a new method by integrating a path finding mathematical model, inspired by Physarum polycephalum, with extracted one heuristic rule to solve SP problem has been proposed, which is called Rapid Physarum Algorithm (RPA). Simulation experiments have been carried out on three different network topologies with varying number of nodes. It is noted that the proposed RPA can find the optimal path as the path finding model does for most networks. What is more, experimental results show that the performance of RPA surpasses the path finding model on both iterations and solution time. © 2014 Elsevier B.V.

A Physarum-inspired multi-agent system to solve maze

Physarum Polycephalum is a primitive unicellular organism. Its foraging behavior demonstrates a unique feature to form a shortest path among food sources, which can be used to solve a maze. This paper proposes a Physarum-inspired multi-agent system to reveal the evolution of Physarum transportation networks. Two types of agents – one type for search and the other for convergence – are used in the proposed model, and three transition rules are identified to simulate the foraging behavior of Physarum. Based on the experiments conducted, the proposed multiagent system can solve the two possible routes of maze, and exhibits the reconfiguration ability when cutting down one route. This indicates that the proposed system is a new way to reveal the intelligence of Physarum during the evolution process of its transportation networks.

A new multi-agent system to simulate the foraging behaviors of Physarum

Physarum Polycephalum is a unicellular and multi-headed slime mold, which can form high efficient networks connecting spatially separated food sources in the process of foraging. Such adaptive networks exhibit a unique characteristic in which network length and fault tolerance are appropriately balanced. Based on the biological observations, the foraging process of Physarum demonstrates two self-organized behaviors, i.e., search and contraction. In this paper, these two behaviors are captured in a multi-agent system. Two types of agents and three transition rules are designed to imitate the search and the contraction behaviors of Physarum based on the necessary and the sufficient conditions of a self-organized computational system. Some simulations of foraging process are used to investigate the characteristics of our system. Experimental results show that our system can autonomously search for food sources and then converge to a stable solution, which replicates the foraging process of Physarum. Specially, a case study of maze problem is used to estimate the path-finding ability of the foraging behaviors of Physarum. What’s more, the model inspired by the foraging behaviors of Physarum is proposed to optimize meta-heuristic algorithms for solving optimization problems. Through comparing the optimized algorithms and the corresponding traditional algorithms, we have found that the optimization strategies have a higher computational performance than their corresponding traditional algorithms, which further justifies that the foraging behaviors of Physarum have a higher computational ability.

A physarum-inspired vacant-particle model with shrinkage for transport network design

Physarum can form a higher efficient and stronger robust network in the processing of foraging. The vacant-particle model with shrinkage (VP-S model), which captures the relationship between the movement of Physarum and the process of network formation, can construct a network with a good balance between exploration and exploitation. In this paper, the VP-S model is applied to design a transport network. We compare the performance of the network designed based on the VP-S model with the real-world transport network in terms of average path length, network efficiency and topology robustness. Experimental results show that the network designed based on the VP-S model has better performance than the real-world transport network in all measurements. Our study indicates that the Physarum-inspired model can provide useful suggestions to the real-world transport network design.

On the Dynamics of the Adenylate Energy System: Homeorhesis vs Homeostasis

Biochemical energy is the fundamental element that maintains both the adequate turnover of the biomolecular structures and the functional metabolic viability of unicellular organisms. The levels of ATP, ADP and AMP reflect roughly the energetic status of the cell, and a precise ratio relating them was proposed by Atkinson as the adenylate energy charge (AEC). Under growth-phase conditions, cells maintain the AEC within narrow physiological values, despite extremely large fluctuations in the adenine nucleotides concentration. Intensive experimental studies have shown that these AEC values are preserved in a wide variety of organisms, both eukaryotes and prokaryotes. Here, to understand some of the functional elements involved in the cellular energy status, we present a computational model conformed by some key essential parts of the adenylate energy system. Specifically, we have considered (I) the main synthesis process of ATP from ADP, (II) the main catalyzed phosphotransfer reaction for interconversion of ATP, ADP and AMP, (III) the enzymatic hydrolysis of ATP yielding ADP, and (IV) the enzymatic hydrolysis of ATP providing AMP. This leads to a dynamic metabolic model (with the form of a delayed differential system) in which the enzymatic rate equations and all the physiological kinetic parameters have been explicitly considered and experimentally tested in vitro. Our central hypothesis is that cells are characterized by changing energy dynamics (homeorhesis). The results show that the AEC presents stable transitions between steady states and periodic oscillations and, in agreement with experimental data these oscillations range within the narrow AEC window. Furthermore, the model shows sustained oscillations in the Gibbs free energy and in the total nucleotide pool. The present study provides a step forward towards the understanding of the fundamental principles and quantitative laws governing the adenylate energy system, which is a fundamental element for unveiling the dynamics of cellular life.

An amoeboid algorithm for shortest path in fuzzy weighted networks

Taking the uncertainty existing in edge weights of networks into consideration, finding shortest path in such fuzzy weighted networks has been widely studied in various practical applications. In this paper, an amoeboid algorithm is proposed, combing fuzzy sets theory with a path finding model inspired by an amoeboid organism, Physarum polycephalum. With the help of fuzzy numbers, uncertainty is well represented and handled in our algorithm. What's more, biological intelligence of Physarum polycephalum has been incorporate into the algorithm. A numerical example on a transportation network is demonstrated to show the efficiency and flexibility of our proposed amoeboid algorithm.

An amoeboid algorithm for solving linear transportation problem

Transportation Problem (TP) is one of the basic operational research problems, which plays an important role in many practical applications. In this paper, a bio-inspired mathematical model is proposed to handle the Linear Transportation Problem (LTP) in directed networks by modifying the original amoeba model Physarum Solver. Several examples are used to prove that the provided model can effectively solve Balanced Transportation Problem (BTP), Unbalanced Transportation Problem (UTP), especially the Generalized Transportation Problem (GTP), in a nondiscrete way. © 2013 Elsevier B.V. All rights reserved.

Swarm constructability: designing through embedded tectonic behaviors in swarm robotic systems

Con questa tesi di laurea si muovono i primi passi di una ricerca applicata finalizzata alla costruzione-deposizione di materiale da parte di sciami di mini-robot dal comportamento indipendente che si coordinano tramite segnali lasciati e rilevati nell’ambiente in cui si muovono. Lo sviluppo di tecniche di progettazione e fabbricazione digitale ha prodotto un aumento nel grado di interconnessione tra tecnologia e design, dunque, di nuove possibilità tettoniche. Le relazioni tettoniche tradizionali stanno infatti subendo una trasformazione radicale, potendo essere esplicitamente informate e dunque mediate attraverso gli strumenti digitali dall’ideazione alla produzione. Questa mediazione informata del contenuto tettonico (che opera costantemente) è distintivo di un approccio material-based alla progettazione che aumenta l’integrazione tra struttura, materia e forma entro le tecnologie di fabbricazione (R.Oxman). Dei numerosi processi di fabbricazione per l’architettura che si servono di tecnologia robotica, pochi sono capaci di superare la logica gerarchica, rigida e lineare-sequenziale che serve di fatto agli obiettivi di automazione ed ottimizzazione. La distribuzione di forme di intelligenza semplificata ad un numero elevato di unità robot è quindi qui proposta come alternativa al modello appena descritto. Incorporando semplici decisioni di carattere architettonico negli agenti-robot che costituiscono il sistema distribuito di entità autonome, la loro interazione e le decisioni prese individualmente producono comportamento collettivo e l’integrazione delle suddette relazioni tettoniche. Nello sviluppo del progetto, si è fatto così riferimento a modelli comportamentali collettivi (di sciame) osservabili in specie comunitarie che organizzano strutture materiali -come termiti e vespe- ed in organismi semplici -come le muffe cellulari della specie Physarum polycephalum. Per queste specie biologiche il processo di costruzione non dipende da un ‘piano generale’ ma è guidato esclusivamente da azioni dei singoli individui che comunicano lasciando tracce chimiche nell’ambiente e modificano il loro comportamento rilevando le tracce lasciate dagli altri individui. A questo scopo, oltre alle simulazioni in digitale, è stato indispensabile sviluppare dei prototipi funzionali di tipo fisico, ovvero la realizzazione di mini-robot dal movimento indipendente, in grado di coordinarsi tra loro tramite segnali lasciati nell’ambiente e capaci di depositare materiale.

PhysarumSpreader: a new bio-Inspired methodology for identifying influential spreaders in complex networks

Identifying influential spreaders in networks, which contributes to optimizing the use of available resources and efficient spreading of information, is of great theoretical significance and practical value. A random-walk-based algorithm LeaderRank has been shown as an effective and efficient method in recognizing leaders in social network, which even outperforms the well-known PageRank method. As LeaderRank is initially developed for binary directed networks, further extensions should be studied in weighted networks. In this paper, a generalized algorithm PhysarumSpreader is proposed by combining LeaderRank with a positive feedback mechanism inspired from an amoeboid organism called Physarum Polycephalum. By taking edge weights into consideration and adding the positive feedback mechanism, PhysarumSpreader is applicable in both directed and undirected networks with weights. By taking two real networks for examples, the effectiveness of the proposed method is demonstrated by comparing with other standard centrality measures.