Evaluation of dynamic behavior forecasting parameters in the process of transition rule induction of unidimensional cellular automata

The simulation of the dynamics of a cellular systems based on cellular automata (CA) can be computationally expensive. This is particularly true when such simulation is part of a procedure of rule induction to find suitable transition rules for the CA. Several efforts have been described in the literature to make this problem more treatable. This work presents a study about the efficiency of dynamic behavior forecasting parameters (DBFPs) used for the induction of transition rules of CA for a specific problem: the classification by the majority rule. A total of 8 DBFPs were analyzed for the 31 best-performing rules found in the literature. Some of these DBFPs were highly correlated each other, meaning they yield the same information. Also, most rules presented values of the DBFPs very close each other. An evolutionary algorithm, based on gene expression programming, was developed for finding transition rules according a given preestablished behavior. The simulation of the dynamic behavior of the CA is not used to evaluate candidate transition rules. Instead, the average values for the DBFPs were used as reference. Experiments were done using the DBFPs separately and together. In both cases, the best induced transition rules were not acceptable solutions for the desired behavior of the CA. We conclude that, although the DBFPs represent interesting aspects of the dynamic behavior of CAs, the transition rule induction process still requires the simulation of the dynamics and cannot rely only on the DBFPs.     

A cellular automata model of tumor-immune system interactions

We present a hybrid cellular automata-partial differential equation model of moderate complexity to describe the interactions between a growing tumor next to a nutrient source and the immune system of the host organism. The model allows both temporal and two-dimensional spatial evolution of the system under investigation and is comprised of biological cell metabolism rules derived from both the experimental and mathematical modeling literature. We present numerical simulations that display behaviors which are qualitatively similar to those exhibited in tumor-immune system interaction experiments. These include spherical tumor growth, stable and unstable oscillatory tumor growth, satellitosis and tumor infiltration by immune cells. Finally, the relationship between these different growth regimes and key system parameters is discussed.     

Evolution of cellular automata with memory: The Density Classification Task

The Density Classification Task is a well known test problem for two-state discrete dynamical systems. For many years researchers have used a variety of evolutionary computation approaches to evolve solutions to this problem. In this paper, we investigate the evolvability of solutions when the underlying Cellular Automaton is augmented with a type of memory based on the Least Mean Square algorithm.     

Dynamics of HIV infection studied with cellular automata and conformon-P systems

Recently a cellular automaton (CA) has been used to model the dynamics of HIV infection, with interesting results. We replicate and further test this model, and we introduce an alternative model based on conformon-P (cP) systems. We find (in common with other recently published comments) that the CA model is very sensitive to initial conditions and produces appropriate qualitative dynamics only for a narrow range of rule probabilities. In contrast, the cP system model is robust to a wide range of conditions and parameters, with more reproducible qualitative agreement to the overall dynamics and to the densities of healthy and infected cells observed in vivo.     

Ecological risk assessment of genetically modified crops based on cellular automata modeling

The assessment of ecological risk in genetically modified (GM) biological systems is critically important for decision-making and public acceptance. Cellular automata (CA) provide a potential modeling and simulation framework for representing relationships and interspecies interactions both temporally and spatially. In this paper, a simple subsystem contains only four species: crop, target pest, non-target pest and enemy insect, and a three layer arrangement of L × L stochastic cellular automata with a periodic boundary were established. The simulation of this simplified system showed abundant and sufficient complexity in population assembly and densities, suggesting a prospective application in ecological risk assessment of GM crops.     

Using cellular automata to generate image representation for biological sequences

Summary. A novel approach to visualize biological sequences is developed based on cellular automata (Wolfram, S. Nature 1984, 311, 419–424), a set of discrete dynamical systems in which space and time are discrete. By transforming the symbolic sequence codes into the digital codes, and using some optimal space-time evolvement rules of cellular automata, a biological sequence can be represented by a unique image, the so-called cellular automata image. Many important features, which are originally hidden in a long and complicated biological sequence, can be clearly revealed thru its cellular automata image. With biological sequences entering into databanks rapidly increasing in the post-genomic era, it is anticipated that the cellular automata image will become a very useful vehicle for investigation into their key features, identification of their function, as well as revelation of their fingerprint. It is anticipated that by using the concept of the pseudo amino acid composition (Chou, K.C. Proteins: Structure, Function, and Genetics, 2001, 43, 246–255), the cellular automata image approach can also be used to improve the quality of predicting protein attributes, such as structural class and subcellular location.     

Numerically evaluated functional equivalence between chaotic dynamics in neural networks and cellular automata under totalistic rules

Chaotic dynamics in a recurrent neural network model and in two-dimensional cellular automata, where both have finite but large degrees of freedom, are investigated from the viewpoint of harnessing chaos and are applied to motion control to indicate that both have potential capabilities for complex function control by simple rule(s). An important point is that chaotic dynamics generated in these two systems give us autonomous complex pattern dynamics itinerating through intermediate state points between embedded patterns (attractors) in high-dimensional state space. An application of these chaotic dynamics to complex controlling is proposed based on an idea that with the use of simple adaptive switching between a weakly chaotic regime and a strongly chaotic regime, complex problems can be solved. As an actual example, a two-dimensional maze, where it should be noted that the spatial structure of the maze is one of typical ill-posed problems, is solved with the use of chaos in both systems. Our computer simulations show that the success rate over 300 trials is much better, at least, than that of a random number generator. Our functional simulations indicate that both systems are almost equivalent from the viewpoint of functional aspects based on our idea, harnessing of chaos.     

“生态位”元胞自动机在土地可持续规划模型中的应用

快速城市化带来了一系列的环境生态问题。有必要把生态学的概念引进城市规划中,以减少城市发展带来的弊端。提出了基于“生态位”的元胞自动机（CA）的新模型,并将其应用在土地利用规划中。探讨了如何通过“生态位”元胞自动机和GIS的结合进行城市土地可持续利用的规划。该模型可方便地探索不同土地利用政策下城市土地利用发展情景,能够为城市规划提供有用的决策支持。旨在探索通过模拟的手段对城市土地利用进行合理的规划。将该模型应用于快速发展的广州市,并取得了较有意义的结果。     

生物废水处理系统的细胞自动机模型

建立了活性污泥处理生物废水的细胞自动机模型,对活性污泥生物量与有机物浓度动态进行了研究,提出了计算活性污泥回流循环比的方法.结果表明,在Moore邻居模型下废水达标排放所需时间较Von. Neumann邻居模型少,不同生长阶段的微生物浓度波动具有时滞性.稳定期有机物浓度和生物量不受活性污泥初始浓度的影响.活性污泥处理生物废水的细胞自动机模型有助于为污水处理提供理论依据.     

利用元胞自动机研究一类捕食食饵模型中的斑块扩散现象

利用概率元胞自动机模型对空间隐式的、食饵具Allee效应的一类捕食食饵模型进行模拟,发现随着相关参数的变化,种群的空间扩散前沿由连续的扩散波逐渐转变为一种相互隔离的斑块向外扩散,这种斑块扩散现象与以往的扩散模式有所不同。研究结果表明：(1)在斑块扩散的情况下,相关参数的微小变化会导致种群灭绝或者形成连续的扩散波,即斑块扩散发生在种群趋于灭绝和连续扩散之间;(2)当种群的空间扩散方式为斑块扩散时,种群的扩散速度会变慢,与其他扩散方式下的速度有着明显的区别。该研究结果对生物入侵控制和外来物种监测有重要的启发和指导作用。     

基于元胞自动机的喀斯特石漠化格局模拟研究

西南喀斯特山区的石漠化问题是目前我国西部地区最为突出的地域环境问题之一,其迅速发展已经严重影响到当地人们的生产生活。以导致石漠化发生发展的自然、人文因子为切入点,通过模拟影响喀斯特系统地表覆被变化的基本生态过程(如植物定居、植物死亡、水蚀风蚀引起的土地退化以及岩石成土过程等),利用随机元胞自动机具有的简单邻域规则产生复杂空间格局的特点,使喀斯特系统地表覆被植被-裸土-裸岩状态在一定概率下发生状态转换,并结合RS和GIS技术,构建了简单、有效的喀斯特石漠化模拟及预测模型(KarstCA)。以典型喀斯特石漠化地区关岭县为研究区,在自然、人文驱动因素共同影响以及只考虑自然驱动因素情景下,KarstCA模型模拟的研究区2007年石漠化空间分布格局的差异主要分布在中部和南部,其主要是不同空间范围上人类活动作用方式和强度差异所致。在16a中(1992—2007年)喀斯特地区地物(植被-裸土-裸岩)丰度变化成非线性关系,当植被覆达到54%以上并继续增加时,裸岩发展趋势与之呈明显的负相关(P<0.01)。在模拟期内人类活动对研究区石漠化的发展起到抑制作用,人类活动的正效应(植树造林等)与负效应(乱砍滥伐、过度放牧等)在一定程度上抵消了植被总面积的剧烈变化趋势。将地表过程耦合进元胞自动机模型,突破了以往该类研究只通过概率考虑状态转换,而对其机理认识的不足;同时本研究考虑了自然、人文驱动因素在不同空间尺度上作用于石漠化现象的复杂性,对于探索这些因素是如何作用于地表过程及其贡献率等研究具有一定的参考价值。     

A cellular automata model for simulating fed-batch penicillin fermentation process

A cellular automata model to simulate penicillin fed-batch fermentation process (CAPFM) was established in this study, based on a morphologically structured dynamic penicillin production model, that is in turn based on the growth mechanism of penicillin producing microorganisms and the characteristics of penicillin fed-batch fermentation. CAPFM uses the three-dimensional cellular automata as a growth space, and a Moore-type neighborhood as the cellular neighborhood. The transition rules of CAPFM are designed based on mechanical and structural kinetic models of penicillin batch-fed fermentation processes. Every cell of CAPFM represents a single or specific number of penicillin producing microorganisms, and has various state. The simulation experimental results show that CAPFM replicates the evolutionary behavior of penicillin batch-fed fermentation processes described by the structured penicillin production kinetic model accordingly. __________ Translated from ACTA BIOPHYSICA, 2005, 21(2) [译自: 生物物理学报, 2005,21(2)]     

A cellular automata model for simulating fed-batch penicillin fermentation process

A cellular automata model to simulate penicillin fed-batch fermentation process(CAPFM)was established in this study,based on a morphologically structured dynamic penicillin production model,that is in turn based on the growth mechanism of penicillin producing microorganisms and the characteristics of penicillin fed-batch fermentation.CAPFM uses the three-dimensional cellular automata as a growth space,and a Moore-type neighborhood as the cellular neighborhood.The transition roles of CAPFM are designed based on mechanical and structural kinetic models of penicillin batch-fed fermentation processes.Every cell of CAPFM represents a single or specific number of penicillin producing microorganisms,and has various state.The simulation experimental results show that CAPFM replicates the evolutionary behavior of penicillin batch-fed fermentation processes described by the structured penicillin production kinetic model accordingly.     

Using cellular automata images and pseudo amino acid composition to predict protein subcellular location

Summary. The avalanche of newly found protein sequences in the post-genomic era has motivated and challenged us to develop an automated method that can rapidly and accurately predict the localization of an uncharacterized protein in cells because the knowledge thus obtained can greatly speed up the process in finding its biological functions. However, it is very difficult to establish such a desired predictor by acquiring the key statistical information buried in a pile of extremely complicated and highly variable sequences. In this paper, based on the concept of the pseudo amino acid composition (Chou, K. C. PROTEINS: Structure, Function, and Genetics, 2001, 43: 246–255), the approach of cellular automata image is introduced to cope with this problem. Many important features, which are originally hidden in the long amino acid sequences, can be clearly displayed through their cellular automata images. One of the remarkable merits by doing so is that many image recognition tools can be straightforwardly applied to the target aimed here. High success rates were observed through the self-consistency, jackknife, and independent dataset tests, respectively.     

Asynchronous spatial evolutionary games

Over the past 50 years, much attention has been given to the Prisoner's Dilemma as a metaphor for problems surrounding the evolution and maintenance of cooperative and altruistic behavior. The bulk of this work has dealt with the successfulness and robustness of various strategies. Nowak and May (1992) considered an alternative approach to studying evolutionary games. They assumed that players were distributed across a two-dimensional (2D) lattice, interactions between players occurred locally, rather than at long range as in the well mixed situation. The resulting spatial evolutionary games display dynamics not seen in their well-mixed counterparts. An assumption underlying much of the work on spatial evolutionary games is that the state of all players is updated in unison or in synchrony. Using the framework outlined in Nowak and May (1992), we examine the effect of various asynchronous updating schemes on the dynamics of spatial evolutionary games. There are potential implications for the dynamics of a wide variety of spatially extended systems in biology, physics and chemistry.     

入侵杂草化感作用的细胞自动机模拟研究

细胞自动机模型(Cellular Automata Model,简称CA模型)是一种能够表现系统复杂行为的模拟方法,适于研究植物群落时空动态过程.本文利用CA模型,模拟具有化感作用的外来种入侵原有物种所构成植被的过程.模型由产生化感物质的外来种和两个对化感物质敏感性不同的本地种组合成不同类型的群落,利用化感物质作用下受体物种生物活性响应模型及种子扩散负指数分布模型,模拟外来杂草和本地种分布格局的时空动态变化.结果表明,外来种可成功地完全入侵由两个对化感物质敏感的本地种构成的群落空间,但对于由对化感物质敏感的一个本地种及对化感物质具有抗性的另一个本地种构成的群落,外来种只能够与本地种共存.     

基于元胞自动机的城市空间动态模拟

城市空间动态的模拟与预测可以为城市可持续发展规划与管理提供重要的参考依据。SLEUTH元胞自动机模型在城市空间模拟中较强的适用性和可移植性,该模型通过对历史数据的蒙特卡洛迭代自动寻找城市增长误差最小的参数组合,解决了传统元胞自动机模型中转换规则不易确定的问题。以武汉市为研究案例,运用SLEUTH模型进行了城市空间动态模拟与情景预测。2007年至2011年的城市空间模拟结果显示,模拟结果与实际历史数据可以获得良好的空间匹配度,Lee-Sallee形状指数均在0.6以上,显示SLEUTH元胞自动机模型经过本地化校正后具有较强的适用性和满意的模拟精度。进而,设置了现状趋势、基本保护、严格保护等3种情景对武汉2025年城市空间动态进行了预测,结果显示,各情景模式下城市居住用地均明显增长,农业用地、林地、水域等均有所减少;现状趋势情景和基本保护情景下农田、林地、水域减少的幅度较大,会加剧区域的生境破碎、耕地功能下降、水资源匮乏、湖滨湿地萎缩等生态问题,说明这两种情景不能有效满足城市生态系统健康和可持续发展的需要。严格保护情景下,城市居住用地扩张的程度得到了明显的控制,水域和林地得到了有效的保护,对于重要的自然生态系统组分保护及其服务能力维持可以起到显著作用。     

基于多智能体与元胞自动机的上海城市扩展动态模拟

利用元胞自动机模型和多智能体模型相结合的方法,在GIS技术手段的支持下构建了一个能够兼顾两种模型优点的城市扩展动态模型,并以上海市为实证对象,模拟了上海市2005年的城市扩展动态,分别预测了2010年和2020年上海城市扩展的动态演化结果。在元胞自动机模型中定义城市系统中的各种自然、社会和交通等要素,在智能体模型中定义政府和居民智能体的行为。模型将二者结合起来,模拟上海中心城区、城郊区及外围区的城市用地扩展模式。对模型模拟的上海2005年土地利用状态和实际土地利用状态进行验证,Kappa系数的平均值达到0.75以上,说明模型具有较高的可信度。对预测出2010年和2020年上海市土地利用状态分析表明,城镇用地以向东部和南部扩张最为明显。     

Computational models of signalling networks for non-linear control

Artificial signalling networks (ASNs) are a computational approach inspired by the signalling processes inside cells that decode outside environmental information. Using evolutionary algorithms to induce complex behaviours, we show how chaotic dynamics in a conservative dynamical system can be controlled. Such dynamics are of particular interest as they mimic the inherent complexity of non-linear physical systems in the real world. Considering the main biological interpretations of cellular signalling, in which complex behaviours and robust cellular responses emerge from the interaction of multiple pathways, we introduce two ASN representations: a stand-alone ASN and a coupled ASN. In particular we note how sophisticated cellular communication mechanisms can lead to effective controllers, where complicated problems can be divided into smaller and independent tasks.