Designing convergent cellular automata

Cellular automata (CA) have been used by biologists to study dynamic non-linear systems where the interaction between cell behaviour and end-pattern is investigated. It is difficult to achieve convergence of a CA towards a specific static pattern and a common solution is to use genetic algorithms and evolve a ruleset that describes cell behaviour. This paper presents an alternative means of designing CA to converge to specific static patterns. A matrix model is introduced and analysed then a design algorithm is demonstrated. The algorithm is significantly less computationally intensive than equivalent evolutionary algorithms, and not limited in scale, complexity or number of dimensions.     

Therapeutic potential of RNA interference against cellular targets of HIV infection

RNA interference is not only very promising in identifying new targets for drug development, siRNA/shRNA themselves may be directly used as therapeutic agents. In inhibiting viral infections by RNA interference, both viral targets and cellular proteins have been evaluated. Most of the early studies in this field had chosen viral targets for RNA interference. However, recent efforts are mainly focusing on cellular proteins for RNA silencing due to the realization that a variety of viral responses substantially minimize siRNA effects. With the application of siRNA approaching, many new cellular targets relevant to HIV infection have been identified. The value of siRNA/shRNA in the treatment of AIDS is largely dependent on better understanding of the biology of HIV replication. Efforts in the identification of cellular processes with the employment of siRNA/shRNA have shed some new lights on our understanding of how HIV infection occurs. Furthermore, the relative specific effects and simplicity of design makes siRNA/shRNA themselves to be favorable drug leads. J. Zhang and Y. O. Wu contributed equally to this article.     

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.     

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.     

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.     

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.     

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.     

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.     

A hybrid systems framework for cellular processes

With the availability of technologies that allow us to obtain stimulus-response time series data for modeling and system identification, there is going to be an increasing need for conceptual frameworks in which to formulate and test hypotheses about intra- and inter-cellular dynamics, in general and not just dependent on a particular cell line, cell type, organism, or technology. While the semantics can be quite different, biologists and systems scientists use in many cases a similar language (notion of feedback, regulation, etc.). A more abstract system-theoretic framework for signals, systems, and control could provide the biologist with an interface between the domains. Apart from recent examples to identify functional elements and describing them in engineering terms, there have been various more abstract developments to describe dynamics at the cell level in the past. This includes Rosen's (M,R)-systems. This paper presents an abstract and general compact mathematical framework of intracellular dynamics, regulation and regime switching inspired by (M,R)-theory and based on hybrid automata.     

具免疫时滞的HIV感染模型动力学性质分析

讨论了一类具免疫时滞的HIV感染模型.分析了未感染平衡点的全局渐近稳定性,给出了感染无免疫平衡点及感染免疫平衡点局部渐近稳定的充分条件.数值模拟结果表明,当易感细胞生成率的取值使得基本再生数满足平衡存在的条件且低于某一临界值时,时滞对平衡点的稳定性没有影响;若大于该临界值,随着时滞增大,稳定性开关发生,平衡点不稳定,出现一系列Hopf分支,最终表现为周期波动模式.     

The probability of HIV infection in a new host and its reduction with microbicides

We use a simple mathematical model to estimate the probability and its time dependence that one or more HIV virions successfully infect target cells. For the transfer of a given number of virions to target cells we derive expressions for the probability P(inf), of infection. Thus, in the case of needlestick transfer we determine P(inf) and an approximate time window for post-exposure prophylaxis (PEP). For heterosexual transmission, where the transfer process is more complicated, a parameter gamma is employed which measures the strength of the infection process. For the smaller value of gamma, P(inf) is from 6x10(-5) to 0.93 or from 7.82x10(-6) to 0.29, where the lower figures are for the transfer of 100 virions and the upper figures are for the transfer of 4.4 million virions. We estimate the reductions in P(inf) which occur with a microbicide of a given efficacy. It is found that reductions may be approximately as stated when the number of virions transferred is less than about 10(5), but declines to zero for viral loads above that number. It is concluded that PEP should always be applied immediately after a needlestick incident. Further, manufacturers of microbicides should be encouraged to investigate and report their effectiveness at various transferred viral burdens.     

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.     

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.     

Association of interleukin‐27 gene polymorphisms with susceptibility to HIV infection and disease progression

Interleukin‐27 (IL‐27) gene polymorphisms are linked to infectious disease susceptibility and IL‐27 plasma level is associated with HIV infection. Therefore, we aimed to investigate the association between IL‐27 polymorphisms and susceptibility to HIV infection and disease progression. A total of 300 patients with HIV infection (48 long‐term nonprogressors and 252 typical progressors) and 300 healthy controls were genotyped for three IL‐27 polymorphisms, rs17855750, rs181206, rs40837 which were performed by using multiple single nucleotide primer extension technique. Significant association was found between IL‐27 rs40837 polymorphisms with susceptibility to HIV infection (AG vs AA: adjusted OR = 1.60, 95% CI, 1.11‐2.30, P = 0.012; AG+GG vs AA: adjusted OR = 1.44, 95% CI, 1.02‐2.03, P = 0.038) and disease progression (LTNP: AG vs AA: adjusted OR = 2.33, 95% CI, 1.13‐4.80, P = 0.021; TP: AG vs AA: adjusted OR = 1.50, 95% CI, 1.04‐2.24, P = 0.030). Serum IL‐27 levels were significantly lower in cases compared to controls (P < 0.001). There were lower serum IL‐27 levels in TPs than in LTNPs (P < 0.001). We further found that LTNPs with rs40837 AG or GG genotype had lower serum IL‐27 levels than with AA genotype (P < 0.05). The CD4⁺T counts in cases were significantly lower than controls (P < 0.001). In contrast, individuals with rs40837 AG genotype had lower CD4⁺T counts than with AA genotype in cases (P < 0.05). In addition, CD4⁺T counts in TPs were significantly lower than LTNPs (P < 0.001). IL‐27 rs40837 polymorphism might influence the susceptibility to HIV infection and disease progression probably by regulating the level of serum IL‐27 or the quantity of CD4⁺T.     

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.     

Impact of gene-modified T cells on HIV infection dynamics

Previous clinical trials in HIV-infected patients involving infusion of T cells protected by an antiviral gene have failed to show any therapeutic benefit. The value of such a treatment approach is thus still highly controversial. In this study, the anticipated effects of gene-modified cells on virus and T-cell kinetics are analysed by mathematical modeling. Because technically only a small fraction of all T cells in a patient can be manipulated ex vivo, therapeutic success will depend on the accumulation of gene-modified cells after infusion into the patient by in vivo selection. Our simulations predict that a significant therapeutic benefit is conferred only by antiviral genes that inhibit HIV replication before virus integration (class I genes). Genes that inhibit viral protein expression (class II, used in previous clinical trials), require a much higher inhibitory activity than class I genes to promote the regeneration of T cells and reduce the viral load. Inhibition of virus assembly and release alone (class III) confers no selective advantage to the T cell and is therefore ineffective unless combined with class I (or, possibly, class II) genes. Also crucial in determining the clinical outcome are the regenerative capacity of the gene-modified cells and the level of HIV replication in the patient. These results can be important for guiding future strategies in the field of gene therapy for HIV infection.