On a logical paradox implicit in the notion of a self-reproducing automation

The notion of automaton as used by J. von Neumann is formalized according to methods previously described (Rosen, 1958,Bull. Math. Biophysics 20, 245–60; 317–41). It is observed that a logical paradox arises when one attempts to describe the notion of self-reproducing automaton in this formalism. This paradox is discussed, together with some of the recent attempts to construct automata which exhibit self-reproduction. The relation of these results to biological problems is then investigated.     

Efficient Deterministic Finite Automata Minimization Based on Backward Depth Information

Obtaining a minimal automaton is a fundamental issue in the theory and practical implementation of deterministic finite automatons (DFAs). A minimization algorithm is presented in this paper that consists of two main phases. In the first phase, the backward depth information is built, and the state set of the DFA is partitioned into many blocks. In the second phase, the state set is refined using a hash table. The minimization algorithm has a lower time complexity O(n) than a naive comparison of transitions O(n²). Few states need to be refined by the hash table, because most states have been partitioned by the backward depth information in the coarse partition. This method achieves greater generality than previous methods because building the backward depth information is independent of the topological complexity of the DFA. The proposed algorithm can be applied not only to the minimization of acyclic automata or simple cyclic automata, but also to automata with high topological complexity. Overall, the proposal has three advantages: lower time complexity, greater generality, and scalability. A comparison to Hopcroft’s algorithm demonstrates experimentally that the algorithm runs faster than traditional algorithms.     

A cellular automaton model of morphogenesis in Arabidopsis thaliana

Development of organisms is a very complex process in that a lot of gene networks of different cell types are to be integrated. Development of cellular automata that model the morphodynamics of different cell types is the first step in understanding and analyzing the regulatory mechanisms that underlie the developmental gene networks. We have developed a model of a cellular automaton that simulates the embryonic development of the shoot meristem in Arabidopsis thaliana. The model adequately describes the basic stages in the development of this organ in wild type and mutants.

     

Stochastic automaton models for the temporal pattern discrimination of nerve impulse sequences

The application of stochastic automata to the input-output relations of single neurons is considered. For this, some stochastic properties of temporal pattern discrimination in single synaptic cells are used to suggest stochastic automaton models. The models have only three possible states, the active, the absolute refractory and the relative refractory states, which are sufficient for temporal pattern sensitivity. From such an application, it was found that the temporal pattern discriminating structures in the models are similar to those used for experimental data and computer simulation (real-time neuron models). Extensions related to temporal pattern learning are discussed.     

Probabilistic automata as a model for epigenesis of cellular networks

Probabilistic automata are compared with deterministic ones in simulations of growing networks made of dividing interconnected cells. On examples of chains, wheels and tree-like structures made of large numbers of cells it is shown that the number of necessary states in the initial generating cell automaton is reduced drastically when the automaton is probabilistic rather than deterministic. Since the price being paid is a decrease in the accuracy of the generated network, conditions under which reasonable compromises can be achieved are studied. They depend on the degree of redundancy of the final network (defined from the complexity of a deterministic automaton capable of generating it with maximum accuracy), on the "entropy" of the generating probabilistic automaton, and on the effects of different inputs on its transition probabilities (as measured by its "'capacity" in the sense of Shannon's information theory). The results are used to discuss and make more precise the notion of biological specificity. It is suggested that the weak metaphor of a genetic program, classically used to account for the role of DNA in specific genetic determinations, is replaced by that of inputs to biochemical probabilistic automata.     

A cellular automaton model of morphogenesis in <Emphasis Type="Italic">Arabidopsis thaliana</Emphasis>

Development of organisms is a very complex process in that a lot of gene networks of different cell types are to be integrated. Development of cellular automata that model the morphodynamics of different cell types is the first step in understanding and analyzing the regulatory mechanisms that underlie the developmental gene networks. We have developed a model of a cellular automaton that simulates the embryonic development of the shoot meristem in Arabidopsis thaliana. The model adequately describes the basic stages in the development of this organ in wild type and mutants.     

Facilitating arrhythmia simulation: the method of quantitative cellular automata modeling and parallel running

Background

Many arrhythmias are triggered by abnormal electrical activity at the ionic channel and cell level, and then evolve spatio-temporally within the heart. To understand arrhythmias better and to diagnose them more precisely by their ECG waveforms, a whole-heart model is required to explore the association between the massively parallel activities at the channel/cell level and the integrative electrophysiological phenomena at organ level.

Methods

We have developed a method to build large-scale electrophysiological models by using extended cellular automata, and to run such models on a cluster of shared memory machines. We describe here the method, including the extension of a language-based cellular automaton to implement quantitative computing, the building of a whole-heart model with Visible Human Project data, the parallelization of the model on a cluster of shared memory computers with OpenMP and MPI hybrid programming, and a simulation algorithm that links cellular activity with the ECG.

Results

We demonstrate that electrical activities at channel, cell, and organ levels can be traced and captured conveniently in our extended cellular automaton system. Examples of some ECG waveforms simulated with a 2-D slice are given to support the ECG simulation algorithm. A performance evaluation of the 3-D model on a four-node cluster is also given.

Conclusions

Quantitative multicellular modeling with extended cellular automata is a highly efficient and widely applicable method to weave experimental data at different levels into computational models. This process can be used to investigate complex and collective biological activities that can be described neither by their governing differentiation equations nor by discrete parallel computation. Transparent cluster computing is a convenient and effective method to make time-consuming simulation feasible. Arrhythmias, as a typical case, can be effectively simulated with the methods described.

     

Inheritance of resistance to potyviruses in Phaseolus vulgaris L. IV. Inheritance, linkage relations, and environmental effects on systemic resistance to four potyviruses

We have examined the genetics of systemic resistance in Phaseolus vulgaris to azuki bean mosaic virus (AzMV) and cowpea aphid-borne mosaic virus (CABMV) and the relationship of this resistance to a phenotypically similar resistance to watermelon mosaic virus (WMV) and soybean mosaic virus (SMV). In P. vulgaris cv Great Northern 1140 (GN1140), resistance to SMV and WMV has been attributed to the genes Smv and Wmv, respectively, which have been shown to segregate as a unit. Systemic resistance to AzMV is conferred by two incompletely dominant alleles, Azm1 and Azm2, at unlinked loci. At least three resistance alleles must be present at these two loci for systemic resistance to be expressed in the plant. Systemic resistance to CABMV in GN 1140 is conditioned by a dominant allele that has been designated Cam2. Under some environmental conditions, a recessive allele at an unlinked locus, cam3, also controls a resistant response to CABMV. Resistance to AzMV and CABMV does not assort independently from Wmv/Smv, but also does not consistently cosegregate, suggesting that perhaps in each case one of the factors involved in resistance is associated with Smv/Wmv.     

Inheritance of resistance to potyviruses in Phaseolus vulgaris L. III. Cosegregation of phenotypically similar dominant responses to nine potyviruses

We have identified monogenic dominant resistance to azuki bean mosaic poty virus (AzMV), passionfruit woodiness potyvirus-K (PWV-K), zucchini yellow mosaic potyvirus (ZYMV), and a dominant factor that conditioned lethal necrosis to Thailand Passiflora potyvirus (ThPV), in Phaseolus vulgaris Black Turtle Soup 1. Resistance to AzMV, PWV-K, ZYMV, watermelon mosaic potyvirus, cowpea aphid-borne mosaic potyvirus, blackeye cowpea mosaic potyvirus, and lethal necrosis to soybean mosaic potyvirus and ThPV cosegregated as a unit with the I gene for resistance to bean common mosaic potyvirus.     

Predictability in Cellular Automata

Modelled as finite homogeneous Markov chains, probabilistic cellular automata with local transition probabilities in (0, 1) always posses a stationary distribution. This result alone is not very helpful when it comes to predicting the final configuration; one needs also a formula connecting the probabilities in the stationary distribution to some intrinsic feature of the lattice configuration. Previous results on the asynchronous cellular automata have showed that such feature really exists. It is the number of zero-one borders within the automaton''s binary configuration. An exponential formula in the number of zero-one borders has been proved for the 1-D, 2-D and 3-D asynchronous automata with neighborhood three, five and seven, respectively. We perform computer experiments on a synchronous cellular automaton to check whether the empirical distribution obeys also that theoretical formula. The numerical results indicate a perfect fit for neighbourhood three and five, which opens the way for a rigorous proof of the formula in this new, synchronous case.     

Optimization and control of a continuous stirred tank fermenter using learning system

A variable structure learning automaton is used as an optimization and control of a continuous stirred tank fermenter. The algorithm requires no modelling of the process. The use of appropriate learning rules enables to locate the optimum dilution rate in order to maximize an objective cost function. It is shown that a hierarchical structure of automata can adapt to environmental changes and can also modify efficiently the domain of variation of the control variable in order to encompass the optimum value.List of Symbols f Random number - F Dimensionless flow rate (F/V ₀) - F m³/h Flow rate - F ₀ m³/h Inlet flow rate - J Objective function - K _i Dimensionless constant in Eq. (3) (k _i/s₀) - k _i · kg/m³ Substrate inhibition constant in Haldane model - K _m Dimensionless constant in equation (3) (k _s/s₀) - k _m kg/m³ Substrate inhibition constant in Haldane model - L Number of levels of the hierarchical system of automata - N Number of possible control actions - p Probability - S Dimensionless substrate concentration (s/s ₀) - s kg/m³ Substrate concentration - T Dimensionless sampling period - t h Time - v Dimensionless volume (V/V ₀) - V m³ Liquid volume in fermenter - W Input to the stochastic automaton - X Dimensionless biomass concentration - x kg/m³ Biomass concentration - Y Biomass/substrate yield coefficient - Weighting factor in Eq. (4) - Dimensionless specific growth rate (/ ^*) - ^* h^–1 Maximum specific growth rate - h^–1 Specific growth rate - Dimensionless time ( t)     

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.     

Very Simple Models, the Self-Modifying Automata and Chain of Self-Modifying Automata, Can Explain Self-Referential Properties of Living Beings

Very often, living beings seem able to change their functioning when external conditions vary. In order to study this property, we have devised abstract machines whose internal organisation changes whenever the external conditions vary. The internal organisations of these machines (or programs), are as simple as possible, functions of discrete variables. We call such machines self-modifying automata.These machines stabilise after any transient steps when they go indefinitely through a loop called p-cycle or limit cycle of length p. More often than not, the p in the cycle is equal to one and the cycle reduces to a fixed point.In this case the external value (v) can be considered as the index of function f such as: f_v(v)^–v and the machine has the property of self-replication and to be self-referential. Many authors, in computer and natural science, consider that self-referential objects are a main concept in comprehension of perception, behaviour and associations.In the third part, we have studied chains of automata. Only one automaton changes its internal organisation at each step. Chains of automata have better performances than single self-modifying automata: Higher frequency of fixed point occurrence and a shorter transient length. The performances of the chains of automata improve when the value of their internal states increases whereas the performances of single automata decrease.     

Germline and somatic mosaicism in a female carrier of Duchenne muscular dystrophy

The family of a male with Duchenne muscular dystrophy (DMD) and a deletion within the dystrophin gene has been studied. Polymerase chain reaction analysis of ectopic mRNA from peripheral blood T+B lymphocytes and the use of (CA) _n repeat polymorphisms in and around the deleted region showed the proband's mother to be both a germline mosaic and a somatic mosaic for the deletion seen in her son. The mutation therefore occurred as a mitotic event early in embryogenesis.     

First report on embryonic and larval development of 2n/3n mosaic sterlet

Mosaicism is frequently observed in aquaculture practices, and it adversely affects the production as well as the restoration programme of the sturgeon. The purpose of the present study was the induction of 2n/3n mosaic in sterlet, Acipenser ruthenus L., and compare their embryonic and larval development with diploid control sterlet. Microsatellite DNA loci genotyping was conducted for the identification of the genotypes and parentage analysis. Embryonic development was monitored in experimental groups at every 24 h interval. Identification of individual stages of embryonic development was recorded based on a 36-degree scale of development. Additionally, the BW and body length (LT) of experimental fishes were taken during 110 days of the rearing period. The Fulton’s condition coefficient (F), length-weight parameters, and specific growth rate (SGR) coefficient were calculated. The analysis of embryonic development of the 2n/3n mosaic and the diploid control group did not show differences. However, higher mortality (88%) was observed in 2n/3n mosaic groups in comparison to the diploid control groups (55%). BW and body length of 2n/3n mosaic sterlet were slightly lower than the diploid control sterlet, but the differences were not statistically significant. F analysis did not confirm a lower growth performance of the fishes in the 2n/3n mosaic group. Microsatellite DNA loci genotyping confirmed both the incidence of polyspermy and retention of the second polar body. This paper presents the first report on embryonic development and growth performance of 2n/3n mosaic sturgeons.     

Occurrence and distribution of potyviruses infecting sorghum in Kaduna and Kano States,Nigeria

Field surveys were conducted during the 2012 wet season in five Local Government Areas (LGAs) each of Kaduna and Kano States, Nigeria, to determine the incidence of potyviruses infecting sorghum. Sorghum leaves (n = 450) showing mosaic, streak, dwarfism and asymptomatic ones were collected from 30 fields. Based on the fields, the virus disease incidence in Kaduna State was 19.3, 17.1, 53.7, 35.4 and 32.7% for Sabon Gari, Giwa, Lere, Kubau and Makarfi LGAs, respectively. In Kano State, the incidence was 13.3, 32.9, 28.9, 34.3 and 50.9% for Tsanyawa, Minjibir, Wudil, Sumaila and Garun Malam LGAs, respectively. Enzyme-linked immunosorbent assay (ELISA) was employed in the detection of the viruses. There was overall incidence of Sorghum mosaic virus 2.8%, Maize dwarf mosaic virus 2.0%, Sugarcane mosaic virus 3.3% and unidentified potyvirus 4.2% from the two states.     

Optimal amnesic probabilistic automata or how to learn and classify proteins in linear time and space.

Statistical modeling of sequences is a central paradigm of machine learning that finds multiple uses in computational molecular biology and many other domains. The probabilistic automata typically built in these contexts are subtended by uniform, fixed-memory Markov models. In practice, such automata tend to be unnecessarily bulky and computationally imposing both during their synthesis and use. Recently, D. Ron, Y. Singer, and N. Tishby built much more compact, tree-shaped variants of probabilistic automata under the assumption of an underlying Markov process of variable memory length. These variants, called Probabilistic Suffix Trees (PSTs) were subsequently adapted by G. Bejerano and G. Yona and applied successfully to learning and prediction of protein families. The process of learning the automaton from a given training set S of sequences requires theta(Ln2) worst-case time, where n is the total length of the sequences in S and L is the length of a longest substring of S to be considered for a candidate state in the automaton. Once the automaton is built, predicting the likelihood of a query sequence of m characters may cost time theta(m2) in the worst case. The main contribution of this paper is to introduce automata equivalent to PSTs but having the following properties: Learning the automaton, for any L, takes O (n) time. Prediction of a string of m symbols by the automaton takes O (m) time. Along the way, the paper presents an evolving learning scheme and addresses notions of empirical probability and related efficient computation, which is a by-product possibly of more general interest.     

Virus diseases of lupins

• I. INTRODUCTION
• II. APHID-TRANSMITTED VIRUSES

• (a) Potyvirus group

• (i) Bean yellow mosaic virus
• (ii) Clover yellow vein virus
• (iii) Bean common mosaic virus
• (iv) Peanut mottle virus
• (v) Bidens mottle virus

• (b) Cucumovirus group

• (i) Cucumber mosaic virus
• (ii) Peanut stunt virus

• (c) Alfalfa mosaic virus
• (d) Fabavirus group: broad bean wilt virus
• (e) Pea enation mosaic virus
• (f) Luteovirus group: soybean dwarf virus
• (g) Rhabdovirus group: lettuce necrotic yellows virus

• III. THRIPS-TRANSMITTED VIRUSES
• Tomato spotted wilt virus

• IV. NEMATODE-TRANSMITTED VIRUSES

• (a) Tobravirus group: pea early browning virus
• (b) Nepovirus group: tomato black ring virus

• V. VIRUS-LIKE DISEASES

• (a) Lupin leaf curl ‘virus’
• (b) ‘Lupin witches’ broom disease

• VI. CONTROL VII. CONCLUSIONS
• VII. CONCLUSIONS

     

A reporter gene under the control of tms or aux promoters is differentially expressed in tobacco and barley protoplasts

Summary Agrobacterium tumefaciens and some Agrobacterium rhizogenes strains possess auxin biosynthesis genes (tms and aux genes respectively), responsible for a de novo auxin biosynthetic pathway in transformed plant cells. A comparison is presented of the potential expression of these genes in a monocotyledonous (barley) and a dicotyledonous plant (tobacco). The promoters of the genes were translationally fused to the -glucuronidase reporter gene and analysed in transient expression experiments. The tms and aux fusions were highly expressed in tobacco, but not in barley. However, the aux enhancer active in tobacco, conferred low -glucuronidase expression in barley when fused to a truncated cauliflower mosaic virus 35S promoter. The results are discussed in relation to the differential responses to Agrobacterium infection in monocots and dicots.Abbreviations CaMV cauliflower mosaic virus - PCR polymerase chain reaction - PEG polyethylene glycol - Mu 4-methyl umbelliferone