大亚湾人工鱼礁附着生物的初步研究

1988年8月至1989年7月在大亚湾鱼礁区进行附着生物调查研究,记录79种生物,大约有82.7％的生物种类是鱼礁区鱼虾的饵料生物(包括35种不带壳和32种带壳的饵料生物),有17.3％的生物种类为非饵料生物。礁区附着生物种类多,附着量大,生长迅速,投礁半年后100％被生物覆盖,附着厚度30mm,附着量达17.487kg/m~2。礁区生物一年四季都能繁殖附着,主要附着期4—10月,高峰期7、8、9三个月,是投礁的最佳时间。     

Use of evolved artificial regulatory networks to simulate 3D cell differentiation

Cell differentiation has a crucial role in both artificial and natural developments. This paper presents results from simulations in which a genetic algorithm (GA) was used to evolve artificial regulatory networks (ARNs) to produce predefined 3D cellular structures through the selective activation and inhibition of genes. The ARNs used in this work are extensions of a model previously used to create 2D geometrical patterns. The GA worked by evolving the gene regulatory networks that were used to control cell reproduction, which took place in a testbed based on cellular automata (CA). After the final chromosomes were produced, a single cell in the middle of the CA lattice was allowed to replicate controlled by the ARN found by the GA, until the desired cellular structures were formed. Two simple cubic layered structures were first developed to test multiple gene synchronization. The model was then applied to the problem of generating a 3D French flag pattern using morphogenetic gradients to provide cells with positional information that constrained cellular replication.     

The incorporation of epigenetics in artificial gene regulatory networks

Artificial gene regulatory networks are computational models that draw inspiration from biological networks of gene regulation. Since their inception they have been used to infer knowledge about gene regulation and as methods of computation. These computational models have been shown to possess properties typically found in the biological world, such as robustness and self organisation. Recently, it has become apparent that epigenetic mechanisms play an important role in gene regulation. This paper describes a new model, the Artificial Epigenetic Regulatory Network (AERN) which builds upon existing models by adding an epigenetic control layer. Our results demonstrate that AERNs are more adept at controlling multiple opposing trajectories when applied to a chaos control task within a conservative dynamical system, suggesting that AERNs are an interesting area for further investigation.     

Modelling the evolution of genetic regulatory networks

An evolutionary model of genetic regulatory networks is developed, based on a model of network encoding and dynamics called the Artificial Genome (AG). This model derives a number of specific genes and their interactions from a string of (initially random) bases in an idealized manner analogous to that employed by natural DNA. The gene expression dynamics are determined by updating the gene network as if it were a simple Boolean network. The generic behaviour of the AG model is investigated in detail. In particular, we explore the characteristic network topologies generated by the model, their dynamical behaviours, and the typical variance of network connectivities and network structures. These properties are demonstrated to agree with a probabilistic analysis of the model, and the typical network structures generated by the model are shown to lie between those of random networks and scale-free networks in terms of their degree distribution. Evolutionary processes are simulated using a genetic algorithm, with selection acting on a range of properties from gene number and degree of connectivity through periodic behaviour to specific patterns of gene expression. The evolvability of increasingly complex patterns of gene expression is examined in detail. When a degree of redundancy is introduced, the average number of generations required to evolve given targets is reduced, but limits on evolution of complex gene expression patterns remain. In addition, cyclic gene expression patterns with periods that are multiples of shorter expression patterns are shown to be inherently easier to evolve than others. Constraints imposed by the template-matching nature of the AG model generate similar biases towards such expression patterns in networks in initial populations, in addition to the somewhat scale-free nature of these networks. The significance of these results on current understanding of biological evolution is discussed.     

On the evolution of scale-free topologies with a gene regulatory network model

A novel approach to generating scale-free network topologies is introduced, based on an existing artificial gene regulatory network model. From this model, different interaction networks can be extracted, based on an activation threshold. By using an evolutionary computation approach, the model is allowed to evolve, in order to reach specific network statistical measures. The results obtained show that, when the model uses a duplication and divergence initialisation, such as seen in nature, the resulting regulation networks not only are closer in topology to scale-free networks, but also require only a few evolutionary cycles to achieve a satisfactory error value.     

A novel method for measuring the thermal conductivity of organisms

Based on the theories of tissue optics and artificial neural network, the relationship between the optical properties and biological parameters was studied, and a new experimental method was derived. The properties of the organism were obtained indirectly by a black-box model derived by self-study of the artificial neural network between optical parameters and thermo-physical properties without using the heat transfer equation. In this method, the energy of light in diffuse radiation, diffuse transmission and collimated transmission was absorbed by a dual-integrating sphere experimental system of a spectrometer, and the spectrogram of the energy was obtained. Combining these spectral data of the energy, the diffuse-reflecting power, the diffuse transmissivity and the collimated transmissivity were calculated. The calculated results were taken as the input parameters of a black-box model. The experimental results show that there are apparent differences between the spectrogram of the energy on the diffuse radiation, the diffuse transmission and the collimated transmission of different matters, while there is a little difference in the same matter. Each spectrogram has its own characteristic. The values of the four thermal properties including the density, the constant pressure specific heat, the thermal diffusivity and the viscosity were calculated using the black-box model. Compared with the real values the calculated one has an average relative error between −5% and 5%. The conductivity of the tongue is 0.68 W/(m K) that calculated from the value of the density, the constant pressure specific heat and the thermal diffusivity. The results also show that there is a little difference on the conductivities in the longitudinal cross-section and the transverse section, but the effect of temperature on the conductivity of the tongue is not apparent. The difference implies the anisotropy of the properties of the organism, which cannot be easily obtained by a conventional experimental method.     

Robust stability of genetic regulatory networks with distributed delay

This paper investigates robust stability of genetic regulatory networks with distributed delay. Different from other papers, distributed delay is induced. It says that the concentration of macromolecule depends on an integral of the regulatory function of over a specified range of previous time, which is more realistic. Based on Lyapunov stability theory and linear matrix inequality (LMI), sufficient conditions for genetic regulatory networks to be global asymptotic stability and robust stability are derived in terms of LMI. Two numerical examples are given to illustrate the effectiveness of our theoretical results.     

从《生物安全议定书》审视我国基因修饰生物越境转移规则

基因修饰生物(GMO)越境转移对生物多样性保护和人类健康具有重要意义。《生物安全议定书》(议定书)分别5种情况对改性活生物体(LMO)越境转移予以不同的处理。我国行政法规和规章规定了一些GMO越境转移规则。以议定书为标准,可以观察出我国农业部和卫生部的相关规章也存在交叉和冲突,GMO越境转移规则体系尚待完善。     

Prediction of N-linked glycan branching patterns using artificial neural networks

A model was developed for novel prediction of N-linked glycan branching pattern classification for CHO-derived N-linked glycoproteins. The model consists of 30 independent recurrent neural networks and uses predicted quantities of secondary structure elements and residue solvent accessibility as an input vector. The model was designed to predict the major component of a heterogeneous mixture of CHO-derived glycoforms of a recombinant protein under normal growth conditions. Resulting glycosylation prediction is classified as either complex-type or high mannose. The incorporation of predicted quantities in the input vector allowed for theoretical mutant N-linked glycan branching predictions without initial experimental analysis of protein structures. Primary amino acid sequence data were effectively eliminated from the input vector space based on neural network prediction analyses. This provided further evidence that localized protein secondary structure elements and conformational structure may play more important roles in determining glycan branching patterns than does the primary sequence of a polypeptide. A confidence interval parameter was incorporated into the model to enable identification of false predictions. The model was further tested using published experimental results for mutants of the tissue-type plasminogen activator protein [J. Wilhelm, S.G. Lee, N.K. Kalyan, S.M. Cheng, F. Wiener, W. Pierzchala, P.P. Hung, Alterations in the domain structure of tissue-type plasminogen activator change the nature of asparagine glycosylation. Biotechnology (N.Y.) 8 (1990) 321-325].     

Optimization of a fermentation medium using neural networks and genetic algorithms

Artificial neural networks and genetic algorithms are used to model and optimize a fermentation medium for the production of the enzyme hydantoinase by Agrobacterium radiobacter. Experimental data reported in the literature were used to build two neural network models. The concentrations of four medium components served as inputs to the neural network models, and hydantoinase or cell concentration served as a single output of each model. Genetic algorithms were used to optimize the input space of the neural network models to find the optimum settings for maximum enzyme and cell production. Using this procedure, two artificial intelligence techniques have been effectively integrated to create a powerful tool for process modeling and optimization.     

Analysis of ethanol–glucose mixtures by two microbial sensors: application of chemometrics and artificial neural networks for data processing

Although biosensors based on whole microbial cells have many advantages in terms of convenience, cost and durability, a major limitation of these sensors is often their inability to distinguish between different substrates of interest. This paper demonstrates that it is possible to use sensors entirely based upon whole microbial cells to selectively measure ethanol and glucose in mixtures. Amperometric sensors were constructed using immobilized cells of either Gluconobacter oxydans or Pichia methanolica. The bacterial cells of G. oxydans were sensitive to both substrates, while the yeast cells of P. methanolica oxidized only ethanol. Using chemometric principles of polynomial approximation, data from both of these sensors were processed to provide accurate estimates of glucose and ethanol over a concentration range of 1.0–8.0 mM (coefficients of determination, R²=0.99 for ethanol and 0.98 for glucose). When data were processed using an artificial neural network, glucose and ethanol were accurately estimated over a range of 1.0–10.0 mM (R²=0.99 for both substrates). The described methodology extends the sphere of utility for microbial sensors.     

Determination of compound aminopyrine phenacetin tablets by using artificial neural networks combined with principal components analysis

A method for simultaneous, nondestructive analysis of aminopyrine and phenacetin in compound aminopyrine phenacetin tablets with different concentrations has been developed by principal component artificial neural networks (PC-ANNs) on near-infrared (NIR) spectroscopy. In PC-ANN models, the spectral data were initially analyzed by principal component analysis. Then the scores of the principal components were chosen as input nodes for the input layer instead of the spectral data. The artificial neural network models using the spectral data as input nodes were also established and compared with the PC-ANN models. Four different preprocessing methods (first-derivative, second-derivative, standard normal variate (SNV), and multiplicative scatter correction) were applied to three sets of NIR spectra of compound aminopyrine phenacetin tablets. The PC-ANNs approach with SNV preprocessing spectra was found to provide the best results. The degree of approximation was performed as the selective criterion of the optimum network parameters.     

Recognition of culture state using two-dimensional gel electrophoresis with an artificial neural network

Proteomic technologies were applied to the examination of nutrient components in culture broth. In bioprocesses, many types of media have been proposed and used on the commercial scale. Natural nutrients, the chemical components of which cannot be identified completely, are often used in fermentation processes such as in the production of baker's yeast, alcoholic beverages, amino acids, and pharmaceuticals. The catabolic activities of the microorganisms in these processes vary with the species used. We used an artificial neural network (ANN) to recognize the sufficiency of chemical elements based on the protein spots resolved in 2-DE, and we evaluated this technique using the leave-one-out method. We also attempted to reduce the number of input data for spot selection based on sensitivity analysis of the ANN, and the selected data were used to improve accuracy.     

概率布尔型基因调节网络的分解方法研究

基于Iyla Shumlevich等提出的概率布尔型网络(PBN)模型,计算网络中任意两基因之间量化了的相互影响,给出相应的有向带权图模型。通过对模型的标准化分析,找出关键节点,以各关键节点为中心,对网络划分,通过计算子网络间交互信息,确定各子网络边界,以达到对网络的最优分解。     

基于GES机制遗传算法构建基因调控网络

目的：基因调控网络在药物研发与疾病防治方面有重要的生物学意义。目前基于芯片数据构建网络的方法普遍效率不高,准确度较低,为此提出了一种新的高效调控网络结构预测算法。方法：提出了一种基于贪婪等价搜索机制的遗传算法构建基因调控网络模型。通过引入遗传算法的多点并行性,使得算法易于摆脱局部最优。通过编码网络结构作为遗传算法的染色体和设计基于GES机制的变异算子,使网络的进化过程基于马尔科夫等价空间而不是有向无环图空间。结果：通过对标准网络ASIA和酵母调控网络的预测,与近期Xue-wen Chen等提出的Order K2算法进行了比较,在网络构建准确率上获得了更佳的结果。与标准遗传算法比较下在执行效率上大大提高。结论：提出的算法在网络结构预测准确率上相对于最近提出的Order K2算法在准确率上效果更佳,并且相较标准遗传算法网络在进化过程上效率更高。