Computing a Predictor Set Influence Zone through a Multi-Layer Genetic Network to Explore the Role of Estrogen in Breast Cancer

Modeling inter-relationships of genes over a specific genetic network is one of the most challenging studies in systems biology. Among the families of models proposed one commonly used is the discrete stochastic, based on conditionally independent Markov chains. In practice, this model is estimated from time sequential sampling, usually obtained by microarray experiments. In order to improve the accuracy of the estimation method, we can use biological knowledge. In this paper, we decided to apply this idea to study the role of estrogen in breast cancer proliferation. The n-influence zone of a set S of genes in a given multi-layer genetic network is a set L of genes regulated, directly or indirectly, by genes in S, after at most n-1 layers. In this manuscript we describe a new approach for computing the n-influence zone of S through the estimation of a multi-layer genetic network from gene expression time series, measured by microarrays, and biological knowledge. Using seed genes related to cell proliferation, our method was able to add to the third layer of the network other genes related to this biological function and validated in the literature. Using a set of genes directly influenced by estrogen, we could find a new role for cell adhesion genes estrogen dependent. Our pipeline is user-friendly and does not have high system requirements. We believe this paper could contribute to improve the data mining for biologists in microarray time series.

Stochastic Noise in Auto-regulatory Genetic Network:Model-dependence and Statistical Complication

For the single gene network model, there are two basic types. For convenience, we call them Type I and Type II, respectively. The Type I model describes both the dynamics of mRNA and protein. The Type II model is a simplification of the Type I model based on the assumption that the change rate of mRNA is much faster than protein because the half-life of mRNA is short compared with that of protein, the Type II model describes only the dynamics of protein. The analysis of the Type I model is based on the assumption that the ratio of the protein decay rate to the mRNA decay rate is small enough. The main results for Type I model show that the Fano factor of the protein must be bigger than one if there is no negative feedback on the transcription. If there is negative feedback, the relative fluctuation strength in the number of proteins is determined by the size of the feedback regulation strength. For the Type II model, the Fano factor of the protein depends on the effect of the feedback regulation on the translation, i.e., the Fano factor equals one if there is no feedback, and is less than one （or bigger than one） if there is negative feedback （or positive feedback）. These results show clearly that the analysis of the steady-state statistical properties of single gene network is model-dependent.

Dissection of genetic network underlying important agronomic traits accelerates modern breeding in soybean

With the support by the National Natural Science Foundation of China and the'Strategic Priority Research Program'of the Chinese Academy of Sciences,a collaborative study by the research groups led by Professors Tian Zhixi(田志喜),Wang Guodong(王国栋),and Zhu Baoge(朱保葛)from the

Numeral eddy current sensor modelling based on genetic neural network

This paper presents a method used to the numeral eddy current sensor modelling based on the genetic neural network to settle its nonlinear problem. The principle and algorithms of genetic neural network are introduced. In this method, the nonlinear model parameters of the numeral eddy current sensor are optimized by genetic neural network （GNN） according to measurement data. So the method remains both the global searching ability of genetic algorithm and the good local searching ability of neural network. The nonlinear model has the advantages of strong robustness, on-line modelling and high precision. The maximum nonlinearity error can be reduced to 0.037% by using GNN. However, the maximum nonlinearity error is 0.075% using the least square method.

Oscillatory and anti-oscillatory motifs in genetic regulatory networks

Recently, self-sustained oscillatory genetic regulatory networks （GRNs） have attracted significant attention in the biological field. Given a GRN, it is important to anticipate whether the network could generate oscillation with proper parameters, and what the key ingredients for the oscillation are. In this paper the ranges of some function-related parameters which are favorable to sustained oscillations are considered. In particular, some oscillatory motifs appearing with high-frequency in most of the oscillatory GRNs are observed. Moreover, there are some anti-oscillatory motifs which have a strong oscillation repressing effect. Some conclusions analyzing these motif effects and constructing oscillatory GRNs are provided.

Fuzzy Optimization of an Elevator Mechanism Applying the Genetic Algorithm and Neural Networks

Considering the indefinite character of the value of design parameters and being satisfied with load-bearing capacity and stiffness, the fuzzy optimization mathematical model is set up to minimize the volume of tooth corona of a worm gear in an elevator mechanism. The method of second-class comprehensive evaluation was used based on the optimal level cut set, thus the optimal level value of every fuzzy constraint can be attained; the fuzzy optimization is transformed into the usual optimization. The Fast Back Propagation of the neural networks algorithm are adopted to train feed-forward networks so as to fit a relative coefficient. Then the fitness function with penalty terms is built by a penalty strategy, a neural networks program is recalled, and solver functions of the Genetic Algorithm Toolbox of Matlab software are adopted to solve the optimization model.

A New Modeling Method Based on Genetic Neural Network for Numeral Eddy Current Sensor

In this paper,we present a method used to the numeral eddy current sensor modeling based on genetic neural network to settle its nonlinear problem.The principle and algorithms of genetic neural network are introduced.In this method, the nonlinear model parameters of the numeral eddy current sensor are optimized by genetic neural network (GNN) according to measurement data.So the method remains both the global searching ability of genetic algorithm and the good local searching ability of neural network.The nonlinear model has the advantages of strong robustness,on-line scaling and high precision.The maximum nonlinearity error can be reduced to 0.037% using GNN.However,the maximum nonlinearity error is 0.075% using least square method (LMS).

Dynamics of network motifs in genetic regulatory networks

Network motifs hold a very important status in genetic regulatory networks. This paper aims to analyse the dynamical property of the network motifs in genetic regulatory networks. The main result we obtained is that the dynamical property of a single motif is very simple with only an asymptotically stable equilibrium point, but the combination of several motifs can make more complicated dynamical properties emerge such as limit cycles. The above-mentioned result shows that network motif is a stable substructure in genetic regulatory networks while their combinations make the genetic regulatory network more complicated.

Stability of piecewise-linear models of genetic regulatory networks

This paper investigates the stability of the equilibria of the piecewise-linear models of genetic regulatory networks on the intersection of the thresholds of all variables. It first studies circling trajectories and derives some stability conditions by quantitative analysis in the state transition graph. Then it proposes a common Lyapunov function for convergence analysis of the piecewise-linear models and gives a simple sign condition. All the obtained conditions are only related to the constant terms on the right-hand side of the differential equation after bringing the equilibrium to zero.

Designing Genetic Regulatory Networks Using Fuzzy Petri Nets Approach

In this paper, we have successfully presented a fuzzy Petri net （FPN） model to design the genetic regulatory network. Based on the FPN model, an efficient algorithm is proposed to automatically reason about imprecise and fuzzy information. By using the reasoning algorithm for the FPN, we present an alternative approach that is more promising than the fuzzy logic. The proposed FPN approach offers more flexible reasoning capability because it is able to obtain results with fuzzy intervals rather than point values. In this paper, a novel model with a new concept of hidden fuzzy transition （HFT） to design the genetic regulatory network is developed. We have built the FPN model and classified the input data in terms of time point and obtained the output data, so the system can be viewed as the two-input and one output system. This method eliminates possible false predictions from the classical fuzzy model thereby allowing a wider search space for inferring regulatory relationship. The experimental results show the proposed approach is feasible and acceptable to design the genetic regulatory network and investigate the dynamical behaviors of gene network.

Structure and Dynamics of Artificial Regulatory Networks Evolved by Segmental Duplication and Divergence Model

Based on a model of network encoding and dynamics called the artificial genome, we propose a segmental duplication and divergence model for evolving artificial regulatory networks. We find that this class of networks share structural properties with natural transcriptional regulatory networks. Specifically, these networks can display scale-free and small-world structures. We also find that these networks have a higher probability to operate in the ordered regimen, and a lower probability to operate in the chaotic regimen. That is, the dynamics of these networks is similar to that of natural networks. The results show that the structure and dynamics inherent in natural networks may be in part due to their method of generation rather than being exclusively shaped by subsequent evolution under natural selection.

GA-BASED PID NEURAL NETWORK CONTROL FOR MAGNETIC BEARING SYSTEMS

In order to overcome the system non-linearity and uncertainty inherent in magnetic bearing systems, a GA（genetic algnrithm）-based PID neural network controller is designed and trained tO emulate the operation of a complete system （magnetic bearing, controller, and power amplifiers）. The feasibility of using a neural network to control nonlinear magnetic bearing systems with unknown dynamics is demonstrated. The key concept of the control scheme is to use GA to evaluate the candidate solutions （chromosomes）, increase the generalization ability of PID neural network and avoid suffering from the local minima problem in network learning due to the use of gradient descent learning method. The simulation results show that the proposed architecture provides well robust performance and better reinforcement learning capability in controlling magnetic bearing systems.

Hydraulic Optimization of a Double-channel Pump's Impeller Based on Multi-objective Genetic Algorithm

Computational fluid dynamics（CFD） can give a lot of potentially very useful information for hydraulic optimization design of pumps, however, it cannot directly state what kind of modification should be made to improve such hydrodynamic performance. In this paper, a more convenient and effective approach is proposed by combined using of CFD, multi-objective genetic algorithm（MOGA） and artificial neural networks（ANN） for a double-channel pump＇s impeller, with maximum head and efficiency set as optimization objectives, four key geometrical parameters including inlet diameter, outlet diameter, exit width and midline wrap angle chosen as optimization parameters. Firstly, a multi-fidelity fitness assignment system in which fitness of impellers serving as training and comparison samples for ANN is evaluated by CFD, meanwhile fitness of impellers generated by MOGA is evaluated by ANN, is established and dramatically reduces the computational expense. Then, a modified MOGA optimization process, in which selection is performed independently in two sub-populations according to two optimization objectives, crossover and mutation is performed afterword in the merged population, is developed to ensure the global optimal solution to be found. Finally, Pareto optimal frontier is found after 500 steps of iterations, and two optimal design schemes are chosen according to the design requirements. The preliminary and optimal design schemes are compared, and the comparing results show that hydraulic performances of both pumps 1 and 2 are improved, with the head and efficiency of pump 1 increased by 5.7% and 5.2%, respectively in the design working conditions, meanwhile shaft power decreased in all working conditions, the head and efficiency of pump 2 increased by 11.7% and 5.9%, respectively while shaft power increased by 5.5%. Inner flow field analyses also show that the backflow phenomenon significantly diminishes at the entrance of the optimal impellers 1 and 2, both the area of vortex and intensity of vortex decreases in the whole flow channel. This paper provides a promising tool to solve the hydraulic optimization problem of pumps＇ impellers.

Improvement of a Genetic Back Propagation Algorithm and Its Application to Diagnosis in Mechanical Failure

A new improved genetic BP algorithm was put forward in the paper. To determine whether the network falls into local minimum point, a discriminant of local minimum was put forth in the training process of a neural network. A genetic algorithm was used to revise the weights of the neural network if the BP algorithm fell into minimums. The mechanical faults were diagnosed using the algorithm put forward in the paper, which verified the validity of this improved genetic BP algorithm.

Bifurcation and Turing instability for genetic regulatory networks with diffusion

In this paper,a diffusive genetic regulatory network under Neumann boundary conditions is considered.First,the criteria for the local stability and diffusion-driven instability of the positive stationary solution without and with diffusion are investigated,respectively.Moreover,Turing regions and pattern formation are obtained in the plane of diffusion coeficients.Second,the existence and multiplicity of spatially homogeneous/nonhomogeneous non-constant steady-states are studied by using the Lyapunov-Schmidt reduction.Finally,some numerical simulations are carried out to illustrate the theoretical results.

Understanding the genetic and epigenetic architecture in complex network of rice flowering pathways

Although the molecular basis of flowering time control is well dissected in the long day （LD） plant Arabidopsis, it is still largely unknown in the short day （SD） plant rice. Rice flowering time （heading date） is an important agronomic trait for season adaption and grain yield, which is affected by both genetic and environmental factors. During the last decade, as the nature of florigen was identified, notable progress has been made on exploration how florigen gene ,expression is genetically controlled. In Arabidopsis expression of certain key flowering integrators such as FLOWERING LOCUS C （FLC） and FLOWERING LOCUS T （FT） are also epige- netically regulated by various chromatin modifications, however, very little is known in rice on this aspect until very recently. This review summarized the advances of both genetic networks and chromatin modifications in rice flowering time control, attempting to give a complete view of the genetic and epigenetic architecture in complex network of rice flowering pathways.

From genes to networks: The genetic control of leaf development

Substantial diversity exists for both the size andshape of the leaf,the main photosynthetic organofflowering plants.The two major forms of leaf aresimple leaves,in which the leaf blade is undivided,and compound leaves,which comprise severalleaflets.Leaves form at the shoot apical meristemfrom a group of undifferentiated cells,whichfirstestablish polarity,then grow and differentiate.Each of these processes is controlled by a com-bination of transcriptional regulators,microRNAsand phytohormones.The present review docu-ments recent advances in our understanding ofhow these various factors modulate the develop-ment of both simple leaves(focusing mainly on themodel plantArabidopsis thaliana)and compoundleaves(focusing mainly on the model legumespeciesMedicago truncatula).

我国社区卫生人力资源预测

目的 分析我国社区卫生人力资源发展趋势,以期为健康中国建设提供参考。方法 通过MATLAB R 2018 A建立灰色遗传算法优化(genetic algorithm-back propagation,GA-BP)神经网络组合模型,预测2021—2023年我国社区卫生人力资源,并比较各单预测模型与组合模型预测精度。结果 组合预测模型精度较好,卫生人员和卫生技术人员网络模型的均方误差(mean squared error,MSE) 和平均绝对百分比误差(mean absolute percentage error,MAPE) 的值分别为0.020 6、0.216 2%和0.019 5、0.167 4%,优于单模型预测。模型预测结果合理,我国社区卫生人员数和卫生技术人员数均保持增长趋势,2023年可分别达到71.403 8万人和60.029 0万人。结论 灰色-GA-BP神经网络组合预测模型适合我国社区卫生人力资源预测,随着医疗服务需求量的增加和新型冠状病毒肺炎疫情防控的常态化,社区卫生人力资源发展规模将逐渐提升,应注重各类卫生人才培训,保障社区卫生人员的切身利益,提升社区医疗服务能力。

Winter wheat leaf area index inversion by the genetic algorithms neural network model based on SAR data

The leaf area index(LAI)is an important agroecological physiological parameter affecting vegetation growth.To apply the genetic algorithms neural network model(GANNM)to the remote sensing inversion of winter wheat LAI throughout the growth cycle and based on GaoFen-3 Synthetic aperture radar(GF-3 SAR)images and GaoFen-1 Wide Field of View(GF-1 WFV)images,the Xiangfu District in the east of Kaifeng City,Henan Province,was selected as the testing region.Winter wheat LAI data from five growth stages were combined,and optical and microwave polarization decomposition vegetation index models were used.The backscattering coefficient was extracted by modified water cloud model(MWCM),and the LAI was obtained by MWCM inversion as input factors to construct GANNM to invert LAI.The root mean square error(RMSE)and determination coefficient(R2)were used as evaluation indicators of the model.The fitting accuracy of winter wheat LAI in five growth stages by GANNM inversion was better than that of the BP neural network model;the R2 was higher than 0.8,and RMSE was lower than 0.3,indicating that the model could accurately invert the growth status of winter wheat in five growth stages.

Genetic Algorithm with Variable Length Chromosomes for Network Intrusion Detection

Genetic algorithm(GA) has received significant attention for the design and implementation of intrusion detection systems. In this paper, it is proposed to use variable length chromosomes(VLCs) in a GA-based network intrusion detection system.Fewer chromosomes with relevant features are used for rule generation. An effective fitness function is used to define the fitness of each rule. Each chromosome will have one or more rules in it. As each chromosome is a complete solution to the problem, fewer chromosomes are sufficient for effective intrusion detection. This reduces the computational time. The proposed approach is tested using Defense Advanced Research Project Agency(DARPA) 1998 data. The experimental results show that the proposed approach is efficient in network intrusion detection.