遗传算法优化真菌深层培养过程神经网络模型物的研究

提出一种简单的真菌深层培养过程网络模型。输入变量为可在线测量的排气中的二氧化碳浓度,网络权数采用遗传算法进行优化训练。所获神经网络模型能准确预测培养过程的状态变量。研究表明遗传算法训练此类神经网络系统是可行的。     

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。     

模型鼠低氧预适应适宜氧气浓度研究

目的:研究低氧预适应训练的适宜氧气浓度。方法:设计了短期和长期两种间歇性低氧暴露模式,研究了一系列不同浓度的低氧环境对模型鼠体重、血氧饱和度、游泳能力等方面的影响,进而探讨低氧预适应效应与氧气浓度之间的内在联系。结果:模型鼠长期暴露于低氧环境中,其体重增长率逐步下降;在15%～8%的低氧浓度区间,模型鼠血氧饱和度随氧气浓度降低呈现平台似缓慢下降趋势;低氧预适应训练后的模型鼠游泳能力显著提高,经在10%低氧环境中进行低氧预适应训练后的昆明小鼠游泳能力提高最为明显。结论:适当浓度的低氧预适应训练可以改善模型鼠低氧耐受能力,显著提高模型鼠运动能力。15%～10%氧气浓度区间可视为低氧预适应有益作用区间。10%氧气浓度为模型鼠低氧预适应训练的较适宜浓度。     

用遗传算法进行重组大肠杆菌发酵动力学的分析

重组大肠杆菌在诱导表达人表皮生长因子的过程促使细菌的生长受到抑制,一部分重组菌丧失了分裂能力,但仍保持着一定的代谢活力,分离成为存活但不能培养的细菌,根据大肠杆菌在表达外源蛋白过程中细胞生理状态的不同将细菌分为三类,提出一个描述诱导表达过程中重组大肠杆菌分离、生长的动力学模型．应用遗传算法对不同底物浓度的细胞生长、分离和产物合成的动力学参数进行了有效地估计,避免了传统算法可能陷于局部最优的问题,模型计算结果与实验结果吻合良好．分离模型在初始糖浓为5-20g/L的范围内可以较好地描述发酵过程中细胞生长、分离和目标产物表达的过程并具有一定的预测能力．     

培养海马神经元网络学习模型的构建

对于培养的神经元网络而言,学习是外界刺激与网络响应之间联系建立和调控的过程.为构建合适的神经元网络学习模型,采用闭环低频(1 Hz)成对电极的电刺激模拟认知任务,在多通道微电极阵列系统中对培养的海马神经元网络进行训练,使其发生网络层次上的学习行为.经过训练后,神经元网络在刺激后20～80ms内的早期突触后响应明显增加,响应/刺激比(在闭环训练中,电极上任一阶段连续10次刺激的早期突触后响应的个数/10)增大,响应时延减小,并且响应具有选择性,即表明,神经元网络与外界刺激之间已建立可调控的联系,该可调控联系是通过网络的响应来表现的,建立神经元网络与外界刺激之间的可调控联系即网络层次的学习.     

基于人工神经网络-遗传算法的樟芝发酵培养基优化

采用优化模型对药用丝状真菌樟芝的复杂发酵过程进行建模,并获得最优发酵培养基组成.对樟芝发酵过程中的形态变化过程进行了观察,并分别采用人工神经网络(ANN)和响应面法(RSM)对樟芝发酵过程进行建模,同时采用遗传算法(GA)优化了发酵培养基组成.结果表明,ANN模型比RSM模型具有更好的实验数据拟合能力和预测能力,GA计算得到樟芝生物量理论最大值为6.2 g/L,并获得发酵最佳接种量及培养基组成:孢子浓度1.76× 105个/mL,葡萄糖29.1 g/L,蛋白胨9.4 g/L,黄豆粉2.8 g/L.在最佳培养条件下,樟芝生物量为(6.1±0.2)g/L.基于ANN-GA的优化方法可用于优化其他丝状真菌的复杂发酵过程,从而获得生物量或活性代谢产物.     

真菌深层培养过程的房室结构神经网络模型

在对横纹黑蛋巢菌深层培养过程进行分析的基础上,提出一种房室结构的神经网络模型,利用ＲＢＦ网络这各房室的输入,输出关系,并进一步对整个生化过程作了建模型研究,计算结果表明,所建模型性能较佳,对真菌培养过程的观测数据拟合结果令人满意。     

外来入侵物种的风险评估定量模型及应用

预防生物入侵的一个重要手段是对外来物种进行风险评估,应用模型则是定量评估的必备方法。本文简述了常用的适生性风险评估模型,概述了诸如遗传算法、模糊包络模型、自组织特征映射网络等较新的理论方法,它们使用环境变量和物种实际分布数据,利用不同的机理模型预测物种潜在分布区。本文还综述了适用于研究物种扩散性的模型,积分差分方程模型可以模拟物种扩散行为,元胞自动机模型可以揭示种间竞争关系,景观中性模型大多用于种群动态等生态过程的研究。     

基于BP神经网络和遗传算法的面包酵母高密度发酵培养基优化

为实现面包酵母的高密度发酵培养,构建一个BP神经网络模型,用于回归面包酵母高密度发酵培养基中显著影响因子与菌体密度之间的非线性关系,并在此基础上结合遗传算法进对此模型进行全局寻优,得到关键因子最佳浓度分别为:葡萄糖52.3 g/L,酵母浸出粉10.4 g/L,(NH4)2SO41.9 g/L.采用此优化配方进行摇瓶培养,所得菌体密度为3.95×108个/mL,比对照提高了61.2％.结果证实了人工神经网络的模拟和预测功能在微生物培养基优化方面有一定应用价值.     

重组类人胶原蛋白工程菌补料发酵过程建模

对产类人胶原蛋白的重组大肠杆菌Escherichia coli(E. Coli) 的批式和分批-补料培养动力学进行了研究。通过检测发酵过程的基质浓度、菌体量和产物浓度,建立了一组反映发酵的动力学模型,并考虑了非工程菌存在的影响,分析了细胞生长、底物消耗、基因工程产物生成的过程,结果显示该动力学模型可以很好的拟合发酵过程。     

人工神经网络与遗传算法相结合在作物估产中的应用——以吉林省玉米估产为例

在遗传算法(Genetic Algorithm)与误差反传(Back Propagation)网络结构模型相结合的基础上,设计了用遗传算法训练神经网络权重的新方法,并对吉林省梨树和德惠县的玉米进行了估产研究,同时与BP算法和灰色系统理论模型进行了比较.经检验,计算值与实际值接近,并优于灰色理论模型,具有良好的预测效果,从而为农作物估产提供了新方法.     

Template learning of cellular neural network using genetic programming

A new learning algorithm for space invariant Uncoupled Cellular Neural Network is introduced. Learning is formulated as an optimization problem. Genetic Programming has been selected for creating new knowledge because they allow the system to find new rules both near to good ones and far from them, looking for unknown good control actions. According to the lattice Cellular Neural Network architecture, Genetic Programming will be used in deriving the Cloning Template. Exploration of any stable domain is possible by the current approach. Details of the algorithm are discussed and several application results are shown.     

An FPGA hardware/software co-design towards evolvable spiking neural networks for robotics application

This paper presents an approach that permits the effective hardware realization of a novel Evolvable Spiking Neural Network (ESNN) paradigm on Field Programmable Gate Arrays (FPGAs). The ESNN possesses a hybrid learning algorithm that consists of a Spike Timing Dependent Plasticity (STDP) mechanism fused with a Genetic Algorithm (GA). The design and implementation direction utilizes the latest advancements in FPGA technology to provide a partitioned hardware/software co-design solution. The approach achieves the maximum FPGA flexibility obtainable for the ESNN paradigm. The algorithm was applied as an embedded intelligent system robotic controller to solve an autonomous navigation and obstacle avoidance problem.     

采用人工生命方法模拟七星瓢虫捕食行为进化

自从 2 0世纪 70年代Ｂｕｒｋｓ[1] 提出人工生命的概念后 ,人工生命作为一个全新的研究领域 ,以其特有的优势在近些年来得到迅猛地发展。人工生命的基本思想是去构造某种人工系统以达到对生物的生长、发育、遗传、变异、生殖、进化、学习等生命过程重要特征的模拟 ,从而认清这些生命现象的本质。人工生命有广泛的应用 ,它所使用的方法也是多样的。粗略地说 ,可分为湿件 (Ｗｅｔｗａｒｅ ,意为采用化学方法模拟 )、硬件 (Ｈａｒｄｗａｒｅ ,意为用机器人模拟 )和软件 (Ｓｏｆｔｗａｒｅ ,意为用程序模拟 )。本文集中在采用软件方法进行行为…     

Serum proteome profiling for diagnostics of ovarian cancer using ClinProt magnetic technique and MALDI-TOF mass spectrometry

Using ClinProt magnetic beads with reverse-phase (MB-HIC 8 and HB-HIC 18), weak cation exchange (MB-WCX) and metal affinity (MB-IMAC Cu) surfaces fractions of peptides and proteins were isolated from human sera for their profiling by MALDI-TOF mass spectrometry. Proteome profiling of sera from basically healthy women (47 subjects, average age 49) and from women with verified ovarian cancer (stages 1-IV, 47 patients, average age 51) by means of MB-WCX beads allowed to generate the best diagnostic models based on Genetic Algorithm and Supervised Neural Network classifiers; these models demonstrated 100% sensitivity and specificity during analysis of the test set. Introduction of additional sera from patients with colorectal cancer (19) and ulcerous colitis (5) to the statistical model confirmed 100% ovarian cancer recognition. Statistical analysis of mass-spectrometry peak areas included to the diagnostic classifiers showed 3 peaks characteristic for ovarian cancer and 4 peak areas exhibiting changes associated with both ovarian and colorectal cancer.     

Predicting protein solubility with a hybrid approach by pseudo amino acid composition

Protein solubility plays a major role for understanding the crystal growth and crystallization process of protein. How to predict the propensity of a protein to be soluble or to form inclusion body is a long but not fairly resolved problem. After choosing almost 10,000 protein sequences from NCBI database and eliminating the sequences with 90% homologous similarity by CD-HIT, 5692 sequences remained. By using Chou's pseudo amino acid composition features, we predict the soluble protein with the three methods: support vector machine (SVM), back propagation neural network (BP Neural Network) and hybrid method based on SVM and BP Neural Network, respectively. Each method is evaluated by re-substitution test and 10-fold cross-validation test. In the re-substitution test, the BP Neural Network performs with the best results, in which the accuracy achieves 0.9288 and Matthews Correlation Coefficient (MCC) achieves 0.8513. Meanwhile, the other two methods are better than BP Neural Network in 10-fold cross-validation test. The hybrid method based on SVM and BP Neural Network is the best. The average accuracy is 0.8678 and average MCC is 0.7233. Although all of the three methods achieve considerable evaluations, the hybrid method is deemed to be the best, according to the performance comparison.     

Identifying GPCRs and their types with Chou's pseudo amino acid composition: an approach from multi-scale energy representation and position specific scoring matrix

G-protein coupled receptor (GPCR) is a membrane protein family, which serves as an interface between cell and the outside world. They are involved in various physiological processes and are the targets of more than 50% of the marketed drugs. The function of GPCRs can be known by conducting Biological experiments. However, the rapid increase of GPCR sequences entering into databanks, it is very time consuming and expensive to determine their function based only on experimental techniques. Hence, the computational prediction of GPCRs is very much demanding for both pharmaceutical and educational research. Feature extraction of GPCRs in the proposed research is performed using three techniques i.e. Pseudo amino acid composition, Wavelet based multi-scale energy and Evolutionary information based feature extraction by utilizing the position specific scoring matrices. For classification purpose, a majority voting based ensemble method is used; whose weights are optimized using genetic algorithm. Four classifiers are used in the ensemble i.e. Nearest Neighbor, Probabilistic Neural Network, Support Vector Machine and Grey Incidence Degree. The performance of the proposed method is assessed using Jackknife test for a number of datasets. First, the individual performances of classifiers are assessed for each dataset using Jackknife test. After that, the performance for each dataset is improved by using weighted ensemble classification. The weights of ensemble are optimized using various runs of Genetic Algorithm. We have compared our method with various other methods. The significance in performance of the proposed method depicts it to be useful for GPCRs classification.     

体外神经干细胞克隆球的超微结构-透射电镜观察

为观察培养的神经干细胞克隆球内部的超微结构特征,采用无血清培养技术,在体外进行小鼠纹状体神经干细胞克隆球的培养传代,经过免疫细胞化学鉴定后,对单一的神经干细胞克隆球进行固定,常规透射电镜观察。结果表明,神经干细胞可以在bFGF等生长因子存在的情况下,在无血清培养液内增殖生成悬浮状态的神经干细胞克隆球,这种克隆可被诱导生成神经细胞和神经胶质细胞,电镜下,神经干细胞克隆球内部细胞相互间可形成特化的膜性结构,细胞内可有小泡出现,部分细胞有凋亡的形态。     

Genetic algorithm based fuzzy logic control of a fed-batch fermentor

In the normal fuzzy logic control (FLC) system, both the membership functions and the rule sets are usually decided upon subjectively, case by case. The application of Genetic Algorithm(GA) could lead to proper selection of membership functions and rule base objectively. In this paper, the optimisation of membership functions of a FLC for a fed-batch fermentor is carried out with help of Genetic Algorithm (GA). Results are found to be satisfactory.