Identification of bioprocesses using Genetic Algorithm

In this paper, Genetic Algorithm (GA) is used for identifying two bioprocesses. Bioprocesses are highly nonlinear systems and hence conventional techniques such as Recursive Least Squares (RLS) cannot be used to identify these systems. Systems chosen for identification are fed batch fermentor and bioreactor. Simulation results are presented for the above cases and are found to be satisfactory.     

Propulsive Velocity Optimization of 3-Joint Fish Robot Using Genetic-Hill Climbing Algorithm

Underwater robot is a new research field which is emerging quickly in recent years.Previous researches in this field focuson Remotely Operated Vehicles(ROVs),Autonomous Underwater Vehicles(AUVs),underwater manipulators,etc.Fish robot,which is a new type of underwater biomimetic robot,has attracted great attention because of its silence in moving and energyefficiency compared to conventional propeller-oriented propulsive mechanism.However,most of researches on fish robots have been carried out via empirical or experimental approaches,not based ondynamic optimality.In this paper,we proposed an analytical optimization approach which can guarantee the maximum propulsivevelocity of fish robot in the given parametric conditions.First,a dynamic model of 3-joint(4 links)carangiform fishrobot is derived,using which the influences of parameters of input torque functions,such as amplitude,frequency and phasedifference,on its velocity are investigated by simulation.Second,the maximum velocity of the fish robot is optimized bycombining Genetic Algorithm(GA)and Hill Climbing Algorithm(HCA).GA is used to generate the initial optimal parametersof the input functions of the system.Then,the parameters are optimized again by HCA to ensure that the final set of parametersis the"near"global optimization.Finally,both simulations and primitive experiments are carried out to prove the feasibility ofthe proposed method.     

Antifungal Activity of Geldanamycin Alone or in Combination with Fluconazole Against Candida species

A standardized broth microdilution method was used to test the antifungal activity of geldanamycin (GA), an inhibitor of heat shock protein 90 (Hsp90), alone or in combination with the antifungal agent fluconazole (FLC) against 32 clinical isolates of Candida spp. In addition, a disk diffusion test was also used to evaluate the antifungal effect of these two drugs against Candida spp. by measuring the inhibition zone diameters. We found that the range of minimal inhibitory concentrations (MICs) for GA alone against Candida spp. was 3.2–12.8 mg/L and the geometric mean of MICs was 6.54 mg/L. In addition, the combination of GA with FLC showed synergistic effects in vitro against 2 FLC-susceptible and 6 FLC-resistant isolates of C. albicans. As for the other isolates, indifference but no antagonism was observed. In the disk diffusion assay, the diameter of inhibition zones for FLC combined with GA against FLC-resistant C. albicans isolates was 30 mm, while no inhibition was observed with FLC alone. These results demonstrate that GA possesses antifungal activity against Candida spp., and the combination of GA with FLC shows in vitro synergistic activity against some C. albicans isolates, especially those resistant to FLC.     

基于粒子群优化算法的脑磁图源定位

脑磁图作为一种新型的脑探测技术,具有较高定位精度和毫秒级时间分辨率的特点。快速准确地利用脑磁图技术对三维空间中的脑神经活动源进行定位,对于脑功能研究和医学临床应用都具有重要的应用价值。可是,目前的脑磁图源定位广泛采用了多信号分类方法,它要求对三维大脑空间进行全局扫描,需要大量的计算,存在速度慢的缺点。针对这一问题,提出了一种基于粒子群优化算法的脑磁图源定位方法。先利用粒子群优化算法全局搜索能力强的特点寻找出目标函数的全局最优值,进行初步的脑磁图源定位;然后,再在小范围内进行小网格的搜索,进一步实现精确的定位。实验结果表明,基于粒子群优化算法的脑磁图源定位能够很好地解决上述问题,具有计算速度快、定位精度高的特点。     

The massively parallel genetic algorithm for RNA folding: MIMD implementation and population variation

A massively parallel Genetic Algorithm (GA) has been applied to RNA sequence folding on three different computer architectures. The GA, an evolution-like algorithm that is applied to a large population of RNA structures based on a pool of helical stems derived from an RNA sequence, evolves this population in parallel. The algorithm was originally designed and developed for a 16384 processor SIMD (Single Instruction Multiple Data) MasPar MP-2. More recently it has been adapted to a 64 processor MIMD (Multiple Instruction Multiple Data) SGI ORIGIN 2000, and a 512 processor MIMD CRAY T3E. The MIMD version of the algorithm raises issues concerning RNA structure data-layout and processor communication. In addition, the effects of population variation on the predicted results are discussed. Also presented are the scaling properties of the algorithm from the perspective of the number of physical processors utilized and the number of virtual processors (RNA structures) operated upon.     

Systematic search for the rate constants that control the exocytotic process from chromaffin cells by a genetic algorithm

We have recently created a kinetic model that reproduces the dynamics of exocytosis with high accuracy. The reconstruction necessitated a search, in a multi-dimensional parameter space, for 37 parameters that described the system, with no assurance that the parameters, which reconstructed the observations, are a unique set. In the present study, a Genetic Algorithm (GA) was used for a thorough search in the unknown parameter space, using a strategy of gradual increase of the complexity of the analyzed input data. Upon systematic incorporation of one to four measurable parameters, used as input signals for the analysis, the constraint set on the GA search imposed the convergence of the free parameters into a single narrow range. The mean values for each adjustable parameter represent a minimum for the fitness function in the multi-dimensional parameter space. The GA search demonstrates that the parameters that control the kinetics of exocytosis are the rate constants of the steps downstream to synaptotagmin binding, and that the equilibrium constant of the binding of calcium to Munc13 controls the calcium-dependent priming process. Thus, the systematic use of the GA creates a link between specific reactions in the process of exocytosis and experimental phenotypes.     

Container loading and unloading scheduling for a Mobile Harbor system: a global and local search method

Mobile Harbor (MH) is a type of mobile floating port system with an on-board crane for off-shore container handling capability. Due to its unique operational features, it creates a new type of operational scheduling problem. Container loading and unloading sequence schedule for the MH on-board crane is one such problem. An optimal schedule should minimize the on-board crane’s moves in a stem-to-stern direction while satisfying MH’s stability constraint. This paper presents a mathematical programming model to formally define the problem, and two heuristic methods, Genetic Algorithm (GA) method and local search method, are developed. Experimental results show that both GA and the local search method generate solutions of similar quality when the stability constraint is loose and that the local search method is not effective in finding a feasible solution for problems with a tight stability constraint.     

Gene selection for sample classification based on gene expression data: study of sensitivity to choice of parameters of the GA/KNN method.

MOTIVATION: We recently introduced a multivariate approach that selects a subset of predictive genes jointly for sample classification based on expression data. We tested the algorithm on colon and leukemia data sets. As an extension to our earlier work, we systematically examine the sensitivity, reproducibility and stability of gene selection/sample classification to the choice of parameters of the algorithm. METHODS: Our approach combines a Genetic Algorithm (GA) and the k-Nearest Neighbor (KNN) method to identify genes that can jointly discriminate between different classes of samples (e.g. normal versus tumor). The GA/KNN method is a stochastic supervised pattern recognition method. The genes identified are subsequently used to classify independent test set samples. RESULTS: The GA/KNN method is capable of selecting a subset of predictive genes from a large noisy data set for sample classification. It is a multivariate approach that can capture the correlated structure in the data. We find that for a given data set gene selection is highly repeatable in independent runs using the GA/KNN method. In general, however, gene selection may be less robust than classification. AVAILABILITY: The method is available at http://dir.niehs.nih.gov/microarray/datamining CONTACT: LI3@niehs.nih.gov     

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。     

FLC基因表达在植物春化过程中的作用

在对以往有关不同开花途径研究简要总结的基础上综述了FLC基因在春化过程中的作用。近期以拟南芥不同生态型和突变体为模式的研究结果表明基因FLC可能是春化反应的关键基因。研究发现 ,FLC的表达水平与植株低温处理的时间呈数量关系 ,低温处理时间越长 ,FLC的表达越弱 ,去甲基化也可能对FLC起负调控的作用。同时FLC也存在于自主开花途径中 ,与其他基因共同作用以调节植株开花时间。而FLC的表达对开花起抑制作用。一系列研究表明 ,春化的低温作用可能在于相关基因的去甲基化 ,消除了FLC对开花的抑制作用 ,从而解除赤霉素合成途径的封锁最终导致植株在一定时期开花。     

Automated generation and refinement of protein signatures: case study with G-protein coupled receptors

MOTIVATION: Previous work had established that it was possible to derive sparse signatures (essentially sequence-length motifs) by examining points of contact between residues in proteins of known three-dimensional (3D) structure. Many interesting protein families have very little tertiary structural information. Methods for deriving signatures using only primary and secondary-structural information were therefore developed. RESULTS: Two methods for deriving protein signatures using protein sequence information and predicted secondary structures are described. One method is based on a scoring approach, the other on the Genetic Algorithm (GA). The effectiveness of the method was tested on the superfamily of GPCRs and compared with the established hidden Markov model (HMM) method. The signature method is shown to perform well, detecting 68% of superfamily members before the first false positive sequence and detecting several distant relationships. The GA population was used to provide information on alignment regions of particular importance for selection of key residues.     

遗传算法——进化论思想与遗传学原理在工程优化中的应用

优胜劣汰是自然界物种进化的法则,近年来,生物科学中的进化论思想与遗传学原理被成功地应用于工程中优化问题的计算,于是产生了一种不同于传统算法的优化算法———遗传算法（ＧＡ）。本文介绍遗传算法所采用的进化论思想和遗传学原理,遗传算法的基本操作、算法步骤、不同于传统算法的特点以及遗传算法的发展历史与应用情况等,并对遗传算法对生物科学的可能应用作了简单的展望。