谷氨酸发酵过程的模型化和参数估计

本文讨论了直接利用工厂报表数据构筑各氨酸发酵过程的数学模型以及估计模型中参数的方法。模型结构由理论分析得出,可用于分析模型和预测模型。参数估计主要介绍了辨识方法。考虑到模型方程可能病态,因此采用了平方根法滤波。文中还详细介绍了适用于实时在线辨识的递推平方根算法。仿真结果表明,利用上述方法求得的模型和工厂数据符合得很好。作为预测模型,利用前18h的数据就能成功地预测全过程的状态。     

农药残留预测模型可靠性的检验与改良

对农药残留预测模型可靠性问题进行了分析。根据矩阵的条件数理论,提供了检验预测模型可靠性的方法,基于岭回归和广义岭回归估计理论,建立了对不可靠预测模型改良的方法。最后通过氰戊菊酯在甘篮上的残留动态预测,对所建改良方法进行了检验,结果表明,预测模型的精度得到了大幅度提高.     

矩阵的加号逆理论在参数估计中的应用

本文从矩阵的加号逆理论出发,根据求矛盾线性方程组最佳逼近解的方法,建立起一种新的参数估计方法,同时给出了显著性检验方法,这种方法更简单更精确．     

非等距情形的正交多项式回归模型

本文向生物、医学、气象、环境等领域的科技人员介绍一种非等距情形的正交多项式回归模型.这种回归模型的优点是:(1)不需要解正规方程组,因而可有效地避免正规方程出现病态;(2)计算量比一般的多项式回归模型小;(3)各回归系数之间不存在相关性;(4)回归模型的检验比较简单.     

复杂的时间序列Granger因果模型的参数估计

Granger因果关系是根据时间序列的联合回归方程和自回归方程的拟合精度来进行计算的,本文利用普通的最小二乘法(OLS)得出了一种复杂的Granger因果模型(EGCM)参数估计的矩阵表达,进而得到一般的Granger因果模型(GCM)参数估计的矩阵表达.最后,利用Matlab编程加以实现.     

树干径流模型

以红松代表针叶树,栎树代表阔叶树,通过模拟实验研究树干径流过程,根据实验结果从机理上构造了模型。树干径流模型为一个方程组用“辗转迭代法”求出了数值解。该方程组基本上揭示了树干径流的规律,从实验与模拟结果对比分析中得到了佐证。     

贝叶斯方法估计阈性状育种值的原理和计算技术

本文介绍了估计阈性状育种值的贝叶斯方法的原理,演示了描述阈性状观察值、建立后验概率密度函数、以及导出非线性方程组的方法．并就这一估计方法的计算技术进行了讨论,针对动物遗传育种中方程组系数矩阵往往很大,超出计算机内存的情况,提出了不需要建立方程组,在数据上迭代求解的计算方法．本文还综述了这一非线性方法与线性方法在阈性状育种值估计上的比较．     

土壤水分含量的理论分析及预测模型

本文应用物理学中的电介质和电磁理论,分析和研究土壤的组成成分,得到了反映土壤水分含量的理论表达式,并在自行研制的测试仪器上,对相关变量进行测量,由此建立了土壤水分含量的预测模型,统计检验和国代结果显示了理论模型和预测模型的合理性．     

运用隔室理论研究中药材吸水膨胀动力学总室线性乳突数学模型及参数分析

目的:运用隔室模型研究中药材吸水膨胀动力学总室线性乳突数学模型并对参数进行分析;方法:运用隔室理论及动力学方法建立中药材吸水膨胀动力学数学模型,用拉氏变换法求解,研究总室与单独隔室的数学模型的关系,并对总室模型求算的各参数进行分析.结果:中药材吸水膨胀总室,与各隔室一样遵循一级线性乳突模型,V_T为e的指数函数之和形式,总室膨胀为各分室膨胀之和.V_T~∞、V_i~∞可通过非线性曲线参数估计算得,各参数(转运常数)由其与α_i的关系方程组算得.结论:中药材吸水膨胀动力学表现为一线性乳突模型.总室与单独室一样可求得各动力学参数,该模型在一定条件下可直接转变为药物动力学数学模型.     

使用线性微分方程构建初步的乳腺癌转移相关基因网络模型

随着基因芯片的技术的推广,越来越多的表达数据需要被处理和分析．利用这些表达数据提取基因调控矩阵从而构建基因网络是一个重要的问题．通过线性微分方程模型可以初步构建基因网络,了解网络结构,提取最显著的信息．然而由于分子生物学的条件限制或者数据来源的限制,导致实验数据不充分,使方程组无解．本文使用三次样条方法,对26例临床、病理资料完备的具有淋巴结转移的乳腺癌基因表达数据进行插值处理,使表达数据满秩,从而使用最小二乘法解出加权矩阵,构建初步的表达基因调控网络．通过对构建的基因网络的初步分析表明:乳腺癌转移的形成是由多基因异常引起多条传导通路异常,致使细胞恶性转化的结果,这与生物学上公认的看法是相一致的．因此,利用此线性模型方法对基因表达谱进行分析兵有一定可行性,在认识乳腺癌转移机制,乳腺癌诊断和治疗方面具有一定的理论和应用价值．     

基于不同可加性方法的黑龙江省红松人工林林分生物量模型

大尺度估算森林生物量一直是人们关注的焦点,而构建林分水平的生物量模型是一种估算森林乔木层生物量的方法。本研究基于聚合法1、聚合法2、平差法、分解法构建红松人工林林分生物量模型,并对比分析4种可加性方法的预测精度,为黑龙江省红松人工林的生物量预测提供科学依据。各模型均使用权函数来消除各模型的异方差,并以留一交叉验证法(LOOCV)作为各模型的检验方法。结果表明: 平差法的整体预测能力略优于聚合法1、聚合法2和分解法,预测精度排序为平差法>聚合法1>聚合法2>分解法;分别对比不同林分断面积的预测能力时,4种可加性方法的预测精度不一致。当红松人工林的林分断面积分布于0～10或50～60 m²·hm^-2区间时,建议采用分解法的参数估计值,而林分断面积分布于其他区间时,建议采用平差法的参数估计值。     

Hidden markov model for the prediction of transmembrane proteins using MATLAB

Since membranous proteins play a key role in drug targeting therefore transmembrane proteins prediction is active and challenging area of biological sciences. Location based prediction of transmembrane proteins are significant for functional annotation of protein sequences. Hidden markov model based method was widely applied for transmembrane topology prediction. Here we have presented a revised and a better understanding model than an existing one for transmembrane protein prediction. Scripting on MATLAB was built and compiled for parameter estimation of model and applied this model on amino acid sequence to know the transmembrane and its adjacent locations. Estimated model of transmembrane topology was based on TMHMM model architecture. Only 7 super states are defined in the given dataset, which were converted to 96 states on the basis of their length in sequence. Accuracy of the prediction of model was observed about 74 %, is a good enough in the area of transmembrane topology prediction. Therefore we have concluded the hidden markov model plays crucial role in transmembrane helices prediction on MATLAB platform and it could also be useful for drug discovery strategy. AVAILABILITY: The database is available for free at bioinfonavneet@gmail.comvinaysingh@bhu.ac.in.     

ESTIMATION OF THE PHYSIOLOGICAL PARAMETERS OF A MEMBRANE CHANNEL EXPOSED TO ELECTROMAGNETIC FIELDS USING THE GPOF APPROACH

A method is proposed for the estimation of kinetic parameters of ionic channels in the cell membrane. The method is based on the generalized pencil-of-function approach, which exploits transient current signals from single channels to derive the frequency of the system poles. The proposed approach is validated for the well-known potassium channel by comparing the estimated values with the theoretical values given by Hodgkin and Huxley. The approach is superior to previous spectral approaches, both for its accuracy and for its robustness. It is especially useful for parameter estimation when the channel is exposed to electromagnetic fields. Results are given for exposure to 200-Hz and 915-MHz signals, to demonstrate the effect of fields on the kinetic parameters of the channel.     

Modelling human tibia structural vibrations

Mode shapes and natural frequencies of human long bones play an important role in the interpretation, prediction and control of their dynamic response to external mechanical loads. This paper describes an experimental and theoretical study of free vibrations in an excised human tibia. Experimentally, seven tibial natural frequencies in the range 0-3 kHz were identified through measured structural transfer functions. Theoretically, a beam type Finite Element model of a human tibia is suggested. Unknown parameters in this model are determined by a Bayesian parameter estimation approach, by which very fine model/observation-accordance was achieved with realistic parameter estimates. A sensitivity analysis of the model confirms that the human tibia in a vibrational sense is more uniform than its complicated geometry would immediately suggest. Accordingly, two simple tibia models are identified, based on uniform beam theory with inclusion of shear deformations.     

Diallel analysis for sex-linked and maternal effects

Genetic models including sex-linked and maternal effects as well as autosomal gene effects are described. Monte Carlo simulations were conducted to compare efficiencies of estimation by minimum norm quadratic unbiased estimation (MINQUE) and restricted maximum likelihood (REML) methods. MINQUE(1), which has 1 for all prior values, has a similar efficiency to MINQUE(), which requires prior estimates of parameter values. MINQUE(1) has the advantage over REML of unbiased estimation and convenient computation. An adjusted unbiased prediction (AUP) method is developed for predicting random genetic effects. AUP is desirable for its easy computation and unbiasedness of both mean and variance of predictors. The jackknife procedure is appropriate for estimating the sampling variances of estimated variances (or covariances) and of predicted genetic effects. A t-test based on jackknife variances is applicable for detecting significance of variation. Worked examples from mice and silkworm data are given in order to demonstrate variance and covariance estimation and genetic effect prediction.     

PHYLOGENETIC ANALYSIS OF THE EVOLUTIONARY CORRELATION USING LIKELIHOOD

Many evolutionary processes can lead to a change in the correlation between continuous characters over time or on different branches of a phylogenetic tree. Shifts in genetic or functional constraint, in the selective regime, or in some combination thereof can influence both the evolution of continuous traits and their relation to each other. These changes can often be mapped on a phylogenetic tree to examine their influence on multivariate phenotypic diversification. We propose a new likelihood method to fit multiple evolutionary rate matrices (also called evolutionary variance–covariance matrices) to species data for two or more continuous characters and a phylogeny. The evolutionary rate matrix is a matrix containing the evolutionary rates for individual characters on its diagonal, and the covariances between characters (of which the evolutionary correlations are a function) elsewhere. To illustrate our approach, we apply the method to an empirical dataset consisting of two features of feeding morphology sampled from 28 centrarchid fish species, as well as to data generated via phylogenetic numerical simulations. We find that the method has appropriate type I error, power, and parameter estimation. The approach presented herein is the first to allow for the explicit testing of how and when the evolutionary covariances between characters have changed in the history of a group.     

HIV model parameter estimates from interruption trial data including drug efficacy and reservoir dynamics

Mathematical models based on ordinary differential equations (ODE) have had significant impact on understanding HIV disease dynamics and optimizing patient treatment. A model that characterizes the essential disease dynamics can be used for prediction only if the model parameters are identifiable from clinical data. Most previous parameter identification studies for HIV have used sparsely sampled data from the decay phase following the introduction of therapy. In this paper, model parameters are identified from frequently sampled viral-load data taken from ten patients enrolled in the previously published AutoVac HAART interruption study, providing between 69 and 114 viral load measurements from 3-5 phases of viral decay and rebound for each patient. This dataset is considerably larger than those used in previously published parameter estimation studies. Furthermore, the measurements come from two separate experimental conditions, which allows for the direct estimation of drug efficacy and reservoir contribution rates, two parameters that cannot be identified from decay-phase data alone. A Markov-Chain Monte-Carlo method is used to estimate the model parameter values, with initial estimates obtained using nonlinear least-squares methods. The posterior distributions of the parameter estimates are reported and compared for all patients.     

多元统计中Logistic回归模型参数估计的一种方法及应用

本文提出一多元统计中Logistics回归模型参数的估计方法──极大似然估计法,使得很容易在计算机上应用,并建立了大兴安岭地区秋季人为火发生预报模型。     

RankAggreg, an R package for weighted rank aggregation

Background

Determining the parameters of a mathematical model from quantitative measurements is the main bottleneck of modelling biological systems. Parameter values can be estimated from steady-state data or from dynamic data. The nature of suitable data for these two types of estimation is rather different. For instance, estimations of parameter values in pathway models, such as kinetic orders, rate constants, flux control coefficients or elasticities, from steady-state data are generally based on experiments that measure how a biochemical system responds to small perturbations around the steady state. In contrast, parameter estimation from dynamic data requires time series measurements for all dependent variables. Almost no literature has so far discussed the combined use of both steady-state and transient data for estimating parameter values of biochemical systems.

Results

In this study we introduce a constrained optimization method for estimating parameter values of biochemical pathway models using steady-state information and transient measurements. The constraints are derived from the flux connectivity relationships of the system at the steady state. Two case studies demonstrate the estimation results with and without flux connectivity constraints. The unconstrained optimal estimates from dynamic data may fit the experiments well, but they do not necessarily maintain the connectivity relationships. As a consequence, individual fluxes may be misrepresented, which may cause problems in later extrapolations. By contrast, the constrained estimation accounting for flux connectivity information reduces this misrepresentation and thereby yields improved model parameters.

Conclusion

The method combines transient metabolic profiles and steady-state information and leads to the formulation of an inverse parameter estimation task as a constrained optimization problem. Parameter estimation and model selection are simultaneously carried out on the constrained optimization problem and yield realistic model parameters that are more likely to hold up in extrapolations with the model.     

脑磁源的定位研究

在脑电脑磁研究中,常将大脑视为一电磁系统,利用准静态近似下的麦克斯韦方程,可以发现代表脑神经元活动的原在电流密度与脑外磁场之间呈线性关系。在高斯哭喊杨存在的情况下,采用极大似然估计理论,讨论了一种基于脑磁场时域－罕注解磁源定位问题的一般方法。球对称导体模型下的模拟计算表明,这一方法是有效的。对于考虑真实头模型下的磁源定位问题求解磁源定位,提出了一种联合使用脑磁脑电数据的近似方法。