基元模式分析在生物网络和途径分析中的应用

基元模式分析是应用最广泛的代谢途径分析方法。基元模式分析的研究对象从代谢网络发展到信号传导网络;研究尺度从细胞到生物反应器,甚至生态系统;数学描述从稳态分解到动态解析;研究领域从微生物代谢到人类疾病。以下综述了基元模式分析的算法和软件开发现状,以及其在代谢途径与鲁棒性、代谢通量分解、稳态代谢通量分析、动态模型与生物过程模拟、网络结构与调控、菌株设计和信号传导网络等方面的应用。开发新的算法解决组合爆炸问题,探索基元模式与代谢调控的关系以及提高菌株设计算法效率是今后基元模式的重要发展方向。     

蛋白质定向进化及其在微生物代谢调控中的应用

蛋白质定向进化是非理性改造蛋白质的一种有效方法。利用蛋白质定向进化技术可以改变代谢流,扩展或构建新的代谢途径,弱化或消除不必要或有害的代谢途径,从而达到提高某种代谢产物产率或降解有害物质的目的。蛋白质定向进化技术在代谢调控中的应用有效拓宽了代谢工程的应用范围。本文介绍了主要的蛋白质定向进化技术如易错PCR技术、DNA改组技术、交错延伸技术和临时模板随机嵌合技术等,评述了蛋白质定向进化技术对微生物细胞代谢中的关键蛋白进行定向改造,从而改善其代谢能力,调控微生物代谢等的应用。     

代谢组及其在真菌研究中的应用

代谢组学是利用现代分析化学手段对一定条件下生物体内小分子代谢产物（初级和次级代谢产物）定性及定量,从而揭示生命现象及其内在规律的学科。相对于基因组、转录组和蛋白质组,代谢组是一定条件下生物学过程完成后的最终代谢产物的集合,因而是各种组学研究中最接近表型的一种组学,可以直接动态地反映出细胞的生理生化过程,从而有效地检测和发现特定的生化途径,准确地解释生理或者病理现象。代谢组学作为系统生物学中基因组学、转录组学以及蛋白质组学三大组学的延伸和补充,是目前的研究热点之一。目前代谢组学在真菌领域的研究得到日益重视和发展。本文首先从历史发展和技术路线简述了代谢组学的发展历程和常见的代谢组学研究方法。接着从真菌的分类鉴定、生物膜研究、代谢途径、代谢工程、天然产物发现与植物互作这6个方面介绍了代谢组学在真菌研究领域的应用。     

铁代谢与蛋白质泛素化．降解调控研究进展

铁元素为几乎所有的生命体所必需,维持铁代谢稳态对机体的正常功能至关重要。铁代谢紊乱与人类多种疾病的发生和发展有关。已知铁代谢稳态受到一系列参与铁代谢环节的关键蛋白质,如IRP2等的精确调节。这些重要蛋白质的稳定性、生理活性的动态变化及其协调作用是细胞维持铁代谢平衡的分子基础。除了转录和转录后水平的调控,泛素化等翻译后修饰方式和蛋白质降解是细胞精确调控参与铁代谢的蛋白质的水平及功能普遍而有效的方式之一;同时,细胞的铁代谢状态也影响细胞内参与泛素化等翻译后修饰途径的酶类的活性和稳定性,从而在铁代谢和蛋白质修饰．降解途径之间形成反馈机制,实时和动态地完成对细胞内铁代谢水平的精确调控。就相关领域的最新进展作简要综述。     

急性热暴露对人体氨基酸代谢影响的初步研究

热环境对蛋白质代谢有较大影响,汗氮丢失与氮平衡有直接关系。国外一些研究表明,氨基酸在汗液丢失氮中占有相当的比例。国内有关这方面的资料并不多见。为了探索炎热环境下人体蛋白质代谢的某些特点,本实验对此进行了初步研究。     

基因组尺度代谢网络研究进展

基因组尺度代谢网络从基因组序列出发,结合基因、蛋白质、代谢数据库和实验数据,从系统的角度定量研究生命体的代谢过程,了解各个组分之间的相互作用关系。这类网络模型对于生命活动理论研究和优良工程菌的构建都具有重要的理论和实践意义。以下结合作者的实际研究经验,对基因组尺度代谢网络从重构到模拟直至应用进行了较为详细的介绍,并讨论了一些目前存在的难题和未来的研究方向。     

肉苁蓉细胞悬浮培养的代谢动力学研究

通过研究肉苁蓉细胞悬浮培养过程中碳源、氮源、磷源的消耗,pH、电导率的变化,以及细胞的生长、胞内外蛋白质含量和次生代谢产物苯乙醇苷、总黄酮和多糖合成的情况,掌握了细胞生长、营养消耗与次生代谢产物积累的基本规律,为建立结构化动力学模型奠定了基础。     

自噬与代谢及代谢性疾病

细胞自噬是真核生物中一种高度保守的代谢过程,可以清除受损的细胞器,降解糖原、脂类、蛋白质等生物大分子物质供细胞重新利用,对维持细胞内代谢平衡有重要意义。自噬障碍常被发现与代谢性疾病相关,如酸性麦芽糖酶缺乏症、肥胖和神经退行性疾病等。该文就近年来关于自噬与代谢及代谢性疾病的研究作一综述。     

温度对禁食大鲵碳氮代谢的影响

本文研究温度对禁食大鲵碳代谢和氮代谢的影响,结果表明：随着温度的升高、禁食大鲵的碳代谢和氮代谢均上升,其代谢率升高,禁食大鲵的代谢率很低,１５℃时,每日每公斤体重内的蛋白质消耗为０．７克左右,从理论上证明大鲵的耐饥能力很强,雄性大鲵比雌性大鲵的代谢率稍高。     

促花和抑花处理对柑桔成花及芽肉蛋白质组份的影响

对金柑、柑和温州蜜柑施行促、抑花调控处理,并利用SDS－PAGE和IEF－PAGE电泳技术,分析了金柑、柑和温州蜜柑在受促、抑花处理后芽体的蛋白质图谱变化动态。结果表明,在柑桔花芽诱导期,经GA3或BA抑花处理的芽组织中均有新的小分子蛋白质或中性至弱碱性蛋白质出现。随着花芽由生理分化向形态分化过渡,环割处理芽中出现小分子或酸性特异蛋白质。可能表明柑桔成花或抑花具有不同的基因表达与蛋白质代谢特征。进一步证实了因处理时间不同,BA对柑桔花芽分化具有促进或抑制的双重作用．     

枯落物分解研究方法和模型讨论

由于研究目的、尺度范围和要求精度的不同,枯落物分解的研究方法各异：野外分解袋法是最直接和最准确的方法,但是耗时太长;室内分解培养法与野外分解袋法相似,但具有灵活设计试验方案的优越性;现量估算法方法简捷,但是只对进入稳定发展和动态平衡阶段的生态系统可以获得较好的精度;综合平衡法能反映整个生长历史时期的枯落物分解速率平均水平,但其准确性取决于对历年凋落物量预测的精度。在枯落物分解模型中,分解率概算模型只适合于林地枯落物积累达到动态平衡时的情况,所以作者提出了另外的枯落物动态平衡模型予以修正;时间衰减模型以Olson指数模型为典型代表,但由于在应用过程中存在一些问题,也提出了相应的修正办法;影响因子关系模型包括基质质量因子关系模型、非生物环境因子关系模型和生物因子关系模型等类型。作者提出构建过程模型将是未来枯落物分解模型研究的方向。     

Statistical methods for estimating doubling time in in vitro cell growth

Summary Doubling time has been widely used to represent the growth pattern of cells. A traditional method for finding the doubling time is to apply gray-scaled cells, where the logarithmic transformed scale is used. As an alternative statistical method, the log-linear model was recently proposed, for which actual cell numbers are used instead of the transformed gray-scaled cells. In this paper, I extend the log-linear model and propose the extended log-linear model. This model is designed for extra-Poisson variation, where the log-linear model produces the less appropriate estimate of the doubling time. Moreover, I compare statistical properties of the gray-scaled method, the log-linear model, and the extended log-linear model. For this purpose, I perform a Monte Carlo simulation study with three data-generating models: the additive error model, the multiplicative error model, and the overdispersed Poisson model. From the simulation study, I found that the gray-scaled method highly depends on the normality assumption of the gray-scaled cells; hence, this method is appropriate when the error model is multiplicative with the log-normally distributed errors. However, it is less efficient for other types of error distributions, especially when the error model is additive or the errors follow the Poisson distribution. The estimated standard error for the doubling time is not accurate in this case. The log-linear model was found to be efficient when the errors follow the Poisson distribution or nearly Poisson distribution. The efficiency of the log-linear model was decreased accordingly as the overdispersion increased, compared to the extended log-linear model. When the error model is additive or multiplicative with Gamma-distributed errors, the log-linear model is more efficient than the gray-scaled method. The extended log-linear model performs well overall for all three data-generating models. The loss of efficiency of the extended log-linear model is observed only when the error model is multiplicative with log-normally distributed errors, where the gray-scaled method is appropriate. However, the extended log-linear model is more efficient than the log-linear model in this case.     

QTL mapping in outbred half-sib families using Bayesian model selection

In this article, we propose a model selection method, the Bayesian composite model space approach, to map quantitative trait loci (QTL) in a half-sib population for continuous and binary traits. In our method, the identity-by-descent-based variance component model is used. To demonstrate the performance of this model, the method was applied to map QTL underlying production traits on BTA6 in a Chinese half-sib dairy cattle population. A total of four QTLs were detected, whereas only one QTL was identified using the traditional least square (LS) method. We also conducted two simulation experiments to validate the efficiency of our method. The results suggest that the proposed method based on a multiple-QTL model is efficient in mapping multiple QTL for an outbred half-sib population and is more powerful than the LS method based on a single-QTL model.     

人──板系统最佳蹬伸动作的控制模型及数值分析

本文基于Hanavan模型和人体测量学参数,以人体各环节间的相对运动作为控制量,建立了人-板系统中起跳蹬伸动作的数学模型,给出了模型的数值计算方法,在此基础上给出了实现最佳蹬伸用力过程的计算实验途径与运动技术诊断方法。     

Constructing stochastic models from deterministic process equations by propensity adjustment

Background

Models of biochemical systems are typically complex, which may complicate the discovery of cardinal biochemical principles. It is therefore important to single out the parts of a model that are essential for the function of the system, so that the remaining non-essential parts can be eliminated. However, each component of a mechanistic model has a clear biochemical interpretation, and it is desirable to conserve as much of this interpretability as possible in the reduction process. Furthermore, it is of great advantage if we can translate predictions from the reduced model to the original model.

Results

In this paper we present a novel method for model reduction that generates reduced models with a clear biochemical interpretation. Unlike conventional methods for model reduction our method enables the mapping of predictions by the reduced model to the corresponding detailed predictions by the original model. The method is based on proper lumping of state variables interacting on short time scales and on the computation of fraction parameters, which serve as the link between the reduced model and the original model. We illustrate the advantages of the proposed method by applying it to two biochemical models. The first model is of modest size and is commonly occurring as a part of larger models. The second model describes glucose transport across the cell membrane in baker's yeast. Both models can be significantly reduced with the proposed method, at the same time as the interpretability is conserved.

Conclusions

We introduce a novel method for reduction of biochemical models that is compatible with the concept of zooming. Zooming allows the modeler to work on different levels of model granularity, and enables a direct interpretation of how modifications to the model on one level affect the model on other levels in the hierarchy. The method extends the applicability of the method that was previously developed for zooming of linear biochemical models to nonlinear models.     

Efficient approximate k‐fold and leave‐one‐out cross‐validation for ridge regression

In model building and model evaluation, cross‐validation is a frequently used resampling method. Unfortunately, this method can be quite time consuming. In this article, we discuss an approximation method that is much faster and can be used in generalized linear models and Cox’ proportional hazards model with a ridge penalty term. Our approximation method is based on a Taylor expansion around the estimate of the full model. In this way, all cross‐validated estimates are approximated without refitting the model. The tuning parameter can now be chosen based on these approximations and can be optimized in less time. The method is most accurate when approximating leave‐one‐out cross‐validation results for large data sets which is originally the most computationally demanding situation. In order to demonstrate the method's performance, it will be applied to several microarray data sets. An R package penalized, which implements the method, is available on CRAN.     

痛风性关节炎动物模型的改良

目的通过改良痛风性关节炎动物模型,制备出更符合人类痛风性关节炎机制的动物模型。方法通过Coderre法与次黄嘌呤相结合（模型B）、Coderre法与氧嗪酸相结合（模型A）的方法建立痛风性关节炎动物模型,采用全自动生化分析仪测定血尿酸水平,观察关节滑膜组织形态学改变及大鼠不同时相步态变化,并将两种方法加以比较。结果模型B组大鼠血尿酸水平显著降低,关节滑膜组织形态学及不同时相大鼠步态改变明显,与模型A组比较有统计学意义。结论Coderre法与次黄嘌呤相结合方法建立大鼠痛风性关节炎动物模型更符合人类痛风性关节炎机制的动物模型。     

基于局部调整动态轮廓模型提取超声图像乳腺肿瘤边缘

提出一种基于局部调整动态轮廓模型提取超声图像乳腺肿瘤边缘的算法。该算法在Chan—Vese（CV）模型基础上,定义了一个局部调整项,采用基于水平集的动态轮廓模型提取超声图像乳腺肿瘤边缘。将该算法应用于89例临床超声图像乳腺肿瘤的边缘提取实验,结果表明：该算法比CV模型更适用于具有区域非同质性的超声图像的分割,可有效实现超声图像乳腺肿瘤边缘的提取。     

A generalized estimating equations approach to quantitative trait locus detection of non-normal traits

To date, most statistical developments in QTL detection methodology have been directed at continuous traits with an underlying normal distribution. This paper presents a method for QTL analysis of non-normal traits using a generalized linear mixed model approach. Development of this method has been motivated by a backcross experiment involving two inbred lines of mice that was conducted in order to locate a QTL for litter size. A Poisson regression form is used to model litter size, with allowances made for under- as well as over-dispersion, as suggested by the experimental data. In addition to fixed parity effects, random animal effects have also been included in the model. However, the method is not fully parametric as the model is specified only in terms of means, variances and covariances, and not as a full probability model. Consequently, a generalized estimating equations (GEE) approach is used to fit the model. For statistical inferences, permutation tests and bootstrap procedures are used. This method is illustrated with simulated as well as experimental mouse data. Overall, the method is found to be quite reliable, and with modification, can be used for QTL detection for a range of other non-normally distributed traits.     

Mapping quantitative trait loci for binary traits using a heterogeneous residual variance model: an application to Marek's disease susceptibility in chickens

A typical problem in mapping quantitative trait loci (QTLs) comes from missing QTL genotype. A routine method for parameter estimation involving missing data is the mixture model maximum likelihood method. We developed an alternative QTL mapping method that describes a mixture of several distributions by a single model with a heterogeneous residual variance. The two methods produce similar results, but the heterogeneous residual variance method is computationally much faster than the mixture model approach. In addition, the new method can automatically generate sampling variances of the estimated parameters. We derive the new method in the context of QTL mapping for binary traits in a F2 population. Using the heterogeneous residual variance model, we identified a QTL on chromosome IV that controls Marek's disease susceptibility in chickens. The QTL alone explains 7.2% of the total disease variation. This revised version was published online in July 2006 with corrections to the Cover Date.