吐鲁番市维吾尔族的糖精味盲基因频率

FrequencyofTaste-blindnessGeneofSaccharininUygurofTurpanAbudulaBakhy(DepartmentofBiology,XinjiangUniversity,Urumqi830046)有关糖精（Saccharin）味盲性状的研究不多,且无定论。1991年;南京师范大学余多慰等研究了汉族人3种物质的尝味能力,得出肯定结论,认为糖精味育是存在的,它跟苯硫脲（PTC）味盲性状一样,是隐性遗传。70年代,刘鸿权等作过新疆维吾尔族苯硫脲（PTC）味盲的遗传学分析。但维吾尔族糖精味盲的遗传分析未见报道。本文采用阈值法测试了新疆吐鲁番市维吾尔族的糖精尝味能力,并分析其基因频率。1对象和…     

突变质体随细胞分裂而传递的性质和规律初探

设一初始细胞中共有ｍ个质体,其中ｉ个是突变的,我们研究了它们随细胞分裂而分裂并在细胞间进行随机分配的过程中,一细胞分配ｍ个突变质体的概率。令ｘ（ｔ）＝１表示在第ｔ次分裂时,一细胞只含突变质体这一随机事件,ｘ（ｔ）＝０为其对立事件。我们得到了随机过程｛ｘ（ｔ）,ｔ∈Ｔ｝的一些性质、证明了该过程为一马尔可夫链,给出了细胞质内质体杂化（含突变与非突变两类质体）与质体纯化（仅含一类质体）两种状态之间的转移概率矩阵,揭示了突变质体随细胞分裂而传递的若干规律。     

Extinction times for a birth-death process with two phases

Many populations have a negative impact on their habitat or upon other species in the environment if their numbers become too large. For this reason they are often subjected to some form of control. One common control regime is the reduction regime: when the population reaches a certain threshold it is controlled (for example culled) until it falls below a lower predefined level. The natural model for such a controlled population is a birth-death process with two phases, the phase determining which of two distinct sets of birth and death rates governs the process. We present formulae for the probability of extinction and the expected time to extinction, and discuss several applications.     

Gaußsche Markovketten zweiter Ordnung

We find the general form of the proper Gaussian Markov chains of second order and give an example for them. Comparing with the Gaussian Markov processes they can be used as an improved growth model.     

A Markov chain model for N-linked protein glycosylation – towards a low-parameter tool for model-driven glycoengineering

Glycosylation is a critical quality attribute of most recombinant biotherapeutics. Consequently, drug development requires careful control of glycoforms to meet bioactivity and biosafety requirements. However, glycoengineering can be extraordinarily difficult given the complex reaction networks underlying glycosylation and the vast number of different glycans that can be synthesized in a host cell. Computational modeling offers an intriguing option to rationally guide glycoengineering, but the high parametric demands of current modeling approaches pose challenges to their application. Here we present a novel low-parameter approach to describe glycosylation using flux-balance and Markov chain modeling. The model recapitulates the biological complexity of glycosylation, but does not require user-provided kinetic information. We use this method to predict and experimentally validate glycoprofiles on EPO, IgG as well as the endogenous secretome following glycosyltransferase knock-out in different Chinese hamster ovary (CHO) cell lines. Our approach offers a flexible and user-friendly platform that can serve as a basis for powerful computational engineering efforts in mammalian cell factories for biopharmaceutical production.     

Convergence to stationary distributions in two-species stochastic competition models

Two sets of sufficient conditions are given for convergence to stationary distributions, for some general models of two species competing in a randomly varying environment. The models are nonlinear stochastic difference equations which define Markov chains. One set of sufficient conditions involves strong continuity and -irreducibility of the transition probability for the chain. The second set has a much weaker irreducibility condition, but is only applicable to monotonic models. The results are applied to a stochastic two-species Ricker model, and to Chesson's lottery model with vacant space, to illustrate how the assumptions can be checked in specific models.     

Time-varying Markov regression random-effect model with Bayesian estimation procedures: Application to dynamics of functional recovery in patients with stroke

The rates of functional recovery after stroke tend to decrease with time. Time-varying Markov processes (TVMP) may be more biologically plausible than time-invariant Markov process for modeling such data. However, analysis of such stochastic processes, particularly tackling reversible transitions and the incorporation of random effects into models, can be analytically intractable. We make use of ordinary differential equations to solve continuous-time TVMP with reversible transitions. The proportional hazard form was used to assess the effects of an individual’s covariates on multi-state transitions with the incorporation of random effects that capture the residual variation after being explained by measured covariates under the concept of generalized linear model. We further built up Bayesian directed acyclic graphic model to obtain full joint posterior distribution. Markov chain Monte Carlo (MCMC) with Gibbs sampling was applied to estimate parameters based on posterior marginal distributions with multiple integrands. The proposed method was illustrated with empirical data from a study on the functional recovery after stroke.     

Influence of the Interaction for Fitness on the Population Structure

The incidence of non-allelic interaction for fitness in a diallelic digenic model is analyzed. Three types of interaction between the fitnesses are considered, namely: i) additive, ii) exponential and, iii) logarithmic. The model of MATHER and JINKS (1970) is applied in the computation of the interaction parameters. The theory of finite absorbing Markov chains (FELLER, 1968) is utilized in the representation of the genotypic structure of the selfing population over time. Expressions depending on the parameters of interaction are obtained for the mean number of generations that the process of genetic information flow delays in transitions among transient states. Also, a numerical example is studied in which the mean absorption times and the variances are calculated. Finally, the impact of fitness interaction on the genetic diversity of some populations is analyzed through the calculation of the genetic information neg-entropy (BRILLOUIN, 1959).     

A search for patterns in the nucleotide sequence of the MS2 genome

Summary The nucleotide sequence of the RNA of the bacteriophage MS2 was examined by computer for internal patterns. We used a technique which analyzes a nucleotide sequence as a Markov chain. This led us to discover patterns within the translated and untranslated regions of the RNA in addition to those patterns formed by the codons. One of the more surprising results of this analysis was the discovery that the non-coding sequences in the genome are as highly ordered, although in a different sense, as the genes themselves. Also of interest was the discovery that the codon frequency distributions for the three genes are similar.     

On parameter estimation in population models

We describe methods for estimating the parameters of Markovian population processes in continuous time, thus increasing their utility in modelling real biological systems. A general approach, applicable to any finite-state continuous-time Markovian model, is presented, and this is specialised to a computationally more efficient method applicable to a class of models called density-dependent Markov population processes. We illustrate the versatility of both approaches by estimating the parameters of the stochastic SIS logistic model from simulated data. This model is also fitted to data from a population of Bay checkerspot butterfly (Euphydryas editha bayensis), allowing us to assess the viability of this population.     

A Clustering Method for the States of a Markov Chain with an Application to Primate Communication

In some applications clustering problems in stochastic processes arise when the experimenter wants to improve the insight into the structure of the observed system by lumping its states into clusters. As measure of goodness of a cluster solution we choose the rate of predictability of the forthcoming events which can be quantified by means of the entropy of the lumped process. The entropy in turn can be estimated by making use of a Shannon-McMillan-type theorem. We will establish some asymptotic results for the estimate of entropy in the case where the underlying stochastic process is a Markov chain. Our main tool will be a representation of functions of Markov chains which is due to Blackwell (1957). The method proposed here is applied to data on communication processes in monkeys. The object of our analysis will be to find a subgroup structure in the animal group.     

In love and war: altruism, norm formation, and two different types of group selection

We analyse simulations reported in "The co-evolution of individual behaviors and social institutions" by Bowles et al., 2003 in the Journal of Theoretical Biology 223, 135-147, and begin with distinguishing two types of group selection models. The literature does not provide different names for them, but they are shown to be fundamentally different and have quite different empirical implications. The working of the first one depends on the answer to the question "is the probability that you also are an altruist large enough", while the other needs an affirmative answer to "are our interests enough in line". The first one therefore can also be understood as a kin selection model, while the working of the second can also be described in terms of the direct benefits. The actual simulation model is a combination of the two. It is also a Markov chain, which has important implications for how the output data should be handled.     

Bias explorer: measurements of compositional bias in EMBL and GenBank sequence files

A Windows application for compositional analysis of sequenced genomes (EMBL or GenBank flat files) is available as freeware. The application allows the user to quantify word bias using Markov chain analysis and it allows the user to generate sliding window data for GC-skew, AT-skew, purine excess, keto excess and discrete word counts. The mathematical routines reside in a dynamic link library (DLL), which can be used independently by other applications. The software is available for download at http://www.dfuni.dk/~anfu/Bioinformatics/Main.htm. This revised version was published online in June 2006 with corrections to the Cover Date.