Prediction of gene expression specificity by promoter sequence patterns.

The housekeeping (ubiquitously expressed) genes in the mammal genome were shown here to be on average slightly GC-richer than tissue-specific genes. Both housekeeping and tissue-specific genes occupy similar ranges of GC content, but the former tend to concentrate in the upper part of the range. In the human genome, tissue-specific genes show two maxima, GC-poor and GC-rich. The strictly tissue-specific human genes tend to concentrate in the GC-poor region; their distribution is left-skewed and thus reciprocal to the distribution of housekeeping genes. The intermediately tissue-specific genes show an intermediate GC content and the right-skewed distribution. Both in the human and mouse, genes specific for some tissues (e.g., parts of the central nervous system) have a higher average GC content than housekeeping genes. Since they are not transcribed in the germ line (in contrast to housekeeping genes), and therefore have a lower probability of inheritable gene conversion, this finding contradicts the biased gene conversion (BGC) explanation for elevated GC content in the heavy isochores of mammal genome. Genes specific for germ-line tissues (ovary, testes) show a low average GC content, which is also in contradiction to the BGC explanation. Both for the total data set and for the most part of tissues taken separately, a weak positive correlation was found between gene GC content and expression level. The fraction of ubiquitously expressed genes is nearly 1.5-fold higher in the mouse than in the human. This suggests that mouse tissues are comparatively less differentiated (on the molecular level), which can be related to a less pronounced isochoric structure of the mouse genome. In each separate tissue (in both species), tissue-specific genes do not form a clear-cut frequency peak (in contrast to housekeeping genes), but constitute a continuum with a gradually increasing degree of tissue-specificity, which probably reflects the path of cell differentiation and/or an independent use of the same protein in several unrelated tissues.

Statistical analysis of combinatorial transcriptional regulatory motifs in human intron-containing promoter sequences

Do housekeeping genes, which are turned on most of the time in almost every tissue, evolve more slowly than genes that are turned on only at specific developmental times or tissues? Recent large-scale gene expression studies enable us to have a better definition of housekeeping genes and to address the above question in detail. In this study, we examined 1581 human-mouse orthologous gene pairs for their patterns of sequence evolution, contrasting housekeeping genes with tissue-specific genes. Our results show that, in comparison to tissue-specific genes, housekeeping genes on average evolve more slowly and are under stronger selective constraints as reflected by significantly smaller values of Ka/Ks. Besides stronger purifying selection, we explored several other factors that can possibly slow down nonsynonymous rates in housekeeping genes. Although mutational bias might slightly slow the nonsynonymous rates in housekeeping genes, it is unlikely to be the major cause of the rate difference between the two types of genes. The codon usage pattern of housekeeping genes does not seem to differ from that of tissue-specific genes. Moreover, contrary to the old textbook concept, we found that approximately 74% of the housekeeping genes in our study belong to multigene families, not significantly different from that of the tissue-specific genes ( approximately 70%). Therefore, the stronger selective constraints on housekeeping genes are not due to a lower degree of genetic redundancy.

Introns are shorter in housekeeping genes than in tissue- or development-specific genes. Differing explanations have been offered for this phenomenon: selection for economy (in housekeeping genes), mutation bias or "genomic design." The large-scale implementation in this present paper of a rigorous local sequence alignment algorithm revealed an unprecedented fraction of evolutionarily conserved DNA in human-mouse introns ( approximately 60% of human and approximately 70% of mouse intron length remained after masking for lineage-specific repeats). The length distributions of both conserved and nonconserved regions are very broad but show peaks close to nucleosomal and di-nucleosomal DNA. Both the fraction of conserved sequence and its absolute length were higher in introns of tissue-specific genes than housekeeping genes. This difference remained after control for between-species identity of the conserved fraction, mutation rate, and GC content. In a more direct control, the product of the conserved sequence fraction and the between-species identity of this fraction (which can be considered to be the fraction of conserved nucleotides) was greater in introns of tissue-specific genes than housekeeping genes. Neither the fraction of intron length covered by repeats nor the balance of small insertions and deletions (indels) can explain the greater length of introns in tissue-specific genes. The length of the conserved intronic DNA in a gene is correlated with the number of functional domains in the protein encoded by that gene. These results suggest that the greater length of introns in tissue-specific genes is not due to selection for economy or mutation bias but instead is related to functional complexity (probably mediated by chromatin condensation), and that the evolution of the bulk of noncoding DNA is not completely neutral.

In this thesis, the properties of some non-standard Markov chain models and their corresponding parameter estimation methods are investigated. Several practical applications and extensions are also discussed.
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\nThe estimation of model parameters plays a key role in the real-world applications of Markov chain models. Some widely used estimation methods for Markov chain models are based on the existence of stationary vectors. In this thesis, some weaker sufficient conditions for the existence of stationary vectors for highorder Markov chain models, multivariate Markov chain models and high-order multivariate Markov chain models are proposed. Furthermore, for multivariate Markov chain models, a new estimation method based on minimizing the prediction error is proposed. Numerical experiments are conducted to demonstrate the efficiency of the proposed estimation methods with an application in demand prediction.
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\nHidden Markov Model (HMM) is a bivariate stochastic process such that one of the process is hidden and the other is observable. The distribution of observable sequence depends on the hidden sequence. In a traditional HMM, the hidden states directly affect the observable states but not vice versa. However, in reality, observable sequence may also have effect on the hidden sequence. For this reason, the concept of Interactive Hidden Markov Model (IHMM) is introduced, whose key idea is that the transitions of the hidden states depend on the observable states too. In this thesis, efforts are devoted in building a highorder IHMM where the probability laws governing both observable and hidden states can be written as a pair of high-order stochastic difference equations. We also propose a new model by capturing the effect of observable sequence on the hidden sequence through using the threshold principle. In this case, reference probability methods are adopted in estimating the optimal model parameters, while for unknown threshold parameter, Akaike Information Criterion (AIC) is used. We explore asset allocation problems from both domestic and foreign perspective where asset price dynamics follows autoregressive HMM. The object of an investor is not only to maximize the expected utility of the terminal wealth, but also to ensure that the risk of the portfolio described by the Value-at-Risk (VaR) does not exceed a specified level.
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\nIn many decision processes, fuzziness is a major source of imprecision. As a perception of usual Markov chains, the definition of fuzzy Markov chains is introduced. Compared to traditional Markov chain models, fuzzy Markov chains are relatively new and many properties of them are still unknown. Due to the potential applications of fuzzy Markov chains, we provide some characterizations to ensure the ergodicity of these chains under both max-min and max-product compositions.

We have developed an experimental procedure to analyze the spatial proximity of nuclear matrix-bound DNA fragments. This protocol, referred to as Matrix 3C (M3C), includes a high salt extraction of nuclei, the removal of distal parts of unfolded DNA loops using restriction enzyme treatment, ligation of the nuclear matrix-bound DNA fragments and a subsequent analysis of ligation frequencies. Using the M3C procedure, we have demonstrated that CpG islands of at least three housekeeping genes that surround the chicken α-globin gene domain are assembled into a complex (presumably, a transcription factory) that is stabilized by the nuclear matrix in both erythroid and non-erythroid cells. In erythroid cells, the regulatory elements of the α-globin genes are attracted to this complex to form a new assembly: an active chromatin hub that is linked to the pre-existing transcription factory. The erythroid-specific part of the assembly is removed by high salt extraction. Based on these observations, we propose that mixed transcription factories that mediate the transcription of both housekeeping and tissue-specific genes are composed of a permanent compartment containing integrated into the nuclear matrix promoters of housekeeping genes and a ‘guest’ compartment where promoters and regulatory elements of tissue-specific genes can be temporarily recruited.

Simple SummaryUnderstanding and predicting vector population and related disease dynamics, is crucial for gaining insight into the abundance and dynamics of arthropod disease vectors, and for the design of effective vector control strategies. Several mathematical and standard epidemiological models have been proposed in studying vector transmitted infectious disease dynamics. However, most models are of deterministic nature and are not able to estimate other relevant metrics such as the probability of vector population emergence as well as the probability and expected time to reach certain population and/or infection state. Here we are focusing on stochastic modeling of mosquito abundance data using weather driven Markov chains (MCs) and are particularly interested in estimating transition probabilities (TPs) between different population levels. A MC model is based on the assumption that the future state of the variable is only dependent on the present state and is suitable in cases of short and noisy data characterized by a complex and random behavior. The aim is to introduce and generalize a formulation of conditional Markov chain models (CMSs) for predicting probability transition estimates of arthropod vector populations. In this context, first we present the basic principles and assumptions behind Markov chain modeling approach, with an intuitive interpretation of the integration of conditional Markov chains (CMCs) and then demonstrate the usefulness of the approach in predicting the abundance of Culex sp. We conclude that the conditional Markov chain technique is recommended as viable for modeling populations that explicit random dynamics and predict their future evolution. Although, the Markov models generated in this work provide an accurate abstraction of the vector disease progress observed within the dataset used for their generation, we envision the current approach as an entry point into the medical entomology literature and methods for predicting arthropod vector diseases dynamics.Understanding and predicting mosquito population dynamics is crucial for gaining insight into the abundance of arthropod disease vectors and for the design of effective vector control strategies. In this work, a climate-conditioned Markov chain (CMC) model was developed and applied for the first time to predict the dynamics of vectors of important medical diseases. Temporal changes in mosquito population profiles were generated to simulate the probabilities of a high population impact. The simulated transition probabilities of the mosquito populations achieved from the trained model are very near to the observed data transitions that have been used to parameterize and validate the model. Thus, the CMC model satisfactorily describes the temporal evolution of the mosquito population process. In general, our numerical results, when temperature is considered as the driver of change, indicate that it is more likely for the population system to move into a state of high population level when the former is a state of a lower population level than the opposite. Field data on frequencies of successive mosquito population levels, which were not used for the data inferred MC modeling, were assembled to obtain an empirical intensity transition matrix and the frequencies observed. Our findings match to a certain degree the empirical results in which the probabilities follow analogous patterns while no significant differences were observed between the transition matrices of the CMC model and the validation data (ChiSq = 14.58013, df = 24, p = 0.9324451). The proposed modeling approach is a valuable eco-epidemiological study. Moreover, compared to traditional Markov chains, the benefit of the current CMC model is that it takes into account the stochastic conditional properties of ecological-related climate variables. The current modeling approach could save costs and time in establishing vector eradication programs and mosquito surveillance programs.

A flexible transcription initiation architecture and a uniform regulatory grammar buffer expression variability and the effects of genetic variants on essential genes while a rigid transcription initiation architecture and a diverse regulatory grammar ensure condition-specific responsiveness of other genes.

RNA is a single stranded nucleic acid type, produced by DNA via a process of transcription.RNA tend to form secondary structure by pairing the complementary base pairs within it.The RNA secondary structure is an essential parameter to understand its functions and expression into a protein.This research is based to understand the link between RNA secondary structure and abiotic stresses in two leading organisms viz; Arabidopsis thaliana and Oryza sativa.For this purpose the RNA secondary structures analyses for abiotic stress resistant and housekeeping genes were done in A. thaliana and O. sativa.Total 138 abiotic stress resistant and 2l housekeeping genes were selected through literature survey and subjected to RNA secondary structure tool.RNA secondary structure were analyzed and characterized in terms of minimum free energy, number of stems and loops.The 80% of abiotic stress and 67% of housekeeping genes show significant difference in the minimum free energy (MFE) while there were no significant variation in the number of stems and loops.The open reading frame analysis, as an additional parameter, of the selected genes were also done, that revealed the preference of +1 frame by most of the genes, i.e., 42% of abiotic stress and 43% of housekeeping genes.RNA secondary structure analysis help us to engineer stress resistant genes with stable RNA structures.

A practical probabilistic data association filter is proposed for tracking multiple targets in clutter. The number of joint data association events increases combinatorially with the number of measurements and the number of targets, which may become computationally impractical for even small numbers of closely located targets in real target-tracking applications in heavily cluttered environments. In this paper, a Markov chain model is proposed to generate a set of feasible joint events (FJEs) for multiple target tracking that is used to approximate the multi-target data association probabilities and the probabilities of target existence of joint integrated probabilistic data association (JIPDA). A Markov chain with the transition probabilities obtained from the integrated probabilistic data association (IPDA) for single-target tracking is designed to generate a random sequence composed of the predetermined number of FJEs without incurring additional computational cost. The FJEs generated are adjusted for the multi-target tracking environment. A computationally tractable set of these random sequences is utilized to evaluate the track-to-measurement association probabilities such that the computational burden is substantially reduced compared to the JIPDA algorithm. By a series of simulations, the track confirmation rates and target retention statistics of the proposed algorithm are compared with the other existing algorithms including JIPDA to show the effectiveness of the proposed algorithm.

In many applications, successive observations of a process, say X 1, X 2,…, have an inherent time component associated with them. For example, the X i could be the state of the weather at a particular location on the i th day, counting from some fixed day. In a simplistic model, the state of the weather could be “dry” or “wet,” quantified as, say, 0 and 1. It is hard to believe that in such an example, the sequence X 1, X 2,… could be mutually independent. The question then arises how to model the dependence among the X i ′s. Probabilists have numerous dependency models. A particular model that has earned a very special status is called the Markov chain. In a Markov chain model, we assume that the future, given the entire past and the present state of a process, depends only on the present state. In the weather example, suppose we want to assign a probability that tomorrow, say March 10, will be dry, and suppose that we have available to us the precipitation history for each day from January 1 to March 9. In a Markov chain model, our probability that March 10 will be dry will depend only on the state of the weather on March 9, even though the entire past precipitation history was available to us. As simple as it sounds, Markov chains are enormously useful in applications, perhaps more than any other specific dependency model. They also are independently relevant to statistical computing in very important ways. The topic has an incredibly rich and well-developed theory, with links to many other topics in probability theory. Familiarity with basic Markov chain terminology and theory is often considered essential for anyone interested in studying statistics and probability.We present an introduction to basic Markov chain theory in this chapter.KeywordsMarkov ChainStationary DistributionMarkov Chain ModelTransition Probability MatrixStationary Markov ChainThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

In a comprehensive study, the temporal replication of tissue-specific genes and flanking sequences was compared in nine cell lines exhibiting different tissue-specific functions. Some of the rules we have determined for the replication of these tissue specific genes include the following. (i) Actively transcribed genes usually replicate during the first quarter of the S phase. (ii) Some immunoglobulin genes replicate during the first half of S phase even when no transcriptional activity is detected but appear to replicate even earlier in cell lines where they are transcribed. (iii) Nontranscribed genes can replicate during any interval of S phase. (iv) Multigene families arranged in clusters of 250 kilobases or less define a temporal compartment comprising approximately one-quarter of S phase. While these rules, and others that are discussed, apply to the tissue-specific genes studied here, all tissue-specific genes may not follow this pattern. In addition, housekeeping genes did not follow some of these rules. These results provide the first molecular evidence that the coordinate timing of replication of contiguous sequences within a multigene family is a general property of the mammalian genome. The relationship between replication very early during S phase and the transcriptional activity within a chromosomal domain is discussed.

An environmental problem which is of concern across the globe nowadays is air pollution. The extent of air pollution is often studied based on data on the observed level of air pollution. Although the analysis of air pollution data that is available in the literature is numerous, studies on the dynamics of air pollution with the allowance for spatial interaction effects through the use of the Markov chain model are very limited. Accordingly, this study aims to explore the potential impact of spatial dependence over time and space on the distribution of air pollution based on the spatial Markov chain (SMC) model using the longitudinal air pollution index (API) data. This SMC model is pertinent to be applied since the daily data of API from 2012 to 2014 that have been gathered from 37 different air quality stations in Peninsular Malaysia is found to exhibit the property of spatial autocorrelation. Based on the spatial transition probability matrices found from the SMC model, specific characteristics of air pollution are studied in the regional context. These characteristics are the long-run proportion and the mean first passage time for each state of air pollution. It is found that the probability for a particular station's state to remain good is 0.814 if its neighbors are in a good state of air pollution and 0.7082 if its neighbors are in a moderate state. For a particular station having neighbors in a good state of air pollution, the proportion of time for it to continue being in a good state is 0.6. This proportion reduces to 0.4, 0.01, and 0 for the cell of moderate, unhealthy, and very unhealthy states, respectively. In addition, there exists a significant spatial dependence of API, indicating that air pollution for a particular station is dependent on the states of the neighboring stations.

<p>Mixed traffic systems are dynamically complex since there are many parameters and variables that influence the interactions between the different kinds of vehicles. Modeling the behavior of vehicles, especially motorcycle which has erratic behavior is still being developed continuously, especially in developing countries which have heterogeneous traffic. To get a better understanding of motorcycle behavior, one can look at maneuvers performed by drivers. In this research, we tried to build a model of motorcycle movement which only focused on maneuver action to avoid the obstacle along with the trajectories using a Markov Chain approach. In Markov Chain, the maneuver of motorcycle will described by state transition. The state transition model is depend on probability function which will use for determine what action will be executed next. The maneuver of motorcycle using Markov Chain model was validated by comparing the analytical result with the naturalistic data, with similarity is calculated using MSE. In order to know how good our proposed method can describe the maneuver of motorcycle, we try to compare the MSE of the trajectory based on Markov Chain model with those using polynomial approach. The MSE results showed the performance of Markov Chain Model give the smallest MSE which 0.7666 about 0.24 better than 4th order polynomial.</p>

기존의 정상성 Markov Chain 모형은 자료 자체의 Markov 특성만을 고려하여 모의하는 기법으로서 수자원 설계에서 여러 가지 목적으로 이용되어 지고 있다. 그러나 일강수량의 천이확률 및 매개변수 등이 과거와 일정하다는 정상성을 기본 가정으로 하기 때문에 평균의 변동성 등과 같은 외부충격을 모형에 적용할 수 없다. 이러한 관점에서 본 연구의 가장 큰 목적은 기존일강수량 모형을 외부인자를 받아들일 수 있는 모형으로 개발하는 것이다. 즉, Markov Chain 모형의 매개변수인 천이확률과 확률분포형의 매개변수 등을 연결함수(link function)를 통해 외부인자와 연동하도록 하였으며 정준상관분석을 통해 매개변수를 추정하였다. 개발된 모형을 서울지방 1961-2006년까지의 일강수량 자료를 대상으로 검증하는 절차를 가졌다. 추정된 결과를 보면 계절강수량의 특성뿐만 아니라 일강수량의 특성 또한 적절하게 모의되는 것을 확인할 수 있다. 따라서 본 연구에서 개발된 모형은 GCM 예측결과를 입력자료로 활용한다면 일강수계열의 장단기 모의를 위한 downscaling 기법으로 사용될 수 있다. 또한, 기후변화 시나리오가 입력자료로 이용된다면 기후변화에 따른 수자원 영향 평가를 위한 downscaling 기법으로 활용이 가능할 것으로 판단된다. A stationary Markov chain model is a stochastic process with the Markov property. Having the Markov property means that, given the present state, future states are independent of the past states. The Markov chain model has been widely used for water resources design as a main tool. A main assumption of the stationary Markov model is that statistical properties remain the same for all times. Hence, the stationary Markov chain model basically can not consider the changes of mean or variance. In this regard, a primary objective of this study is to develop a model which is able to make use of exogenous variables. The regression based link functions are employed to dynamically update model parameters given the exogenous variables, and the model parameters are estimated by canonical correlation analysis. The proposed model is applied to daily rainfall series at Seoul station having 46 years data from 1961 to 2006. The model shows a capability to reproduce daily and seasonal characteristics simultaneously. Therefore, the proposed model can be used as a short or mid-term prediction tool if elaborate GCM forecasts are used as a predictor. Also, the nonstationary Markov chain model can be applied to climate change studies if GCM based climate change scenarios are provided as inputs.