Recovering Genetic Regulatory Networks from Chromatin Immunoprecipitation and Steady-State Microarray Data

<p/> <p>Recent advances in high-throughput DNA microarrays and chromatin immunoprecipitation (ChIP) assays have enabled the learning of the structure and functionality of genetic regulatory networks. In light of these heterogeneous data sets, this paper proposes a novel approach for reconstruction of genetic regulatory networks based on the posterior probabilities of gene regulations. Built within the framework of Bayesian statistics and computational Monte Carlo techniques, the proposed approach prevents the dichotomy of classifying gene interactions as either being connected or disconnected, thereby it reduces significantly the inference errors. Simulation results corroborate the superior performance of the proposed approach relative to the existing state-of-the-art algorithms. A genetic regulatory network for <it>Saccharomyces cerevisiae</it> is inferred based on the published real data sets, and biological meaningful results are discussed.</p

Elasmobranch (sharks and rays) interaction with plastic pollution from global and local perspectives, via entanglement within anthropogenic debris and synthetic fibre ingestion

Plastic pollution is a known threat to a host of marine organisms across the world. Research in recent years has exposed numerous negative impacts on some of the world’s most threatened marine species, including turtles, cetaceans and pinnipeds. The impact of plastic pollution on elasmobranchs, however, has been relatively understudied. Sharks and rays are widely accepted to be two of the most threatened marine species in the oceans, most notably due to anthropogenic impacts including direct fisheries and bycatch. Their relationship with plastic pollution is only now being investigated in further detail. Previous studies have alluded to damaging effects on sharks and rays as a result of plastic pollution but have lacked in wide synthesis of existing information and empirical evidence. In this thesis, the impact of entanglement within and ingestion of plastic is highlighted for sharks and rays both globally and locally in the North-East Atlantic. Chapter one aimed to collect existing information on the occurrence and distribution of elasmobranch entanglement events, using a systematic literature review and novel data collection from social media site “Twitter”. Our results highlighted ghost fishing gear to be the most common entangling material for sharks and rays globally, consistent with previous studies on other marine species. The review also highlighted the lack of standardised reporting for elasmobranch entanglement and therefore resulted in the creation of an online entanglement report form for sharks and rays (ShaREN), allowing citizen scientists across the world to report entanglement incidents quickly and efficiently. Chapter two investigated the presence of microplastics and synthetic contaminant particles in four species of demersal shark found in the North-East Atlantic. Almost 70% of sharks analysed contained at least one contaminant particle, 2 however no significant relationship between size/weight and number of contaminants was identified, although further analysis was recommended. The study highlighted the ubiquity of synthetic fibres such as rayon and viscose, commonly found in clothing items, as contaminants in the marine environment. Chapter two presents the first empirical evidence of microplastic ingestion by UK shark species and highlights the pervasive nature of microplastic pollution off the English coast. While these two threats are unlikely to have significant population impacts on sharks and rays globally, similar to that of direct fisheries and bycatch, they are identified to be of clear animal welfare concern for these species. Entanglement within and ingestion of plastic is symptomatic of a degraded marine environment and highlights the need for policy-makers, scientists and stakeholders to work together to mitigate this issue for all marine species

A case study of the televised international newsflow of Raidió Teilifís Éireann and The Canadian Broadcasting Corporation: A comparative content analysis

The objective of this comparative newsflow study was to analyse the televised international news broadcast in the national public service of Canada and the Republic of Ireland over a thirty-day term. In doing so, a quantitative content analysis comparing the output of two national public service providers (PSB), Raidió Teilifís Éireann (RTÉ) and the Canadian Broadcasting Corporation (CBC) is offered. In identifying the national origin of the international news, those reports utilizing the foreign correspondents of the PSBs were quantified. Finally, the ratio of international to domestic reportage and the volume of international news reports by quantity and duration are also compared. This study reviews the literature of cultural, corporate and state sovereignty as it looks to the regulatory structures of the broadcasters. Gatekeeping dynamics and the critical media ecology of a re-feudalizing public sphere are addressed as are the roles of framing and domestication. An exploration of cultural imperialism and the newsflow studies of globalization and deregulation are also undertaken. The commercialization of international news values, compassion fatigue and declining demand are similarly explored. Satellite broadcasting and the influence of the news agencies is considered as is the literature pertaining to crisis-news driven parachute journalism and the role of the foreign correspondent. The study revealed that the real sovereignty of both the CBC and RTÉ is demonstrably limited in terms of their ability to control the production chain from the source of the news through to the audiences. It’s argued that larger outputs of international news increase the value accrued to civic knowledge and therein the value of the service offered. In terms of the offered ‘value for public money’ it’s concluded that audiences of the CBC routinely receive greater value than do those of RTÉ

Stochastic Oscillations in Genetic Regulatory Networks: Application to Microarray Experiments

<p/> <p>We analyze the stochastic dynamics of genetic regulatory networks using a system of nonlinear differential equations. The system of <inline-formula><graphic file="1687-4153-2006-59526-i1.gif"/></inline-formula>-functions is applied to capture the role of RNA polymerase in the transcription-translation mechanism. Using probabilistic properties of chemical rate equations, we derive a system of stochastic differential equations which are analytically tractable despite the high dimension of the regulatory network. Using stationary solutions of these equations, we explain the apparently paradoxical results of some recent time-course microarray experiments where mRNA transcription levels are found to only weakly correlate with the corresponding transcription rates. Combining analytical and simulation approaches, we determine the set of relationships between the size of the regulatory network, its structural complexity, chemical variability, and spectrum of oscillations. In particular, we show that temporal variability of chemical constituents may decrease while complexity of the network is increasing. This finding provides an insight into the nature of "functional determinism" of such an inherently stochastic system as genetic regulatory network.</p

Параметрическая идентификация S-системы с применением модифицированного алгоритма клонального отбора

Предложен метод реконструкции генных регуляторных сетей, который в сочетании с алгоритмом оптимизации (эволюционным или иммунным) позволяет повысить скорость и точность решения задачи параметрической идентификации S-системы. Изложена суть метода последовательной трансформации пространства решений, управляемой результатами работы алгоритма оптимизации.Запропоновано метод реконструкції генних регуляторних мереж, який в поєднанні з алгоритмом оптимізації (еволюційним або імунним) дозволяє підвищити швидкість і точність розв’язання задачі параметричної ідентифікації S-системи. Викладено суть методу послідовної трансформації простору розв’язань, керованої результатами роботи алгоритму оптимізації.Purpose. The aim of this work is to create an effective method of the optimal parameters of the mathematical model of a gene regulatory network searching based on the ordinary differential equations system represented in the form of S-system. Method. A method is based on the successive transformation of decision space, guided by the results of the separate starting of clonal selection algorithm, hereupon space compresses in the vicinity of the global optimum. Results. A method for reconstructing the gene regulatory networks based on a modified clonal selection algorithm is developed. The method uses time series data of the gene expression profiles for searching interconnections between GRN components. The efficiency of the proposed method is confirmed by the experimental studies. Conclusion. The developed method and the algorithm increase the speed of the convergence of the optimization algorithms, and at the same time improve their accuracy in solving the problem of parametric identification of S-System. The proposed method can be used for modification of the evolutionary algorithms or artificial immune systems. Besides, in our future research we plan to test the method effectiveness on the real biological data

Гибридный подход при реконструкции генных регуляторных сетей

Описан гибридный метод реконструкции генной регуляторной сети по временным рядам данных экспрессии генов. В качестве модели сети предложена система обыкновенных дифференциальных уравнений. Для проверки эффективности метода проведены исследования на двух моделях искусственных регуляторных сетей.Розроблено гібридний метод реконструкції генної регуляторної мережі у часових рядах даних експресії генів. Як модель запропоновано систему звичайних диференціальних рівнянь. Для перевірки ефективності методу проведено дослідження на двох моделях штучних регуляторних мереж.Introduction. Although there are a variety of models and methods for gene regulatory networks reconstruction, the problem of obtaining an adequate model based on experimental data is still urgent. In this regard, many studies use a fixed record of the differential equations based on the S-system. A significant disadvantage of such fixed record of the differential equations is the lack of flexibility of the model, what limits the scope of its application. Purpose. The purpose of this work is development of the hybrid procedure of the solution of the gene networks reconstruction problem based on the ordinary differential equations. Method. Models of the ordinary differential equations are used to model the gene regulatory networks. To solve the differential equations, wavelet-neural networks are used. The topology and tuning of the parameters is determined using the algorithm of the clonal selection. To find the concentration of gene expression products, which are represented by the method of solving the Cauchy problem, the Runge-Kutta method of the fourth order is applied. Results. A hybrid method is developed that implemented the procedure for reconstructing gene regulatory networks based on the gene expression data. The effectiveness of the proposed method is proved by experimental studies that confirm the applicability of this approach to find the relationships between the components of the GRN. Conclusion. The proposed work is a new Wavelet Neural Network and Clonal Algorithm approach for inferring Gene Regulatory Network which is expressed in terms of the ordinary differential model. The result of the proposed procedure is that further improvement of the technology, combined with preprocessing methods, will allow the effective reconstruction of real GRN. The main directions of further research we have chosen to create a meta-procedure for automatic configuration of parameters of a hybrid algorithm. This meta-procedure will reduce the search space by dynamically changing the intervals of representation of the elements values that make up the individuals of the AIS. Using this new method the bioinformatics and biologists can infer any Gene Regulatory Network of their interest. Also, they can understand the regulatory mechanism of the specific genes which causes the combat diseases

カゴシマケンデハッセイシタサトイモカンプビョウノビョウゲンFusarium spp.トセイブツテキボウジョニカンスルケンキュウ

東京農工大学Tokyo University of Agriculture and Technology博士（農学）Doctor of Philosophy (Agriculture)doctoral thesi

Flexibility of the Cytoplasmic Domain of the Phototaxis Transducer II from Natronomonas pharaonis

Chemo- and phototaxis systems in bacteria and archaea serve as models for more complex signal transduction mechanisms in higher eukaryotes. Previous studies of the cytoplasmic fragment of the phototaxis transducer (pHtrII-cyt) from the halophilic archaeon Natronomonas pharaonis showed that it takes the shape of a monomeric or dimeric rod under low or high salt conditions, respectively. CD spectra revealed only approximately 24% helical structure, even in 4 M KCl, leaving it an open question how the rod-like shape is achieved. Here, we conducted CD, FTIR, and NMR spectroscopic studies under different conditions to address this question. We provide evidence that pHtrII-cyt is highly dynamic with strong helical propensity, which allows it to change from monomeric to dimeric helical coiled-coil states without undergoing dramatic shape changes. A statistical analysis of predicted disorder for homologous sequences suggests that structural flexibility is evolutionarily conserved within the methyl-accepting chemotaxis protein family

NML Computation Algorithms for Tree-Structured Multinomial Bayesian Networks

Typical problems in bioinformatics involve large discrete datasets. Therefore, in order to apply statistical methods in such domains, it is important to develop efficient algorithms suitable for discrete data. The minimum description length (MDL) principle is a theoretically well-founded, general framework for performing statistical inference. The mathematical formalization of MDL is based on the normalized maximum likelihood (NML) distribution, which has several desirable theoretical properties. In the case of discrete data, straightforward computation of the NML distribution requires exponential time with respect to the sample size, since the definition involves a sum over all the possible data samples of a fixed size. In this paper, we first review some existing algorithms for efficient NML computation in the case of multinomial and naive Bayes model families. Then we proceed by extending these algorithms to more complex, tree-structured Bayesian networks