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BackgroundSeveral dynamic models of a gene regulatory network of the light-induced floral transition process in Arabidopsis have been developed to capture the behavior of gene transcription and infer predictions based on experimental observations. It has been proven that the models can make accurate and novel predictions, which generate testable hypotheses.Two major issues were addressed in this study. First, construction of dynamic models for gene regulatory networks requires the use of mathematic modeling that comprises equations of a large number of parameters. Second, the binding mechanism of the transcription factor with DNA is another factor that requires detailed modeling. The first issue was tackled by adopting an optimization algorithm, and the second was addressed by comparing the performance of three alternative modeling approaches, namely the S-system, the Michaelis-Menten model and the Mass-action model. The efficiencies of parameter estimation and modeling performance were calculated based on least square error (O(p)), mean relative error (MRE) and Akaike Information Criterion (AIC).ResultsWe compared three models to describe gene regulation of the flowering transition process in Arabidopsis. The Mass-action model is the simplest and has the least parameters. It is therefore less computation-intensive with the smallest AIC value. The disadvantage, however, is that it assumes the system is simply a second order reaction which is not the case in our study. The Michaelis-Menten model also assumes the system is homogeneous and ignores the intracellular protein transport process. The S-system model has the best performance and it does describe the diffusion effects. A disadvantage of the S-system is that it involves the most parameters. The largest AIC value also implies an over-fitting may occur in parameter estimation.ConclusionsThree dynamic models were adopted to describe the dynamics of the gene regulatory network of the flowering transition process in Arabidopsis. Based on MRE, the least square error and global sensitivity analysis, the S-system has the best performance. However, the fact that it has the highest AIC suggests an over-fitting may occur in parameter estimation. The result of this study may need to be applied carefully when modeling complex gene regulatory networks.

This paper presents a Semantic Software Development Model (SSDM ) for object-oriented software. It organizes all the information generated during the software development lifecycle including requirements, design, implementation, testing, and maintenance. Based on SSDM, software testing and maintenance can be carried out in a more systematic, efficient and complete manner, and can be enhanced by a set of proactive rules defined. IntroductionDeveloping software with high quality is one of the important objectives of software engineering. In the past, numerous testing and maintenance techniques have been developed to facilitate software testing and maintenance. Nowadays people consider more and more about the completeness and effectiveness of such techniques in order to increase the developers' confidence in software quality.To some extent, all these techniques have been developed based on the information collected about software requirements, design, implementation, testing and maintenance. If the information can be organized into a complete and tight-coupled model, the model can then be used to support more systematic, complete and efficient software testing and maintenance.In this paper, we will discuss model-based test case generation and model-based regression test selection for software testing and maintenance. Related WorkModel-based testing and maintenance have been provided in many software development systems. For example , Rhapsody [1] integrated a set of technologies to develop software systems in a highly reusable and platform-independent way based on the idea of executable models [2], where model-based testing and maintenance are able to specify and run tests, and to detect defects from the test results by visualizing the failure points within the model. It also provides a model-based testing component to facilitate consistency checking between the requirements and the system constructed. Model-based test case generation techniques have been well-developed. These techniques are suitable for state-based systems that are modeled by a formal or semi -formal description language, like Extended Finite State Machine (EFSM), Specification Description Language (SDL), etc. A set of tools ([6], [9], [10], [11], etc) have been developed based on these techniques to generate system-level test suites from system models. Instead of using models, some other methodologies such as Genetic algorithms (GAs) have been proposed for test case generation. For example, i n the paper about path testing [3], Jin-Cherng Lin et al. used a set of GAs to generate test cases to test a selected path. They developed a new fitness function (SIMILARITY) to determine the distance between the exercised path and the target path for a given target path. In a paper about real-time system testing [4], J. Wegener, et al. declared an appropriate fitness function to measure the execution time for a real-time system. Through establishing the longest and shortest execution times, GAs can be used to validate the temporal correctness. In a paper abo...

Semantic Computing extends Semantic Web both in breadth and depth. It bridges, and integrates, technologies such as software engineering, user interface, natural language processing, artificial intelligence, programming language, grid computing and pervasive computing, among others, into a complete and unified theme. Cloud Computing, the dream of computing as a utility, shifts user programs and data from personal computers to the clouds, providing all kinds of resources over the Internet as services to customers and letting customers pay for them in a way much like they pay for traditional utilities such as electricity. This paper analyzes both Semantic Computing and Cloud Computing, and introduces the Semantic Search Engine, an infrastructure and implementation of Semantic Computing, that demonstrates how Semantic Computing can benefit Cloud Computing.

Molecular and isotopic compositions of void gases from Sites 618 and 619 were determined. The gases were predominantly methane with the C L /C 2 ratios averaging 4,000 and 26,000 at Sites 618 and 619, respectively. The δ 13 C-Cj values at Site 618 were nearly constant, ranging from -70.1 to -73.7‰. δ 13 C-CHj values for Site 619 between 76 and 178 meters sub-bottom ranged from -94.8 to -70.8‰, becoming progressively heavier with depth. The molecular and isotopic compositions at both sites are characteristic of biogenic methane.Small spheres (~ 1-2 mm diameter) of gas hydrates were observed in the Orca Basin between 20 and 40 m sub-bottom and were often associated with sandy sections. The hydrate was predominantly methane with a δ 13 -Cj of -71‰ and trace amounts of ethane, propane, and carbon dioxide. The isotopic value for the methane was similar to that of the nearest gas pocket, indicating that hydrate was formed from predominantly biogenic methane without isotopic fractionation.