A windows program for the derivation of steady-state equations in enzyme systems

Despite the number of computer-assisted methods described for the derivation of steady-state equations of enzyme systems, most of them are focused on strict steady-state conditions or are not able to solve complex reaction mechanisms. Moreover, many of them are based on computer programs that are either not readily available or have limitations. We present here a computer program called WinStes, which derives equations for both strict steady-state systems and those with the assumption of rapid equilibrium, for branched or unbranched mechanisms, containing both reversible and irreversible conversion steps. It solves reaction mechanisms involving up to 255 enzyme species, connected by up to 255 conversion steps. The program provides all the advantages of the Windows programs, such as a user-friendly graphical interface, and has a short computation time. WinStes is available free of charge on request from the authors. (c) 2006 Elsevier Inc. All rights reserved.

Managing ontologies: A comparative study of ontology servers

An ontology is increasingly becoming an essential tool for solving problems in many research areas. The ontology is a complex information object. It can contain millions of concepts in complex relationships. When we want to manage complex information objects, we generally turn to information systems technology. An information system intended to manage ontology is called an ontology server. The ontology server technology is at the time of writing quite immature. Therefore, this paper reviews and compares the main ontology servers that have been reported in the literatures. As a result, we point out several research questions related to server technology

Uses for JNK: the many and varied substrates of the c-Jun N-terminal kinases

The c-Jun N-terminal kinases (JNKs) are members of a larger group of serine/ threonine (Ser/Thr) protein kinases from the mitogen-activated protein kinase family. JNKs were originally identified as stress-activated protein kinases in the livers of cycloheximide-challenged rats. Their subsequent purification, cloning, and naming as JNKs have emphasized their ability to phosphorylate and activate the transcription factor c-Jun. Studies of c-Jun and related transcription factor substrates have provided clues about both the preferred substrate phosphorylation sequences and additional docking domains recognized by JNK There are now more than 50 proteins shown to be substrates for JNK These include a range of nuclear substrates, including transcription factors and nuclear hormone receptors, heterogeneous nuclear ribonucleoprotein K and the Pol I-specific transcription factor TIF-IA, which regulates ribosome synthesis. Many nonnuclear substrates have also been characterized, and these are involved in protein degradation (e.g., the E3 ligase Itch), signal transduction (e.g., adaptor and scaffold proteins and protein kinases), apoptotic cell death (e.g., mitochondrial Bcl2 family members), and cell movement (e.g., paxillin, DCX, microtubule-associated proteins, the stathmin family member SCG10, and the intermediate filament protein keratin 8). The range of JNK actions in the cell is therefore likely to be complex. Further characterization of the substrates of JNK should provide clearer explanations of the intracellular actions of the JNKs and may allow new avenues for targeting the JNK pathways with therapeutic agents downstream of JNK itself.

Hydrogen from coal: Production and utilisation technologies

Error condition detected Although coal may be viewed as a dirty fuel due to its high greenhouse emissions when combusted, a strong case can be made for coal to be a major world source of clean H-2 energy. Apart from the fact that resources of coal will outlast oil and natural gas by centuries, there is a shift towards developing environmentally benign coal technologies, which can lead to high energy conversion efficiencies and low air pollution emissions as compared to conventional coal fired power generation plant. There are currently several world research and industrial development projects in the areas of Integrated Gasification Combined Cycles (IGCC) and Integrated Gasification Fuel Cell (IGFC) systems. In such systems, there is a need to integrate complex unit operations including gasifiers, gas separation and cleaning units, water gas shift reactors, turbines, heat exchangers, steam generators and fuel cells. IGFC systems tested in the USA, Europe and Japan employing gasifiers (Texaco, Lurgi and Eagle) and fuel cells have resulted in energy conversions at efficiency of 47.5% (HHV) which is much higher than the 30-35% efficiency of conventional coal fired power generation. Solid oxide fuel cells (SOFC) and molten carbonate fuel cells (MCFC) are the front runners in energy production from coal gases. These fuel cells can operate at high temperatures and are robust to gas poisoning impurities. IGCC and IGFC technologies are expensive and currently economically uncompetitive as compared to established and mature power generation technology. However, further efficiency and technology improvements coupled with world pressures on limitation of greenhouse gases and other gaseous pollutants could make IGCC/IGFC technically and economically viable for hydrogen production and utilisation in clean and environmentally benign energy systems. (c) 2005 Elsevier B.V. All rights reserved.

Semi-supervised learning of hierarchical latent trait models for data visualisation

An interactive hierarchical Generative Topographic Mapping (HGTM) ¸iteH_GTM has been developed to visualise complex data sets. In this paper, we build a more general visualisation system by extending the HGTM visualisation system in 3 directions: bf (1) We generalize HGTM to noise models from the exponential family of distributions. The basic building block is the Latent Trait Model (LTM) developed in ¸iteKaban_pami. bf (2) We give the user a choice of initializing the child plots of the current plot in either em interactive, or em automatic mode. In the interactive mode the user interactively selects ``regions of interest'' as in ¸iteH_GTM, whereas in the automatic mode an unsupervised minimum message length (MML)-driven construction of a mixture of LTMs is employed. bf (3) We derive general formulas for magnification factors in latent trait models. Magnification factors are a useful tool to improve our understanding of the visualisation plots, since they can highlight the boundaries between data clusters. The unsupervised construction is particularly useful when high-level plots are covered with dense clusters of highly overlapping data projections, making it difficult to use the interactive mode. Such a situation often arises when visualizing large data sets. We illustrate our approach on a toy example and apply our system to three more complex real data sets.

Data visualisation with missing data: A non-linear approach

Exploratory analysis of data in all sciences seeks to find common patterns to gain insights into the structure and distribution of the data. Typically visualisation methods like principal components analysis are used but these methods are not easily able to deal with missing data nor can they capture non-linear structure in the data. One approach to discovering complex, non-linear structure in the data is through the use of linked plots, or brushing, while ignoring the missing data. In this technical report we discuss a complementary approach based on a non-linear probabilistic model. The generative topographic mapping enables the visualisation of the effects of very many variables on a single plot, which is able to incorporate far more structure than a two dimensional principal components plot could, and deal at the same time with missing data. We show that using the generative topographic mapping provides us with an optimal method to explore the data while being able to replace missing values in a dataset, particularly where a large proportion of the data is missing.