http://www.biomedcentral.com/bmcsystbiol/ The editor's pick of recent articles published by BMC Systems Biology 2012-05-30T00:00:00Z Background: Alzheimer's disease (AD) is the most common cause of dementia among the elderly. To clarify pathogenesis of AD, thousands of reports have been accumulating. However, knowledge of signaling pathways in the field of AD has not been compiled as a database before.DescriptionHere, we have constructed a publicly available pathway map called "AlzPathway" that comprehensively catalogs signaling pathways in the field of AD. We have collected and manually curated over 100 review articles related to AD, and have built an AD pathway map using CellDesigner. AlzPathway is currently composed of 1347 molecules and 1070 reactions in neuron, brain blood barrier, presynaptic, postsynaptic, astrocyte, and microglial cells and their cellular localizations. AlzPathway is available as both the SBML (Systems Biology Markup Language) map for CellDesigner and the high resolution image map. AlzPathway is also available as a web service (online map) based on Payao system, a community-based, collaborative web service platform for pathway model curation, enabling continuous updates by AD researchers. Conclusions: AlzPathway is the first comprehensive map of intra, inter and extra cellular AD signaling pathways which can enable mechanistic deciphering of AD pathogenesis. The AlzPathway map is accessible at http://alzpathway.org/. http://www.biomedcentral.com/1752-0509/6/52 Satoshi Mizuno Risa Iijima Soichi Ogishima Masataka Kikuchi Yukiko Matsuoka Samik Ghosh Tadashi Miyamoto Akinori Miyashita Ryozo Kuwano Hiroshi Tanaka BMC Systems Biology 2012, 6:52 2012-05-30T00:00:00Z ${item.identifier} A pathway map for Alzheimer AlzPathway is a publicly available database collating known Alzheimer?s disease intra, inter and extra cellular signalling pathways into a comprehensive pathway map, providing a useful resource in deciphering Alzheimer?s disease pathogenesis. /content/figures/1752-0509-6-52-toc.gif BMC Systems Biology 1752-0509 6 52 2012-05-30T00:00:00Z PDF Background: It is well known that the deterministic dynamics of biochemical reaction networks can be more easily studied if timescale separation conditions are invoked (the quasi-steady-state assumption). In this case the deterministic dynamics of a large network of elementary reactions are well described by the dynamics of a smaller network of eective reactions. Each of the latter represents a group of elementary reactions in the large network and has associated with it an effective macroscopic rate law. A popular method to achieve model reduction in the presence of intrinsic noise consists of using the effective macroscopic rate laws to heuristically deduce effective probabilities for the effective reactions which then enables simulation via the stochastic simulation algorithm (SSA). The validity of this heuristic SSA method is a priori doubtful because the reaction probabilities for the SSA have only been rigorously derived from microscopic physics arguments for elementary reactions. Results: We here obtain, by rigorous means and in closed-form, a reduced linear Langevin equation description of the stochastic dynamics of monostable biochemical networks in conditions characterized by small intrinsic noise and timescale separation. The slow-scale linear noise approximation (ssLNA), as the new method is called, is used to calculate the intrinsic noise statistics of enzyme and gene networks. The results agree very well with SSA simulations of the non-reduced network of elementary reactions. In contrast the conventional heuristic SSA is shown to overestimate the size of noise for Michaelis-Menten kinetics, considerably under-estimate the size of noise for Hill-type kinetics and in some cases even miss the prediction of noise-induced oscillations. Conclusions: A new general method, the ssLNA, is derived and shown to correctly describe the statistics of intrinsic noise about the macroscopic concentrations under timescale separation conditions. The ssLNA provides a simple and accurate means of performing stochastic model reduction and hence it is expected to be of widespread utility in studying the dynamics of large noisy reaction networks, as is common in computational and systems biology. http://www.biomedcentral.com/1752-0509/6/39 Philipp Thomas Arthur V Straube Ramon Grima BMC Systems Biology 2012, 6:39 2012-05-14T00:00:00Z 10.1186/1752-0509-6-39 ssLNA algorithm improves intrinsic noise statistics A new method, called the slow-scale linear noise approximation (ssLNA), provides a simple and accurate means of performing stochastic model reduction under timescale separation conditions, giving more accurate noise estimates that the heuristic stochastic simulation algorithm. /content/figures/1752-0509-6-39-toc.gif BMC Systems Biology 1752-0509 6 39 2012-05-14T00:00:00Z PDF Background: Pichia stipitis and Pichia pastoris have long been investigated due to their native abilities to metabolize every sugar from lignocellulose and to modulate methanol consumption, respectively. The latter has been driving the production of several recombinant proteins. As a result, significant advances in their biochemical knowledge, as well as in genetic engineering and fermentation methods have been generated. The release of their genome sequences has allowed systems level research. Results: In this work, genome-scale metabolic models (GEMs) of P. stipitis (iSS884) and P. pastoris (iLC915) were reconstructed. iSS884 includes 1332 reactions, 922 metabolites, and 4 compartments. iLC915 contains 1423 reactions, 899 metabolites, and 7 compartments. Compared with the previous GEMs of P. pastoris, PpaMBEL1254 and iPP668, iLC915 contains more genes and metabolic functions, as well as improved predictive capabilities. Simulations of physiological responses for the growth of both yeasts on selected carbon sources using iSS884 and iLC915 closely reproduced the experimental data. Additionally, the iSS884 model was used to predict ethanol production from xylose at different oxygen uptake rates. Simulations with iLC915 closely reproduced the effect of oxygen uptake rate on physiological states of P. pastoris expressing a recombinant protein. The potential of P. stipitis for the conversion of xylose and glucose into ethanol using reactors in series, and of P. pastoris to produce recombinant proteins using mixtures of methanol and glycerol or sorbitol are also discussed. Conclusions: In conclusion the first GEM of P. stipitis (iSS884) was reconstructed and validated. The expanded version of the P. pastoris GEM, iLC915, is more complete and has improved capabilities over the existing models. Both GEMs are useful frameworks to explore the versatility of these yeasts and to capitalize on their biotechnological potentials. http://www.biomedcentral.com/1752-0509/6/24 Luis Caspeta Saeed Shoaie Rasmus Agren Intawat Nookaew Jens Nielsen BMC Systems Biology 2012, 6:24 2012-04-04T00:00:00Z 10.1186/1752-0509-6-24 Modeling Pichia metabolism A new genome-scale metabolic model of the industrially important yeast species Pichia stipitis and Pichia pastoris enables the modeling of growth on xylose, which is essential to optimising the production of ethanol from biomass. /content/figures/1752-0509-6-24-toc.gif BMC Systems Biology 1752-0509 6 24 2012-04-04T00:00:00Z XML Background: Carbon-13 (13C) analysis is a commonly used method for estimating reaction rates in biochemical networks. The choice of carbon labeling pattern is an important consideration when designing these experiments. We present a novel Monte Carlo algorithm for finding the optimal substrate input label for a particular experimental objective (flux or flux ratio). Unlike previous work, this method does not require assumption of the flux distribution beforehand. Results: Using a large E. coli isotopomer model, different commercially available substrate labeling patterns were tested computationally for their ability to determine reaction fluxes. The choice of optimal labeled substrate was found to be dependent upon the desired experimental objective. Many commercially available labels are predicted to be outperformed by complex labeling patterns. Based on Monte Carlo Sampling, the dimensionality of experimental data was found to be considerably less than anticipated, suggesting that effectiveness of 13C experiments for determining reaction fluxes across a large-scale metabolic network is less than previously believed. Conclusions: While 13C analysis is a useful tool in systems biology, high redundancy in measurements limits the information that can be obtained from each experiment. It is however possible to compute potential limitations before an experiment is run and predict whether, and to what degree, the rate of each reaction can be resolved. http://www.biomedcentral.com/1752-0509/6/9 Jan Schellenberger Daniel C Zielinski Wing Choi Sunthosh Madireddi Vasiliy Portnoy David A Scott Jennifer L Reed Andrei L Osterman Bernhard ∅ Palsson BMC Systems Biology 2012, 6:9 2012-01-30T00:00:00Z 10.1186/1752-0509-6-9 Predicting outcomes of 13C experiments A new Monte Carlo sampling algorithm determines the capability of 13C labeling experiments to resolve fluxes in metabolic networks, and is demonstrated on an E. coli isotopomer model to determine reaction fluxes. /content/figures/1752-0509-6-9-toc.gif BMC Systems Biology 1752-0509 6 9 2012-01-30T00:00:00Z XML Background: The increasing use of computational simulation experiments to inform modern biological research creates new challenges to annotate, archive, share and reproduce such experiments. The recently published Minimum Information About a Simulation Experiment (MIASE) proposes a minimal set of information that should be provided to allow the reproduction of simulation experiments among users and software tools. Results: In this article, we present the Simulation Experiment Description Markup Language (SED-ML). SED-ML encodes in a computer-readable exchange format the information required by MIASE to enable reproduction of simulation experiments. It has been developed as a community project and it is defined in a detailed technical specification and additionally provides an XML schema. The version of SED-ML described in this publication is Level 1 Version 1. It covers the description of the most frequent type of simulation experiments in the area, namely time course simulations. SED-ML documents specify which models to use in an experiment, modifications to apply on the models before using them, which simulation procedures to run on each model, what analysis results to output, and how the results should be presented. These descriptions are independent of the underlying model implementation. SED-ML is a software-independent format for encoding the description of simulation experiments; it is not specific to particular simulation tools. Here, we demonstrate that with the growing software support for SED-ML we can effectively exchange executable simulation descriptions. Conclusions: With SED-ML, software can exchange simulation experiment descriptions, enabling the validation and reuse of simulation experiments in different tools. Authors of papers reporting simulation experiments can make their simulation protocols available for other scientists to reproduce the results. Because SED-ML is agnostic about exact modeling language(s) used, experiments covering models from different fields of research can be accurately described and combined. http://www.biomedcentral.com/1752-0509/5/198 Dagmar Waltemath Richard Adams Frank T Bergmann Michael Hucka Fedor Kolpakov Andrew K Miller Ion I Moraru David Nickerson Sven Sahle Jacky L Snoep Nicolas Le Novère BMC Systems Biology 2011, 5:198 2011-12-15T00:00:00Z 10.1186/1752-0509-5-198 Standardizing simulations with SED-ML Simulation Experiment Description Markup Language (SED-ML) is a computer-readable exchange format that encodes all the information required to reuse and validate simulation experiments, ensuring they conform to the "Minimum Information About a Simulation Experiment" (MIASE) guidelines. /content/figures/1752-0509-5-198-toc.gif BMC Systems Biology 1752-0509 5 198 2011-12-15T00:00:00Z XML