SBSI:an extensible distributed software infrastructure for parameter estimation in systems biology

Complex computational experiments in Systems Biology, such as fitting model parameters to experimental data, can be challenging to perform. Not only do they frequently require a high level of computational power, but the software needed to run the experiment needs to be usable by scientists with varying levels of computational expertise, and modellers need to be able to obtain up-to-date experimental data resources easily. We have developed a software suite, the Systems Biology Software Infrastructure (SBSI), to facilitate the parameter-fitting process. SBSI is a modular software suite composed of three major components: SBSINumerics, a high-performance library containing parallelized algorithms for performing parameter fitting; SBSIDispatcher, a middleware application to track experiments and submit jobs to back-end servers; and SBSIVisual, an extensible client application used to configure optimization experiments and view results. Furthermore, we have created a plugin infrastructure to enable project-specific modules to be easily installed. Plugin developers can take advantage of the existing user-interface and application framework to customize SBSI for their own uses, facilitated by SBSI’s use of standard data formats

The Input Signal Step Function (ISSF), a Standard Method to Encode Input Signals in SBML Models with Software Support, Applied to Circadian Clock Models

LetterThis is the final version of the article. Available from SAGE Publications via the DOI in this record.Time-dependent light input is an important feature of computational models of the circadian clock. However, publicly available models encoded in standard representations such as the Systems Biology Markup Language (SBML) either do not encode this input or use different mechanisms to do so, which hinders reproducibility of published results as well as model reuse. The authors describe here a numerically continuous function suitable for use in SBML for models of circadian rhythms forced by periodic light-dark cycles. The Input Signal Step Function (ISSF) is broadly applicable to encoding experimental manipulations, such as drug treatments, temperature changes, or inducible transgene expression, which may be transient, periodic, or mixed. It is highly configurable and is able to reproduce a wide range of waveforms. The authors have implemented this function in SBML and demonstrated its ability to modify the behavior of publicly available models to accurately reproduce published results. The implementation of ISSF allows standard simulation software to reproduce specialized circadian protocols, such as the phase-response curve. To facilitate the reuse of this function in public models, the authors have developed software to configure its behavior without any specialist knowledge of SBML. A community-standard approach to represent the inputs that entrain circadian clock models could particularly facilitate research in chronobiology.K.S. was supported by the UK BBSRC grant BB/E015263/1. SynthSys Edinburgh is a Centre for Integrative Systems Biology (CISB) funded by BBSRC and EPSRC, reference BB/D019621/1

BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models

Background: Quantitative models of biochemical and cellular systems are used to answer a variety of questions in the biological sciences. The number of published quantitative models is growing steadily thanks to increasing interest in the use of models as well as the development of improved software systems and the availability of better, cheaper computer hardware. To maximise the benefits of this growing body of models, the field needs centralised model repositories that will encourage, facilitate and promote model dissemination and reuse. Ideally, the models stored in these repositories should be extensively tested and encoded in community-supported and standardised formats. In addition, the models and their components should be cross-referenced with other resources in order to allow their unambiguous identification. Description: BioModels Database http://www.ebi.ac.uk/biomodels/ is aimed at addressing exactly these needs. It is a freely-accessible online resource for storing, viewing, retrieving, and analysing published, peer-reviewed quantitative models of biochemical and cellular systems. The structure and behaviour of each simulation model distributed by BioModels Database are thoroughly checked; in addition, model elements are annotated with terms from controlled vocabularies as well as linked to relevant data resources. Models can be examined online or downloaded in various formats. Reaction network diagrams generated from the models are also available in several formats. BioModels Database also provides features such as online simulation and the extraction of components from large scale models into smaller submodels. Finally, the system provides a range of web services that external software systems can use to access up-to-date data from the database. Conclusions: BioModels Database has become a recognised reference resource for systems biology. It is being used by the community in a variety of ways; for example, it is used to benchmark different simulation systems, and to study the clustering of models based upon their annotations. Model deposition to the database today is advised by several publishers of scientific journals. The models in BioModels Database are freely distributed and reusable; the underlying software infrastructure is also available from SourceForge https://sourceforge.net/projects/biomodels/ under the GNU General Public License

A Tutorial on Mathematical Modeling of Biological Signaling Pathways

Mathematical models have been widely used in the studies of biological signaling pathways. Among these studies, two systems biology approaches have been applied: top-down and bottom-up systems biology. The former approach focuses on X-omics researches involving the measurement of experimental data in a large scale, for example proteomics, metabolomics, or fluxomics and transcriptomics. In contrast, the bottom-up approach studies the interaction of the network components and employs mathematical models to gain some insights about the mechanisms and dynamics of biological systems. This chapter introduces how to use the bottom-up approach to establish mathematical models for cell signaling studies

Crosstalk between the lipopolysaccharide and phospholipid pathways during outer membrane biogenesis in Escherichia coli

The outer membrane of gram-negative bacteria is composed of phospholipids in the inner leaflet and lipopolysaccharides (LPS) in the outer leaflet. LPS is an endotoxin that elicits a strong immune response from humans, and its biosynthesis is in part regulated via degradation of LpxC (EC 3.5.1.108) and WaaA (EC 2.4.99.12/13) enzymes by the protease FtsH (EC 3.4.24.-). Because the synthetic pathways for both molecules are complex, in addition to being produced in strict ratios, we developed a computational model to interrogate the regulatory mechanisms involved. Our model findings indicate that the catalytic activity of LpxK (EC 2.7.1.130) appears to be dependent on the concentration of unsaturated fatty acids. This is biologically important because it assists in maintaining LPS/phospholipids homeostasis. Further crosstalk between the phospholipid and LPS biosynthetic pathways was revealed by experimental observations that LpxC is additionally regulated by an unidentified protease whose activity is independent of lipid A disaccharide concentration (the feedback source for FtsH-mediated LpxC regulation) but could be induced in vitro by palmitic acid. Further experimental analysis provided evidence on the rationale for WaaA regulation. Overexpression of waaA resulted in increased levels of 3-deoxy-D-manno-oct-2-ulosonic acid (Kdo) sugar in membrane extracts, whereas Kdo and heptose levels were not elevated in LPS. This implies that uncontrolled production of WaaA does not increase the LPS production rate but rather reglycosylates lipid A precursors. Overall, the findings of this work provide previously unidentified insights into the complex biogenesis of the Escherichia coli outer membrane

Unveiling the reaction mechanism of the das/chechik/marek synthesis of stereodefined quaternary carbon centers

The reaction mechanism of the Cu+‐catalyzed introduction of two all‐carbon‐substituted stereocenters in an ynamide system using a Grignard reagent, a zinc carbenoid, and an aldehyde, was investigated using density‐functional theory. In contrast to the formation of an organocopper(I) compound and subsequent carbocupration reaction, previously postulated as the initial step, the reaction proved to instead proceed through an initial complexation of the substrate alkyne bond by the Cu+‐catalyst, which primes this bond for reaction with the Grignard reagent. Subsequent addition of the zinc carbenoid then enables the nucleophilic attack on the incoming aldehyde, which is revealed as the rate‐limiting step. Our computations have also identified the factors governing the regio‐ and setereoselectivity of this interesting reaction, and suggest possible paths forits further development.info:eu-repo/semantics/publishedVersio

Mining metabolites: extracting the yeast metabolome from the literature

Text mining methods have added considerably to our capacity to extract biological knowledge from the literature. Recently the field of systems biology has begun to model and simulate metabolic networks, requiring knowledge of the set of molecules involved. While genomics and proteomics technologies are able to supply the macromolecular parts list, the metabolites are less easily assembled. Most metabolites are known and reported through the scientific literature, rather than through large-scale experimental surveys. Thus it is important to recover them from the literature. Here we present a novel tool to automatically identify metabolite names in the literature, and associate structures where possible, to define the reported yeast metabolome. With ten-fold cross validation on a manually annotated corpus, our recognition tool generates an f-score of 78.49 (precision of 83.02) and demonstrates greater suitability in identifying metabolite names than other existing recognition tools for general chemical molecules. The metabolite recognition tool has been applied to the literature covering an important model organism, the yeast Saccharomyces cerevisiae, to define its reported metabolome. By coupling to ChemSpider, a major chemical database, we have identified structures for much of the reported metabolome and, where structure identification fails, been able to suggest extensions to ChemSpider. Our manually annotated gold-standard data on 296 abstracts are available as supplementary materials. Metabolite names and, where appropriate, structures are also available as supplementary materials