Oxidative stress is a well-known biological process that occurs in all respiring cells and is involved in pathophysiological processes such as aging and apoptosis. Oxidative stress agents include peroxides such as hydrogen peroxide, cumene hydroperoxide, and linoleic acid hydroperoxide, the thiol oxidant diamide, and menadione, a generator of superoxide, amongst others. The present study analyzed the early temporal genome-wide transcriptional response of Saccharomyces cerevisiae to oxidative stress induced by the aromatic peroxide cumene hydroperoxide. The accurate dataset obtained, supported by the use of temporal controls, biological replicates and well controlled growth conditions, provided a detailed picture of the early dynamics of the process. We identified a set of genes previously not implicated in the oxidative stress response, including several transcriptional regulators showing a fast transient response, suggesting a coordinated process in the transcriptional reprogramming. We discuss the role of the glutathione, thioredoxin and reactive oxygen species-removing systems, the proteasome and the pentose phosphate pathway. A data-driven clustering of the expression patterns identified one specific cluster that mostly consisted of genes known to be regulated by the Yap1p and Skn7p transcription factors, emphasizing their mediator role in the transcriptional response to oxidants. Comparison of our results with data reported for hydrogen peroxide identified 664 genes that specifically respond to cumene hydroperoxide, suggesting distinct transcriptional responses to these two peroxides. Genes up-regulated only by cumene hydroperoxide are mainly related to the cell membrane and cell wall, and proteolysis process, while those down-regulated only by this aromatic peroxide are involved in mitochondrial function.
We present an experimental and computational pipeline for the generation of kinetic models of metabolism, and demonstrate its application to glycolysis in Saccharomyces cerevisiae. Starting from an approximate mathematical model, we employ a “cycle of knowledge” strategy, identifying the steps with most control over flux. Kinetic parameters of the individual isoenzymes within these steps are measured experimentally under a standardised set of conditions. Experimental strategies are applied to establish a set of in vivo concentrations for isoenzymes and metabolites. The data are integrated into a mathematical model that is used to predict a new set of metabolite concentrations and reevaluate the control properties of the system. This bottom-up modelling study reveals that control over the metabolic network most directly involved in yeast glycolysis is more widely distributed than previously thought.
Glycolysis; Systems biology; Enzyme kinetic; Isoenzyme; Modelling
Following a strategy similar to that used in baker’s yeast (Herrgård et al. Nat Biotechnol 26:1155–1160, 2008). A consensus yeast metabolic network obtained from a community approach to systems biology (Herrgård et al. 2008; Dobson et al. BMC Syst Biol 4:145, 2010). Further developments towards a genome-scale metabolic model of yeast (Dobson et al. 2010; Heavner et al. BMC Syst Biol 6:55, 2012). Yeast 5—an expanded reconstruction of the Saccharomyces cerevisiae metabolic network (Heavner et al. 2012) and in Salmonella typhimurium (Thiele et al. BMC Syst Biol 5:8, 2011). A community effort towards a knowledge-base and mathematical model of the human pathogen Salmonellatyphimurium LT2 (Thiele et al. 2011), a recent paper (Thiele et al. Nat Biotechnol 31:419–425, 2013). A community-driven global reconstruction of human metabolism (Thiele et al. 2013) described a much improved ‘community consensus’ reconstruction of the human metabolic network, called Recon 2, and the authors (that include the present ones) have made it freely available via a database at http://humanmetabolism.org/ and in SBML format at Biomodels (http://identifiers.org/biomodels.db/MODEL1109130000). This short analysis summarises the main findings, and suggests some approaches that will be able to exploit the availability of this model to advantage.
Metabolism; Modelling; Systems biology; Networks; Metabolic networks
This study was made to analyze the variability and stability of the serve toss in tennis, on the x (side-to-side), y (back-to-front) and z (vertical) axes, with 12 experienced players under the influence of crosswind (induced aerodynamic flow) produced by an industrial ventilator. The players were analyzed individually after serving at maximum speed and accuracy to the intersection point of the centre line and service line (“T ”point). The results allow us to conclude that the experienced players tend to stabilize the vertical dimension of the service (z axis). Additionally, this study confirms the invariability of the player height ratio: height of impact (1:1.5) in experienced players even when constrained by the “artificial crosswind. ”Given the above, the vertical dimension of the tennis serve is assumed as a constant feature, which is guaranteed in the remaining varying dimensions (y and x axes) of the ball toss. Thus, the variability should be seen as part of the solution and not as something to be avoided by players and coaches.
Analysis of the tennis serve variability under the effect of artificial crosswind
Twelve experienced tennis players performed a set of 20 free serves (without wind constraints), and four other sets of 20 serves under different practice conditions (with different crosswind intensities)
The players tend to stabilize in the z axis and vary in the y- (back-to-front) and x-axes (side-to-side) during the ball toss tennis serve in all the practice conditions (with and without crosswind)
The maintenance of a player height ratio: impact height of approximately 1:1.5 in experienced players, even when constrained by “artificial crosswind”.
Variability; tennis serve; performance; crosswind; motor control
In many situations it is desirable to compare dynamical systems based on their behavior. Similarity of behavior often implies similarity of internal mechanisms or dependency on common extrinsic factors. While there are widely used methods for comparing univariate time series, most dynamical systems are characterized by multivariate time series. Yet, comparison of multivariate time series has been limited to cases where they share a common dimensionality. A semi-metric is a distance function that has the properties of non-negativity, symmetry and reflexivity, but not sub-additivity. Here we develop a semi-metric – SMETS – that can be used for comparing groups of time series that may have different dimensions. To demonstrate its utility, the method is applied to dynamic models of biochemical networks and to portfolios of shares. The former is an example of a case where the dependencies between system variables are known, while in the latter the system is treated (and behaves) as a black box.
A new quantitative strategy has generated a comprehensive rate control map for protein synthesis in exponentially growing yeast cells. This analysis reveals the modularity of the system as well as highly non-stoichiometric relationships between components.
A ‘genetic titration' method has generated a map of the in vivo rate control properties of components of the protein synthesis machinery in Saccharomyces cerevisiae and has been used to parameterize a new comprehensive model of the translation pathway.The translation machinery is found to be a highly modular system in functional terms yet the intracellular concentrations of its components range from a few thousand to one million molecules per cell.This approach identifies non-intuitive features of the system such as the strongest rate control being exercised by high abundance elongation factors.The rate control analysis allows us to identify a surprising fine-control function for duplicated translation factor genes.
Rate control analysis defines the in vivo control map governing yeast protein synthesis and generates an extensively parameterized digital model of the translation pathway. Among other non-intuitive outcomes, translation demonstrates a high degree of functional modularity and comprises a non-stoichiometric combination of proteins manifesting functional convergence on a shared maximal translation rate. In exponentially growing cells, polypeptide elongation (eEF1A, eEF2, and eEF3) exerts the strongest control. The two other strong control points are recruitment of mRNA and tRNAi to the 40S ribosomal subunit (eIF4F and eIF2) and termination (eRF1; Dbp5). In contrast, factors that are found to promote mRNA scanning efficiency on a longer than-average 5′untranslated region (eIF1, eIF1A, Ded1, eIF2B, eIF3, and eIF5) exceed the levels required for maximal control. This is expected to allow the cell to minimize scanning transition times, particularly for longer 5′UTRs. The analysis reveals these and other collective adaptations of control shared across the factors, as well as features that reflect functional modularity and system robustness. Remarkably, gene duplication is implicated in the fine control of cellular protein synthesis.
eukaryotic translation machinery; gene duplication; in vivo rate control; post-transcriptional gene expression; system modularity
The dissolution of CaCO3 is one of the ways ocean acidification can, potentially, greatly affect the ballast of aggregates. A diminution of the ballast could reduce the settling speed of aggregates, resulting in a change in the carbon flux to the deep sea. This would mean lower amounts of more refractory organic matter reaching the ocean floor. This work aimed to determine the effect of ocean acidification on the ballast of sinking surface aggregates. Our hypothesis was that the decrease of pH will increase the dissolution of particulate inorganic carbon ballasting the aggregates, consequently reducing their settling velocity and increasing their residence time in the upper twilight zone. Using a new methodology for simulation of aggregate settling, our results suggest that future pCO2 conditions can significantly change the ballast composition of sinking aggregates. The change in aggregate composition had an effect on the size distribution of the aggregates, with a shift to smaller aggregates. A change also occurred in the settling velocity of the particles, which would lead to a higher residence time in the water column, where they could be continuously degraded. In the environment, such an effect would result in a reduction of the carbon flux to the deep-sea. This reduction would impact those benthic communities, which rely on the vertical flow of carbon as primary source of energy.
The aim of this manuscript is to describe the first year of our experience using extracorporeal membrane oxygenation support.
Ten patients with severe refractory hypoxemia, two with associated severe cardiovascular failure, were supported using venous-venous extracorporeal membrane oxygenation (eight patients) or veno-arterial extracorporeal membrane oxygenation (two patients).
The median age of the patients was 31 yr (range 14–71 yr). Their median simplified acute physiological score three (SAPS3) was 94 (range 84–118), and they had a median expected mortality of 95% (range 87–99%). Community-acquired pneumonia was the most common diagnosis (50%), followed by P. jiroveci pneumonia in two patients with AIDS (20%). Six patients were transferred from other ICUs during extracorporeal membrane oxygenation support, three of whom were transferred between ICUs within the hospital (30%), two by ambulance (20%) and one by helicopter (10%). Only one patient (10%) was anticoagulated with heparin throughout extracorporeal membrane oxygenation support. Eighty percent of patients required continuous venous-venous hemofiltration. Three patients (30%) developed persistent hypoxemia, which was corrected using higher positive end-expiratory pressure, higher inspired oxygen fractions, recruitment maneuvers, and nitric oxide. The median time on extracorporeal membrane oxygenation support was five (range 3–32) days. The median length of the hospital stay was 31 (range 3-97) days. Four patients (40%) survived to 60 days, and they were free from renal replacement therapy and oxygen support.
The use of extracorporeal membrane oxygenation support in severely ill patients is possible in the presence of a structured team. Efforts must be made to recognize the necessity of extracorporeal respiratory support at an early stage and to prompt activation of the extracorporeal membrane oxygenation team.
Extracorporeal Membrane Oxygenation; Respiratory Failure; Mechanical Ventilation; Patient Care Team; Intensive Care Unit
Mathematical modelling has become a standard technique to improve our understanding of complex biological systems. As models become larger and more complex, simulations and analyses require increasing amounts of computational power. Clusters of computers in a high-throughput computing environment can help to provide the resources required for computationally expensive model analysis. However, exploiting such a system can be difficult for users without the necessary expertise.
We present Condor-COPASI, a server-based software tool that integrates COPASI, a biological pathway simulation tool, with Condor, a high-throughput computing environment. Condor-COPASI provides a web-based interface, which makes it extremely easy for a user to run a number of model simulation and analysis tasks in parallel. Tasks are transparently split into smaller parts, and submitted for execution on a Condor pool. Result output is presented to the user in a number of formats, including tables and interactive graphical displays.
Condor-COPASI can effectively use a Condor high-throughput computing environment to provide significant gains in performance for a number of model simulation and analysis tasks. Condor-COPASI is free, open source software, released under the Artistic License 2.0, and is suitable for use by any institution with access to a Condor pool. Source code is freely available for download at
http://code.google.com/p/condor-copasi/, along with full instructions on deployment and usage.
Systems biology; Computational modelling; High-throughput computing; Distributed computing; Simulation
Constraint-based analysis of genome-scale metabolic models typically relies upon maximisation of a cellular objective function such as the rate or efficiency of biomass production. Whilst this assumption may be valid in the case of microorganisms growing under certain conditions, it is likely invalid in general, and especially for multicellular organisms, where cellular objectives differ greatly both between and within cell types. Moreover, for the purposes of biotechnological applications, it is normally the flux to a specific metabolite or product that is of interest rather than the rate of production of biomass per se.
An alternative objective function is presented, that is based upon maximising the correlation between experimentally measured absolute gene expression data and predicted internal reaction fluxes. Using quantitative transcriptomics data acquired from Saccharomyces cerevisiae cultures under two growth conditions, the method outperforms traditional approaches for predicting experimentally measured exometabolic flux that are reliant upon maximisation of the rate of biomass production.
Due to its improved prediction of experimentally measured metabolic fluxes, and of its lack of a requirement for knowledge of the biomass composition of the organism under the conditions of interest, the approach is likely to be of rather general utility. The method has been shown to predict fluxes reliably in single cellular systems. Subsequent work will investigate the method’s ability to generate condition- and tissue-specific flux predictions in multicellular organisms.
Flux balance analysis; Metabolic flux; Metabolic networks; Transcriptomics; RNA-Seq; Exometabolomics
Efforts to improve the computational reconstruction of the Saccharomyces cerevisiae biochemical reaction network and to refine the stoichiometrically constrained metabolic models that can be derived from such a reconstruction have continued since the first stoichiometrically constrained yeast genome scale metabolic model was published in 2003. Continuing this ongoing process, we have constructed an update to the Yeast Consensus Reconstruction, Yeast 5. The Yeast Consensus Reconstruction is a product of efforts to forge a community-based reconstruction emphasizing standards compliance and biochemical accuracy via evidence-based selection of reactions. It draws upon models published by a variety of independent research groups as well as information obtained from biochemical databases and primary literature.
Yeast 5 refines the biochemical reactions included in the reconstruction, particularly reactions involved in sphingolipid metabolism; updates gene-reaction annotations; and emphasizes the distinction between reconstruction and stoichiometrically constrained model. Although it was not a primary goal, this update also improves the accuracy of model prediction of viability and auxotrophy phenotypes and increases the number of epistatic interactions. This update maintains an emphasis on standards compliance, unambiguous metabolite naming, and computer-readable annotations available through a structured document format. Additionally, we have developed MATLAB scripts to evaluate the model’s predictive accuracy and to demonstrate basic model applications such as simulating aerobic and anaerobic growth. These scripts, which provide an independent tool for evaluating the performance of various stoichiometrically constrained yeast metabolic models using flux balance analysis, are included as Additional files 1, 2 and 3.
Yeast 5 expands and refines the computational reconstruction of yeast metabolism and improves the predictive accuracy of a stoichiometrically constrained yeast metabolic model. It differs from previous reconstructions and models by emphasizing the distinction between the yeast metabolic reconstruction and the stoichiometrically constrained model, and makes both available as Additional file 4 and Additional file 5 and at http://yeast.sf.net/ as separate systems biology markup language (SBML) files. Through this separation, we intend to make the modeling process more accessible, explicit, transparent, and reproducible.
Metabolic; Reconstruction; Yeast; Flux balance analysis; GEM; GENRE; Model
The control of biochemical fluxes is distributed and to perturb complex intracellular networks effectively it is often necessary to modulate several steps simultaneously. However, the number of possible permutations leads to a combinatorial explosion in the number of experiments that would have to be performed in a complete analysis. We used a multi-objective evolutionary algorithm (EA) to optimize reagent combinations from a dynamic chemical library of 33 compounds with established or predicted targets in the regulatory network controlling IL-1β expression. The EA converged on excellent solutions within 11 generations during which we studied just 550 combinations out of the potential search space of ~ 9 billion. The top five reagents with the greatest contribution to combinatorial effects throughout the EA were then optimized pairwise. A p38 MAPK inhibitor with either an inhibitor of IκB kinase or a chelator of poorly liganded iron yielded synergistic inhibition of macrophage IL-1β expression. Evolutionary searches provide a powerful and general approach to the discovery of novel combinations of pharmacological agents with potentially greater therapeutic indices than those of single drugs.
The use of computational modeling to describe and analyze biological systems is at the heart of systems biology. This Perspective discusses the development and use of ontologies that are designed to add semantic information to computational models and simulations.
The use of computational modeling to describe and analyze biological systems is at the heart of systems biology. Model structures, simulation descriptions and numerical results can be encoded in structured formats, but there is an increasing need to provide an additional semantic layer. Semantic information adds meaning to components of structured descriptions to help identify and interpret them unambiguously. Ontologies are one of the tools frequently used for this purpose. We describe here three ontologies created specifically to address the needs of the systems biology community. The Systems Biology Ontology (SBO) provides semantic information about the model components. The Kinetic Simulation Algorithm Ontology (KiSAO) supplies information about existing algorithms available for the simulation of systems biology models, their characterization and interrelationships. The Terminology for the Description of Dynamics (TEDDY) categorizes dynamical features of the simulation results and general systems behavior. The provision of semantic information extends a model's longevity and facilitates its reuse. It provides useful insight into the biology of modeled processes, and may be used to make informed decisions on subsequent simulation experiments.
dynamics; kinetics; model; ontology; simulation
It is well known that significant metabolic change take place as cells are transformed from normal to malignant. This review focuses on the use of different bioinformatics tools in cancer metabolomics studies. The article begins by describing different metabolomics technologies and data generation techniques. Overview of the data pre-processing techniques is provided and multivariate data analysis techniques are discussed and illustrated with case studies, including principal component analysis, clustering techniques, self-organizing maps, partial least squares, and discriminant function analysis. Also included is a discussion of available software packages.
Metabolomics; Cancer; Metabolite profiling; NMR; Mass spectrometry; Bioinformatics
In order to understand how a cancer cell is functionally different from a normal cell it is necessary to assess the complex network of pathways involving gene regulation, signaling, and cell metabolism, and the alterations in its dynamics caused by the several different types of mutations leading to malignancy. Since the network is typically complex, with multiple connections between pathways and important feedback loops, it is crucial to represent it in the form of a computational model that can be used for a rigorous analysis. This is the approach of systems biology, made possible by new –omics data generation technologies. The goal of this review is to illustrate this approach and its utility for our understanding of cancer. After a discussion of recent progress using a network-centric approach, three case studies related to diagnostics, therapy, and drug development are presented in detail. They focus on breast cancer, B cell lymphomas, and colorectal cancer. The discussion is centered on key mathematical and computational tools common to a systems biology approach.
systems biology; cancer; mathematical modeling
The behaviour of biological systems can be deduced from their mathematical models. However, multiple sources of data in diverse forms are required in the construction of a model in order to define its components and their biochemical reactions, and corresponding parameters. Automating the assembly and use of systems biology models is dependent upon data integration processes involving the interoperation of data and analytical resources.
Taverna workflows have been developed for the automated assembly of quantitative parameterised metabolic networks in the Systems Biology Markup Language (SBML). A SBML model is built in a systematic fashion by the workflows which starts with the construction of a qualitative network using data from a MIRIAM-compliant genome-scale model of yeast metabolism. This is followed by parameterisation of the SBML model with experimental data from two repositories, the SABIO-RK enzyme kinetics database and a database of quantitative experimental results. The models are then calibrated and simulated in workflows that call out to COPASIWS, the web service interface to the COPASI software application for analysing biochemical networks. These systems biology workflows were evaluated for their ability to construct a parameterised model of yeast glycolysis.
Distributed information about metabolic reactions that have been described to MIRIAM standards enables the automated assembly of quantitative systems biology models of metabolic networks based on user-defined criteria. Such data integration processes can be implemented as Taverna workflows to provide a rapid overview of the components and their relationships within a biochemical system.
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.
Electronic supplementary material
The online version of this article (doi:10.1007/s11306-010-0251-6) contains supplementary material, which is available to authorized users.
Text mining; Named entity recognition; Yeast metabolome
To date, several genome-scale network reconstructions have been used to describe the metabolism of the yeast Saccharomyces cerevisiae, each differing in scope and content. The recent community-driven reconstruction, while rigorously evidenced and well annotated, under-represented metabolite transport, lipid metabolism and other pathways, and was not amenable to constraint-based analyses because of lack of pathway connectivity.
We have expanded the yeast network reconstruction to incorporate many new reactions from the literature and represented these in a well-annotated and standards-compliant manner. The new reconstruction comprises 1102 unique metabolic reactions involving 924 unique metabolites - significantly larger in scope than any previous reconstruction. The representation of lipid metabolism in particular has improved, with 234 out of 268 enzymes linked to lipid metabolism now present in at least one reaction. Connectivity is emphatically improved, with more than 90% of metabolites now reachable from the growth medium constituents. The present updates allow constraint-based analyses to be performed; viability predictions of single knockouts are comparable to results from in vivo experiments and to those of previous reconstructions.
We report the development of the most complete reconstruction of yeast metabolism to date that is based upon reliable literature evidence and richly annotated according to MIRIAM standards. The reconstruction is available in the Systems Biology Markup Language (SBML) and via a publicly accessible database http://www.comp-sys-bio.org/yeastnet/.
Iron is required for survival of mammalian cells. Recently, understanding of iron metabolism and trafficking has increased dramatically, revealing a complex, interacting network largely unknown just a few years ago. This provides an excellent model for systems biology development and analysis. The first step in such an analysis is the construction of a structural network of iron metabolism, which we present here. This network was created using CellDesigner version 3.5.2 and includes reactions occurring in mammalian cells of numerous tissue types. The iron metabolic network contains 151 chemical species and 107 reactions and transport steps. Starting from this general model, we construct iron networks for specific tissues and cells that are fundamental to maintaining body iron homeostasis. We include subnetworks for cells of the intestine and liver, tissues important in iron uptake and storage, respectively; as well as the reticulocyte and macrophage, key cells in iron utilization and recycling. The addition of kinetic information to our structural network will permit the simulation of iron metabolism in different tissues as well as in health and disease.
iron; liver; macrophage; reactive oxygen species; red blood cells
The trajectory, mass fluxes, and deposition of aerosolized particles in a complex tubular system have been predicted. A procedure based on Lagrangian stochastic modeling is proposed to enable the anticipation of such phenomena, taking advantage of experimental results to characterize the air flow. The predictions have been obtained for pharmaceutical aerosols delivered by dry powder inhalers. A critical assessment of the dispersion model has been carried out using data available in the literature. The procedure assumes a low volume fraction of particles in the simulation of turbulent dispersion, but deposition is physically based on the interaction between the particles and both solid and liquid surfaces. The results were confirmed by experimental tests of powder deposition, run according to the European Pharmacopoeia. A parametric study was also carried out with the aim of providing a more complete evaluation of the model’s performance. The comparison between predictions and experimental results has shown that the model properly describes the deposition of aerosolized particles.
aerosol; computational model; dry powder inhaler; lung drug delivery; particle deposition
Advances in bioinformatic techniques and analyses have led to the availability of genome-scale metabolic reconstructions. The size and complexity of such networks often means that their potential behaviour can only be analysed with constraint-based methods. Whilst requiring minimal experimental data, such methods are unable to give insight into cellular substrate concentrations. Instead, the long-term goal of systems biology is to use kinetic modelling to characterize fully the mechanics of each enzymatic reaction, and to combine such knowledge to predict system behaviour.
We describe a method for building a parameterized genome-scale kinetic model of a metabolic network. Simplified linlog kinetics are used and the parameters are extracted from a kinetic model repository. We demonstrate our methodology by applying it to yeast metabolism. The resultant model has 956 metabolic reactions involving 820 metabolites, and, whilst approximative, has considerably broader remit than any existing models of its type. Control analysis is used to identify key steps within the system.
Our modelling framework may be considered a stepping-stone toward the long-term goal of a fully-parameterized model of yeast metabolism. The model is available in SBML format from the BioModels database (BioModels ID: MODEL1001200000) and at http://www.mcisb.org/resources/genomescale/.
An integral part of any systems biology approach is the modelling and simulation of the respective system under investigation. However, the values of many parameters of the system have often not been determined or are not identifiable due to technical experimental difficulties or other constraints. Sensitivity analysis is often employed to quantify the importance of each of the model's parameters in the behaviour of the system. This approach can also be useful in identifying those parts of the system that are most sensitive with the potential of becoming drug targets. A problem of the commonly used methods of sensitivity analysis is that they constitute local methods meaning that they depend directly on the exact parameter space, which in turn is not known exactly. One way to circumvent this problem is to carry out sensitivity analysis over a wide range of values for all parameters, but this is handicapped by expensive computations when the systems are high dimensional. Another approach is to employ global sensitivity analysis, which in this context is mostly based on random sampling methods. In this paper we present an efficient approach that involves using numerical optimizing methods that search a wide region of parameter space for a given model to determine the maximum and minimum values of its metabolic control coefficients. A relevant example for drug development is presented to demonstrate the strategy using the software COPASI.
sensitivity analysis; robustness; optimization
Summary: The Systems Biology Markup Language (SBML) is an established community XML format for the markup of biochemical models. With the introduction of SBML level 2 version 3, specific model entities, such as species or reactions, can now be annotated using ontological terms. These annotations, which are encoded using the resource description framework (RDF), provide the facility to specify definite terms to individual components, allowing software to unambiguously identify such components and thus link the models to existing data resources.
libSBML is an application programming interface library for the manipulation of SBML files. While libSBML provides the facilities for reading and writing such annotations from and to models, it is beyond the scope of libSBML to provide interpretation of these terms. The libAnnotationSBML library introduced here acts as a layer on top of libSBML linking SBML annotations to the web services that describe these ontological terms. Two applications that use this library are described: SbmlSynonymExtractor finds name synonyms of SBML model entities and SbmlReactionBalancer checks SBML files to determine whether specifed reactions are elementally balanced.