Intestinal mucosal block is the transient reduction in iron absorption ability of intestinal epithelial cells (enterocytes) in response to previous iron exposures that occur at the cell scale. The block characteristics have been shown to depend both on iron exposure magnitude and temporality, and understanding block control will enable deeper understanding of how intestinal iron absorption contributes to pathological iron states. Three biochemical mechanisms implicated in driving the block behavior are divalent metal transporter 1 endocytosis, ferritin iron sequestration, and iron regulatory protein regulation of iron related protein expression. In this work, a model of enterocyte iron metabolism is built based on published experimental data that is capable of reproducing the mucosal block phenomena. The model is then used to estimate the quantitative contribution of each of the three mechanisms on the properties of the mucosal block. Analysis reveals that ferritin and iron regulatory proteins are the main intracellular mechanisms contributing to the mucosal block, findings congruent with experimental predictions. Lastly, DMT1 endocytosis is shown to play a role in limiting total iron uptake by enterocytes but does not contribute to the decrease in total iron transfer across their basal membrane seen in the mucosal block.

SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) is a novel virus of the family Coronaviridae. The virus causes the infectious disease COVID-19. The biology of coronaviruses has been studied for many years. However, bioinformatics tools designed explicitly for SARS-CoV-2 have only recently been developed as a rapid reaction to the need for fast detection, understanding and treatment of COVID-19. To control the ongoing COVID-19 pandemic, it is of utmost importance to get insight into the evolution and pathogenesis of the virus. In this review, we cover bioinformatics workflows and tools for the routine detection of SARS-CoV-2 infection, the reliable analysis of sequencing data, the tracking of the COVID-19 pandemic and evaluation of containment measures, the study of coronavirus evolution, the discovery of potential drug targets and development of therapeutic strategies. For each tool, we briefly describe its use case and how it advances research specifically for SARS-CoV-2. All tools are free to use and available online, either through web applications or public code repositories. Contact:[email protected].

It is well known that iron is an essential element for life but is toxic when in excess or in certain forms. Accordingly there are many diseases that result directly from either lack or excess of iron. Yet many molecular and physiological aspects of iron regulation have only been discovered recently and others are still elusive. There is still no good quantitative and dynamic description of iron absorption, distribution, storage and mobilization that agrees with the wide array of phenotypes presented in several iron-related diseases. The present work addresses this issue by developing a mathematical model of iron distribution in mice calibrated with ferrokinetic data and subsequently validated against data from mouse models of iron disorders, such as hemochromatosis, β-thalassemia, atransferrinemia and anemia of inflammation. To adequately fit the ferrokinetic data required inclusion of the following mechanisms: a) transferrin-mediated iron delivery to tissues, b) induction of hepcidin by transferrin-bound iron, c) ferroportin-dependent iron export regulated by hepcidin, d) erythropoietin regulation of erythropoiesis, and e) liver uptake of NTBI. The utility of the model to simulate disease interventions was demonstrated by using it to investigate the outcome of different schedules of transferrin treatment in β-thalassemia.

The metabolism of microorganisms is regulated through two main mechanisms: changes of enzyme capacities as a consequence of gene expression modulation ("hierarchical control") and changes of enzyme activities through metabolite-enzyme interactions. An increasing body of evidence indicates that hierarchical control is insufficient to explain metabolic behaviors, but the system-wide impact of metabolic regulation remains largely uncharacterized. To clarify its role, we developed and validated a detailed kinetic model of Escherichia coli central metabolism that links growth to environment. Metabolic control analyses confirm that the control is widely distributed across the network and highlight strong interconnections between all the pathways. Exploration of the model solution space reveals that several robust properties emerge from metabolic regulation, from the molecular level (e.g. homeostasis of total metabolite pool) to the overall cellular physiology (e.g. coordination of carbon uptake, catabolism, energy and redox production, and growth), while allowing a large degree of flexibility at most individual metabolic steps. These properties have important physiological implications for E. coli and significantly expand the self-regulating capacities of its metabolism.

Iron is essential for all known life due to its redox properties; however, these same properties can also lead to its toxicity in overload through the production of reactive oxygen species. Robust systemic and cellular control are required to maintain safe levels of iron, and the liver seems to be where this regulation is mainly located. Iron misregulation is implicated in many diseases, and as our understanding of iron metabolism improves, the list of iron-related disorders grows. Recent developments have resulted in greater knowledge of the fate of iron in the body and have led to a detailed map of its metabolism; however, a quantitative understanding at the systems level of how its components interact to produce tight regulation remains elusive. A mechanistic computational model of human liver iron metabolism, which includes the core regulatory components, is presented here. It was constructed based on known mechanisms of regulation and on their kinetic properties, obtained from several publications. The model was then quantitatively validated by comparing its results with previously published physiological data, and it is able to reproduce multiple experimental findings. A time course simulation following an oral dose of iron was compared to a clinical time course study and the simulation was found to recreate the dynamics and time scale of the systems response to iron challenge. A disease state simulation of haemochromatosis was created by altering a single reaction parameter that mimics a human haemochromatosis gene (HFE) mutation. The simulation provides a quantitative understanding of the liver iron overload that arises in this disease. This model supports and supplements understanding of the role of the liver as an iron sensor and provides a framework for further modelling, including simulations to identify valuable drug targets and design of experiments to improve further our knowledge of this system.

MOTIVATION: Research in systems biology is carried out through a combination of experiments and models. Several data standards have been adopted for representing models (Systems Biology Markup Language) and various types of relevant experimental data (such as FuGE and those of the Proteomics Standards Initiative). However, until now, there has been no standard way to associate a model and its entities to the corresponding datasets, or vice versa. Such a standard would provide a means to represent computational simulation results as well as to frame experimental data in the context of a particular model. Target applications include model-driven data analysis, parameter estimation, and sharing and archiving model simulations. RESULTS: We propose the Systems Biology Results Markup Language (SBRML), an XML-based language that associates a model with several datasets. Each dataset is represented as a series of values associated with model variables, and their corresponding parameter values. SBRML provides a flexible way of indexing the results to model parameter values, which supports both spreadsheet-like data and multidimensional data cubes. We present and discuss several examples of SBRML usage in applications such as enzyme kinetics, microarray gene expression and various types of simulation results. AVAILABILITY AND IMPLEMENTATION: The XML Schema file for SBRML is available at http://www.comp-sys-bio.org/SBRML under the Academic Free License (AFL) v3.0.

BACKGROUND: 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. RESULTS: 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. CONCLUSIONS: 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.

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.

Eight years have passed since the proposal that the characterization of mutations with silent phenotypes should be carried out by a global view of metabolism [1]. Since then, the field of metabolomics has flourished both in the development of new techniques and in the expansion of its application areas. No longer is metabolomics seen just as a tool of functional genomics, but it has now become an integral part of systems biology. Early approaches were based on metabolite fingerprints and metabolite profiles, but now there are also studies focusing on fluxes. The array of techniques to measure a large number of metabolites has also been expanding, with a clear predominance of mass spectrometry and nuclear magnetic resonance (NMR) methods but with new methods based on probes in development. But anyone who has been initiated in this field will recognize that the applications of metabolomics are currently limited by computational issues. The bigger bioinformatics challenges posed by metabolomics are: to identify the large number of metabolites that are detected but whose chemical nature is unknown (estimates go from 60 to 90% of the total); to identify the active areas of metabolism (pathways and networks) responsible for changes in metabolite profiles and to create standards for data and metadata format and reporting. This issue of Briefings in Bioinformatics carries a series of articles dedicated to addressing the bioinformatics challenges posed by metabolomics. To introduce the problem, a review by Shulaev describes the most popular metabolomics techniques and enumerates the bioinformatic issues at that level. Two reviews address the issue of pathways and networks: Lee et al. describe the relationship between flux balance analysis and metabolomics, and Steuer and Selbig cover the many interesting aspects of metabolite correlations and how they may be related with metabolic networks and regulation. Finally, an article by Castle et al. discusses the new ongoing efforts of an international and interdisciplinary group that is defining standards for metabolomics. This follows several earlier attempts at defining standards [2–4] and unites those efforts. The much anticipated result of this effort will be a set of data standards that cover all of metabolomics and will be compatible with other standards for genomics and proteomics. It is the simultaneous application of metabolomics, proteomics, and transcriptomics that is likely to provide the most comprehensive and informative views of biological systems, and for this to happen it is crucial that we have data standards in place. The next step will be to integrate these data in global models of cellular behavior—indeed a truly systems view of biology!

MOTIVATION: Simulation and modeling is becoming a standard approach to understand complex biochemical processes. Therefore, there is a big need for software tools that allow access to diverse simulation and modeling methods as well as support for the usage of these methods. RESULTS: Here, we present COPASI, a platform-independent and user-friendly biochemical simulator that offers several unique features. We discuss numerical issues with these features; in particular, the criteria to switch between stochastic and deterministic simulation methods, hybrid deterministic-stochastic methods, and the importance of random number generator numerical resolution in stochastic simulation. AVAILABILITY: The complete software is available in binary (executable) for MS Windows, OS X, Linux (Intel) and Sun Solaris (SPARC), as well as the full source code under an open source license from http://www.copasi.org.

MOTIVATION: A major challenge of systems biology is to infer biochemical interactions from large-scale observations, such as transcriptomics, proteomics and metabolomics. We propose to use a partial correlation analysis to construct approximate Undirected Dependency Graphs from such large-scale biochemical data. This approach enables a distinction between direct and indirect interactions of biochemical compounds, thereby inferring the underlying network topology. RESULTS: The method is first thoroughly evaluated with a large set of simulated data. Results indicate that the approach has good statistical power and a low False Discovery Rate even in the presence of noise in the data. We then applied the method to an existing data set of yeast gene expression. Several small gene networks were inferred and found to contain genes known to be collectively involved in particular biochemical processes. In some of these networks there are also uncharacterized ORFs present, which lead to hypotheses about their functions. AVAILABILITY: Programs running in MS-Windows and Linux for applying zeroth, first, second and third order partial correlation analysis can be downloaded at: http://mendes.vbi.vt.edu/tiki-index.php?page=Software. SUPPLEMENTARY INFORMATION: Supplementary information can be found at: URL to be decided.

MOTIVATION: Molecular biotechnology now makes it possible to build elaborate systems models, but the systems biology community needs information standards if models are to be shared, evaluated and developed cooperatively. RESULTS: We summarize the Systems Biology Markup Language (SBML) Level 1, a free, open, XML-based format for representing biochemical reaction networks. SBML is a software-independent language for describing models common to research in many areas of computational biology, including cell signaling pathways, metabolic pathways, gene regulation, and others. AVAILABILITY: The specification of SBML Level 1 is freely available from http://www.sbml.org/

Metabolic profiling applied to functional genomics (metabolomics) is in an early stage of development. Here, the technologies used for metabolite profiling are briefly covered, illustrated by a few pioneering studies. Issues related to bioinformatics, namely data analysis, visualisation and archival, are the main focus of this review. Arguably there is already a need for databases containing metabolite profiles specific for a single organism, and a generic repository containing all metabolite profiling results, regardless of species. Data analyses and visualisations that combine the biological context with chemistry details are suggested as being the most promising.

Abstract Summary: We describe a program for the construction of spatially distributed metabolic models, which may then be simulated using the metabolic simulator Gepasi. This is useful for the modelling of heterogeneous systems whether as liquid cultures or as spatially organised systems with specified interconnections. Availability: MEG runs under Windows 95, 98, 2000 and NT, with either Intel or Alpha processors, and is available from http://gepasi.dbs.aber.ac.uk/softw/meg.html Contact: [email protected] * To whom correspondence should be addressed. Present address: Virginia Bioinformatics Institute, Virginia Polytechnic and State University, 1750 Kraft Drive, Suite 1100, Blacksburg, VA 24061, USA.

Abstract Motivation: Heterogeneity of databases and software resources continues to hamper the integration of biological information. Top-down solutions are not feasible for the full-scale problem of integration across biological species and data types. Bottom-up solutions so far have not integrated, in a maximally flexible way, dynamic and interactive graphical-user-interface components with data repositories and analysis tools. Results: We present a component-based approach that relies on a generalized platform for component integration. The platform enables independently-developed components to synchronize their behavior and exchange services, without direct knowledge of one another. An interface-based data model allows the exchange of information with minimal component interdependency. From these interactions an integrated system results, which we call ISYS\batchmode \documentclass[fleqn,10pt,legalpaper]{article} \usepackage{amssymb} \usepackage{amsfonts} \usepackage{amsmath} \pagestyle{empty} \begin{document} \(^{TM}\) \end{document}. By allowing services to be discovered dynamically based on selected objects, ISYS encourages a kind of exploratory navigation that we believe to be well-suited for applications in genomic research. Availability: A ‘developer’s kit’ for creating software that uses the ISYS platform is available at www.ncgr.org/research/isys/devrel.html. It includes a more refined and fully documented version of the platform and a starting set of ISYS-ready components. Up-to-date descriptions of the ISYS project can be found at www.ncgr.org/research/isys. Contact: [email protected] * To whom correspondence should be addressed.

MOTIVATION: The simulation of biochemical kinetic systems is a powerful approach that can be used for: (i) checking the consistency of a postulated model with a set of experimental measurements, (ii) answering 'what if?' questions and (iii) exploring possible behaviours of a model. Here we describe a generic approach to combine numerical optimization methods with biochemical kinetic simulations, which is suitable for use in the rational design of improved metabolic pathways with industrial significance (metabolic engineering) and for solving the inverse problem of metabolic pathways, i.e. the estimation of parameters from measured variables. RESULTS: We discuss the suitability of various optimization methods, focusing especially on their ability or otherwise to find global optima. We recommend that a suite of diverse optimization methods should be available in simulation software as no single one performs best for all problems. We describe how we have implemented such a simulation-optimization strategy in the biochemical kinetics simulator Gepasi and present examples of its application. AVAILABILITY: The new version of Gepasi (3.20), incorporating the methodology described here, is available on the Internet at http://gepasi.dbs.aber.ac.uk/softw/Gepasi. html. CONTACT: [email protected]

GEPASI is a software system for modelling chemical and biochemical reaction networks on computers running Microsoft Windows. For any system of up to 45 metabolites and 45 reactions, each with any user-defined or one of 35 predefined rate equations, one can produce trajectories of the metabolite concentrations and obtain a steady state (if it does exist). When steady-state solutions are produced, elasticity and control coefficients, as defined in metabolic control analysis, are calculated. GEPASI also allows the automatic generation of a sequence of simulations with different combinations of parameter values, effectively scanning a hyper-solid in parameter space. Together with the ability to produce user-defined columnar data files, these features allow for both very quick and systematic study of biochemical pathway models. The source code (in C) is available on request from the author, and while the user interface is dependent on having MS-Windows as the operating system, the numerical part is portable to other operating systems. GEPASI is suitable both for research and educational purposes. Although GEPASI was written with biochemical pathways in mind, it can equally be used to stimulate other dynamical systems.