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Biological models often contain elements that have inexact numerical values, since they are based on values that are stochastic in nature or data that contains uncertainty. The Systems Biology Markup Language (SBML) Level 3 Core specification does not include an explicit mechanism to include inexact or stochastic values in a model, but it does provide a mechanism for SBML packages to extend the Core specification and add additional syntactic constructs. The SBML Distributions package for SBML Level 3 adds the necessary features to allow models to encode information about the distribution and uncertainty of values underlying a quantity.

The Systems Biology Graphical Notation (SBGN) is an international community effort that aims to standardise the visualisation of pathways and networks for readers with diverse scientific backgrounds as well as to support an efficient and accurate exchange of biological knowledge between disparate research communities, industry, and other players in systems biology. SBGN comprises the three languages Entity Relationship, Activity Flow, and Process Description (PD) to cover biological and biochemical systems at distinct levels of detail. PD is closest to metabolic and regulatory pathways found in biological literature and textbooks. Its well-defined semantics offer a superior precision in expressing biological knowledge. PD represents mechanistic and temporal dependencies of biological interactions and transformations as a graph. Its different types of nodes include entity pools (e.g. metabolites, proteins, genes and complexes) and processes (e.g. reactions, associations and influences). The edges describe relationships between the nodes (e.g. consumption, production, stimulation and inhibition). This document details Level 1 Version 2.0 of the PD specification, including several improvements, in particular: 1) the addition of the equivalence operator, subunit, and annotation glyphs, 2) modification to the usage of submaps, and 3) updates to clarify the use of various glyphs (i.e. multimer, empty set, and state variable).

The Systems Biological Graphical Notation (SBGN) is an international community effort for standardized graphical representations of biological pathways and networks. The goal of SBGN is to provide unambiguous pathway and network maps for readers with different scientific backgrounds as well as to support efficient and accurate exchange of biological knowledge between different research communities, industry, and other players in systems biology. Three SBGN languages, Process Description (PD), Entity Relationship (ER) and Activity Flow (AF), allow for the representation of different aspects of biological and biochemical systems at different levels of detail.

The Systems Biological Graphical Notation (SBGN) is an international community effort for standardized graphical representations of biological pathways and networks. The goal of SBGN is to provide unambiguous pathway and network maps for readers with different scientific backgrounds as well as to support efficient and accurate exchange of biological knowledge between different research communities, industry, and other players in systems biology. Three SBGN languages, Process Description (PD), Entity Relationship (ER) and Activity Flow (AF), allow for the representation of different aspects of biological and biochemical systems at different levels of detail.

The Systems Biological Graphical Notation (SBGN) is an international community effort for standardized graphical representations of biological pathways and networks. The goal of SBGN is to provide unambiguous pathway and network maps for readers with different scientific backgrounds as well as to support efficient and accurate exchange of biological knowledge between different research communities, industry, and other players in systems biology. Three SBGN languages, Process Description (PD), Entity Relationship (ER) and Activity Flow (AF), allow for the representation of different aspects of biological and biochemical systems at different levels of detail.

The modelling of biological intra-cellular pathways requires the systematic capture and representation of interactions between components that are biologically correct and computationally rigorous. The challenge is two fold, to verify and extend our understanding of such pathways by comparing in silico models to experiments; and to ensure that such models are understandable by biologists and for checking biological validity. In this report we present a graphical notation, the Edinburgh Pathway Notation (EPN), which satisfies the central biologically driven requirements while providing a strict formal framework for analysis. The EPN emphasises the use of a logical representation of pathways, which is particularly suited to pathways were some mechanisms are not known in detail.

Abstract Background Macrophages play an integral role in the host immune system, bridging innate and adaptive immunity. As such, they are finely attuned to extracellular and intracellular stimuli and respond by rapidly initiating multiple signalling cascades with diverse effector functions. The macrophage cell is therefore an experimentally and clinically amenable biological system for the mapping of biological pathways. The goal of the macrophage expression atlas is to systematically investigate the pathway biology and interaction network of macrophages challenged with a variety of insults, in particular via infection and activation with key inflammatory mediators. As an important first step towards this we present a single searchable database resource containing high-throughput macrophage gene expression studies. Description The GPX Macrophage Expression Atlas (GPX-MEA) is an online resource for gene expression based studies of a range of macrophage cell types following treatment with pathogens and immune modulators. GPX-MEA follows the MIAME standard and includes an objective quality score with each experiment. It places special emphasis on rigorously capturing the experimental design and enables the searching of expression data from different microarray experiments. Studies may be queried on the basis of experimental parameters, sample information and quality assessment score. The ability to compare the expression values of individual genes across multiple experiments is provided. In addition, the database offers access to experimental annotation and analysis files and includes experiments and raw data previously unavailable to the research community. Conclusion GPX-MEA is the first example of a quality scored gene expression database focussed on a macrophage cellular system that allows efficient identification of transcriptional patterns. The resource will provide novel insights into the phenotypic response of macrophages to a variety of benign, inflammatory, and pathogen insults. GPX-MEA is available through the GPX website at http://www.gti.ed.ac.uk/GPX.

Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developed SBML Level 3 over the past decade. Its modular form consists of a core suited to representing reaction‐based models and packages that extend the core with features suited to other model types including constraint‐based models, reaction‐diffusion models, logical network models, and rule‐based models. The format leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single‐cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and how SBML Level 3 provides the foundation needed to support this evolution. [ABSTRACT FROM AUTHOR]

The Drug Disease Model Resources (DDMoRe) Interoperability Framework (IOF) enables pharmacometric model encoding and execution via Model Description Language (MDL) and R language, through the ddmore package. Through its components and converter plugins, the IOF can execute pharmacometric tasks using different target tools, starting from a single MDL‐encoded model. In this article, we present the WinBUGS plugin and show how its integration in the IOF enables an easy implementation of complex Bayesian workflows. We selected a published diabetes‐linked study as a real‐world example, in which two inter‐related models are used to estimate insulin secretion rate in response to a glucose stimulus from intravenous glucose tolerance test (IVGTT) data. This model was implemented following different approaches to propagate uncertainty, via diverse IOF target tools (NONMEM, WinBUGS, PsN, and Xpose). The developed software supports a plethora of pharmacokinetic/pharmacodynamic (PK/PD) modeling features. It provides solutions to reproducibility and interoperability issues in Bayesian modeling, and facilitates the difficult encoding of complex PK/PD models in WinBUGS. [ABSTRACT FROM AUTHOR]

In this paper, we present the implementation of a novel domain-specific language (DSL) for pharmacometric modeling called the Modelling Description Language (MDL). MDL is a modular, declarative language with block structures that allows users to abstract data, processes and mathematical models from auxiliary code, and hence, improves model readability, reusability and opportunities for collaborative research. The main aim of this DSL is interoperability between core software tools used in pharmacometrics. We describe the MDL-IDE, an integrated development environment for MDL, which assists users in writing MDL code. The paper focuses on language constructs and design decisions, briefly explains how models are validated and converted to a machine-readable format for processing by existing model simulation and estimation software tools. [ABSTRACT FROM AUTHOR]

Background With the ever increasing use of computational models in the biosciences, the need to share models and reproduce the results of published studies efficiently and easily is becoming more important. To this end, various standards have been proposed that can be used to describe models, simulations, data or other essential information in a consistent fashion. These constitute various separate components required to reproduce a given published scientific result. Results We describe the Open Modeling EXchange format (OMEX). Together with the use of other standard formats from the Computational Modeling in Biology Network (COMBINE), OMEX is the basis of the COMBINE Archive, a single file that supports the exchange of all the information necessary for a modeling and simulation experiment in biology. An OMEX file is a ZIP container that includes a manifest file, listing the content of the archive, an optional metadata file adding information about the archive and its content, and the files describing the model. The content of a COMBINE Archive consists of files encoded in COMBINE standards whenever possible, but may include additional files defined by an Internet Media Type. Several tools that support the COMBINE Archive are available, either as independent libraries or embedded in modeling software. Conclusions The COMBINE Archive facilitates the reproduction of modeling and simulation experiments in biology by embedding all the relevant information in one file. Having all the information stored and exchanged at once also helps in building activity logs and audit trails. We anticipate that the COMBINE Archive will become a significant help for modellers, as the domain moves to larger, more complex experiments such as multi-scale models of organs, digital organisms, and bioengineering. [ABSTRACT FROM AUTHOR]

Circuit diagrams and Unified Modeling Language diagrams are just two examples of standard visual languages that help accelerate work by promoting regularity, removing ambiguity and enabling software tool support for communication of complex information. Ironically, despite having one of the highest ratios of graphical to textual information, biology still lacks standard graphical notations. The recent deluge of biological knowledge makes addressing this deficit a pressing concern. Toward this goal, we present the Systems Biology Graphical Notation (SBGN), a visual language developed by a community of biochemists, modelers and computer scientists. SBGN consists of three complementary languages: process diagram, entity relationship diagram and activity flow diagram. Together they enable scientists to represent networks of biochemical interactions in a standard, unambiguous way. We believe that SBGN will foster efficient and accurate representation, visualization, storage, exchange and reuse of information on all kinds of biological knowledge, from gene regulation, to metabolism, to cellular signaling. [ABSTRACT FROM AUTHOR]

Background: Macrophages play an integral role in the host immune system, bridging innate and adaptive immunity. As such, they are finely attuned to extracellular and intracellular stimuli and respond by rapidly initiating multiple signalling cascades with diverse effector functions. The macrophage cell is therefore an experimentally and clinically amenable biological system for the mapping of biological pathways. The goal of the macrophage expression atlas is to systematically investigate the pathway biology and interaction network of macrophages challenged with a variety of insults, in particular via infection and activation with key inflammatory mediators. As an important first step towards this we present a single searchable database resource containing high-throughput macrophage gene expression studies. Description: The GPX Macrophage Expression Atlas (GPX-MEA) is an online resource for gene expression based studies of a range of macrophage cell types following treatment with pathogens and immune modulators. GPX-MEA follows the MIAME standard and includes an objective quality score with each experiment. It places special emphasis on rigorously capturing the experimental design and enables the searching of expression data from different microarray experiments. Studies may be queried on the basis of experimental parameters, sample information and quality assessment score. The ability to compare the expression values of individual genes across multiple experiments is provided. In addition, the database offers access to experimental annotation and analysis files and includes experiments and raw data previously unavailable to the research community. Conclusion: GPX-MEA is the first example of a quality scored gene expression database focussed on a macrophage cellular system that allows efficient identification of transcriptional patterns. The resource will provide novel insights into the phenotypic response of macrophages to a variety of benign, inflammatory, and pathogen insults. GPX-MEA is available through the GPX website at http://www.gti.ed.ac.uk/GPX. [ABSTRACT FROM AUTHOR]

A correction to the article "The Systems Biology Graphical Notation" in the August 7, 2009 issue that was published online is presented.

The Computational Modeling in Biology Network (COMBINE), is an initiative to coordinate the development of the various community standards and formats in computational systems biology and related fields. This report summarizes the activities pursued at the first annual COMBINE meeting held in Edinburgh on October 6-9 2010 and the first HARMONY hackathon, held in New York on April 18-22 2011. The first of those meetings hosted 81 attendees. Discussions covered both official COMBINE standards-(BioPAX, SBGN and SBML), as well as emerging efforts and interoperability between different formats. The second meeting, oriented towards software developers, welcomed 59 participants and witnessed many technical discussions, development of improved standards support in community software systems and conversion between the standards. Both meetings were resounding successes and showed that the field is now mature enough to develop representation formats and related standards in a coordinated manner.

Resistance to targeted cancer therapies such as trastuzumab is a frequent clinical problem not solely because of insufficient expression of HER2 receptor but also because of the overriding activation states of cell signaling pathways. Systems biology approaches lend themselves to rapid in silico testing of factors, which may confer resistance to targeted therapies. Inthis study, we aimed to develop a new kinetic model that could be interrogated to predict resistance to receptor tyrosine kinase (RTK) inhibitor therapies and directly test predictions in vitro and in clinical samples. The new mathematical model included RTK inhibitor antibody binding, HER2/HER3 dimerization and inhibition, AKT/mitogen-activated protein kinase cross-talk, and the regulatory properties of PTEN. The model was parameterized using quantitative phosphoprotein expression data from cancer cell lines using reverse-phase protein microarrays. Quantitative PTEN protein expression was found to be the key determinant of resistance to anti-HER2 therapy in silico, which was predictive of unseen experiments in vitro using the PTEN inhibitor bp(V). When measured in cancer cell lines, PTEN expression predicts sensitivity to anti-HER2 therapy; furthermore, this quantitative measurement is more predictive of response (relative risk, 3.0; 95% confidence interval, 1.6-5.5; P < 0.0001) than other pathway components taken in isolation and when tested by multivariate analysis in a cohort of 122 breast cancers treated with trastuzumab. For the first time, a systems biology approach has successfully been used to stratify patients for personalized therapy in cancer and is further compelling evidence that PTEN, appropriately measured in the clinical setting, refines clinical decision making in patients treated with anti-HER2 therapies.

We describe the challenges faced when developing a Linux/PC-based cluster to apply bioinformatics algorithms to the rapidly increasing raw genomics data available. The calculations, which take around two months to complete, result in a powerful resource that can be used for data mining--most obviously for the human genome. Our current infrastructure consists of a 1314 node cluster with 1734 processors supporting both production and research. This paper highlights the problems in achieving high data throughput with such systems and shows that raw computer power is only one component of a complex problem.