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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.

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 developedSBMLLevel 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 ofSBMLand 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 howSBMLLevel 3 provides the foundation needed to support this evolution.

AimIt is of interest to predict possible lifetime risk of type 1 diabetes (T1D) in young children for recruiting high-risk subjects into longitudinal studies of effective prevention strategies. MethodsUtilizing a case-control study in Sweden, we applied a recently developed next generation targeted sequencing technology to genotype class II genes and applied an object-oriented regression to build and validate a prediction model for T1D. ResultsIn the training set, estimated risk scores were significantly different between patients and controls (P=8.12x10(-92)), and the area under the curve (AUC) from the receiver operating characteristic (ROC) analysis was 0.917. Using the validation data set, we validated the result with AUC of 0.886. Combining both training and validation data resulted in a predictive model with AUC of 0.903. Further, we performed a biological validation by correlating risk scores with 6 islet autoantibodies, and found that the risk score was significantly correlated with IA-2A (Z-score=3.628, Pamp;lt;0.001). When applying this prediction model to the Swedish population, where the lifetime T1D risk ranges from 0.5% to 2%, we anticipate identifying approximately 20 000 high-risk subjects after testing all newborns, and this calculation would identify approximately 80% of all patients expected to develop T1D in their lifetime. ConclusionThrough both empirical and biological validation, we have established a prediction model for estimating lifetime T1D risk, using class II HLA. This prediction model should prove useful for future investigations to identify high-risk subjects for prevention research in high-risk populations., Funding Agencies|European Foundation for the Study of Diabetes (EFSD); Swedish Child Diabetes Foundation (Barndiabetesfonden); National Institutes of Health [DK26190, DK63861]; Swedish Research Council; Skane County Council; Swedish Association of Local Authorities and Regions (SKL); National Institute of Diabetes and Digestive and Kidney Diseases [16-05-MH]; Fred Hutchinson Cancer Research Center

Stem cells, believed to be the cellular origin of glioma, are able to generate gliomas, according to experimental studies. Here we investigated the potential and circumstances of more differentiated cells to generate glioma development. We and others have shown that oligodendrocyte precursor cells (OPCs) can also be the cell of origin for experimental oligodendroglial tumors. However, the question of whether OPCs have the capacity to initiate astrocytic gliomas remains unanswered. Astrocytic and oligodendroglial tumors represent the two most common groups of glioma and have been considered as distinct disease groups with putatively different origins. Here we show that mouse OPCs can give rise to both types of glioma given the right circumstances. We analyzed tumors induced by K-RAS and AKT and compared them to oligodendroglial platelet-derived growth factor B-induced tumors in Ctv-a mice with targeted deletions of Cdkn2a (p16(Ink4a-/-), p19(Arf-/-), Cdkn2a(-/-)). Our results showed that glioma can originate from OPCs through overexpression of K-RAS and AKT when combined with p19(Arf) loss, and these tumors displayed an astrocytic histology and high expression of astrocytic markers. We argue that OPC shave the potential to develop both astrocytic and oligodendroglial tumors given loss of p19(Arf), and that oncogenic signaling is dominant to cell of origin in determining glioma phenotype. Our mouse data are supported by the fact that human astrocytoma and oligodendroglioma display a high degree of overlap in global gene expression with no clear distinctions between the two diagnoses.

An analysis of the degree of genomic variation between two individual genomes suggests that there may be considerable biochemical differences among individuals. Examination of DNA sequence variations in 14 canonical signaling pathways and Monte-Carlo simulation modeling suggest that the kinetic and quantitative behavior of signaling pathways in many individuals may be significantly perturbed from the 'healthy' norm. Signal transduction pathways in some individuals may suffer context-specific failures, or they may function normally but fail easily in the face of additional environmental perturbations or somatic mutations. These findings argue for new systems biology approaches that can predict pathway status in individuals using personal genome sequences and biomarker data.

A model was built to examine the kinetics of regulatory cascades such as occur in developmental gene networks. The model relates occupancy of cis-regulatory target sites to transcriptional initiation rate, and thence to RNA and protein output. The model was used to simulate regulatory cascades in which genes encoding transcription factors are successively activated. Using realistic parameter ranges based on extensive earlier measurements in sea urchin embryos, we find that transitions of regulatory states occur sharply in these simulations, with respect to time or changing transcription factor concentrations. As is often observed in developing systems, the simulated regulatory cascades display a succession of gene activations separated by delays of some hours. The most important causes of this behavior are cooperativity in the assembly of cis-regulatory complexes and the high specificity of transcription factors for their target sites. Successive transitions in state occur long in advance of the approach to steady-state levels of the molecules that drive the process. The kinetics of such developmental systems thus depend mainly on the initial output rates of genes activated in response to the advent of new transcription factors.

Researchers in computational biology today make use of a large number of different software packages for modeling, analysis, and data manipulation and visualization. In this paper, we describe the ERATO Systems Biology Workbench (SBW), a software framework that allows these heterogeneous application components--written in diverse programming languages and running on different platforms--to communicate and use each others' data and algorithmic capabilities. Our goal is to create a simple, open-source software infrastructure which is effective, easy to implement and easy to understand. SBW uses a broker-based architecture and enables applications (potentially running on separate, distributed computers) to communicate via a simple network protocol. The interfaces to the system are encapsulated in client-side libraries that we provide for different programming languages. We describe the SBW architecture and the current set of modules, as well as alternative implementation technologies.

The goal of the ERATO Systems Biology Workbench (SBW) project is to create an integrated, easy-to-use software environment that enables sharing of resources for systems biology. Our initial focus is on achieving interoperability between 7 existing simulators. Our long-term goal is to develop a flexible and adaptable environment that provides the ability to interact with a wide variety of software tools applicable to the systems biology field including databases and experimental devices. We place high value on ease of use and ease of extensibility as important qualities of software for use in biological investigations. The software products of this project will be open source, and portable to Windows and Linux. The Systems Biology Workbench is a vehicle for collaboration between developers of bioinformatics technology. We are actively seeking other collaborators to extend the workbench. The motivation is to reduce the time spent by developers both creating software infrastructure and creating tools that exist in a similar form in other packages, allowing developers to concentrate on new algorithm and model development.

Systems biology researchers make use of a large number of different software packages for computational modeling and analysis as well as data manipulation and visualization. To help developers easily provide the ability for their applications to communicate with other tools, we have developed a simple, open-source, application integration framework, the ERATO Systems Biology Workbench (SBW). In this paper, we discuss the architecture of SBW, focusing on our motivations for various design decisions including the choice of the message-oriented communications infrastructure.

Over the years, a variety of biochemical network modeling packages have been developed and used by researchers in biology. No single package currently answers all the needs of the biology community; nor is one likely to do so in the near future, because the range of tools needed is vast and new techniques are emerging too rapidly. It seems unavoidable that, for the foreseeable future, systems biology researchers are likely to continue using multiple packages to carry out their work. In this chapter, we describe the ERATO Systems Biology Workbench (SBW) and the Systems Biology Markup Language (SBML), two related efforts directed at the problems of software package interoperability. The goal of the SBW project is to create an integrated, easy-to-use software environment that enables sharing of models and resources between simulation and analysis tools for systems biology. SBW uses a modular, plug-in architecture that permits easy introduction of new components. SBML is a proposed standard XML-based language for representing models communicated between software packages; it is used as the format of models communicated between components in SBW.

In principle, all the problems in biology could be solved by solving the time-dependent Schroedinger equation (quantum mechanics, QM). This would lead to a detailed under-standing of the role that molecular-level interactions play in determining the fundamental biochemistry at the heart of biology and pharmacology. The difficulty is the vast range of length and time scales, from a nitrous oxide molecule to an organ (heart, lung), which makes a QM solution both impractical and useless. It is impractical because there are too many degrees of freedom describing the motions of the electrons and atoms, whereas in the functioning of an organ it may be only the rate of transfer across some membrane. The solution to both problems is the hierarchical strategy outlined in figure 6.1. We average over the scale of electrons (from QM) to describe the forces on atoms (the force field, FF), then average over the dynamics of atoms (molecular dynamics, MD) to describe the motions of large biomolecules, then average over the molecular scale to obtain the properties of membranes, then average over the components in a cell, then average over the cells to describe a part of an organ. The strategy is to develop a methodology for going between these various levels so that first principle’s theory can be used to predict the properties of new systems.