Distinct Classes of Complex Structural Variation Uncovered across Thousands of Cancer Genome Graphs.

Cancer genomes often harbor hundreds of somatic DNA rearrangement junctions, many of which cannot be easily classified into simple (e.g., deletion) or complex (e.g., chromothripsis) structural variant classes. Applying a novel genome graph computational paradigm to analyze the topology of junction copy number (JCN) across 2,778 tumor whole-genome sequences, we uncovered three novel complex rearrangement phenomena: pyrgo, rigma, and tyfonas. Pyrgo are "towers" of low-JCN duplications associated with early-replicating regions, superenhancers, and breast or ovarian cancers. Rigma comprise "chasms" of low-JCN deletions enriched in late-replicating fragile sites and gastrointestinal carcinomas. Tyfonas are "typhoons" of high-JCN junctions and fold-back inversions associated with expressed protein-coding fusions, breakend hypermutation, and acral, but not cutaneous, melanomas. Clustering of tumors according to genome graph-derived features identified subgroups associated with DNA repair defects and poor prognosis.

VSCode-Antimony: a source editor for building, analyzing, and translating antimony models.

MOTIVATION: Developing biochemical models in systems biology is a complex, knowledge-intensive activity. Some modelers (especially novices) benefit from model development tools with a graphical user interface. However, as with the development of complex software, text-based representations of models provide many benefits for advanced model development. At present, the tools for text-based model development are limited, typically just a textual editor that provides features such as copy, paste, find, and replace. Since these tools are not "model aware," they do not provide features for: (i) model building such as autocompletion of species names; (ii) model analysis such as hover messages that provide information about chemical species; and (iii) model translation to convert between model representations. We refer to these as BAT features. RESULTS: We present VSCode-Antimony, a tool for building, analyzing, and translating models written in the Antimony modeling language, a human readable representation of Systems Biology Markup Language (SBML) models. VSCode-Antimony is a source editor, a tool with language-aware features. For example, there is autocompletion of variable names to assist with model building, hover messages that aid in model analysis, and translation between XML and Antimony representations of SBML models. These features result from making VSCode-Antimony model-aware by incorporating several sophisticated capabilities: analysis of the Antimony grammar (e.g. to identify model symbols and their types); a query system for accessing knowledge sources for chemical species and reactions; and automatic conversion between different model representations (e.g. between Antimony and SBML). AVAILABILITY AND IMPLEMENTATION: VSCode-Antimony is available as an open source extension in the VSCode Marketplace https://marketplace.visualstudio.com/items?itemName=stevem.vscode-antimony. Source code can be found at https://github.com/sys-bio/vscode-antimony.

BioSimulators: a central registry of simulation engines and services for recommending specific tools.

Computational models have great potential to accelerate bioscience, bioengineering, and medicine. However, it remains challenging to reproduce and reuse simulations, in part, because the numerous formats and methods for simulating various subsystems and scales remain siloed by different software tools. For example, each tool must be executed through a distinct interface. To help investigators find and use simulation tools, we developed BioSimulators (https://biosimulators.org), a central registry of the capabilities of simulation tools and consistent Python, command-line and containerized interfaces to each version of each tool. The foundation of BioSimulators is standards, such as CellML, SBML, SED-ML and the COMBINE archive format, and validation tools for simulation projects and simulation tools that ensure these standards are used consistently. To help modelers find tools for particular projects, we have also used the registry to develop recommendation services. We anticipate that BioSimulators will help modelers exchange, reproduce, and combine simulations.

Adapting modeling and simulation credibility standards to computational systems biology.

Computational models are increasingly used in high-impact decision making in science, engineering, and medicine. The National Aeronautics and Space Administration (NASA) uses computational models to perform complex experiments that are otherwise prohibitively expensive or require a microgravity environment. Similarly, the Food and Drug Administration (FDA) and European Medicines Agency (EMA) have began accepting models and simulations as forms of evidence for pharmaceutical and medical device approval. It is crucial that computational models meet a standard of credibility when using them in high-stakes decision making. For this reason, institutes including NASA, the FDA, and the EMA have developed standards to promote and assess the credibility of computational models and simulations. However, due to the breadth of models these institutes assess, these credibility standards are mostly qualitative and avoid making specific recommendations. On the other hand, modeling and simulation in systems biology is a narrower domain and several standards are already in place. As systems biology models increase in complexity and influence, the development of a credibility assessment system is crucial. Here we review existing standards in systems biology, credibility standards in other science, engineering, and medical fields, and propose the development of a credibility standard for systems biology models.

SBML Level 3: an extensible format for the exchange and reuse of biological models.

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.

CNValidator: validating somatic copy-number inference.

MOTIVATION: CNValidator assesses the quality of somatic copy-number calls based on coherency of haplotypes across multiple samples from the same individual. It is applicable to any copy-number calling algorithm, which makes calls independently for each sample. This test is useful in assessing the accuracy of copy-number calls, as well as choosing among alternative copy-number algorithms or tuning parameter values. RESULTS: On a dataset of somatic samples from individuals with Barrett's Esophagus, CNValidator provided feedback on the correctness of sample ploidy calls and also detected data quality issues. AVAILABILITY AND IMPLEMENTATION: CNValidator is available on GitHub at https://github.com/kuhnerlab/CNValidator. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Apoglobin Stability Is the Major Factor Governing both Cell-free and in Vivo Expression of Holomyoglobin.

Expression levels in animal muscle tissues and in Escherichia coli vary widely for naturally occurring mammalian myoglobins (Mb). To explore this variation, we developed an in vitro transcription and wheat germ extract-based translation assay to examine quantitatively the factors that govern expression of holoMb. We constructed a library of naturally occurring Mbs from two terrestrial and four deep-diving aquatic mammals and three distal histidine mutants designed to enhance apoglobin stability but decrease hemin affinity. A strong linear correlation is observed between cell-free expression levels of holo-metMb variants and their corresponding apoglobin stabilities, which were measured independently by guanidine HCl-induced unfolding titrations using purified proteins. In contrast, there is little dependence of expression on hemin affinity. Our results confirm quantitatively that deep diving mammals have highly stable Mbs that express to higher levels in animal myocytes, E. coli, and the wheat germ cell-free system than Mbs from terrestrial mammals. Our theoretical analyses show that the rate of aggregation of unfolded apoMb is very large, and as a result, the key factor for high level expression of holoMb, and presumably other heme proteins, is an ultra high fraction of folded, native apoglobin that is capable of rapidly binding hemin. This fraction is determined by the overall equilibrium folding constant and not hemin affinity. These results also demonstrate that the cell-free transcription/translation system can be used as a high throughput platform to screen for apoglobin stability without the need to generate large amounts of protein for in vitro unfolding measurements.

SBML and CellML translation in antimony and JSim.

MOTIVATION: The creation and exchange of biologically relevant models is of great interest to many researchers. When multiple standards are in use, models are more readily used and re-used if there exist robust translators between the various accepted formats. SUMMARY: Antimony 2.4 and JSim 2.10 provide translation capabilities from their own formats to SBML and CellML. All provided unique challenges, stemming from differences in each format's inherent design, in addition to differences in functionality. AVAILABILITY AND IMPLEMENTATION: Both programs are available under BSD licenses; Antimony from http://antimony.sourceforge.net/and JSim from http://physiome.org/jsim/. CONTACT: lpsmith@u.washington.edu.

MakeSBML: a tool for converting between Antimony and SBML.

We describe a web-based tool, MakeSBML (https://sys-bio.github.io/makesbml/), that provides an installation-free application for creating, editing, and searching the Biomodels repository for SBML-based models. MakeSBML is a client-based web application that translates models expressed in human-readable Antimony to the System Biology Markup Language (SBML) and vice-versa. Since MakeSBML is a web-based application it requires no installation on the user's part. Currently, MakeSBML is hosted on a GitHub page where the client-based design makes it trivial to move to other hosts. This model for software deployment also reduces maintenance costs since an active server is not required. The SBML modeling language is often used in systems biology research to describe complex biochemical networks and makes reproducing models much easier. However, SBML is designed to be computer-readable, not human-readable. We therefore employ the human-readable Antimony language to make it easy to create and edit SBML models.

The simulation experiment description markup language (SED-ML): language specification for level 1 version 5.

Modern biological research is increasingly informed by computational simulation experiments, which necessitate the development of methods for annotating, archiving, sharing, and reproducing the conducted experiments. These simulations increasingly require extensive collaboration among modelers, experimentalists, and engineers. The Minimum Information About a Simulation Experiment (MIASE) guidelines outline the information needed to share simulation experiments. SED-ML is a computer-readable format for the information outlined by MIASE, created as a community project and supported by many investigators and software tools. Level 1 Version 5 of SED-ML expands the ability of modelers to define simulations in SED-ML using the Kinetic Simulation Algorithm Onotoloy (KiSAO). While it was possible in Version 4 to define a simulation entirely using KiSAO, Version 5 now allows users to define tasks, model changes, ranges, and outputs using the ontology as well. SED-ML is supported by a growing ecosystem of investigators, model languages, and software tools, including various languages for constraint-based, kinetic, qualitative, rule-based, and spatial models, and many simulation tools, visual editors, model repositories, and validators. Additional information about SED-ML is available at https://sed-ml.org/.

MakeSBML: A tool for converting between Antimony and SBML.

We describe a web-based tool, MakeSBML (https://sys-bio.github.io/makesbml/), that provides an installation-free application for creating, editing, and searching the Biomodels repository for SBML-based models. MakeSBML is a client-based web application that translates models expressed in human-readable Antimony to the System Biology Markup Language (SBML) and vice-versa. Since MakeSBML is a web-based application it requires no installation on the user's part. Currently, MakeSBML is hosted on a GitHub page where the client-based design makes it trivial to move to other hosts. This model for software deployment also reduces maintenance costs since an active server is not required. The SBML modeling language is often used in systems biology research to describe complex biochemical networks and makes reproducing models much easier. However, SBML is designed to be computer-readable, not human-readable. We therefore employ the human-readable Antimony language to make it easy to create and edit SBML models.

Somatic whole genome dynamics of precancer in Barrett's esophagus reveals features associated with disease progression.

While the genomes of normal tissues undergo dynamic changes over time, little is understood about the temporal-spatial dynamics of genomes in premalignant tissues that progress to cancer compared to those that remain cancer-free. Here we use whole genome sequencing to contrast genomic alterations in 427 longitudinal samples from 40 patients with stable Barrett's esophagus compared to 40 Barrett's patients who progressed to esophageal adenocarcinoma (ESAD). We show the same somatic mutational processes are active in Barrett's tissue regardless of outcome, with high levels of mutation, ESAD gene and focal chromosomal alterations, and similar mutational signatures. The critical distinction between stable Barrett's versus those who progress to cancer is acquisition and expansion of TP53-/- cell populations having complex structural variants and high-level amplifications, which are detectable up to six years prior to a cancer diagnosis. These findings reveal the timing of common somatic genome dynamics in stable Barrett's esophagus and define key genomic features specific to progression to esophageal adenocarcinoma, both of which are critical for cancer prevention and early detection strategies.

The simulation experiment description markup language (SED-ML): language specification for level 1 version 4.

Computational simulation experiments increasingly inform modern biological research, and bring with them the need to provide ways to annotate, archive, share and reproduce the experiments performed. These simulations increasingly require extensive collaboration among modelers, experimentalists, and engineers. The Minimum Information About a Simulation Experiment (MIASE) guidelines outline the information needed to share simulation experiments. SED-ML is a computer-readable format for the information outlined by MIASE, created as a community project and supported by many investigators and software tools. The first versions of SED-ML focused on deterministic and stochastic simulations of models. Level 1 Version 4 of SED-ML substantially expands these capabilities to cover additional types of models, model languages, parameter estimations, simulations and analyses of models, and analyses and visualizations of simulation results. To facilitate consistent practices across the community, Level 1 Version 4 also more clearly describes the use of SED-ML constructs, and includes numerous concrete validation rules. SED-ML is supported by a growing ecosystem of investigators, model languages, and software tools, including eight languages for constraint-based, kinetic, qualitative, rule-based, and spatial models, over 20 simulation tools, visual editors, model repositories, and validators. Additional information about SED-ML is available at https://sed-ml.org/.

Within-patient phylogenetic reconstruction reveals early events in Barrett's Esophagus.

Barrett's Esophagus is a neoplastic condition which progresses to esophageal adenocarcinoma in 5% of cases. Key events affecting the outcome likely occur before diagnosis of Barrett's and cannot be directly observed; we use phylogenetic analysis to infer such past events. We performed whole-genome sequencing on 4-6 samples from 40 cancer outcome and 40 noncancer outcome patients with Barrett's Esophagus, and inferred within-patient phylogenies of deconvoluted clonal lineages. Spatially proximate lineages clustered in the phylogenies, but temporally proximate ones did not. Lineages with inferred loss-of-function mutations in both copies of TP53 and CDKN2A showed enhanced spatial spread, whereas lineages with loss-of-function mutations in other frequently mutated loci did not. We propose a two-phase model with expansions of TP53 and CKDN2A mutant lineages during initial growth of the segment, followed by relative stasis. Subsequent to initial expansion, mutations in these loci as well as ARID1A and SMARCA4 may show a local selective advantage but do not expand far: The spatial structure of the Barrett's segment remains stable during surveillance even in patients who go on to cancer. We conclude that the cancer/noncancer outcome is strongly affected by early steps in formation of the Barrett's segment.

The limits of fine-scale mapping.

When a novel genetic trait arises in a population, it introduces a signal in the haplotype distribution of that population. Through recombination that signal's history becomes differentiated from the DNA distant to it, but remains similar to the DNA close by. Fine-scale mapping techniques rely on this differentiation to pinpoint trait loci. In this study, we analyzed the differentiation itself to better understand how much information is available to these techniques. Simulated alleles on known recombinant coalescent trees show the upper limit for fine-scale mapping. Varying characteristics of the population being studied increase or decrease this limit. The initial uncertainty in map position has the most direct influence on the final precision of the estimate, with wider initial areas resulting in wider final estimates, though the increase is sigmoidal rather than linear. The Theta of the trait (4Nmu) is also important, with lower values for Theta resulting in greater precision of trait placement up to a point--the increase is sigmoidal as Theta decreases. Collecting data from more individuals can increase precision, though only logarithmically with the total number of individuals, so that each added individual contributes less to the final precision. However, a case/control analysis has the potential to greatly increase the effective number of individuals, as the bulk of the information lies in the differential between affected and unaffected genotypes. If haplotypes are unknown due to incomplete penetrance, much information is lost, with more information lost the less indicative phenotype is of the underlying genotype.

Antimony: a modular model definition language.

MOTIVATION: Model exchange in systems and synthetic biology has been standardized for computers with the Systems Biology Markup Language (SBML) and CellML, but specialized software is needed for the generation of models in these formats. Text-based model definition languages allow researchers to create models simply, and then export them to a common exchange format. Modular languages allow researchers to create and combine complex models more easily. We saw a use for a modular text-based language, together with a translation library to allow other programs to read the models as well. SUMMARY: The Antimony language provides a way for a researcher to use simple text statements to create, import, and combine biological models, allowing complex models to be built from simpler models, and provides a special syntax for the creation of modular genetic networks. The libAntimony library allows other software packages to import these models and convert them either to SBML or their own internal format. AVAILABILITY: The Antimony language specification and the libAntimony library are available under a BSD license from http://antimony.sourceforge.net/.

Systems Biology Markup Language (SBML) Level 3 Package: Distributions, Version 1, Release 1.

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.

Systems biology markup language (SBML) level 3 package: multistate, multicomponent and multicompartment species, version 1, release 2.

Rule-based modeling is an approach that permits constructing reaction networks based on the specification of rules for molecular interactions and transformations. These rules can encompass details such as the interacting sub-molecular domains and the states and binding status of the involved components. Conceptually, fine-grained spatial information such as locations can also be provided. Through "wildcards" representing component states, entire families of molecule complexes sharing certain properties can be specified as patterns. This can significantly simplify the definition of models involving species with multiple components, multiple states, and multiple compartments. The systems biology markup language (SBML) Level 3 Multi Package Version 1 extends the SBML Level 3 Version 1 core with the "type" concept in the Species and Compartment classes. Therefore, reaction rules may contain species that can be patterns and exist in multiple locations. Multiple software tools such as Simmune and BioNetGen support this standard that thus also becomes a medium for exchanging rule-based models. This document provides the specification for Release 2 of Version 1 of the SBML Level 3 Multi package. No design changes have been made to the description of models between Release 1 and Release 2; changes are restricted to the correction of errata and the addition of clarifications.

The Systems Biology Markup Language (SBML): Language Specification for Level 3 Version 2 Core Release 2.

Computational models can help researchers to interpret data, understand biological functions, and make quantitative predictions. The Systems Biology Markup Language (SBML) is a file format for representing computational models in a declarative form that different software systems can exchange. SBML is oriented towards describing biological processes of the sort common in research on a number of topics, including metabolic pathways, cell signaling pathways, and many others. By supporting SBML as an input/output format, different tools can all operate on an identical representation of a model, removing opportunities for translation errors and assuring a common starting point for analyses and simulations. This document provides the specification for Release 2 of Version 2 of SBML Level 3 Core. The specification defines the data structures prescribed by SBML as well as their encoding in XML, the eXtensible Markup Language. Release 2 corrects some errors and clarifies some ambiguities discovered in Release 1. This specification also defines validation rules that determine the validity of an SBML document, and provides many examples of models in SBML form. Other materials and software are available from the SBML project website at http://sbml.org/.