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.

Guided proposals for efficient weighted stochastic simulation.

Rare event probabilities play an important role in the understanding of the behavior of biochemical systems. Due to the intractability of the most natural Markov jump process representation of a system of interest, rare event probabilities are typically estimated using importance sampling. While the resulting algorithm is reasonably well developed, the problem of choosing a suitable importance density is far from straightforward. We therefore leverage recent developments on simulation of conditioned jump processes to propose an importance density that is simple to implement and requires no tuning. Our results demonstrate superior performance over some existing approaches.

Chronic, Active Inflammation in Patients With Failed Total Knee Replacements Undergoing Revision Surgery.

Chronic pain and restricted knee motion is a significant problem following the total knee arthroplasty (TKA). The molecular pathogenesis of pain post-TKA is not known and no targeted therapeutic intervention is available. The aim of this study was to investigate whether pro-inflammatory mediators are elevated in revision knee patients, indicating an active, ongoing inflammatory process that may contribute to pain. Twelve key markers (pro-inflammatory cytokines granulocyte-macrophage colony-stimulating factor [GM-CSF], interleukin 5 [IL-5], IL-8 and IL-10, chemokines CCL2, CCL3, CCL4, and CCL13, mediators of angiogenesis Flt-1, vascular endothelial growth factor, and cell migration vascular cell adhesion molecule 1 and intercellular adhesion molecule 1) were measured in knee tissue and synovial fluid (SF) from primary TKA (n = 29) and revision patients (n = 32). Indications for surgery were osteoarthritis (OA) for primary TKA, and component loosening (n = 11), stiffness (n = 11), laxity pattern (n = 8), or progression of OA in patella resurfacing (n = 3) for revision surgery. Pain levels (WOMAC score) were higher in revision than primary patients (p ≤ 0.05). Time from primary to revision ranged from 8 months to 30 years (median 10 years). All markers were elevated in revision TKA; there was no trend toward decreasing levels with greater time from primary surgery for any marker studied in SF. Similar results were seen in knee tissue. We found no differences comparing indications for revision surgery (p ≥ 0.05). The elevation of inflammatory mediators in painful post-TKA knees requiring revision suggests active, chronic inflammation. Characterization of upregulated markers provides rationale for targeted therapy, even many years from the primary surgery. © 2019 The Authors. Journal of Orthopaedic Research® published by Wiley Periodicals, Inc. on behalf of Orthopaedic Research Society. J Orthop Res 37:2316-2324, 2019.

B-cell activity markers are associated with different disease activity domains in primary Sjögren's syndrome.

OBJECTIVES: B-cell activating factor (BAFF), ß-2 microglobulin (ß2M) and serum free light chains (FLCs) are elevated in primary SS (pSS) and associated with disease activity. We aimed to investigate their association with the individual disease activity domains of the EULAR Sjögren's Syndrome Disease Activity Index (ESSDAI) in a large well-characterized pSS cohort. METHODS: Sera from pSS patients enrolled in the UK Primary Sjögren's Syndrome Registry (UKPSSR) (n = 553) and healthy controls (n = 286) were analysed for FLC (κ and λ), BAFF and ß2 M. Pearson correlation coefficients were calculated for patient clinical characteristics, including salivary flow, Schirmer's test, EULAR Sjögren's Syndrome Patient Reported Index and serum IgG levels. Poisson regression was performed to identify independent predictors of total ESSDAI and ClinESSDAI (validated ESSDAI minus the biological domain) scores and their domains. RESULTS: Levels of BAFF, ß2M and FLCs were higher in pSS patients compared to controls. All three biomarkers associated significantly with the ESSDAI and the ClinESSDAI. BAFF associated with the peripheral nervous system domain of the ESSDAI, whereas ß2M and FLCs associated with the cutaneous, biological and renal domains. Multivariate analysis showed BAFF, ß2M and their interaction to be independent predictors of ESSDAI/ClinESSDAI. FLCs were also shown to associate with the ESSDAI/ClinESSDAI but not independent of serum IgG. CONCLUSION: All biomarkers were associated with total ESSDAI scores but with differing domain associations. These findings should encourage further investigation of these biomarkers in longitudinal studies and against other disease activity measures.

Profiling inflammation and tissue injury markers in perfusate and bronchoalveolar lavage fluid during human ex vivo lung perfusion.

Objectives: Availability of donor lungs suitable for transplant falls short of current demand and contributes to waiting list mortality. Ex vivo lung perfusion (EVLP) offers the opportunity to objectively assess and recondition organs unsuitable for immediate transplant. Identifying robust biomarkers that can stratify donor lungs during EVLP to use or non-use or for specific interventions could further improve its clinical impact. Methods: In this pilot study, 16 consecutive donor lungs unsuitable for immediate transplant were assessed by EVLP. Key inflammatory mediators and tissue injury markers were measured in serial perfusate samples collected hourly and in bronchoalveolar lavage fluid (BALF) collected before and after EVLP. Levels were compared between donor lungs that met criteria for transplant and those that did not. Results: Seven of the 16 donor lungs (44%) improved during EVLP and were transplanted with uniformly good outcomes. Tissue and vascular injury markers lactate dehydrogenase, HMGB-1 and Syndecan-1 were significantly lower in perfusate from transplanted lungs. A model combining IL-1ß and IL-8 concentrations in perfusate could predict final EVLP outcome after 2 h assessment. In addition, perfusate IL-1ß concentrations showed an inverse correlation to recipient oxygenation 24 h post-transplant. Conclusions: This study confirms the feasibility of using inflammation and tissue injury markers in perfusate and BALF to identify donor lungs most likely to improve for successful transplant during clinical EVLP. These results support examining this issue in a larger study.

Growth rate control of flagellar assembly in Escherichia coli strain RP437.

The flagellum is a rotary motor that enables bacteria to swim in liquids and swarm over surfaces. Numerous global regulators control flagellar assembly in response to cellular and environmental factors. Previous studies have also shown that flagellar assembly is affected by the growth-rate of the cell. However, a systematic study has not yet been described under controlled growth conditions. Here, we investigated the effect of growth rate on flagellar assembly in Escherichia coli using steady-state chemostat cultures where we could precisely control the cell growth-rate. Our results demonstrate that flagellar abundance correlates with growth rate, where faster growing cells produce more flagella. They also demonstrate that this growth-rate dependent control occurs through the expression of the flagellar master regulator, FlhD4C2. Collectively, our results demonstrate that motility is intimately coupled to the growth-rate of the cell.

Diagnostics for assessing the linear noise and moment closure approximations.

Solving the chemical master equation exactly is typically not possible, so instead we must rely on simulation based methods. Unfortunately, drawing exact realisations, results in simulating every reaction that occurs. This will preclude the use of exact simulators for models of any realistic size and so approximate algorithms become important. In this paper we describe a general framework for assessing the accuracy of the linear noise and two moment approximations. By constructing an efficient space filling design over the parameter region of interest, we present a number of useful diagnostic tools that aids modellers in assessing whether the approximation is suitable. In particular, we leverage the normality assumption of the linear noise and moment closure approximations.

A Transcriptional Signature of Fatigue Derived from Patients with Primary Sjögren's Syndrome.

BACKGROUND: Fatigue is a debilitating condition with a significant impact on patients' quality of life. Fatigue is frequently reported by patients suffering from primary Sjögren's Syndrome (pSS), a chronic autoimmune condition characterised by dryness of the eyes and the mouth. However, although fatigue is common in pSS, it does not manifest in all sufferers, providing an excellent model with which to explore the potential underpinning biological mechanisms. METHODS: Whole blood samples from 133 fully-phenotyped pSS patients stratified for the presence of fatigue, collected by the UK primary Sjögren's Syndrome Registry, were used for whole genome microarray. The resulting data were analysed both on a gene by gene basis and using pre-defined groups of genes. Finally, gene set enrichment analysis (GSEA) was used as a feature selection technique for input into a support vector machine (SVM) classifier. Classification was assessed using area under curve (AUC) of receiver operator characteristic and standard error of Wilcoxon statistic, SE(W). RESULTS: Although no genes were individually found to be associated with fatigue, 19 metabolic pathways were enriched in the high fatigue patient group using GSEA. Analysis revealed that these enrichments arose from the presence of a subset of 55 genes. A radial kernel SVM classifier with this subset of genes as input displayed significantly improved performance over classifiers using all pathway genes as input. The classifiers had AUCs of 0.866 (SE(W) 0.002) and 0.525 (SE(W) 0.006), respectively. CONCLUSIONS: Systematic analysis of gene expression data from pSS patients discordant for fatigue identified 55 genes which are predictive of fatigue level using SVM classification. This list represents the first step in understanding the underlying pathophysiological mechanisms of fatigue in patients with pSS.

Likelihood free inference for Markov processes: a comparison.

Approaches to Bayesian inference for problems with intractable likelihoods have become increasingly important in recent years. Approximate Bayesian computation (ABC) and "likelihood free" Markov chain Monte Carlo techniques are popular methods for tackling inference in these scenarios but such techniques are computationally expensive. In this paper we compare the two approaches to inference, with a particular focus on parameter inference for stochastic kinetic models, widely used in systems biology. Discrete time transition kernels for models of this type are intractable for all but the most trivial systems yet forward simulation is usually straightforward. We discuss the relative merits and drawbacks of each approach whilst considering the computational cost implications and efficiency of these techniques. In order to explore the properties of each approach we examine a range of observation regimes using two example models. We use a Lotka-Volterra predator-prey model to explore the impact of full or partial species observations using various time course observations under the assumption of known and unknown measurement error. Further investigation into the impact of observation error is then made using a Schlögl system, a test case which exhibits bi-modal state stability in some regions of parameter space.

Simulation of stochastic kinetic models.

A growing realization of the importance of stochasticity in cell and molecular processes has stimulated the need for statistical models that incorporate intrinsic (and extrinsic) variability. In this chapter we consider stochastic kinetic models of reaction networks leading to a Markov jump process representation of a system of interest. Traditionally, the stochastic model is characterized by a chemical master equation. While the intractability of such models can preclude a direct analysis, simulation can be straightforward and may present the only practical approach to gaining insight into a system's dynamics. We review exact simulation procedures before considering some efficient approximate alternatives.

An approach to describing and analysing bulk biological annotation quality: a case study using UniProtKB.

MOTIVATION: Annotations are a key feature of many biological databases, used to convey our knowledge of a sequence to the reader. Ideally, annotations are curated manually, however manual curation is costly, time consuming and requires expert knowledge and training. Given these issues and the exponential increase of data, many databases implement automated annotation pipelines in an attempt to avoid un-annotated entries. Both manual and automated annotations vary in quality between databases and annotators, making assessment of annotation reliability problematic for users. The community lacks a generic measure for determining annotation quality and correctness, which we look at addressing within this article. Specifically we investigate word reuse within bulk textual annotations and relate this to Zipf's Principle of Least Effort. We use the UniProt Knowledgebase (UniProtKB) as a case study to demonstrate this approach since it allows us to compare annotation change, both over time and between automated and manually curated annotations. RESULTS: By applying power-law distributions to word reuse in annotation, we show clear trends in UniProtKB over time, which are consistent with existing studies of quality on free text English. Further, we show a clear distinction between manual and automated analysis and investigate cohorts of protein records as they mature. These results suggest that this approach holds distinct promise as a mechanism for judging annotation quality. AVAILABILITY: Source code is available at the authors website: http://homepages.cs.ncl.ac.uk/m.j.bell1/annotation. CONTACT: phillip.lord@newcastle.ac.uk.

A stochastic step model of replicative senescence explains ROS production rate in ageing cell populations.

Increases in cellular Reactive Oxygen Species (ROS) concentration with age have been observed repeatedly in mammalian tissues. Concomitant increases in the proportion of replicatively senescent cells in ageing mammalian tissues have also been observed. Populations of mitotic human fibroblasts cultured in vitro, undergoing transition from proliferation competence to replicative senescence are useful models of ageing human tissues. Similar exponential increases in ROS with age have been observed in this model system. Tracking individual cells in dividing populations is difficult, and so the vast majority of observations have been cross-sectional, at the population level, rather than longitudinal observations of individual cells.One possible explanation for these observations is an exponential increase in ROS in individual fibroblasts with time (e.g. resulting from a vicious cycle between cellular ROS and damage). However, we demonstrate an alternative, simple hypothesis, equally consistent with these observations which does not depend on any gradual increase in ROS concentration: the Stochastic Step Model of Replicative Senescence (SSMRS). We also demonstrate that, consistent with the SSMRS, neither proliferation-competent human fibroblasts of any age, nor populations of hTERT overexpressing human fibroblasts passaged beyond the Hayflick limit, display high ROS concentrations. We conclude that longitudinal studies of single cells and their lineages are now required for testing hypotheses about roles and mechanisms of ROS increase during replicative senescence.

Stochastic simulation of chemically reacting systems using multi-core processors.

In recent years, computer simulations have become increasingly useful when trying to understand the complex dynamics of biochemical networks, particularly in stochastic systems. In such situations stochastic simulation is vital in gaining an understanding of the inherent stochasticity present, as these models are rarely analytically tractable. However, a stochastic approach can be computationally prohibitive for many models. A number of approximations have been proposed that aim to speed up stochastic simulations. However, the majority of these approaches are fundamentally serial in terms of central processing unit (CPU) usage. In this paper, we propose a novel simulation algorithm that utilises the potential of multi-core machines. This algorithm partitions the model into smaller sub-models. These sub-models are then simulated, in parallel, on separate CPUs. We demonstrate that this method is accurate and can speed-up the simulation by a factor proportional to the number of processors available.

Moment closure approximations for stochastic kinetic models with rational rate laws.

Stochastic models are often used when modelling chemical species that have low numbers of molecules. However, as these models become large, it can become computationally expensive to simulate even a single realisation of the system since even efficient simulation techniques have a high computational cost. One possible technique to approximate the stochastic system is moment closure. The moment closure approximation is used to provide analytic approximations to non-linear stochastic models. Until now, this approximation has only been applied to models with polynomial rate laws. In this paper we extend the moment closure method to cover models with rational rate laws.

Analysing time course microarray data using Bioconductor: a case study using yeast2 Affymetrix arrays.

BACKGROUND: Large scale microarray experiments are becoming increasingly routine, particularly those which track a number of different cell lines through time. This time-course information provides valuable insight into the dynamic mechanisms underlying the biological processes being observed. However, proper statistical analysis of time-course data requires the use of more sophisticated tools and complex statistical models. FINDINGS: Using the open source CRAN and Bioconductor repositories for R, we provide example analysis and protocol which illustrate a variety of methods that can be used to analyse time-course microarray data. In particular, we highlight how to construct appropriate contrasts to detect differentially expressed genes and how to generate plausible pathways from the data. A maintained version of the R commands can be found at http://www.mas.ncl.ac.uk/~ncsg3/microarray/. CONCLUSIONS: CRAN and Bioconductor are stable repositories that provide a wide variety of appropriate statistical tools to analyse time course microarray data.

CaliBayes and BASIS: integrated tools for the calibration, simulation and storage of biological simulation models.

Dynamic simulation modelling of complex biological processes forms the backbone of systems biology. Discrete stochastic models are particularly appropriate for describing sub-cellular molecular interactions, especially when critical molecular species are thought to be present at low copy-numbers. For example, these stochastic effects play an important role in models of human ageing, where ageing results from the long-term accumulation of random damage at various biological scales. Unfortunately, realistic stochastic simulation of discrete biological processes is highly computationally intensive, requiring specialist hardware, and can benefit greatly from parallel and distributed approaches to computation and analysis. For these reasons, we have developed the BASIS system for the simulation and storage of stochastic SBML models together with associated simulation results. This system is exposed as a set of web services to allow users to incorporate its simulation tools into their workflows. Parameter inference for stochastic models is also difficult and computationally expensive. The CaliBayes system provides a set of web services (together with an R package for consuming these and formatting data) which addresses this problem for SBML models. It uses a sequential Bayesian MCMC method, which is powerful and flexible, providing very rich information. However this approach is exceptionally computationally intensive and requires the use of a carefully designed architecture. Again, these tools are exposed as web services to allow users to take advantage of this system. In this article, we describe these two systems and demonstrate their integrated use with an example workflow to estimate the parameters of a simple model of Saccharomyces cerevisiae growth on agar plates.

The SBML discrete stochastic models test suite.

MOTIVATION: Stochastic simulation is a very important tool for mathematical modelling. However, it is difficult to check the correctness of a stochastic simulator, since any two realizations from a single model will typically be different. RESULTS: We have developed a test suite of stochastic models that have been solved either analytically or using numerical methods. This allows the accuracy of stochastic simulators to be tested against known results. The test suite is already being used by a number of stochastic simulator developers. AVAILABILITY: The latest version of the test suite can be obtained from http://www.calibayes.ncl.ac.uk/Resources/dsmts/ and is licensed under GNU Lesser General Public License.

An improved saddlepoint approximation.

Given a set of third- or higher-order moments, not only is the saddlepoint approximation the only realistic 'family-free' technique available for constructing an associated probability distribution, but it is 'optimal' in the sense that it is based on the highly efficient numerical method of steepest descents. However, it suffers from the problem of not always yielding full support, and whilst [S. Wang, General saddlepoint approximations in the bootstrap, Prob. Stat. Lett. 27 (1992) 61.] neat scaling approach provides a solution to this hurdle, it leads to potentially inaccurate and aberrant results. We therefore propose several new ways of surmounting such difficulties, including: extending the inversion of the cumulant generating function to second-order; selecting an appropriate probability structure for higher-order cumulants (the standard moment closure procedure takes them to be zero); and, making subtle changes to the target cumulants and then optimising via the simplex algorithm.

Tools for the SBML Community.

MOTIVATION: SBML is quickly becoming the standard format to exchange biochemical models. The tools presented in this paper are loosely-coupled, and are intended to be incorporated into SBML aware applications. The rationale for this is to reduce the amount of repeated work carried out within the community and to create tools that offer a greater number of features to the end-user. AVAILABILITY: All tools described are available from http://www.basis.ncl.ac.uk/software and are licensed under GNU General Public License.