Motivation: The growing field of systems biology has driven demand for flexible tools to model and simulate biological systems. Two established problems in the modeling of biological processes are model selection and the estimation of associated parameters. A number of statistical approaches, both frequentist and Bayesian, have been proposed to answer these questions.Results: Here we present a Python package, ABC-SysBio, that implements parameter inference and model selection for dynamical systems in an approximate Bayesian computation (ABC) framework. ABC-SysBio combines three algorithms: ABC rejection sampler, ABC SMC for parameter inference and ABC SMC for model selection. It is designed to work with models written in Systems Biology Markup Language (SBML). Deterministic and stochastic models can be analyzed in ABC-SysBio.Availability: http://abc-sysbio.sourceforge.netContact: christopher.barnes@imperial.ac.uk; ttoni@imperial.ac.uk

The size and complexity of cellular systems make building predictive models an extremely difficult task. In principle dynamical time-course data can be used to elucidate the structure of the underlying molecular mechanisms, but a central and recurring problem is that many and very different models can be fitted to experimental data, especially when the latter are limited and subject to noise. Even given a model, estimating its parameters remains challenging in real-world systems. Here we present a comprehensive analysis of 180 systems biology models, which allows us to classify the parameters with respect to their contribution to the overall dynamical behaviour of the different systems. Our results reveal candidate elements of control in biochemical pathways that differentially contribute to dynamics. We introduce sensitivity profiles that concisely characterize parameter sensitivity and demonstrate how this can be connected to variability in data. Systematically linking data and model sloppiness allows us to extract features of dynamical systems that determine how well parameters can be estimated from time-course measurements, and associates the extent of data required for parameter inference with the model structure, and also with the global dynamical state of the system. The comprehensive analysis of so many systems biology models reaffirms the inability to estimate precisely most model or kinetic parameters as a generic feature of dynamical systems, and provides safe guidelines for performing better inferences and model predictions in the context of reverse engineering of mathematical models for biological systems.

Climate change can influence the transmission of vector-borne diseases (VBDs) through altering the habitat suitability of insect vectors. Here we present global climate model simulations and evaluate the associated uncertainties in view of the main meteorological factors that may affect the distribution of the Asian tiger mosquito (Aedes albopictus), which can transmit pathogens that cause chikungunya, dengue fever, yellow fever and various encephalitides. Using a general circulation model at 50 km horizontal resolution to simulate mosquito survival variables including temperature, precipitation and relative humidity, we present both global and regional projections of the habitat suitability up to the middle of the twenty-first century. The model resolution of 50 km allows evaluation against previous projections for Europe and provides a basis for comparative analyses with other regions. Model uncertainties and performance are addressed in light of the recent CMIP5 ensemble climate model simulations for the RCP8.5 concentration pathway and using meteorological re-analysis data (ERA-Interim/ECMWF) for the recent past. Uncertainty ranges associated with the thresholds of meteorological variables that may affect the distribution of Ae. albopictus are diagnosed using fuzzy-logic methodology, notably to assess the influence of selected meteorological criteria and combinations of criteria that influence mosquito habitat suitability. From the climate projections for 2050, and adopting a habitat suitability index larger than 70%, we estimate that approximately 2.4 billion individuals in a land area of nearly 20 million km2 will potentially be exposed to Ae. albopictus. The synthesis of fuzzy-logic based on mosquito biology and climate change analysis provides new insights into the regional and global spreading of VBDs to support disease control and policy making.

Thin-basement-membrane nephropathy (TBMN) and Alport syndrome (AS) are progressive collagen IV nephropathies caused by mutations in COL4A3/A4/A5 genes. These nephropathies invariably present with microscopic hematuria and frequently progress to proteinuria and CKD or ESRD during long-term follow-up. Nonetheless, the exact molecular mechanisms by which these mutations exert their deleterious effects on the glomerulus remain elusive. We hypothesized that defective trafficking of the COL4A3 chain causes a strong intracellular effect on the cell responsible for COL4A3 expression, the podocyte. To this end, we overexpressed normal and mutant COL4A3 chains (G1334E mutation) in human undifferentiated podocytes and tested their effects in various intracellular pathways using a microarray approach. COL4A3 overexpression in the podocyte caused chain retention in the endoplasmic reticulum (ER) that was associated with activation of unfolded protein response (UPR)-related markers of ER stress. Notably, the overexpression of normal or mutant COL4A3 chains differentially activated the UPR pathway. Similar results were observed in a novel knockin mouse carrying the Col4a3-G1332E mutation, which produced a phenotype consistent with AS, and in biopsy specimens from patients with TBMN carrying a heterozygous COL4A3-G1334E mutation. These results suggest that ER stress arising from defective localization of collagen IV chains in human podocytes contributes to the pathogenesis of TBMN and AS through activation of the UPR, a finding that may pave the way for novel therapeutic interventions for a variety of collagenopathies.

BackgroundThe unfolded protein response (UPR) is a major signalling cascade acting in the quality control of protein folding in the endoplasmic reticulum (ER). The cascade is known to play an accessory role in a range of genetic and environmental disorders including neurodegenerative and cardiovascular diseases, diabetes and kidney diseases. The three major receptors of the ER stress involved with the UPR, i.e. IRE1 α, PERK and ATF6, signal through a complex web of pathways to convey an appropriate response. The emerging behaviour ranges from adaptive to maladaptive depending on the severity of unfolded protein accumulation in the ER; however, the decision mechanism for the switch and its timing have so far been poorly understood.ResultsHere, we propose a mechanism by which the UPR outcome switches between survival and death. We compose a mathematical model integrating the three signalling branches, and perform a comprehensive bifurcation analysis to investigate possible responses to stimuli. The analysis reveals three distinct states of behaviour, low, high and intermediate activity, associated with stress adaptation, tolerance, and the initiation of apoptosis. The decision to adapt or destruct can, therefore, be understood as a dynamic process where the balance between the stress and the folding capacity of the ER plays a pivotal role in managing the delivery of the most appropriate response. The model demonstrates for the first time that the UPR is capable of generating oscillations in translation attenuation and the apoptotic signals, and this is supplemented with a Bayesian sensitivity analysis identifying a set of parameters controlling this behaviour.ConclusionsThis work contributes largely to the understanding of one of the most ubiquitous signalling pathways involved in protein folding quality control in the metazoan ER. The insights gained have direct consequences on the management of many UPR-related diseases, revealing, in addition, an extended list of candidate disease modifiers. Demonstration of stress adaptation sheds light to how preconditioning might be beneficial in manifesting the UPR outcome to prevent untimely apoptosis, and paves the way to novel approaches for the treatment of many UPR-related conditions.

The Asian tiger mosquito, Aedes albopictus, is a highly invasive vector species. It is a proven vector of dengue and chikungunya viruses, with the potential to host a further 24 arboviruses. It has recently expanded its geographical range, threatening many countries in the Middle East, Mediterranean, Europe and North America. Here, we investigate the theoretical limitations of its range expansion by developing an environmentally-driven mathematical model of its population dynamics. We focus on the temperate strain of Ae. albopictus and compile a comprehensive literature-based database of physiological parameters. As a novel approach, we link its population dynamics to globally-available environmental datasets by performing inference on all parameters. We adopt a Bayesian approach using experimental data as prior knowledge and the surveillance dataset of Emilia-Romagna, Italy, as evidence. The model accounts for temperature, precipitation, human population density and photoperiod as the main environmental drivers, and, in addition, incorporates the mechanism of diapause and a simple breeding site model. The model demonstrates high predictive skill over the reference region and beyond, confirming most of the current reports of vector presence in Europe. One of the main hypotheses derived from the model is the survival of Ae. albopictus populations through harsh winter conditions. The model, constrained by the environmental datasets, requires that either diapausing eggs or adult vectors have increased cold resistance. The model also suggests that temperature and photoperiod control diapause initiation and termination differentially. We demonstrate that it is possible to account for unobserved properties and constraints, such as differences between laboratory and field conditions, to derive reliable inferences on the environmental dependence of Ae. albopictus populations.

Heparin binding epidermal growth factor (HBEGF) is expressed in podocytes and was shown to play a role in glomerular physiology. MicroRNA binding sites on the 3′UTR of HBEGF were predicted using miRWalk algorithm and followed by DNA sequencing in 103 patients diagnosed with mild or severe glomerulopathy. A single nucleotide polymorphism, miRSNP C1936T (rs13385), was identified at the 3′UTR of HBEGF that corresponds to the second base of the hsa-miR-1207-5p seed region. When AB8/13 undifferentiated podocytes were transfected with miRNA mimics of hsa-miR-1207-5p, the HBEGF protein levels were reduced by about 50%. A DNA fragment containing the miRSNP allele-1936C was cloned into the pMIR-Report Luciferase vector and co-transfected with miRNA mimics of hsa-miR-1207-5p into AB8/13 podocytes. In agreement with western blot data, this resulted in reduced luciferase expression demonstrating the ability of hsa-miR-1207-5p to directly regulate HBEGF expression. On the contrary, in the presence of the miRSNP 1936T allele, this regulation was abolished. Collectively, these results demonstrate that variant 1936T of this miRSNP prevents hsa-miR-1207-5p from down-regulating HBEGF in podocytes. We hypothesized that this variant has a functional role as a genetic modifier. To this end, we showed that in a cohort of 78 patients diagnosed with CFHR5 nephropathy (also known as C3-glomerulopathy), inheritance of miRSNP 1936T allele was significantly increased in the group demonstrating progression to chronic renal failure on long follow-up. No similar association was detected in a cohort of patients with thin basement membrane nephropathy. This is the first report associating a miRSNP as genetic modifier to a monogenic renal disorder.

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