Extracting falsifiable predictions from sloppy models

Successful predictions are among the most compelling validations of any model. Extracting falsifiable predictions from nonlinear multiparameter models is complicated by the fact that such models are commonly sloppy, possessing sensitivities to different parameter combinations that range over many decades. Here we discuss how sloppiness affects the sorts of data that best constrain model predictions, makes linear uncertainty approximations dangerous, and introduces computational difficulties in Monte-Carlo uncertainty analysis. We also present a useful test problem and suggest refinements to the standards by which models are communicated.Comment: 4 pages, 2 figures. Submitted to the Annals of the New York Academy of Sciences for publication in "Reverse Engineering Biological Networks: Opportunities and Challenges in Computational Methods for Pathway Inference

Who uses foodbanks and why? Exploring the impact of financial strain and adverse life events on food insecurity

Background Rising use of foodbanks highlights food insecurity in the UK. Adverse life events (e.g. unemployment, benefit delays or sanctions) and financial strains are thought to be the drivers of foodbank use. This research aimed to explore who uses foodbanks, and factors associated with increased food insecurity. Methods We surveyed those seeking help from front line crisis providers from foodbanks (N = 270) and a comparison group from Advice Centres (ACs) (N = 245) in relation to demographics, adverse life events, financial strain and household food security. Results About 55.9% of foodbank users were women and the majority were in receipt of benefits (64.8%). Benefit delays (31.9%), changes (11.1%) and low income (19.6%) were the most common reasons given for referral. Compared to AC users, there were more foodbank users who were single men without children, unemployed, currently homeless, experiencing more financial strain and adverse life events (P = 0.001). Food insecurity was high in both populations, and more severe if they also reported financial strain and adverse life events. Conclusions Benefit-related problems appear to be a key reason for foodbank referral. By comparison with other disadvantaged groups, foodbank users experienced more financial strain, adverse life events, both increased the severity of food insecurity

Universally Sloppy Parameter Sensitivities in Systems Biology

Quantitative computational models play an increasingly important role in modern biology. Such models typically involve many free parameters, and assigning their values is often a substantial obstacle to model development. Directly measuring \emph{in vivo} biochemical parameters is difficult, and collectively fitting them to other data often yields large parameter uncertainties. Nevertheless, in earlier work we showed in a growth-factor-signaling model that collective fitting could yield well-constrained predictions, even when it left individual parameters very poorly constrained. We also showed that the model had a `sloppy' spectrum of parameter sensitivities, with eigenvalues roughly evenly distributed over many decades. Here we use a collection of models from the literature to test whether such sloppy spectra are common in systems biology. Strikingly, we find that every model we examine has a sloppy spectrum of sensitivities. We also test several consequences of this sloppiness for building predictive models. In particular, sloppiness suggests that collective fits to even large amounts of ideal time-series data will often leave many parameters poorly constrained. Tests over our model collection are consistent with this suggestion. This difficulty with collective fits may seem to argue for direct parameter measurements, but sloppiness also implies that such measurements must be formidably precise and complete to usefully constrain many model predictions. We confirm this implication in our signaling model. Our results suggest that sloppy sensitivity spectra are universal in systems biology models. The prevalence of sloppiness highlights the power of collective fits and suggests that modelers should focus on predictions rather than on parameters.Comment: Submitted to PLoS Computational Biology. Supplementary Information available in "Other Formats" bundle. Discussion slightly revised to add historical contex

Detection of organic compound signatures in infra-red, limb emission spectra observed by the MIPAS-B2 instrument

International audienceOrganic compounds play a central role in troposphere chemistry and increasingly are a viable target for remote sensing observations. In this paper, infra-red spectral features of three organic compounds are investigated in thermal emission spectra recorded by a balloon-borne instrument, MIPAS-B2, operating at high spectral resolution. It is demonstrated, for the first time, that PAN and acetone can be detected in infra-red remote sensing spectra of the upper troposphere; detection results are presented at tangent altitudes of 10.4 km and 7.5 km (not acetone). In addition, the results provide the first observation of spectral features of formic acid in thermal emission, as opposed to solar occultation, and confirm that concentrations of this gas are likely to be measurable in the free troposphere, given accurate spectroscopic data. For PAN, two bands are observed centred at 794 cm?1 and 1163 cm?1. For acetone and formic acid, one band has been detected for each so far with band centres at 1218 cm?1 and 1105 cm?1 respectively. Mixing ratios inferred at 10.4 km tangent altitude are 180 pptv and 530 pptv for PAN and acetone respectively, and 200 pptv for formic acid with HITRAN 2000 spectroscopy. Accuracies are on the order of 30 to 50%. The detection technique applied here is verified by examining weak but known signatures of CFC-12 and HCFC-22 in the same spectral regions as those of the organic compounds, with results confirming the quality of both the instrument and the radiative transfer model. The results suggest the possibility of global sensing of the organic compounds studied here which would be a major step forward in verifying and interpreting global tropospheric model calculations

The sloppy model universality class and the Vandermonde matrix

In a variety of contexts, physicists study complex, nonlinear models with many unknown or tunable parameters to explain experimental data. We explain why such systems so often are sloppy; the system behavior depends only on a few `stiff' combinations of the parameters and is unchanged as other `sloppy' parameter combinations vary by orders of magnitude. We contrast examples of sloppy models (from systems biology, variational quantum Monte Carlo, and common data fitting) with systems which are not sloppy (multidimensional linear regression, random matrix ensembles). We observe that the eigenvalue spectra for the sensitivity of sloppy models have a striking, characteristic form, with a density of logarithms of eigenvalues which is roughly constant over a large range. We suggest that the common features of sloppy models indicate that they may belong to a common universality class. In particular, we motivate focusing on a Vandermonde ensemble of multiparameter nonlinear models and show in one limit that they exhibit the universal features of sloppy models.Comment: New content adde

Developing an open data portal for the ESA climate change initiative

We introduce the rationale for, and architecture of, the European Space Agency Climate Change Initiative (CCI) Open Data Portal (http://cci.esa.int/data/). The Open Data Portal hosts a set of richly diverse datasets – 13 “Essential Climate Variables” – from the CCI programme in a consistent and harmonised form and to provides a single point of access for the (>100 TB) data for broad dissemination to an international user community. These data have been produced by a range of different institutions and vary across both scientific and spatio-temporal characteristics. This heterogeneity of the data together with the range of services to be supported presented significant technical challenges. An iterative development methodology was key to tackling these challenges: the system developed exploits a workflow which takes data that conforms to the CCI data specification, ingests it into a managed archive and uses both manual and automatically generated metadata to support data discovery, browse, and delivery services. It utilises both Earth System Grid Federation (ESGF) data nodes and the Open Geospatial Consortium Catalogue Service for the Web (OGC-CSW) interface, serving data into both the ESGF and the Global Earth Observation System of Systems (GEOSS). A key part of the system is a new vocabulary server, populated with CCI specific terms and relationships which integrates OGC-CSW and ESGF search services together, developed as part of a dialogue between domain scientists and linked data specialists. These services have enabled the development of a unified user interface for graphical search and visualisation – the CCI Open Data Portal Web Presence