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

Neon and Sulfur Abundances of Planetary Nebulae in the Magellanic Clouds

The chemical abundances of neon and sulfur for 25 planetary nebulae (PNe) in the Magellanic Clouds are presented. These abundances have been derived using mainly infrared data from the Spitzer Space Telescope. The implications for the chemical evolution of these elements are discussed. A comparison with similarly obtained abundances of Galactic PNe and HII regions and Magellanic Clouds HII regions is also given. The average neon abundances are 6.0x10(-5) and 2.7x10(-5) for the PNe in the Large and Small Magellanic Clouds respectively. These are ~1/3 and 1/6 of the average abundances of Galactic planetary nebulae to which we compare. The average sulfur abundances for the LMC and SMC are respectively 2.7x10(-6) and 1.0x10(-6). The Ne/S ratio (23.5) is on average higher than the ratio found in Galactic PNe (16) but the range of values in both data sets is similar for most of the objects. The neon abundances found in PNe and HII regions agree with each other. It is possible that a few (3-4) of the PNe in the sample have experienced some neon enrichment, but for two of these objects the high Ne/S ratio can be explained by their very low sulfur abundances. The neon and sulfur abundances derived in this paper are also compared to previously published abundances using optical data and photo-ionization models.Comment: 13 pages, 4 tables, 5 figures. Accepted for publication in Ap

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

Abundances of planetary nebulae in the Galactic bulge

Context. Planetary nebulae (PNe) abundances are poorly known for those nebulae in the Galactic bulge. This is because of the high and uneven extinction in the bulge which makes visual spectral measurements difficult. In addition, the extinction corrections may be unreliable. Elements considered are O, N, Ne, S, Ar, and Cl. Aims. We determine the abundances in 19 PNe, 18 of which are located in the bulge. This doubles the number of PNe abundance determinations in the bulge. The Galactic abundance gradient is discussed for five elements. Methods. The mid-infrared spectra measured by the Spitzer Space Telescope are used to determine the abundances. This part of the spectrum is little affected by extinction for which an uncertain correction is no longer necessary. In addition the connection with the visible and ultraviolet spectrum becomes simpler because hydrogen lines are observed both in the infrared and in the visible spectra. In this way we more than double the number of PNe with reliable abundances. Results. Reliable abundances are obtained for O, N, Ne, S, and Ar for Galactic bulge PNe. Conclusions. The Galactic abundance gradient is less steep than previously thought. This is especially true for oxygen. The sulfur abundance is reliable because all stages of ionization expected have been measured. It is not systematically low compared to oxygen as has been found for some Galactic PNe

Demes:A standard format for demographic models

Understanding the demographic history of populations is a key goal in population genetics, and with improving methods and data, ever more complex models are being proposed and tested. Demographic models of current interest typically consist of a set of discrete populations, their sizes and growth rates, and continuous and pulse migrations between those populations over a number of epochs, which can require dozens of parameters to fully describe. There is currently no standard format to define such models, significantly hampering progress in the field. In particular, the important task of translating the model descriptions in published work into input suitable for population genetic simulators is labor intensive and error prone. We propose the Demes data model and file format, built on widely used technologies, to alleviate these issues. Demes provide a well-defined and unambiguous model of populations and their properties that is straightforward to implement in software, and a text file format that is designed for simplicity and clarity. We provide thoroughly tested implementations of Demes parsers in multiple languages including Python and C, and showcase initial support in several simulators and inference methods. An introduction to the file format and a detailed specification are available at https://popsim-consortium.github.io/demes-spec-docs/

Effect of 1918 PB1-F2 Expression on Influenza A Virus Infection Kinetics

Relatively little is known about the viral factors contributing to the lethality of the 1918 pandemic, although its unparalleled virulence was likely due in part to the newly discovered PB1-F2 protein. This protein, while unnecessary for replication, increases apoptosis in monocytes, alters viral polymerase activity in vitro, enhances inflammation and increases secondary pneumonia in vivo. However, the effects the PB1-F2 protein have in vivo remain unclear. To address the mechanisms involved, we intranasally infected groups of mice with either influenza A virus PR8 or a genetically engineered virus that expresses the 1918 PB1-F2 protein on a PR8 background, PR8-PB1-F2(1918). Mice inoculated with PR8 had viral concentrations peaking at 72 hours, while those infected with PR8-PB1-F2(1918) reached peak concentrations earlier, 48 hours. Mice given PR8-PB1-F2(1918) also showed a faster decline in viral loads. We fit a mathematical model to these data to estimate parameter values. The model supports a higher viral production rate per cell and a higher infected cell death rate with the PR8-PB1-F2(1918) virus. We discuss the implications these mechanisms have during an infection with a virus expressing a virulent PB1-F2 on the possibility of a pandemic and on the importance of antiviral treatments

Reply to Comment on “Sloppy models, parameter uncertainty, and the role of experimental design"

available in PMC 2012 November 10.We welcome the commentary from Chachra, Transtrum, and Sethna1 regarding our paper “Sloppy models, parameter uncertainty, and the role of experimental design,”2 as their intriguing work shaped our thinking in this area.3 Sethna and colleagues introduced the notion of sloppy models, in which the uncertainty in the values of some combinations of parameters is many orders of magnitude greater than others.4 In our work we explored the extent to which large parameter uncertainties are an intrinsic characteristic of systems biology network models, or whether uncertainties are instead closely related to the collection of experiments used for model estimation. We were gratified to find the latter result –– that parameters are in principle knowable, which is important for the field of systems biology. The work also showed that small parameter uncertainties can be achieved and that the process can be greatly accelerated by using computational experimental design approaches5–9 deployed to select sets of experiments that effectively exercise the system in complementary directions