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

The Effects of Prenatal Protein Restriction on β-Adrenergic Signalling of the Adult Rat Heart during Ischaemia Reperfusion

A maternal low-protein diet (MLP) fed during pregnancy leads to hypertension in adult rat offspring. Hypertension is a major risk factor for ischaemic heart disease. This study examined the capacity of hearts from MLP-exposed offspring to recover from myocardial ischaemia-reperfusion (IR) and related this to cardiac expression of β-adrenergic receptors (β-AR) and their associated G proteins. Pregnant rats were fed control (CON) or MLP diets (n = 12 each group) throughout pregnancy. When aged 6 months, hearts from offspring underwent Langendorff cannulation to assess contractile function during baseline perfusion, 30 min ischemia and 60 min reperfusion. CON male hearts demonstrated impaired recovery in left ventricular pressure (LVP) and dP/dtmax (P < 0.01) during reperfusion when compared to MLP male hearts. Maternal diet had no effect on female hearts to recover from IR. MLP males exhibited greater membrane expression of β2-AR following reperfusion and urinary excretion of noradrenaline and dopamine was lower in MLP and CON female rats versus CON males. In conclusion, the improved cardiac recovery in MLP male offspring following IR was attributed to greater membrane expression of β2-AR and reduced noradrenaline and dopamine levels. In contrast, females exhibiting both decreased membrane expression of β2-AR and catecholamine levels were protected from IR injury

The Efficacy and Optimization of Somatosensory Intracortical Microstimulation in Rats

Demand exists for brain-machine interfaces that offer a wide range of sensory feedback along with volitional motor control to individuals with limited control of natural sensory or motor function. As these sensorimotor devices are developed, it is necessary to improve the interaction between the prostheses and higher-level cortical structures. Optimizing these somatosensory stimulation parameters will require the use of a high-throughput experimental design. To address this, one Sprague-Dawley rat was trained to respond to auditory stimuli during a conditioned-avoidance behavior task and then implanted with a penetrating microelectrode array in the part of the somatosensory cortex corresponding to the left forelimb. After implantation, the task was repeated using electrical stimuli instead of auditory signals. Detection threshold data was collected from each electrode site to prove stimulation efficacy. The pulse rate of electrical stimulation was varied to optimize power usage by the neuroprosthesis while still achieving the lowest possible thresholds. Electrical impedance spectroscopy and cyclic voltammetry data were collected to monitor the performance of the electrode. Testing shows that auditory learning can be translated to somatosensory stimulation. As an aggregate, somatosensory detection thresholds are significantly different from those in the auditory cortex (Student’s t-test, p \u3c 0.0003). With these results in mind, future research can further optimize somatosensory intracortical microstimulation to provide more sensory feedback in motor prostheses

Understanding uncertainty in temperature effects on vector-borne disease: A Bayesian approach

Extrinsic environmental factors influence the distribution and population dynamics of many organisms, including insects that are of concern for human health and agriculture. This is particularly true for vector-borne infectious diseases, like malaria, which is a major source of morbidity and mortality in humans. Understanding the mechanistic links between environment and population processes for these diseases is key to predicting the consequences of climate change on transmission and for developing effective interventions. An important measure of the intensity of disease transmission is the reproductive number $R_0$. However, understanding the mechanisms linking $R_0$ and temperature, an environmental factor driving disease risk, can be challenging because the data available for parameterization are often poor. To address this we show how a Bayesian approach can help identify critical uncertainties in components of $R_0$ and how this uncertainty is propagated into the estimate of $R_0$. Most notably, we find that different parameters dominate the uncertainty at different temperature regimes: bite rate from 15-25$^\circ$ C; fecundity across all temperatures, but especially $\sim$25-32$^\circ$ C; mortality from 20-30$^\circ$ C; parasite development rate at $\sim$15-16$^\circ$C and again at $\sim$33-35$^\circ$C. Focusing empirical studies on these parameters and corresponding temperature ranges would be the most efficient way to improve estimates of $R_0$. While we focus on malaria, our methods apply to improving process-based models more generally, including epidemiological, physiological niche, and species distribution models.Comment: 27 pages, including 1 table and 3 figure

Preparation and Crystal Structure of a Platinum(II) Complex of [CH2N(CH2COOH)CH2CONH2]2, the Hydrolysis Product of an Anti-Tumour Bis(3,5-Dioxopiperazin-1-YL)Alkane

The synthesis and crystal and molecular structures of the platinum(II) complex Pt(HL)Cl where H2L is the diacid diamide –[CH2N(CH2COOH)CH2CONH2]2, a hydrolytic metabolite of an antitumour active bis(3,5-dioxopiperazin-1-yl)alkane are reported. The complex is square planar and contains HL– as a tridentate 2N (amino), O (carboxylate) donor. The metal to ligand bond distances are Pt-Cl 2.287(1) Å, Pt-O 2.002 (1) Å, Pt-Ntrans Cl 2.014(1) Å and Pt-Ntrans O 2.073 Å. There is extensive hydrogen bonding, each molecule of Pt(HL)Cl being intermolecularly hydrogen bonded to ten others giving a 3-dimensional network. There is also one intramolecular H-bond

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

Genomics reveals historic and contemporary transmission dynamics of a bacterial disease among wildlife and livestock

Whole-genome sequencing has provided fundamental insights into infectious disease epidemiology, but has rarely been used for examining transmission dynamics of a bacterial pathogen in wildlife. In the Greater Yellowstone Ecosystem (GYE), outbreaks of brucellosis have increased in cattle along with rising seroprevalence in elk. Here we use a genomic approach to examine Brucella abortus evolution, cross-species transmission and spatial spread in the GYE. We find that brucellosis was introduced into wildlife in this region at least five times. The diffusion rate varies among Brucella lineages (∼3 to 8 km per year) and over time. We also estimate 12 host transitions from bison to elk, and 5 from elk to bison. Our results support the notion that free-ranging elk are currently a self-sustaining brucellosis reservoir and the source of livestock infections, and that control measures in bison are unlikely to affect the dynamics of unrelated strains circulating in nearby elk populations