Evaluation of habitat suitability index models by global sensitivity and uncertainty analyses: a case study for submerged aquatic vegetation

Habitat suitability index (HSI) models are commonly used to predict habitat quality and species distributions and are used to develop biological surveys, assess reserve and management priorities, and anticipate possible change under different management or climate change scenarios. Important management decisions may be based on model results, often without a clear understanding of the level of uncertainty associated with model outputs. We present an integrated methodology to assess the propagation of uncertainty from both inputs and structure of the HSI models on model outputs (uncertainty analysis: UA) and relative importance of uncertain model inputs and their interactions on the model output uncertainty (global sensitivity analysis: GSA). We illustrate the GSA/UA framework using simulated hydrology input data from a hydrodynamic model representing sea level changes and HSI models for two species of submerged aquatic vegetation (SAV) in southwest Everglades National Park: Vallisneria americana (tape grass) and Halodule wrightii (shoal grass). We found considerable spatial variation in uncertainty for both species, but distributions of HSI scores still allowed discrimination of sites with good versus poor conditions. Ranking of input parameter sensitivities also varied spatially for both species, with high habitat quality sites showing higher sensitivity to different parameters than low‐quality sites. HSI models may be especially useful when species distribution data are unavailable, providing means of exploiting widely available environmental datasets to model past, current, and future habitat conditions. The GSA/UA approach provides a general method for better understanding HSI model dynamics, the spatial and temporal variation in uncertainties, and the parameters that contribute most to model uncertainty. Including an uncertainty and sensitivity analysis in modeling efforts as part of the decision‐making framework will result in better‐informed, more robust decisions.     

Comparative study of parameter sensitivity analyses of the TCR-activated Erk-MAPK signalling pathway

Parameter estimation is a major challenge for mathematical modelling of biological systems. Given the uncertainties associated with model parameters, it is important to understand how sensitive the model output is to variations in parameter values. A local sensitivity analysis determines the model sensitivity to parameter variations over a localised region around the nominal parameter values, whereas a global sensitivity analysis (GSA) investigates the sensitivity over the entire parameter space. Using a T-cell receptor-activated Erk-MAPK signalling pathway model as an example, the authors present a comparative study of a variety of different sensitivity analysis techniques. These techniques include: local sensitivity analysis, existing GSA methods of partial rank correlation coefficient, Sobol's, extended Fourier amplitude sensitivity test, as well as a weighted average of local sensitivities and a new GSA method to extract global parameter sensitivities from a parameter identification routine. Results of this study revealed critical reactions in the signalling pathway and their impact on the signalling dynamics and provided insights into embedded regulatory mechanisms such as feedback loops in the pathway. From this study, a recommendation emerges for a general sensitivity analysis strategy to efficiently and reliably infer quantitative, dynamic as well as topological properties from systems biology models.     

Development of a physiologically based toxicokinetic model for butadiene and four major metabolites in humans: global sensitivity analysis for experimental design issues

1,3-Butadiene (BD) is metabolized in humans and rodents to mutagenic and carcinogenic species. Our previous work has focused on developing a physiologically based toxicokinetic (PBTK) model for BD to estimate its metabolic rate to 1,2-epoxy-3-butene (EB), using exhaled breath BD concentrations in human volunteers exposed by inhalation. In this paper, we extend our BD model to describe the kinetics of its four major metabolites EB, 1,2:3,4-diepoxybutane (DEB), 3-butene-1,2-diol (BDD), and 3,4-epoxy-1,2-butanediol (EBD), and to test whether the extended model and experimental data (to be collected for BD and metabolites in humans) are together adequate to estimate the metabolic rate constants of each of the above chemicals. Global sensitivity analyses (GSA) were conducted to evaluate the relative importance of the model parameters on model outputs during the 20min of exposure and the 40min after exposure ended. All model parameters were studied together with various potentially measurable model outputs: concentrations of BD and EB in exhaled air, concentrations of BD and all metabolites in venous blood, and cumulated amounts of urinary metabolites excreted within 24h. Our results show that pulmonary absorption of BD and subsequent distribution and metabolism in the well-perfused tissues compartment are the critical processes in the toxicokinetics of BD and metabolites. In particular, three parameters influence numerous outputs: the blood:air partition coefficient for BD, the metabolic rate of BD to EB, and the volume of the well-perfused tissues. Other influential parameters include other metabolic rates, some partition coefficients, and parameters driving the gas exchanges (in particular, for BD outputs). GSA shows that the impact of the metabolic rate of BD to EB on the BD concentrations in exhaled air is greatly increased if a few of the model's important parameters (such as the blood:air partition coefficient for BD) are measured experimentally. GSA also shows that all the transformation pathways described in the PBTK model may not be estimable if only data on the studied outputs are collected, and that data on a specific output for a chemical may not inform all the transformations involving that chemical.     

A global approach for sparse representation of uncertainty in Life Cycle Assessments of waste management systems

Purpose

Identification of key inputs and their effect on results from Life Cycle Assessment (LCA) models is fundamental. Because parameter importance varies greatly between cases due to the interaction of sensitivity and uncertainty, these features should never be defined a priori. However, exhaustive parametrical uncertainty analyses may potentially be complicated and demanding, both with analytical and sampling methods. Therefore, we propose a systematic method for selection of critical parameters based on a simplified analytical formulation that unifies the concepts of sensitivity and uncertainty in a Global Sensitivity Analysis (GSA) framework.

Methods

The proposed analytical method based on the calculation of sensitivity coefficients (SC) is evaluated against Monte Carlo sampling on traditional uncertainty assessment procedures, both for individual parameters and for full parameter sets. Three full-scale waste management scenarios are modelled with the dedicated waste LCA model EASETECH and a full range of ILCD recommended impact categories. Common uncertainty ranges of 10 % are used for all parameters, which we assume to be normally distributed. The applicability of the concepts of additivity of variances and GSA is tested on results from both uncertainty propagation methods. Then, we examine the differences in discernibility analyses results carried out with varying numbers of sampling points and parameters.

Results and discussion

The proposed analytical method complies with the Monte Carlo results for all scenarios and impact categories, but offers substantially simpler mathematical formulation and shorter computation times. The coefficients of variation obtained with the analytical method and Monte Carlo differ only by 1 %, indicating that the analytical method provides a reliable representation of uncertainties and allows determination of whether a discernibility analysis is required. The additivity of variances and the GSA approach show that the uncertainty in results is determined by a limited set of important parameters. The results of the discernibility analysis based on these critical parameters vary only by 1 % from discernibility analyses based on the full set, but require significantly fewer Monte Carlo runs.

Conclusions

The proposed method and GSA framework provide a fast and valuable approximation for uncertainty quantification. Uncertainty can be represented sparsely by contextually identifying important parameters in a systematic manner. The proposed method integrates with existing step-wise approaches for uncertainty analysis by introducing a global importance analysis before uncertainty propagation.

     

Noise propagation in two-step series MAPK cascade

Series MAPK enzymatic cascades, ubiquitously found in signaling networks, act as signal amplifiers and play a key role in processing information during signal transduction in cells. In activated cascades, cell-to-cell variability or noise is bound to occur and thereby strongly affects the cellular response. Commonly used linearization method (LM) applied to Langevin type stochastic model of the MAPK cascade fails to accurately predict intrinsic noise propagation in the cascade. We prove this by using extensive stochastic simulations for various ranges of biochemical parameters. This failure is due to the fact that the LM ignores the nonlinear effects on the noise. However, LM provides a good estimate of the extrinsic noise propagation. We show that the correct estimate of intrinsic noise propagation in signaling networks that contain at least one enzymatic step can be obtained only through stochastic simulations. Noise propagation in the cascade depends on the underlying biochemical parameters which are often unavailable. Based on a combination of global sensitivity analysis (GSA) and stochastic simulations, we developed a systematic methodology to characterize noise propagation in the cascade. GSA predicts that noise propagation in MAPK cascade is sensitive to the total number of upstream enzyme molecules and the total number of molecules of the two substrates involved in the cascade. We argue that the general systematic approach proposed and demonstrated on MAPK cascade must accompany noise propagation studies in biological networks.     

The effect of connective tissue material uncertainties on knee joint mechanics under isolated loading conditions

Although variability in connective tissue parameters is widely reported and recognized, systematic examination of the effect of such parametric uncertainties on predictions derived from a full anatomical joint model is lacking. As such, a sensitivity analysis was performed to consider the behavior of a three-dimensional, non-linear, finite element knee model with connective tissue material parameters that varied within a given interval. The model included the coupled mechanics of the tibio-femoral and patello-femoral degrees of freedom. Seven primary connective tissues modeled as non-linear continua, articular cartilages described by a linear elastic model, and menisci modeled as transverse isotropic elastic materials were included. In this study, a multi-factorial global sensitivity analysis is proposed, which can detect the contribution of influential material parameters while maintaining the potential effect of parametric interactions. To illustrate the effect of material uncertainties on model predictions, exemplar loading conditions reported in a number of isolated experimental paradigms were used. Our findings illustrated that the inclusion of material uncertainties in a coupled tibio-femoral and patello-femoral model reveals biomechanical interactions that otherwise would remain unknown. For example, our analysis revealed that the effect of anterior cruciate ligament parameter variations on the patello-femoral kinematic and kinetic response sensitivities was significantly larger, over a range of flexion angles, when compared to variations associated with material parameters of tissues intrinsic to the patello-femoral joint. We argue that the systematic sensitivity framework presented herein will help identify key material uncertainties that merit further research and provide insight on those uncertainties that may not be as relative to a given response.     

The gravitropic set-point angle (GSA): the identification of an important developmentally controlled variable governing plant architecture*

The angle at which an organ is maintained by gravit-ropism is characteristic of the organ, its developmental state and the prevailing environmental conditions. We propose that this angle be called the gravitropic set-point angle (GSA), defined as the angle with respect to the gravity vector (with a vertically downward orientation being 0°) at which an organ is maintained as a consequence of gravitropism. Studies of the gravitropic behaviour of organs from trailing plants show that the GSA is subject to developmental regulation. Depending on the developmental age and prevailing environmental conditions, the GSA of an organ can he set at any value between 0° and 180° The previously reported reversal of the sign of the gravitropic response in such organs, whether this is brought about developmentally or induced by light, represents the change from one common extreme (GSA = 180°, conventionally referred to as negative orthogravitropism) to another (GSA = 0°, or positive orthogravitropism). The concept of a variable gravitropic set-point offers a more unified view of all forms of gravitropic behaviour than has been advanced previously, and places a new constraint on models of gravitropism. Current models of gravitropism appear to be unable to explain either the ability of organs to change their orientation with respect to gravity as they develop, or the re-orientation that can be observed when some organs are exposed to new environmental conditions.     

Light modulation of the gravitropic set-point angle (GSA).

A study has been made of the means by which light influences the gravitropic set-point angle (GSA) of the nodes of Tradescantia and the hypocotyls of the lazy-2 mutant of tomato. In light-grown Tradescantia there is a light-regulated developmental change in the GSA with the magnitude of this change being dependent on the photon flux density of white light. The photosynthetic inhibitor DCMU abolished the effect of white light. Low fluence rates of red light had no significant effect on the GSA of Tradescantia: It was concluded that there is an interaction between photosynthesis and the GSA in Tradescantia: The light-induced reduction of the GSA of the hypocotyl of lazy-2 tomato has previously been assumed to be solely an action of light acting via phytochrome. However, it can be shown that the GSA of hypocotyls of lazy-2 seedlings grown in white light is sensitive to DCMU and norflurazon treatment, hence the light effects on the GSA of an organ can be mediated via both phytochrome and photosynthesis. The implication of these findings to the study of gravitropism is discussed.     

High School Gay-Straight Alliances (GSAs) and Young Adult Well-Being: An Examination of GSA Presence, Participation, and Perceived Effectiveness

Gay-Straight Alliances (GSAs) are student-led, school-based clubs that aim to provide a safe environment in the school context for lesbian, gay, bisexual, and transgender (LGBT) students, as well as their straight allies. The present study examines the potential for GSAs to support positive youth development and to reduce associations among LGBT-specific school victimization and negative young adult well-being. The sample includes 245 LGBT young adults, ages 21-25, who retrospectively reported on the presence of a GSA in their high school, their participation in their school's GSA, and their perceptions of whether or not their GSA was effective in improving school safety. Findings revealed that the presence of a GSA, participation in a GSA, and perceived GSA effectiveness in promoting school safety were differentially associated with young adult well-being and in some cases, buffered the negative association between LGBT-specific school victimization and well-being. Implications for future research and schools are discussed.     

High School Gay–Straight Alliances (GSAs) and Young Adult Well-Being: An Examination of GSA Presence,Participation, and Perceived Effectiveness

Gay–Straight Alliances (GSAs) are student-led, school-based clubs that aim to provide a safe environment in the school context for lesbian, gay, bisexual, and transgender (LGBT) students, as well as their straight allies. The present study examines the potential for GSAs to support positive youth development and to reduce associations among LGBT-specific school victimization and negative young adult well-being. The sample includes 245 LGBT young adults, ages 21–25, who retrospectively reported on the presence of a GSA in their high school, their participation in their school's GSA, and their perceptions of whether or not their GSA was effective in improving school safety. Findings revealed that the presence of a GSA, participation in a GSA, and perceived GSA effectiveness in promoting school safety were differentially associated with young adult well-being and, in some cases, buffered the negative association between LGBT-specific school victimization and well-being. Implications for future research and schools are discussed.     

Strategies for dealing with incomplete information in the modeling of molecular interaction networks

Modelers of molecular interaction networks encounter the paradoxical situation that while large amounts of data are available, these are often insufficient for the formulation and analysis of mathematical models describing the network dynamics. In particular, information on the reaction mechanisms and numerical values of kinetic parameters are usually not available for all but a few well-studied model systems. In this article we review two strategies that have been proposed for dealing with incomplete information in the study of molecular interaction networks: parameter sensitivity analysis and model simplification. These strategies are based on the biologically justified intuition that essential properties of the system dynamics are robust against moderate changes in the value of kinetic parameters or even in the rate laws describing the interactions. Although advanced measurement techniques can be expected to relieve the problem of incomplete information to some extent, the strategies discussed in this article will retain their interest as tools providing an initial characterization of essential properties of the network dynamics.     

Identification of an intermediate of delta-aminolevulinate biosynthesis in Chlamydomonas by high-performance liquid chromatography

The first committed intermediate of the chlorophyll biosynthetic pathway is delta-aminolevulinic acid (ALA). In plant cells, ALA is formed from glutamate by a pathway not yet clearly defined. One of the proposed pathways involves the reduction of glutamate to glutamate-1-semialdehyde (GSA) via a glutamyl-tRNA intermediate. GSA is then converted to ALA by an aminotransferase. We are studying this pathway using partially purified components from Chlamydomonas reinhardtii in in vitro reactions with [3H]L-glutamate as the substrate and analysis of the radioactive reaction products via HPLC. In reactions either lacking GSA-aminotransferase or containing gabaculine (an inhibitor of aminotransferase), a radioactive intermediate is formed which cochromatographs with synthetic GSA. As observed previously for ALA synthesis, the synthesis of this intermediate has an absolute requirement for RNA, ATP, and active enzymes, while the requirement for NADPH is less stringent. Both the accumulated intermediate and the synthetic GSA can be converted to ALA by the aminotransferase without any additional substrates or cofactors. These results support previous observations that GSA or a very similar compound is an intermediate of ALA synthesis.     

Insights into pathological mechanisms and interventions revealed by analyzing a mathematical model for cone metabolism

This work analyzes a mathematical model for the metabolic dynamics of a cone photoreceptor, which is the first model to account for energy generation from fatty acids oxidation of shed photoreceptor outer segments (POS). Multiple parameter bifurcation analysis shows that joint variations in external glucose, the efficiency of glucose transporter 1 (GLUT1), lipid utilization for POS renewal, and oxidation of fatty acids affect the cone’s metabolic vitality and its capability to adapt under glucose-deficient conditions. The analysis further reveals that when glucose is scarce, cone viability cannot be sustained by only fueling energy production in the mitochondria, but it also requires supporting anabolic processes to create lipids necessary for cell maintenance and repair. In silico experiments are used to investigate how the duration of glucose deprivation impacts the cell without and with a potential GLUT1 or oxidation of fatty acids intervention as well as a dual intervention. The results show that for prolonged duration of glucose deprivation, the cone metabolic system does not recover with higher oxidation of fatty acids and requires greater effectiveness of GLUT1 to recover. Finally, time-varying global sensitivity analysis (GSA) is applied to assess the sensitivity of the model outputs of interest to changes and uncertainty in the parameters at specific times. The results reveal a critical temporal window where there would be more flexibility for interventions to rescue a cone cell from the detrimental consequences of glucose shortage.     

Saltelli Global Sensitivity Analysis and Simulation Modelling to Identify Intervention Strategies to Reduce the Prevalence of Escherichia coli O157 Contaminated Beef Carcasses

Introduction

Strains of Shiga-toxin producing Escherichia coli O157 (STEC O157) are important foodborne pathogens in humans, and outbreaks of illness have been associated with consumption of undercooked beef. Here, we determine the most effective intervention strategies to reduce the prevalence of STEC O157 contaminated beef carcasses using a modelling approach.

Method

A computational model simulated events and processes in the beef harvest chain. Information from empirical studies was used to parameterise the model. Variance-based global sensitivity analysis (GSA) using the Saltelli method identified variables with the greatest influence on the prevalence of STEC O157 contaminated carcasses. Following a baseline scenario (no interventions), a series of simulations systematically introduced and tested interventions based on influential variables identified by repeated Saltelli GSA, to determine the most effective intervention strategy.

Results

Transfer of STEC O157 from hide or gastro-intestinal tract to carcass (improved abattoir hygiene) had the greatest influence on the prevalence of contaminated carcases. Due to interactions between inputs (identified by Saltelli GSA), combinations of interventions based on improved abattoir hygiene achieved a greater reduction in maximum prevalence than would be expected from an additive effect of single interventions. The most effective combination was improved abattoir hygiene with vaccination, which achieved a greater than ten-fold decrease in maximum prevalence compared to the baseline scenario.

Conclusion

Study results suggest that effective interventions to reduce the prevalence of STEC O157 contaminated carcasses should initially be based on improved abattoir hygiene. However, the effect of improved abattoir hygiene on the distribution of STEC O157 concentration on carcasses is an important information gap—further empirical research is required to determine whether reduced prevalence of contaminated carcasses is likely to result in reduced incidence of STEC O157 associated illness in humans. This is the first use of variance-based GSA to assess the drivers of STEC O157 contamination of beef carcasses.     

Growth stimulating activity from lysed cultured arterial smooth muscle cells and skin fibroblasts

Rabbit and bovine arterial smooth muscle cells (SMC) and human skin fibroblasts were lysed by freezing and thawing in the presence of protease inhibitors (PI). The supernatant was assayed for growth stimulating activity (GSA), and it stimulated the growth of SMC and fibroblasts, but not human and bovine endothelial cells. GSA was sensitive to heat and trypsin treatment, stimulated DNA synthesis after a lag time of 12-15 hours, and exhibited marked size and charge heterogeneity when subjected to gel chromatographies. GSA differed from many other known growth factors, mainly platelet derived growth factor (PDGF), through the behavior on ion exchange chromatography, the heat sensitivity and the lack of decline in activity in the presence of anti PDGF antibodies. The data suggests that several growth stimulating proteins can be obtained through the lysis of SMC or fibroblasts with possible implications for atherosclerosis and wound healing.     

Comprehensive,Population-Based Sensitivity Analysis of a Two-Mass Vocal Fold Model

Previous vocal fold modeling studies have generally focused on generating detailed data regarding a narrow subset of possible model configurations. These studies can be interpreted to be the investigation of a single subject under one or more vocal conditions. In this study, a broad population-based sensitivity analysis is employed to examine the behavior of a virtual population of subjects and to identify trends between virtual individuals as opposed to investigating a single subject or model instance. Four different sensitivity analysis techniques were used in accomplishing this task. Influential relationships between model input parameters and model outputs were identified, and an exploration of the model’s parameter space was conducted. Results indicate that the behavior of the selected two-mass model is largely dominated by complex interactions, and that few input-output pairs have a consistent effect on the model. Results from the analysis can be used to increase the efficiency of optimization routines of reduced-order models used to investigate voice abnormalities. Results also demonstrate the types of challenges and difficulties to be expected when applying sensitivity analyses to more complex vocal fold models. Such challenges are discussed and recommendations are made for future studies.     

胰岛素增强糖基化白蛋白对大鼠血管平滑肌细胞的促增殖作用

在糖尿病性大血管病变的发病过程中,高血糖以及晚期糖基化终末产物（advanced glycation end products,AGEs）、脂质异常和高胰岛素血症的相互作用较其单独作用可能更重要。本研究采用糖基化白蛋白（glycated serum albumin,GSA）模拟AGEs,观察胰岛素和GSA对大鼠血管平滑肌细胞（vascular smooth muscle cells,VSMCs）的增殖是否存在协同作用,并初步探讨其作用机制。采用组织贴块法分离培养大鼠VSMCs。经过或不经过各种丝裂原激活蛋白激酶（mitogen-activated protein kinases．MAPKs）抑制剂和氧自由基清除剂N-acetylcysteine（NAC）处理后,加入不同浓度的胰岛素、GSA或GSA＋胰岛素,用MTT法和细胞计数法检测VSMCs的增殖。采用Western blot检测p38MAPK和C-Jun N-terminal kinase1／2（JNK1／2）的磷酸化。结果显示,GSA和胰岛素联合作用促进p38MAPK的磷酸化,而对JNK1／2的磷酸化无明显影响。GSA和胰岛素均可促进VSMCs增殖,而且两者具有协同作用。p38MAPK抑制剂SB203580和NAC可以抑制GSA和胰岛素联合作用引起的VSMCs增殖。以上结果提示,胰岛素和GSA对促进VSMCs增殖有协同作用,这可能是通过氧化应激敏感的p38MAPK通路实现的。胰岛素和AGEs的协同作用在糖尿病性动脉粥样硬化和再狭窄的发病过程中可能起重要作用。     

Practical limits for reverse engineering of dynamical systems: a statistical analysis of sensitivity and parameter inferability in systems biology models

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.     

Prediction of protein-protein interactions using random decision forest framework

MOTIVATION: Protein interactions are of biological interest because they orchestrate a number of cellular processes such as metabolic pathways and immunological recognition. Domains are the building blocks of proteins; therefore, proteins are assumed to interact as a result of their interacting domains. Many domain-based models for protein interaction prediction have been developed, and preliminary results have demonstrated their feasibility. Most of the existing domain-based methods, however, consider only single-domain pairs (one domain from one protein) and assume independence between domain-domain interactions. RESULTS: In this paper, we introduce a domain-based random forest of decision trees to infer protein interactions. Our proposed method is capable of exploring all possible domain interactions and making predictions based on all the protein domains. Experimental results on Saccharomyces cerevisiae dataset demonstrate that our approach can predict protein-protein interactions with higher sensitivity (79.78%) and specificity (64.38%) compared with that of the maximum likelihood approach. Furthermore, our model can be used to infer interactions not only for single-domain pairs but also for multiple domain pairs.