Testing the Australian Weed Risk Assessment with different estimates for invasiveness

The Weed Risk Assessment (WRA) has become an effective tool in predicting invasiveness of exotic plant species. In studies testing the WRA, exotic plant species are usually divided into major weeds, minor weeds and non-weeds. However, these divisions are qualitative, as the categories are assigned by experts. Many studies searching for plant traits that are indicative of plant invasiveness use quantitative estimates to measure invasiveness. We compared how quantitative and qualitative estimates of invasiveness may relate to WRA scores. As quantitative estimates we used regional frequency (spread), change in regional frequency and local dominance of naturalized exotic plant species in The Netherlands. To obtain a qualitative estimate we determined if the exotic plant species occurred on a black list in neighbouring regions. We related WRA scores of the exotic plant species to these qualitative and quantitative estimates of invasiveness. Our results reveal that the WRA predicted the qualitative (black list) estimate more accurately than the quantitative (dominance and spread) ones. The black list estimate matches with the overall impact of exotic species, which is assumed to incorporate regional spread, local dominance and noxiousness. Therefore, the WRA predicts the noxiousness component, but to a lesser extent the spatial components of impact of exotic species. On the other hand, studies that use regional spread and other quantitative estimates of invasiveness tend not to include the noxiousness component of impact. We propose that our analyses may also help to further solve the recent debate on whether or not performing research on exotic species.     

Analysis of affected sib pairs, with covariates--with and without constraints

Covariate models have previously been developed as an extension to affected-sib-pair methods in which the covariate effects are jointly estimated with the degree of excess allele sharing. These models can estimate the differences in sib-pair allele sharing that are associated with measurable environment or genes. When there are no covariates, the pattern of identical-by-descent allele sharing in affected sib pairs is expected to fall within a small triangular region of the potential parameter space, under most genetic models. By restriction of the estimated allele sharing to this triangle, improved power is obtained in tests for genetic linkage. When the affected-sib-pair model is generalized to allow for covariates that affect allele sharing, however, new constraints and new methods for the application of constraints are required. Three generalized constraint methods are proposed and evaluated by use of simulated data. The results compare the power of the different methods, with and without covariates, for a single-gene model with age-dependent onset and for quantitative and qualitative gene-environment and gene-gene interaction models. Covariates can improve the power to detect linkage and can be particularly valuable when there are qualitative gene-environment interactions. In most situations, the best strategy is to assume that there is no dominance variance and to obtain constrained estimates for covariate models under this assumption.     

Segregation analysis comparing liability and quantitative trait models for hypertension using the Genetic Analysis Workshop 13 simulated data

Discrete (qualitative) data segregation analysis may be performed assuming the liability model, which involves an underlying normally distributed quantitative phenotype. The appropriateness of the liability model for complex traits is unclear. The Genetic Analysis Workshop 13 simulated data provides measures on systolic blood pressure, a highly complex trait, which may be dichotomized into a discrete trait (hypertension). We perform segregation analysis under the liability model of hypertensive status as a qualitative trait and compare this with results using systolic blood pressure as a quantitative trait (without prior knowledge at that stage of the true underlying simulation model) using 1050 pedigrees ascertained from four replicates on the basis of at least one affected member. Both analyses identify models with major genes and polygenic components to explain the family aggregation of systolic blood pressure. Neither of the methods estimates the true parameters well (as the true model is considerably more complicated than those considered for the analysis), but both identified the most complicated model evaluated as the preferred model. Segregation analysis of complex diseases using relatively simple models is unlikely to provide accurate parameter estimates but is able to indicate major gene and/or polygenic components in familial aggregation of complex diseases.     

Viral validation strategy for recombinant products derived from established animal cell lines

For products derived from continuous cell lines, regulatory agencies worldwide require that the purification process be validated for its ability to remove or inactivate potential contaminants such as viruses and virus-like particles. New guidance suggests a requirement for statistical evaluation of these studies but the industry has yet to develop such standards. The task of estimating excess capacity is also complicated by variable assays, accumulation of variability in clearance estimates over unit operations, dependence of clearance capacity on operating parameters, and expense of experiments. We propose an experimental strategy to determine the excess clearance capacity of a biopharmaceutical process and to provide statistical estimation of excess capacity in an efficient way. Clearance estimates and their variances are calculated for each orthogonal unit operation and estimates are combined to form an interval estimate of overall process clearance capacity. Poisson regression is suggested as an efficient technique for data analysis of clearance studies. We believe that this approach should meet regulatory guidelines in a cost effective way, while clarifying the roles of qualitative and quantitative components in setting requirements.     

An integrative and practical evolutionary optimization for a complex, dynamic model of biological networks

Computer simulation is an important technique to capture the dynamics of biochemical networks. Numerical optimization is the key to estimate the values of kinetic parameters so that the dynamic model reproduces the behaviors of the existing experimental data. It is required to develop general strategies for the optimization of complex biochemical networks with a huge space of search parameters, under the condition that kinetic and quantitative data are hardly available. We propose an integrative and practical strategy for optimizing a complex dynamic model by using qualitative and incomplete experimental data. The key technologies are the divide and conquer method for reducing the search space, handling of multiple objective functions representing different types of biological behaviors, and design of rule-based objective functions that are suitable for qualitative and error-prone experimental data. This strategy is applied to optimizing a dynamic model of the yeast cell cycle to demonstrate the feasibility of it.     

A Unified Framework Integrating Parent-of-Origin Effects for Association Study

Genetic imprinting is the most well-known cause for parent-of-origin effect (POE) whereby a gene is differentially expressed depending on the parental origin of the same alleles. Genetic imprinting is related to several human disorders, including diabetes, breast cancer, alcoholism, and obesity. This phenomenon has been shown to be important for normal embryonic development in mammals. Traditional association approaches ignore this important genetic phenomenon. In this study, we generalize the natural and orthogonal interactions (NOIA) framework to allow for estimation of both main allelic effects and POEs. We develop a statistical (Stat-POE) model that has the orthogonal estimates of parameters including the POEs. We conducted simulation studies for both quantitative and qualitative traits to evaluate the performance of the statistical and functional models with different levels of POEs. Our results showed that the newly proposed Stat-POE model, which ensures orthogonality of variance components if Hardy-Weinberg Equilibrium (HWE) or equal minor and major allele frequencies is satisfied, had greater power for detecting the main allelic additive effect than a Func-POE model, which codes according to allelic substitutions, for both quantitative and qualitative traits. The power for detecting the POE was the same for the Stat-POE and Func-POE models under HWE for quantitative traits.     

Preclinical metabolism of LB42908, a novel farnesyl transferase inhibitor, and its effects on the cytochrome P450 isozyme activities

Metabolism of LB42908, a novel farnesyl transferase inhibitor, was investigated for preclinical development. In vitro hepatic metabolism of LB42908 gave rise to at least 9 metabolites via phase I biotransformation pathways, which were characterized by HPLC-UV, LC-MS, and LC-MS/MS analyses. N-Dealkylation was shown to be a major phase I metabolic pathway. Species-specific in vitro metabolism of LB42908 was studied in liver fractions of rat, dog, monkey, and human. Order of metabolic stability is human≈dog>rat≈monkey in both S9 and microsomal fractions. Tissue-specific metabolism of LB42908 in various tissue homogenates of rats demonstrated that the liver was the major organ responsible for phase I metabolism of LB42908. The results from both qualitative and quantitative metabolism studies such as metabolic profiling and metabolic clearance indicated that dog would be the animal model of choice for preclinical toxicology studies. In addition, LB42908 was a potent CYP3A4 inhibitor in human liver microsomes and induced the activities of several CYP isozymes, implying that it has the potential for drug-drug interactions. Repeated dosing of LB42908 in rats did not significantly affect its own metabolism, indicating that long-term administration of LB42908 would not alter its pharmacokinetic profiles.     

Modeling Viral Evolutionary Dynamics after Telaprevir-Based Treatment

For patients infected with hepatitis C virus (HCV), the combination of the direct-acting antiviral agent telaprevir, pegylated-interferon alfa (Peg-IFN), and ribavirin (RBV) significantly increases the chances of sustained virologic response (SVR) over treatment with Peg-IFN and RBV alone. If patients do not achieve SVR with telaprevir-based treatment, their viral population is often significantly enriched with telaprevir-resistant variants at the end of treatment. We sought to quantify the evolutionary dynamics of these post-treatment resistant variant populations. Previous estimates of these dynamics were limited by analyzing only population sequence data (20% sensitivity, qualitative resistance information) from 388 patients enrolled in Phase 3 clinical studies. Here we add clonal sequence analysis (5% sensitivity, quantitative) for a subset of these patients. We developed a computational model which integrates both the qualitative and quantitative sequence data, and which forms a framework for future analyses of drug resistance. The model was qualified by showing that deep-sequence data (1% sensitivity) from a subset of these patients are consistent with model predictions. When determining the median time for viral populations to revert to 20% resistance in these patients, the model predicts 8.3 (95% CI: 7.6, 8.4) months versus 10.7 (9.9, 12.8) months estimated using solely population sequence data for genotype 1a, and 1.0 (0.0, 1.4) months versus 0.9 (0.0, 2.7) months for genotype 1b. For each individual patient, the time to revert to 20% resistance predicted by the model was typically comparable to or faster than that estimated using solely population sequence data. Furthermore, the model predicts a median of 11.0 and 2.1 months after treatment failure for viral populations to revert to 99% wild-type in patients with HCV genotypes 1a or 1b, respectively. Our modeling approach provides a framework for projecting accurate, quantitative assessment of HCV resistance dynamics from a data set consisting of largely qualitative information.     

A PHYLOGENETIC APPROACH TO THE ESTIMATION OF PHYTOPLANKTON TRAITS1

The quantitative characterization of the ecology of individual phytoplankton taxa is essential for model resolution of many aspects of aquatic ecosystems. Existing literature cannot directly parameterize all phytoplankton taxa of interest, as many traits and taxa have not been sampled. However, valuable clues on the value of traits are found in the evolutionary history of species and in common correlations between traits. These two resources were exploited with an existing, statistically consistent method built upon evolutionary concepts. From a new data set with >700 observations on freshwater phytoplankton traits and a qualitative phytoplankton phylogeny, estimates were derived for the size, growth rate, phosphate affinity, and susceptibility to predation of 277 phytoplankton types, from evolutionary ancestors to present‐day species. These estimates account simultaneously for phylogenetic relationships between types, as imposed by the phylogeny, and approximate power‐law relationships (e.g., allometric scaling laws) between traits, as reconstructed from the data set. Results suggest that most phytoplankton traits are to some extent conserved in evolution: cross‐validation demonstrated that the use of phylogenetic information significantly improves trait value estimates. By providing trait value estimates as well as uncertainties, these results could benefit most quantitative studies involving phytoplankton.     

Qualitative stability and ambiguity in model ecosystems

Qualitative analysis of stability in model ecosystems has previously been limited to determining whether a community matrix is sign stable or not with little analytical means to assess the impact of complexity on system stability. Systems are seen as either unconditionally or conditionally stable with little distinction and therefore much ambiguity in the likelihood of stability. First, we reexamine Hurwitz's principal theorem for stability and propose two "Hurwitz criteria" that address different aspects of instability: positive feedback and insufficient lower-level feedback. Second, we derive two qualitative metrics based on these criteria: weighted feedback (wF(n)) and weighted determinants (wDelta(n)). Third, we test the utility of these qualitative metrics through quantitative simulations in a random and evenly distributed parameter space in models of various sizes and complexities. Taken together they provide a practical means to assess the relative degree to which ambiguity has entered into calculations of stability as a result of system structure and complexity. From these metrics we identify two classes of models that may have significant relevance to system research and management. This work helps to resolve some of the impasse between theoretical and empirical discussions on the complexity and stability of natural communities.     

Predictive ecosystem research in the Wadden Sea

Predictive ecosystem research needs a pluralistic approach. Retrospective studies reveal the initial causes of ongoing ecological change. In the Wadden Sea, inherent ecosystem stability may be falsely assumed, because the effects of modern coastal architecture and of anthropogenic eutrophication to some extent complement each other. Expected environmental changes often have corresponding phases in the past which may serve as a model to predict ecological implications. Historically, quantitative ecology entered the Wadden Sea, via fisheries research, from the oceanic side. Quantified material fluxes may reveal imbalances which are indicative of the rough direction of ecosystem change. For ecosystem research to contribute to the maintenance of the Wadden Sea as a centre of coastal organisms, quantitative knowledge of resources and ecosystem metabolism must be supplemented by qualitative knowledge of habitat requirements and species interdependences. Qualitative ecology entered the Wadden Sea from the landward side. Extending this approach to anticipatory field experiments may help to predict ecological changes at the species level.     

Molecular dynamics simulations of the lipid bilayer edge

Phospholipid bilayers have been intensively studied by molecular dynamics (MD) simulation in recent years. The properties of bilayer edges are important in determining the structure and stability of pores formed in vesicles and biomembranes. In this work, we use molecular dynamics simulation to investigate the structure, dynamics, and line tension of the edges of bilayer ribbons composed of pure dimyristoylphosphatidylcholine (DMPC) or palmitoyl-oleoylphosphatidylethanolamine (POPE). As expected, we observe a significant reorganization of lipids at and near the edges. The treatment of electrostatic effects is shown to have a qualitative impact on the structure and stability of the edge, and significant differences are observed in the dynamics and structure of edges formed by DMPC and palmitoyl-oleoylphosphatidylethanolamine. From the pressure anisotropy in the simulation box, we calculate a line tension of approximately 10-30 pN for the DMPC edge, in qualitative agreement with experimental estimates for similar lipids.     

Predicting changes of body weight, body fat, energy expenditure and metabolic fuel selection in C57BL/6 mice

The mouse is an important model organism for investigating the molecular mechanisms of body weight regulation, but a quantitative understanding of mouse energy metabolism remains lacking. Therefore, we created a mathematical model of mouse energy metabolism to predict dynamic changes of body weight, body fat, energy expenditure, and metabolic fuel selection. Based on the principle of energy balance, we constructed ordinary differential equations representing the dynamics of body fat mass (FM) and fat-free mass (FFM) as a function of dietary intake and energy expenditure (EE). The EE model included the cost of tissue deposition, physical activity, diet-induced thermogenesis, and the influence of FM and FFM on metabolic rate. The model was calibrated using previously published data and validated by comparing its predictions to measurements in five groups of male C57/BL6 mice (N = 30) provided ad libitum access to either chow or high fat diets for varying time periods. The mathematical model accurately predicted the observed body weight and FM changes. Physical activity was predicted to decrease immediately upon switching from the chow to the high fat diet and the model coefficients relating EE to FM and FFM agreed with previous independent estimates. Metabolic fuel selection was predicted to depend on a complex interplay between diet composition, the degree of energy imbalance, and body composition. This is the first validated mathematical model of mouse energy metabolism and it provides a quantitative framework for investigating energy balance relationships in mouse models of obesity and diabetes.     

SASQuant: a SAS software program to estimate genetic effects and heritabilities of quantitative traits in populations consisting of 6 related generations

Plant breeders are interested in the analysis of phenotypic data to measure genetic effects and heritability of quantitative traits and predict gain from selection. Measurement of phenotypic values of 6 related generations (parents, F(1), F(2), and backcrosses) allows for the simultaneous analysis of both Mendelian and quantitative traits. In 1997, Liu et al. released a SAS software based program (SASGENE) for the analysis of inheritance and linkage of qualitative traits. We have developed a new program (SASQuant) that estimates gene effects (Hayman's model), genetic variances, heritability, predicted gain from selection (Wright's and Warner's models), and number of effective factors (Wright's, Mather's, and Lande's models). SASQuant makes use of traditional genetic models and allows for their easy application to complex data sets. SASQuant is freely available and is intended for scientists studying quantitative traits in plant populations.     

The importance of stereoselective determination of drugs in the clinical laboratory

About 56% of the drugs currently in use are chiral compounds, and 88% of these chiral synthetic drugs are used therapeutically as racemates. Only a few of these drugs qualify for a stereospecific determination in a clinical laboratory for therapeutic drug monitoring of patients. If the qualitative and quantitative pharmacokinetic and pharmacodynamic effects are similar, the enantiomers do not need to be separated. However, if the metabolism of the different stereoisomers is handled by different enzymes which are either polymorphic or can be induced or inhibited, and if their pharmacodynamic effects have differences either in strength or in quality, enantiospecific analysis is urgently needed. Unfortunately, there are many racemic drugs where the stereospecificity of the metabolism and/or the pharmacodynamic effects of the enantiomers is not known today. For these drugs, there is a great need for studies concentrating on these differences to improve treatment of the patients.     

Systemic investigation of a brain-centered model of the human energy metabolism

The regulation of the human energy metabolism is crucial to ensure the functionality of the entire organism. Deregulations may lead to severe pathologies such as diabetes mellitus and obesity. The decisive role of the brain as active controller and heavy consumer in the complex whole-body energy metabolism is the object of recent research. Latest studies suggest the priority of the brain energy supply in the competition between brain and body periphery for the available energy resources. In this paper, a systemic investigation of the human energy metabolism is presented which consists of a compartment model including periphery, blood, and brain as well as signaling paths via insulin, appetite, and ingestion. The presented dynamical system particularly contains the competition for energy between brain and body periphery. Characteristically, the hormone insulin is regarded as central feedback signal of the brain. The model realistically reproduces the qualitative behavior of the energy metabolism. Short-time observations demonstrate the physiological periodic food intake generating the typical oscillating blood glucose variations. Integration over the daily cycle yields a long-term model which shows a stable behavior in accordance with the homeostatic regulation of the energy metabolism on a long-time scale. Two types of abstract constitutive equations describing the interaction between compartments and signals are taken into consideration. These are nonlinear and linear representatives from the class of feasible relations. The robustness of the model against the choice of the representative relation is linked to evolutionary stability of existing organisms.     

Impact of chemo-therapy on optimal control of malaria disease with infected immigrants

We derived and analyzed rigorously a mathematical model that describes the dynamics of malaria infection with the recruitment of infected immigrants, treatment of infectives and spray of insecticides against mosquitoes in the population. Both qualitative and quantitative analysis of the deterministic model are performed with respect to stability of the disease free and endemic equilibria. It is found that in the absence of infected immigrants disease-free equilibrium is achievable and is locally asymptotically stable. Using Pontryagin's Maximum Principle, the optimal strategies for disease control are established. Finally, numerical simulations are performed to illustrate the analytical results.     

On the reliability of a simple method for scoring phenotypes to estimate heritability: A case study with pupal color in Heliconius erato phyllis, Fabricius 1775 (Lepidoptera, Nymphalidae)

In this paper, two methods for assessing the degree of melanization of pupal exuviae from the butterfly Heliconius erato phyllis, Fabricius 1775 (Lepidoptera, Nymphalidae, Heliconiini) are compared. In the first method, which was qualitative, the exuviae were classified by scoring the degree of melanization, whereas in the second method, which was quantitative, the exuviae were classified by optical density followed by analysis with appropriate software. The heritability (h(2)) of the degree of melanization was estimated by regression and analysis of variance. The estimates of h (2) were similar with both methods, indicating that the qualitative method could be particularly suitable for field work. The low estimates obtained for heritability may have resulted from the small sample size (n = 7-18 broods, including the parents) or from the allocation-priority hypothesis in which pupal color would be a lower priority trait compared to morphological traits and adequate larval development.     

Oxidative DNA damage background estimated by a system model of base excision repair

Human DNA can be damaged by natural metabolism through free radical production. It has been suggested that the equilibrium between innate damage and cellular DNA repair results in an oxidative DNA damage background that potentially contributes to disease and aging. Efforts to quantitatively characterize the human oxidative DNA damage background level, based on measuring 8-oxoguanine lesions as a biomarker, have led to estimates that vary over three to four orders of magnitude, depending on the method of measurement. We applied a previously developed and validated quantitative pathway model of human DNA base excision repair, integrating experimentally determined endogenous damage rates and model parameters from multiple sources. Our estimates of at most 100 8-oxoguanine lesions per cell are consistent with the low end of data from biochemical and cell biology experiments, a result robust to model limitations and parameter variation. Our findings show the power of quantitative system modeling to interpret composite experimental data and make biologically and physiologically relevant predictions for complex human DNA repair pathway mechanisms and capacity.