Computer-aided studies on the regulation of oxidative phosphorylation during work transitions

In the present polemic paper the application of computer models of oxidative phosphorylation (OXPHOS) in heart, skeletal muscle and liver to the studies on the regulation of the bioenergetic system in intact cells during work transitions is discussed. Two groups of such models are compared: group I models that involve only a direct activation of ATP usage by Ca²⁺, and group II models that assume a direct activation by some (probably) Ca²⁺-related mechanism of essentially all steps of the system. It is argued that group II models reproduce much better a broad range of variable values and system properties encountered in experimental studies. The consequences of the theoretical and experimental development of Metabolic Control Analysis, within the framework of which it has been shown that the control over the flux through the oxidative phosphorylation system is shared by essentially all components of this system, are analyzed. In particular, it is argued that in order to increase the flux very significantly, and at the same time to maintain relatively constant concentrations of such intermediate metabolites as ADP, ATP, P_i, PCr and NADH, it is necessary to activate directly many, if not all components of the system (the ‘multi-step parallel activation’ mechanism). Generally, it is suggested that this is not a particular form or complexity of computer models, but rather their agreement with a broad range of experimental data concerning ‘macroscopic’ system properties that really matters.The specificity of the regulation of the energetic system of pancreatic β-cells is discussed.     

Ranked retrieval of Computational Biology models

Background  

The study of biological systems demands computational support. If targeting a biological problem, the reuse of existing computational models can save time and effort. Deciding for potentially suitable models, however, becomes more challenging with the increasing number of computational models available, and even more when considering the models' growing complexity. Firstly, among a set of potential model candidates it is difficult to decide for the model that best suits ones needs. Secondly, it is hard to grasp the nature of an unknown model listed in a search result set, and to judge how well it fits for the particular problem one has in mind.     

ModEco: an integrated software package for ecological niche modeling

ModEco is a software package for ecological niche modeling. It integrates a range of niche modeling methods within a geographical information system. ModEco provides a user friendly platform that enables users to explore, analyze, and model species distribution data with relative ease. ModEco has several unique features: 1) it deals with different types of ecological observation data, such as presence and absence data, presence‐only data, and abundance data; 2) it provides a range of models when dealing with presence‐only data, such as presence‐only models, pseudo‐absence models, background vs presence data models, and ensemble models; and 3) it includes relatively comprehensive tools for data visualization, feature selection, and accuracy assessment.     

Rheumatoid arthritis viewed using a headache paradigm

Results and new hypotheses in animal models often stimulate development of new paradigms in how we view rheumatoid arthritis (RA). The complexity of RA does, however, eventually lead to the rejection of these hypotheses. Here, it is argued that the large number of so-far described animal models, when taken together, also reveals a complex disease. Fortunately, detailed study of each of the animal models will reveal this complexity, and may also be helpful in elucidating the complexity of the human disease. Benoist and Mathis [1] recently contributed a new animal model in which an autoimmune response to a ubiquitous antigen leads to an antibody-mediated inflammatory attack in the joints. It is argued that this new model, as with other animal models, is unlikely to explain RA, but it will add to the tools available to reveal the complexity of RA.     

Extracting non-linear integrate-and-fire models from experimental data using dynamic I–V curves

The dynamic I–V curve method was recently introduced for the efficient experimental generation of reduced neuron models. The method extracts the response properties of a neuron while it is subject to a naturalistic stimulus that mimics in vivo-like fluctuating synaptic drive. The resulting history-dependent, transmembrane current is then projected onto a one-dimensional current–voltage relation that provides the basis for a tractable non-linear integrate-and-fire model. An attractive feature of the method is that it can be used in spike-triggered mode to quantify the distinct patterns of post-spike refractoriness seen in different classes of cortical neuron. The method is first illustrated using a conductance-based model and is then applied experimentally to generate reduced models of cortical layer-5 pyramidal cells and interneurons, in injected-current and injected- conductance protocols. The resulting low-dimensional neuron models—of the refractory exponential integrate-and-fire type—provide highly accurate predictions for spike-times. The method therefore provides a useful tool for the construction of tractable models and rapid experimental classification of cortical neurons.     

A coefficient of determination (R2) for generalized linear mixed models

Extensions of linear models are very commonly used in the analysis of biological data. Whereas goodness of fit measures such as the coefficient of determination (R²) or the adjusted R² are well established for linear models, it is not obvious how such measures should be defined for generalized linear and mixed models. There are by now several proposals but no consensus has yet emerged as to the best unified approach in these settings. In particular, it is an open question how to best account for heteroscedasticity and for covariance among observations present in residual error or induced by random effects. This paper proposes a new approach that addresses this issue and is universally applicable for arbitrary variance‐covariance structures including spatial models and repeated measures. It is exemplified using three biological examples.     

A Comparison of Six DNA Bending Models

Abstract

The predictions of six DNA bending models were compared with experimental relative mobility data. The study showed that all the models are reasonably accurate in predicting bending in synthetic sequences and in a natural sequence. The least accurate of these models is the Calladine-Dickerson model. The most consistent model is the ApA Wedge, possibly because it distributes the bends into base-roll and base-tilt components.     

A Statistical Model for In Vivo Neuronal Dynamics

Single neuron models have a long tradition in computational neuroscience. Detailed biophysical models such as the Hodgkin-Huxley model as well as simplified neuron models such as the class of integrate-and-fire models relate the input current to the membrane potential of the neuron. Those types of models have been extensively fitted to in vitro data where the input current is controlled. Those models are however of little use when it comes to characterize intracellular in vivo recordings since the input to the neuron is not known. Here we propose a novel single neuron model that characterizes the statistical properties of in vivo recordings. More specifically, we propose a stochastic process where the subthreshold membrane potential follows a Gaussian process and the spike emission intensity depends nonlinearly on the membrane potential as well as the spiking history. We first show that the model has a rich dynamical repertoire since it can capture arbitrary subthreshold autocovariance functions, firing-rate adaptations as well as arbitrary shapes of the action potential. We then show that this model can be efficiently fitted to data without overfitting. We finally show that this model can be used to characterize and therefore precisely compare various intracellular in vivo recordings from different animals and experimental conditions.     

From elaborate to compact seasonal plant epidemic models and back: is competitive exclusion in the details?

Seasonality, or periodic host absence, is a central feature in plant epidemiology. In this respect, seasonal plant epidemic models take into account the way the parasite overwinters and generates new infections. These are termed primary infections. In the literature, one finds two classes of models: high-dimensional elaborate models and low-dimensional compact models, where primary infection dynamics are explicit and implicit, respectively. Investigating a compact model allowed previous authors to show the existence of a competitive exclusion principle. However, the way compact models derive from elaborate models has not been made explicit yet. This makes it unclear whether results such as competitive exclusion extend to elaborate models as well. Here, we show that assuming primary infection dynamics are fast in a standard elaborate model translates into a compact form. Yet, it is not that usually found in the literature. Moreover, we numerically show that coexistence is possible in this original compact form. Reversing the question, we show that the usual compact form approximates an alternate elaborate model, which differs from the earlier one in that primary infection dynamics are density dependent. We discuss to which extent these results shed light on coexistence within soil- and air-borne plant parasites, such as within the take-all disease of wheat and the grapevine powdery mildew cryptic species complexes, respectively.     

Robust patterns in the stochastic organization of filopodia

Background  

Filopodia are actin-based cellular projections that have a critical role in initiating and sustaining directional migration in vertebrate cells. Filopodia are highly dynamic structures that show a rich diversity in appearance and behavior. While there are several mathematical models of filopodia initiation and growth, testing the capacity of these theoretical models in predicting empirical behavior has been hampered by a surprising shortage of quantitative data related to filopodia. Neither is it clear how quantitatively robust the cellular filopodial network is and how perturbations alter it.     

L1 Penalized Estimation in the Cox Proportional Hazards Model

This article presents a novel algorithm that efficiently computes L₁ penalized (lasso) estimates of parameters in high‐dimensional models. The lasso has the property that it simultaneously performs variable selection and shrinkage, which makes it very useful for finding interpretable prediction rules in high‐dimensional data. The new algorithm is based on a combination of gradient ascent optimization with the Newton–Raphson algorithm. It is described for a general likelihood function and can be applied in generalized linear models and other models with an L₁ penalty. The algorithm is demonstrated in the Cox proportional hazards model, predicting survival of breast cancer patients using gene expression data, and its performance is compared with competing approaches. An R package, penalized , that implements the method, is available on CRAN.     

In vitro models of angiogenesis and vasculogenesis in fibrin gel

In vitro models of endothelial assembly into microvessels are useful for the study of angiogenesis and vasculogenesis. In addition, such models may be used to provide the microvasculature required to sustain engineered tissues. A large range of in vitro models of both angiogenesis and vasculogenesis have utilized fibrin gel as a scaffold. Although fibrin gel is conducive to endothelial assembly, its ultrastructure varies substantially based on the gel formulation and gelation conditions, making it challenging to compare between models. This work reviews existing models of endothelial assembly in fibrin gel and posits that differerences between models are partially caused by microstructural differences in fibrin gel.     

“Wrong,but Useful”: Negotiating Uncertainty in Infectious Disease Modelling

For infectious disease dynamical models to inform policy for containment of infectious diseases the models must be able to predict; however, it is well recognised that such prediction will never be perfect. Nevertheless, the consensus is that although models are uncertain, some may yet inform effective action. This assumes that the quality of a model can be ascertained in order to evaluate sufficiently model uncertainties, and to decide whether or not, or in what ways or under what conditions, the model should be ‘used’. We examined uncertainty in modelling, utilising a range of data: interviews with scientists, policy-makers and advisors, and analysis of policy documents, scientific publications and reports of major inquiries into key livestock epidemics. We show that the discourse of uncertainty in infectious disease models is multi-layered, flexible, contingent, embedded in context and plays a critical role in negotiating model credibility. We argue that usability and stability of a model is an outcome of the negotiation that occurs within the networks and discourses surrounding it. This negotiation employs a range of discursive devices that renders uncertainty in infectious disease modelling a plastic quality that is amenable to ‘interpretive flexibility’. The utility of models in the face of uncertainty is a function of this flexibility, the negotiation this allows, and the contexts in which model outputs are framed and interpreted in the decision making process. We contend that rather than being based predominantly on beliefs about quality, the usefulness and authority of a model may at times be primarily based on its functional status within the broad social and political environment in which it acts.     

Model Selection in Systems Biology Depends on Experimental Design

Experimental design attempts to maximise the information available for modelling tasks. An optimal experiment allows the inferred models or parameters to be chosen with the highest expected degree of confidence. If the true system is faithfully reproduced by one of the models, the merit of this approach is clear - we simply wish to identify it and the true parameters with the most certainty. However, in the more realistic situation where all models are incorrect or incomplete, the interpretation of model selection outcomes and the role of experimental design needs to be examined more carefully. Using a novel experimental design and model selection framework for stochastic state-space models, we perform high-throughput in-silico analyses on families of gene regulatory cascade models, to show that the selected model can depend on the experiment performed. We observe that experimental design thus makes confidence a criterion for model choice, but that this does not necessarily correlate with a model''s predictive power or correctness. Finally, in the special case of linear ordinary differential equation (ODE) models, we explore how wrong a model has to be before it influences the conclusions of a model selection analysis.     

MetaMQAP: A meta-server for the quality assessment of protein models

Background  

Computational models of protein structure are usually inaccurate and exhibit significant deviations from the true structure. The utility of models depends on the degree of these deviations. A number of predictive methods have been developed to discriminate between the globally incorrect and approximately correct models. However, only a few methods predict correctness of different parts of computational models. Several Model Quality Assessment Programs (MQAPs) have been developed to detect local inaccuracies in unrefined crystallographic models, but it is not known if they are useful for computational models, which usually exhibit different and much more severe errors.     

The influence of the type 3 (sigmoid) functional response upon the stability of predator-prey difference models

Sigmoid functional responses are found to exert a stabilizing influence upon a discrete-generation predator-prey model in a way analogous to that found in continuous predator-prey models. The precise effect depends upon the degree to which a predator's feeding history influences its reproductive success. The time delay intrinsic in difference equation models imposes constraints not found in differential models, however, it is shown that in an otherwise unstable model the inclusion of a sigmoid functional response can result in local stability. With the addition of prey self-regulation the stabilizing influence of the functional response acts in concert with self-regulation, as it does in continuous models. These results show that the effect of the sigmoid response upon stability is not dependent upon the assumption of continuity, and reinforces the view that sigmoid responses could be an important factor stabilizing natural communities.     

Gene prioritization and clustering by multi-view text mining

Background  

Text mining has become a useful tool for biologists trying to understand the genetics of diseases. In particular, it can help identify the most interesting candidate genes for a disease for further experimental analysis. Many text mining approaches have been introduced, but the effect of disease-gene identification varies in different text mining models. Thus, the idea of incorporating more text mining models may be beneficial to obtain more refined and accurate knowledge. However, how to effectively combine these models still remains a challenging question in machine learning. In particular, it is a non-trivial issue to guarantee that the integrated model performs better than the best individual model.     

A Bayesian Chi‐Squared Goodness‐of‐Fit Test for Censored Data Models

Summary We propose a Bayesian chi‐squared model diagnostic for analysis of data subject to censoring. The test statistic has the form of Pearson's chi‐squared test statistic and is easy to calculate from standard output of Markov chain Monte Carlo algorithms. The key innovation of this diagnostic is that it is based only on observed failure times. Because it does not rely on the imputation of failure times for observations that have been censored, we show that under heavy censoring it can have higher power for detecting model departures than a comparable test based on the complete data. In a simulation study, we show that tests based on this diagnostic exhibit comparable power and better nominal Type I error rates than a commonly used alternative test proposed by Akritas (1988, Journal of the American Statistical Association 83, 222–230). An important advantage of the proposed diagnostic is that it can be applied to a broad class of censored data models, including generalized linear models and other models with nonidentically distributed and nonadditive error structures. We illustrate the proposed model diagnostic for testing the adequacy of two parametric survival models for Space Shuttle main engine failures.     

Some reflections on evolutionary theories,With a classification of fitness

Using a classical life history model (the Smith & Fretwell model of the evolution of offspring size), it is demonstrated that even in the presence of overwhelming empirical support, the testability of predictions derived from evolutionary models can give no guarantee that the underlying fitness concept is sound. Non-awareness of this problem may cause considerable justified but avoidable criticism. To help understanding the variable use of fitness in evolutionary models and recognizing potentially problematic areas which need careful consideration, a hierarchical classification of definitions of fitness used in evolutionary models is presented. As a conclusion, it is advocated to use the term fitness more conscientiously than currently often practised and to think more about ways to develop fitness-free evolutionary theories compatible with Darwin's ideas.     

Analytical methods for predicting the behaviour of population models with general spatial interactions

Many biologists use population models that are spatial, stochastic and individual based. Analytical methods that describe the behaviour of these models approximately are attracting increasing interest as an alternative to expensive computer simulation. The methods can be employed for both prediction and fitting models to data. Recent work has extended existing (mean field) methods with the aim of accounting for the development of spatial correlations. A common feature is the use of closure approximations for truncating the set of evolution equations for summary statistics. We investigate an analytical approach for spatial and stochastic models where individuals interact according to a generic function of their distance; this extends previous methods for lattice models with interactions between close neighbours, such as the pair approximation. Our study also complements work by Bolker and Pacala (BP) [Theor. Pop. Biol. 52 (1997) 179; Am. Naturalist 153 (1999) 575]: it treats individuals as being spatially discrete (defined on a lattice) rather than as a continuous mass distribution; it tests the accuracy of different closure approximations over parameter space, including the additive moment closure (MC) used by BP and the Kirkwood approximation. The study is done in the context of an susceptible-infected-susceptible epidemic model with primary infection and with secondary infection represented by power-law interactions. MC is numerically unstable or inaccurate in parameter regions with low primary infection (or density-independent birth rates). A modified Kirkwood approximation gives stable and generally accurate transient and long-term solutions; we argue it can be applied to lattice and to continuous-space models as a substitute for MC. We derive a generalisation of the basic reproduction ratio, R(0), for spatial models.