Ingeneue: a versatile tool for reconstituting genetic networks,with examples from the segment polarity network

Here we describe a software tool for synthesizing molecular genetic data into models of genetic networks. Our software program Ingeneue, written in Java, lets the user quickly turn a map of a genetic network into a dynamical model consisting of a set of ordinary differential equations. We developed Ingeneue as part of an ongoing effort to explore the design and evolvability of genetic networks. Ingeneue has three principal advantages over other available mathematical software: it automates instantiation of the same network model in each cell in a 2-D sheet of cells; it constructs model equations from pre-made building blocks corresponding to common biochemical processes; and it automates searches through parameter space, sensitivity analyses, and other common tasks. Here we discuss the structure of the software and some of the issues we have dealt with. We conclude with some examples of results we have achieved with Ingeneue for the Drosophila segment polarity network.     

Modelling tropical fire ant (<Emphasis Type="Italic">Solenopsis geminata</Emphasis>) dynamics and detection to inform an eradication project

Invasive species threaten endangered species worldwide and substantial effort is focused on their control. Eradication projects require critical resource allocation decisions, as they affect both the likelihood of success and the overall cost. However, these complex decisions must often be made within data-poor environments. Here we develop a mathematical framework to assist in resource allocation for invasive species control projects and we apply it to the proposed eradication of the tropical fire ant (Solenopsis geminata) from the islands of Ashmore Reef in the Timor Sea. Our framework contains two models: a population model and a detection model. Our stochastic population model is used to predict ant abundance through time and allows us to estimate the probability of eradication. Using abundance predictions from the population model, we use the detection model to predict the probability of ant detection through time. These models inform key decisions throughout the project, which include deciding how many baiting events should take place, deciding whether to invest in detector dogs and setting surveillance effort to confirm eradication following control. We find that using a combination of insect growth regulator and toxins are required to achieve a high probability of eradication over 2 years, and we find that using two detector dogs may be more cost-effective than the use of lure deployment, provided that they are used across the life of the project. Our analysis lays a foundation for making decisions about control and detection throughout the project and provides specific advice about resource allocation.     

JSBML: a flexible Java library for working with SBML

SUMMARY: The specifications of the Systems Biology Markup Language (SBML) define standards for storing and exchanging computer models of biological processes in text files. In order to perform model simulations, graphical visualizations and other software manipulations, an in-memory representation of SBML is required. We developed JSBML for this purpose. In contrast to prior implementations of SBML APIs, JSBML has been designed from the ground up for the Java programming language, and can therefore be used on all platforms supported by a Java Runtime Environment. This offers important benefits for Java users, including the ability to distribute software as Java Web Start applications. JSBML supports all SBML Levels and Versions through Level 3 Version 1, and we have strived to maintain the highest possible degree of compatibility with the popular library libSBML. JSBML also supports modules that can facilitate the development of plugins for end user applications, as well as ease migration from a libSBML-based backend. AVAILABILITY: Source code, binaries and documentation for JSBML can be freely obtained under the terms of the LGPL 2.1 from the website http://sbml.org/Software/JSBML.     

A toolbox for developing bioinformatics software

Creating useful software is a major activity of many scientists, including bioinformaticians. Nevertheless, software development in an academic setting is often unsystematic, which can lead to problems associated with maintenance and long-term availibility. Unfortunately, well-documented software development methodology is difficult to adopt, and technical measures that directly improve bioinformatic programming have not been described comprehensively. We have examined 22 software projects and have identified a set of practices for software development in an academic environment. We found them useful to plan a project, support the involvement of experts (e.g. experimentalists), and to promote higher quality and maintainability of the resulting programs. This article describes 12 techniques that facilitate a quick start into software engineering. We describe 3 of the 22 projects in detail and give many examples to illustrate the usage of particular techniques. We expect this toolbox to be useful for many bioinformatics programming projects and to the training of scientific programmers.     

A hierarchical Bayesian Beta regression approach to study the effects of geographical genetic structure and spatial autocorrelation on species distribution range shifts

Global climate change (GCC) may be causing distribution range shifts in many organisms worldwide. Multiple efforts are currently focused on the development of models to better predict distribution range shifts due to GCC. We addressed this issue by including intraspecific genetic structure and spatial autocorrelation (SAC) of data in distribution range models. Both factors reflect the joint effect of ecoevolutionary processes on the geographical heterogeneity of populations. We used a collection of 301 georeferenced accessions of the annual plant Arabidopsis thaliana in its Iberian Peninsula range, where the species shows strong geographical genetic structure. We developed spatial and nonspatial hierarchical Bayesian models (HBMs) to depict current and future distribution ranges for the four genetic clusters detected. We also compared the performance of HBMs with Maxent (a presence‐only model). Maxent and nonspatial HBMs presented some shortcomings, such as the loss of accessions with high genetic admixture in the case of Maxent and the presence of residual SAC for both. As spatial HBMs removed residual SAC, these models showed higher accuracy than nonspatial HBMs and handled the spatial effect on model outcomes. The ease of modelling and the consistency among model outputs for each genetic cluster was conditioned by the sparseness of the populations across the distribution range. Our HBMs enrich the toolbox of software available to evaluate GCC‐induced distribution range shifts by considering both genetic heterogeneity and SAC, two inherent properties of any organism that should not be overlooked.     

Large-scale bi-level strain design approaches and mixed-integer programming solution techniques

The use of computational models in metabolic engineering has been increasing as more genome-scale metabolic models and computational approaches become available. Various computational approaches have been developed to predict how genetic perturbations affect metabolic behavior at a systems level, and have been successfully used to engineer microbial strains with improved primary or secondary metabolite production. However, identification of metabolic engineering strategies involving a large number of perturbations is currently limited by computational resources due to the size of genome-scale models and the combinatorial nature of the problem. In this study, we present (i) two new bi-level strain design approaches using mixed-integer programming (MIP), and (ii) general solution techniques that improve the performance of MIP-based bi-level approaches. The first approach (SimOptStrain) simultaneously considers gene deletion and non-native reaction addition, while the second approach (BiMOMA) uses minimization of metabolic adjustment to predict knockout behavior in a MIP-based bi-level problem for the first time. Our general MIP solution techniques significantly reduced the CPU times needed to find optimal strategies when applied to an existing strain design approach (OptORF) (e.g., from ～10 days to ～5 minutes for metabolic engineering strategies with 4 gene deletions), and identified strategies for producing compounds where previous studies could not (e.g., malate and serine). Additionally, we found novel strategies using SimOptStrain with higher predicted production levels (for succinate and glycerol) than could have been found using an existing approach that considers network additions and deletions in sequential steps rather than simultaneously. Finally, using BiMOMA we found novel strategies involving large numbers of modifications (for pyruvate and glutamate), which sequential search and genetic algorithms were unable to find. The approaches and solution techniques developed here will facilitate the strain design process and extend the scope of its application to metabolic engineering.     

The Quetzal Coalescence template library: A C++ programmers resource for integrating distributional,demographic and coalescent models

Genetic samples can be used to understand and predict the behaviour of species living in a fragmented and temporally changing environment. In this regard, models of coalescence conditioned to an environment through an explicit modelling of population growth and migration have been developed in recent years, and simulators implementing these models have been developed, enabling biologists to estimate parameters of interest with Approximate Bayesian Computation techniques. However, model choice remains limited, and developing new coalescence simulators is extremely time consuming because code re‐use is limited. We present Quetzal, a C++ library composed of re‐usable components, which is sufficiently general to efficiently implement a wide range of spatially explicit coalescence‐based environmental models of population genetics and to embed the simulation in an Approximate Bayesian Computation framework. Quetzal is not a simulation program, but a toolbox for programming simulators aimed at the community of scientific coders and research software engineers in molecular ecology and phylogeography. This new code resource is open‐source and available at https://becheler.github.io/pages/quetzal.html along with other documentation resources.     

The quantitative single-neuron modeling competition

As large-scale, detailed network modeling projects are flourishing in the field of computational neuroscience, it is more and more important to design single neuron models that not only capture qualitative features of real neurons but are quantitatively accurate in silico representations of those. Recent years have seen substantial effort being put in the development of algorithms for the systematic evaluation and optimization of neuron models with respect to electrophysiological data. It is however difficult to compare these methods because of the lack of appropriate benchmark tests. Here, we describe one such effort of providing the community with a standardized set of tests to quantify the performances of single neuron models. Our effort takes the form of a yearly challenge similar to the ones which have been present in the machine learning community for some time. This paper gives an account of the first two challenges which took place in 2007 and 2008 and discusses future directions. The results of the competition suggest that best performance on data obtained from single or double electrode current or conductance injection is achieved by models that combine features of standard leaky integrate-and-fire models with a second variable reflecting adaptation, refractoriness, or a dynamic threshold.     

Predicting the compliance of small diameter vascular grafts from uniaxial tensile tests

The compliance hypothesis states that the compliance of vascular grafts should match that of the host artery for optimal patency. Although this has not been proven, the literature shows that much effort has gone into measuring compliance. Uniaxial circumferential tensile tests are simpler than compliance tests, but do not give the compliance (a multiaxial property) directly. Therefore, we have used mechanical models to correlate the two. Simple models suffer from inappropriate simplifying assumptions. In a clinically useful range, a Laplace law model and an incremental elasticity model do not predict the compliance from the rigidity as well as does a model derived from finite elasticity. This latter model has helped locate sources of errors. Variations in graft thickness, diameter, and anisotropy may be responsible for scatter in the experimental correlations between compliance and uniaxial rigidity.     

Animal models of cholangiocarcinoma

Cholangiocarcinoma (CCA) is an aggressive biliary tract malignancy with a poor overall prognosis. There is a critical need to develop effective targeted therapies for the treatment of this lethal disease. In an effort to address this challenge, preclinical in vivo studies have become paramount in understanding CCA carcinogenesis, progression, and therapy. Various CCA animal models exist including carcinogen-based models in which animals develop CCA after exposure to a carcinogen, genetically engineered mouse models in which genetic changes are induced in mice leading to CCA, murine syngeneic orthotopic models, as well as xenograft tumors derived from xenotransplantation of CCA cells, organoids, and patient-derived tissue. Each type has distinct advantages as well as shortcomings. In the ideal animal model of CCA, the tumor arises from the biliary tract in an immunocompetent host with a species-matched tumor microenvironment. Such a model would also be time-efficient, recapitulate the genetic and histopathological features of human CCA, and predict therapeutic response in humans. Recently developed biliary tract transduction and orthotopic syngeneic transplant mouse models encompass several of these elements. Herein, we review the different animal models of CCA, their advantages and deficiencies, as well as features which mimic human CCA.     

A review on Monte Carlo simulation methods as they apply to mutation and selection as formulated in Wright-Fisher models of evolutionary genetics

A case has made for the use of Monte Carlo simulation methods when the incorporation of mutation and natural selection into Wright-Fisher gametic sampling models renders then intractable from the standpoint of classical mathematical analysis. The paper has been organized around five themes. Among these themes was that of scientific openness and a clear documentation of the mathematics underlying the software so that the results of any Monte Carlo simulation experiment may be duplicated by any interested investigator in a programming language of his choice. A second theme was the disclosure of the random number generator used in the experiments to provide critical insights as to whether the generated uniform random variables met the criterion of independence satisfactorily. A third theme was that of a review of recent literature in genetics on attempts to find signatures of evolutionary processes such as natural selection, among the millions of segments of DNA in the human genome, that may help guide the search for new drugs to treat diseases. A fourth theme involved formalization of Wright-Fisher processes in a simple form that expedited the writing of software to run Monte Carlo simulation experiments. Also included in this theme was the reporting of several illustrative Monte Carlo simulation experiments for the cases of two and three alleles at some autosomal locus, in which attempts were to made to apply the theory of Wright-Fisher models to gain some understanding as to how evolutionary signatures may have developed in the human genome and those of other diploid species. A fifth theme was centered on recommendations that more demographic factors, such as non-constant population size, be included in future attempts to develop computer models dealing with signatures of evolutionary process in genomes of various species. A brief review of literature on the incorporation of demographic factors into genetic evolutionary models was also included to expedite and stimulate further development on this theme.     

MSMBuilder: Statistical Models for Biomolecular Dynamics

MSMBuilder is a software package for building statistical models of high-dimensional time-series data. It is designed with a particular focus on the analysis of atomistic simulations of biomolecular dynamics such as protein folding and conformational change. MSMBuilder is named for its ability to construct Markov state models (MSMs), a class of models that has gained favor among computational biophysicists. In addition to both well-established and newer MSM methods, the package includes complementary algorithms for understanding time-series data such as hidden Markov models and time-structure based independent component analysis. MSMBuilder boasts an easy to use command-line interface, as well as clear and consistent abstractions through its Python application programming interface. MSMBuilder was developed with careful consideration for compatibility with the broader machine learning community by following the design of scikit-learn. The package is used primarily by practitioners of molecular dynamics, but is just as applicable to other computational or experimental time-series measurements.     

Phenomenological and molecular-level Petri net modeling and simulation of long-term potentiation

Petri net-based modeling methods have been used in many research projects to represent biological systems. Among these, the hybrid functional Petri net (HFPN) was developed especially for biological modeling in order to provide biologists with a more intuitive Petri net-based method. In the literature, HFPNs are used to represent kinetic models at the molecular level. We present two models of long-term potentiation previously represented by differential equations which we have transformed into HFPN models: a phenomenological synapse model and a molecular-level model of the CaMKII regulation pathway. Through simulation, we obtained results similar to those of previous studies using these models. Our results open the way to a new type of modeling for systems biology where HFPNs are used to combine different levels of abstraction within one model. This approach can be useful in fully modeling a system at the molecular level when kinetic data is missing or when a full study of a system at the molecular level it is not within the scope of the research.     

Distribution models of estuarine fish species: The effect of sampling bias,species ecology and threshold selection on models' accuracy

Species distribution models (SDMs) relate presence/absence data to environmental variables, allowing to predict species environmental requirements and potential distribution. They have been increasingly used in fields such as ecology, biogeography and evolution, and often support conservation priorities and strategies. Thus, it becomes crucial to understand how trustworthy and reliable their predictions are. Different approaches, such as using ensemble methods (combining forecasts of different single models), or applying the most suitable threshold to transform continuous probability maps into species presences or absences, have been used to reduce model-based uncertainty. Taking into account the influence of biased sampling imprecision in species location, small datasets and species ecological characteristics, may also help to detect and compensate for uncertainty in the model building process. To investigate the effect of applying an ensemble approach, several threshold selection criteria and different datasets representing seasonal and spatial sampling bias, on models' accuracy, SDMs were built for four estuarine fish species with distinct use of the estuarine systems. Overall, predictions obtained with the ensemble approach were more accurate. Variability in accuracy metrics obtained with the nine threshold selection criteria applied was more pronounced for species with low prevalence and when sensitivity was calculated. Higher values of accuracy measures were registered with the threshold that maximizes the sum of sensitivity and specificity, and the threshold where the predicted prevalence equals the observed, whereas the 0.5 cut-off was unreliable, originating the lowest values for these metrics. Accuracy of models created from a spatially biased sampling was overall higher than accuracy of models created with a seasonally biased sampling or with the multi-year database created and this pattern was consistently obtained for marine migrant species, which use estuaries as nursery areas, presenting a seasonally and regular use of these ecosystems. The ecological dependence between these fish species and estuaries may add difficulties in the model building process, and needs to be taken into account, to improve their accuracy. The present study highlights the need for a thorough analysis of the critical underlying issues of the complete model building process to predict the distribution of estuarine fish species, due to the particular and dynamic nature of these ecosystems.     

Effect of optimization criterion on spinal force estimates during asymmetric lifting

Optimization-based muscle force prediction models of the lumbar region are used in research and ergonomic practice, usually as a subroutine of a job analysis software package. Various optimization criteria have been put forward for use in rationally selecting a set of muscle forces to satisfy moment equilibrium, including the sum of cubed muscle contraction intensities and a double linear programming procedure for minimizing the spinal compression force and maximum muscle contraction intensity. A laboratory study was conducted to determine whether these two model formulations produce significantly different estimates of spinal forces for a dynamic asymmetric lift. Although statistically significant differences were found between the predictions of the two models, the difference in peak spinal compression force was only 1.1%.     

General quantitative genetic methods for comparative biology: phylogenies,taxonomies and multi‐trait models for continuous and categorical characters

Although many of the statistical techniques used in comparative biology were originally developed in quantitative genetics, subsequent development of comparative techniques has progressed in relative isolation. Consequently, many of the new and planned developments in comparative analysis already have well‐tested solutions in quantitative genetics. In this paper, we take three recent publications that develop phylogenetic meta‐analysis, either implicitly or explicitly, and show how they can be considered as quantitative genetic models. We highlight some of the difficulties with the proposed solutions, and demonstrate that standard quantitative genetic theory and software offer solutions. We also show how results from Bayesian quantitative genetics can be used to create efficient Markov chain Monte Carlo algorithms for phylogenetic mixed models, thereby extending their generality to non‐Gaussian data. Of particular utility is the development of multinomial models for analysing the evolution of discrete traits, and the development of multi‐trait models in which traits can follow different distributions. Meta‐analyses often include a nonrandom collection of species for which the full phylogenetic tree has only been partly resolved. Using missing data theory, we show how the presented models can be used to correct for nonrandom sampling and show how taxonomies and phylogenies can be combined to give a flexible framework with which to model dependence.