Automated Processing of Imaging Data through Multi-tiered Classification of Biological Structures Illustrated Using Caenorhabditis elegans

Quantitative imaging has become a vital technique in biological discovery and clinical diagnostics; a plethora of tools have recently been developed to enable new and accelerated forms of biological investigation. Increasingly, the capacity for high-throughput experimentation provided by new imaging modalities, contrast techniques, microscopy tools, microfluidics and computer controlled systems shifts the experimental bottleneck from the level of physical manipulation and raw data collection to automated recognition and data processing. Yet, despite their broad importance, image analysis solutions to address these needs have been narrowly tailored. Here, we present a generalizable formulation for autonomous identification of specific biological structures that is applicable for many problems. The process flow architecture we present here utilizes standard image processing techniques and the multi-tiered application of classification models such as support vector machines (SVM). These low-level functions are readily available in a large array of image processing software packages and programming languages. Our framework is thus both easy to implement at the modular level and provides specific high-level architecture to guide the solution of more complicated image-processing problems. We demonstrate the utility of the classification routine by developing two specific classifiers as a toolset for automation and cell identification in the model organism Caenorhabditis elegans. To serve a common need for automated high-resolution imaging and behavior applications in the C. elegans research community, we contribute a ready-to-use classifier for the identification of the head of the animal under bright field imaging. Furthermore, we extend our framework to address the pervasive problem of cell-specific identification under fluorescent imaging, which is critical for biological investigation in multicellular organisms or tissues. Using these examples as a guide, we envision the broad utility of the framework for diverse problems across different length scales and imaging methods.     

Dimensionality Reduction of Bistable Biological Systems

Time hierarchies, arising as a result of interactions between system’s components, represent a ubiquitous property of dynamical biological systems. In addition, biological systems have been attributed switch-like properties modulating the response to various stimuli across different organisms and environmental conditions. Therefore, establishing the interplay between these features of system dynamics renders itself a challenging question of practical interest in biology. Existing methods are suitable for systems with one stable steady state employed as a well-defined reference. In such systems, the characterization of the time hierarchies has already been used for determining the components that contribute to the dynamics of biological systems. However, the application of these methods to bistable nonlinear systems is impeded due to their inherent dependence on the reference state, which in this case is no longer unique. Here, we extend the applicability of the reference-state analysis by proposing, analyzing, and applying a novel method, which allows investigation of the time hierarchies in systems exhibiting bistability. The proposed method is in turn used in identifying the components, other than reactions, which determine the systemic dynamical properties. We demonstrate that in biological systems of varying levels of complexity and spanning different biological levels, the method can be effectively employed for model simplification while ensuring preservation of qualitative dynamical properties (i.e., bistability). Finally, by establishing a connection between techniques from nonlinear dynamics and multivariate statistics, the proposed approach provides the basis for extending reference-based analysis to bistable systems.     

Mathematical runtime analysis of ACO algorithms: survey on an emerging issue

The paper gives an overview on the status of the theoretical analysis of Ant Colony Optimization (ACO) algorithms, with a special focus on the analytical investigation of the runtime required to find an optimal solution to a given combinatorial optimization problem. First, a general framework for studying questions of this type is presented, and three important ACO variants are recalled within this framework. Secondly, two classes of formal techniques for runtime investigations of the considered type are outlined. Finally, some available runtime complexity results for ACO variants, referring to elementary test problems that have been introduced in the theoretical literature on evolutionary algorithms, are cited and discussed.     

Translated Chemical Reaction Networks

Many biochemical and industrial applications involve complicated networks of simultaneously occurring chemical reactions. Under the assumption of mass action kinetics, the dynamics of these chemical reaction networks are governed by systems of polynomial ordinary differential equations. The steady states of these mass action systems have been analyzed via a variety of techniques, including stoichiometric network analysis, deficiency theory, and algebraic techniques (e.g., Gröbner bases). In this paper, we present a novel method for characterizing the steady states of mass action systems. Our method explicitly links a network’s capacity to permit a particular class of steady states, called toric steady states, to topological properties of a generalized network called a translated chemical reaction network. These networks share their reaction vectors with their source network but are permitted to have different complex stoichiometries and different network topologies. We apply the results to examples drawn from the biochemical literature.     

Stability and parameter dependency analysis of a facilitation tectal column (FTC) model

Mathematical models and computer simulations have been widely used to study the spatio-temporal characteristics of the processing of information carried out by the central nervous system. When trying to show whether or not a neural model accounts for the phenomena under study, if the number of parameters whose values need to be calculated becomes large, then computer simulations alone become very inefficient to define such values. Here, we developed stability and parameter dependency analyses of the mathematical representation of a single facilitation tectal column (FTC) model, to show how by using techniques from non-linear systems theory we can define the ranges of parameter values under which the model would explain the required performance of the neural net model. The benefits of these analyses can be grouped in two parts: first, the advantage of using non-linear systems techniques to analyze, analytically, the dynamics of neural net models; and second, we get a deeper understanding of why the hypotheses embedded in the models yield the appropriate behaviors and what are the critical situations (parametric combinations) under which these behaviors are displayed.     

Delayed effects in aphid-parasitoid systems: Consequences for evaluating biological control species and their use in augmentation strategies

A particular feature of aphid-parasitoid systems is the existence of a delay between parasitisation (sting) and the death of the host (i.e. mummification). This biological trait is generally not considered important for population stability, except if the delay is very long, and hence it is ignored in most population dynamics studies. However, many crops have relatively short durations, and these time delays may have important consequences and cannot be ignored in a dynamics model. In this study, we are looking for the key-factors that influence an aphidparasitoid system population dynamics during a cropping cycle. Specifically, a simple model based on ordinary and delay differential equations and including biologically meaningful parameters was developed for aphidparasitoid systems and used to examine: (1) effect of biological characteristics of both the aphid and the parasitoid on their dynamics, (2) the effect of parasitoid augmentation on the dynamics of the system (e.g.: date, number and importance of the releases of parasitoids), and (3) to compare observedAphis gossypii — Lysiphlebus testaceipes dynamics in a cucumber crop to the predictions of the model. Good fits between the model and the field data were obtained and suggest that this model may be a powerful tool for selecting species of parasitoid and strategies for their use in biological control augmentation programs for aphid pest management.     

Spatio-temporal dynamics and quantification of daisyworld in two-dimensional coupled map lattices

We spatially extend the daisyworld model on a two-dimensional toroidal coupled map lattice (CML – a generalisation of cellular automata). We investigated whether this tightly coupled system of local nonlinear dynamics with bi-directional life-environment feedback can generate a specific kind of behaviour, characterised by global stability coexisting with local instability. We introduce appropriate metrics to measure the spatio-temporal dynamics of the daisyworld system. Specifically, we evaluate spatial autocorrelation using Moran's I, and local and global temporal fluctuation through the permutation entropy and the temporal standard deviation. We categorise a range of different behaviours that can arise in such scenarios, and relate them through a parameter analysis.     

Biological applications of the theory of birth-and-death processes

In this review, we discuss applications of the theory of birth-and-death processes to problems in biology, primarily, those of evolutionary genomics. The mathematical principles of the theory of these processes are briefly described. Birth-and-death processes, with some straightforward additions such as innovation, are a simple, natural and formal framework for modeling a vast variety of biological processes such as population dynamics, speciation, genome evolution, including growth of paralogous gene families and horizontal gene transfer and somatic evolution of cancers. We further describe how empirical data, e.g. distributions of paralogous gene family size, can be used to choose the model that best reflects the actual course of evolution among different versions of birth-death-and-innovation models. We conclude that birth-and-death processes, thanks to their mathematical transparency, flexibility and relevance to fundamental biological processes, are going to be an indispensable mathematical tool for the burgeoning field of systems biology.     

Boolean Models of Genomic Regulatory Networks: Reduction Mappings,Inference, and External Control

Computational modeling of genomic regulation has become an important focus of systems biology and genomic signal processing for the past several years. It holds the promise to uncover both the structure and dynamical properties of the complex gene, protein or metabolic networks responsible for the cell functioning in various contexts and regimes. This, in turn, will lead to the development of optimal intervention strategies for prevention and control of disease. At the same time, constructing such computational models faces several challenges. High complexity is one of the major impediments for the practical applications of the models. Thus, reducing the size/complexity of a model becomes a critical issue in problems such as model selection, construction of tractable subnetwork models, and control of its dynamical behavior. We focus on the reduction problem in the context of two specific models of genomic regulation: Boolean networks with perturbation (BN_P) and probabilistic Boolean networks (PBN). We also compare and draw a parallel between the reduction problem and two other important problems of computational modeling of genomic networks: the problem of network inference and the problem of designing external control policies for intervention/altering the dynamics of the model.     

Measuring collective behaviour of multicellular ensembles: role of space-time scales

Living systems are spectacular examples of spatiotemporally organized structures. During the development of complex organization there is dynamic equilibrium between the local and global processes acting at the intra-and intercellular levels in multiple space and time scales. Although in modelling studies such spatiotemporal systems can be described by different space-time scales and at many organizational levels, the experimental quantities measured and predictions useful for practical applications are at a macroscopic (coarser or averaged) level/scale; these are limited by the resolution of the measuring method and experimental protocol. In this work, we address whether the spatiotemporal collective dynamics exhibited by a multiscale system can discriminate between, or be borne out by, the coarse-grained and averaged measurements done at different spatial and temporal scales. Using a simple model of a ring of cells, we show that measurements of both spatial and spatiotemporal average behaviour in this multicellular ensemble can mask the variety of collective dynamics observed at other space-time scales, and exhibit completely different behaviours. Such outcomes of measurements can lead to incomplete and incorrect understanding of physiological functions and pathogenesis in multicell ensembles.     

Model membrane systems and their applications

The complexity of biological membranes has motivated the development of a wide variety of simpler model systems whose size, geometry, and composition can be tailored with great precision. Approaches highlighted in this review are illustrated in Figure 1 including vesicles, supported bilayers, and hybrid membrane systems. These have been used to study problems ranging from phase behavior to membrane fusion. Experimental membrane models continue to advance in complexity with respect to architecture, size, and composition, as do computer simulations of their properties and dynamics. Analytical techniques such as imaging secondary ion mass spectrometry have also been developed and refined to give increasing spatial resolution and information content on membrane composition and dynamics.     

Fluorescence microscopy for visualizing single-molecule protein dynamics

BackgroundSingle-molecule fluorescence imaging (smFI) has evolved into a valuable method used in biophysical and biochemical studies as it can observe the real-time behavior of individual protein molecules, enabling understanding of their detailed dynamic features. smFI is also closely related to other state-of-the-art microscopic methods, optics, and nanomaterials in that smFI and these technologies have developed synergistically.Scope of reviewThis paper provides an overview of the recently developed single-molecule fluorescence microscopy methods, focusing on critical techniques employed in higher-precision measurements in vitro and fluorescent nanodiamond, an emerging promising fluorophore that will improve single-molecule fluorescence microscopy.Major conclusionssmFI will continue to improve regarding the photostability of fluorophores and will develop via combination with other techniques based on nanofabrication, single-molecule manipulation, and so on.General significanceQuantitative, high-resolution single-molecule studies will help establish an understanding of protein dynamics and complex biomolecular systems.     

DA-TC: a novel application execution model in multicluster systems

The availability of a large number of separate clusters has given rise to the field of multicluster systems in which these resources are coupled to obtain their combined benefits to solve large-scale compute-intensive applications. However, it is challenging to achieve automatic load balancing of the jobs across these participating autonomic systems. We developed a novel user space execution model named DA-TC to address the workload allocation techniques for the applications with large number of sequential jobs in multicluster systems. Through this model, we can achieve dynamic load balancing for task assignment, and slower resources become beneficial factors rather than bottlenecks for application execution. The effectiveness of this strategy is demonstrated through theoretical analysis. This model is also evaluated through extensive experimental studies and the results show that when compared with the traditional method, the proposed DA-TC model can significantly improve the performance of application execution in terms of application turnaround time and system reliability in multicluster circumstances.     

Feeder use predicts both acquisition and transmission of a contagious pathogen in a North American songbird

Individual heterogeneity can influence the dynamics of infectious diseases in wildlife and humans alike. Thus, recent work has sought to identify behavioural characteristics that contribute disproportionately to individual variation in pathogen acquisition (super-receiving) or transmission (super-spreading). However, it remains unknown whether the same behaviours enhance both acquisition and transmission, a scenario likely to result in explosive epidemics. Here, we examined this possibility in an ecologically relevant host–pathogen system: house finches and their bacterial pathogen, Mycoplasma gallisepticum, which causes severe conjunctivitis. We examined behaviours likely to influence disease acquisition (feeder use, aggression, social network affiliations) in an observational field study, finding that the time an individual spends on bird feeders best predicted the risk of conjunctivitis. To test whether this behaviour also influences the likelihood of transmitting M. gallisepticum, we experimentally inoculated individuals based on feeding behaviour and tracked epidemics within captive flocks. As predicted, transmission was fastest when birds that spent the most time on feeders initiated the epidemic. Our results suggest that the same behaviour underlies both pathogen acquisition and transmission in this system and potentially others. Identifying individuals that exhibit such behaviours is critical for disease management.     

Investigating the nanostructure of a CER[NP]/CER[AP]-based stratum corneum lipid matrix model: A combined neutron diffraction & molecular dynamics simulations approach

The human skin provides a physiochemical and biological protective barrier due to the unique structure of its outermost layer known as the Stratum corneum. This layer consists of corneocytes and a multi-lamellar lipid matrix forming a composite, which is a major determining factor for the barrier function of the Stratum corneum. A substantiated understanding of this barrier is necessary, as controlled breaching or modulation of the same is also essential for various health and personal care applications such as topical drug delivery and cosmetics to a name few.In this study, we discuss the state-of-the-art of neutron diffraction techniques, using specifically deuterated lipids, combined with the information obtained from molecular models using molecular dynamics simulations, to understand the structure and barrier function of the Stratum corneum lipid matrix.As an example, the effect of ceramide concentration on a lipid lamella system consisting of CER[NP]/CER[AP]/Cholesterol/free fatty acid (deprotonated) is studied. This study demonstrates the usefulness of the combined approach of neutron diffraction and molecular dynamics simulations for effective analysis of the model systems created for the Stratum corneum lipid matrix. The optimization of force fields by comparison with experimental data is furthermore an important step in the direction of providing a predictive quality.     

Critical behaviour of the stochastic Wilson-Cowan model

Spontaneous brain activity is characterized by bursts and avalanche-like dynamics, with scale-free features typical of critical behaviour. The stochastic version of the celebrated Wilson-Cowan model has been widely studied as a system of spiking neurons reproducing non-trivial features of the neural activity, from avalanche dynamics to oscillatory behaviours. However, to what extent such phenomena are related to the presence of a genuine critical point remains elusive. Here we address this central issue, providing analytical results in the linear approximation and extensive numerical analysis. In particular, we present results supporting the existence of a bona fide critical point, where a second-order-like phase transition occurs, characterized by scale-free avalanche dynamics, scaling with the system size and a diverging relaxation time-scale. Moreover, our study shows that the observed critical behaviour falls within the universality class of the mean-field branching process, where the exponents of the avalanche size and duration distributions are, respectively, 3/2 and 2. We also provide an accurate analysis of the system behaviour as a function of the total number of neurons, focusing on the time correlation functions of the firing rate in a wide range of the parameter space.     

Accounting for Negative Automaintenance in Pigeons: A Dual Learning Systems Approach and Factored Representations

Animals, including Humans, are prone to develop persistent maladaptive and suboptimal behaviours. Some of these behaviours have been suggested to arise from interactions between brain systems of Pavlovian conditioning, the acquisition of responses to initially neutral stimuli previously paired with rewards, and instrumental conditioning, the acquisition of active behaviours leading to rewards. However the mechanics of these systems and their interactions are still unclear. While extensively studied independently, few models have been developed to account for these interactions. On some experiment, pigeons have been observed to display a maladaptive behaviour that some suggest to involve conflicts between Pavlovian and instrumental conditioning. In a procedure referred as negative automaintenance, a key light is paired with the subsequent delivery of food, however any peck towards the key light results in the omission of the reward. Studies showed that in such procedure some pigeons persisted in pecking to a substantial level despite its negative consequence, while others learned to refrain from pecking and maximized their cumulative rewards. Furthermore, the pigeons that were unable to refrain from pecking could nevertheless shift their pecks towards a harmless alternative key light. We confronted a computational model that combines dual-learning systems and factored representations, recently developed to account for sign-tracking and goal-tracking behaviours in rats, to these negative automaintenance experimental data. We show that it can explain the variability of the observed behaviours and the capacity of alternative key lights to distract pigeons from their detrimental behaviours. These results confirm the proposed model as an interesting tool to reproduce experiments that could involve interactions between Pavlovian and instrumental conditioning. The model allows us to draw predictions that may be experimentally verified, which could help further investigate the neural mechanisms underlying theses interactions.