An analytical tool that quantifies cellular morphology changes from three-dimensional fluorescence images

The most common software analysis tools available for measuring fluorescence images are for two-dimensional (2D) data that rely on manual settings for inclusion and exclusion of data points, and computer-aided pattern recognition to support the interpretation and findings of the analysis. It has become increasingly important to be able to measure fluorescence images constructed from three-dimensional (3D) datasets in order to be able to capture the complexity of cellular dynamics and understand the basis of cellular plasticity within biological systems. Sophisticated microscopy instruments have permitted the visualization of 3D fluorescence images through the acquisition of multispectral fluorescence images and powerful analytical software that reconstructs the images from confocal stacks that then provide a 3D representation of the collected 2D images. Advanced design-based stereology methods have progressed from the approximation and assumptions of the original model-based stereology(1) even in complex tissue sections(2). Despite these scientific advances in microscopy, a need remains for an automated analytic method that fully exploits the intrinsic 3D data to allow for the analysis and quantification of the complex changes in cell morphology, protein localization and receptor trafficking. Current techniques available to quantify fluorescence images include Meta-Morph (Molecular Devices, Sunnyvale, CA) and Image J (NIH) which provide manual analysis. Imaris (Andor Technology, Belfast, Northern Ireland) software provides the feature MeasurementPro, which allows the manual creation of measurement points that can be placed in a volume image or drawn on a series of 2D slices to create a 3D object. This method is useful for single-click point measurements to measure a line distance between two objects or to create a polygon that encloses a region of interest, but it is difficult to apply to complex cellular network structures. Filament Tracer (Andor) allows automatic detection of the 3D neuronal filament-like however, this module has been developed to measure defined structures such as neurons, which are comprised of dendrites, axons and spines (tree-like structure). This module has been ingeniously utilized to make morphological measurements to non-neuronal cells(3), however, the output data provide information of an extended cellular network by using a software that depends on a defined cell shape rather than being an amorphous-shaped cellular model. To overcome the issue of analyzing amorphous-shaped cells and making the software more suitable to a biological application, Imaris developed Imaris Cell. This was a scientific project with the Eidgenössische Technische Hochschule, which has been developed to calculate the relationship between cells and organelles. While the software enables the detection of biological constraints, by forcing one nucleus per cell and using cell membranes to segment cells, it cannot be utilized to analyze fluorescence data that are not continuous because ideally it builds cell surface without void spaces. To our knowledge, at present no user-modifiable automated approach that provides morphometric information from 3D fluorescence images has been developed that achieves cellular spatial information of an undefined shape (Figure 1). We have developed an analytical platform using the Imaris core software module and Imaris XT interfaced to MATLAB (Mat Works, Inc.). These tools allow the 3D measurement of cells without a pre-defined shape and with inconsistent fluorescence network components. Furthermore, this method will allow researchers who have extended expertise in biological systems, but not familiarity to computer applications, to perform quantification of morphological changes in cell dynamics.

A multi-scaled approach for simulating chemical reaction systems

In this paper we give an overview of some very recent work, as well as presenting a new approach, on the stochastic simulation of multi-scaled systems involving chemical reactions. In many biological systems (such as genetic regulation and cellular dynamics) there is a mix between small numbers of key regulatory proteins, and medium and large numbers of molecules. In addition, it is important to be able to follow the trajectories of individual molecules by taking proper account of the randomness inherent in such a system. We describe different types of simulation techniques (including the stochastic simulation algorithm, Poisson Runge-Kutta methods and the balanced Euler method) for treating simulations in the three different reaction regimes: slow, medium and fast. We then review some recent techniques on the treatment of coupled slow and fast reactions for stochastic chemical kinetics and present a new approach which couples the three regimes mentioned above. We then apply this approach to a biologically inspired problem involving the expression and activity of LacZ and LacY proteins in E coli, and conclude with a discussion on the significance of this work. (C) 2004 Elsevier Ltd. All rights reserved.

Inferring biochemical routes from biochemical networks

Metabolism is a defining feature of life, and its study is important to understand how a cell works, alterations that lead to disease and for applications in drug discovery. From a systems perspective, metabolism can be represented as a network that captures all the metabolites as nodes and the inter-conversions among pairs of them as edges. Such an abstraction enables the networks to be studied by applying graph theory, particularly, to infer the flow of chemical information in the networks by identifying relevant metabolic pathways. In this study, different weighting schemes are used to illustrate that appropriately weighted networks can capture the quantitative cellular dynamics quite accurately. Thus, the networks now combine the elegance and simplicity of representation of the system and ease of analysing metabolic graphs. Metabolic routes or paths determined by this therefore are likely to be more biologically meaningful. The usefulness of the approach is demonstrated with two examples, first for understanding bacterial stress response and second for studying metabolic alterations that occurs in cancer cells.

How neurons migrate: a dynamic in-silico model of neuronal migration in the developing cortex.

BACKGROUND: Neuronal migration, the process by which neurons migrate from their place of origin to their final position in the brain, is a central process for normal brain development and function. Advances in experimental techniques have revealed much about many of the molecular components involved in this process. Notwithstanding these advances, how the molecular machinery works together to govern the migration process has yet to be fully understood. Here we present a computational model of neuronal migration, in which four key molecular entities, Lis1, DCX, Reelin and GABA, form a molecular program that mediates the migration process. RESULTS: The model simulated the dynamic migration process, consistent with in-vivo observations of morphological, cellular and population-level phenomena. Specifically, the model reproduced migration phases, cellular dynamics and population distributions that concur with experimental observations in normal neuronal development. We tested the model under reduced activity of Lis1 and DCX and found an aberrant development similar to observations in Lis1 and DCX silencing expression experiments. Analysis of the model gave rise to unforeseen insights that could guide future experimental study. Specifically: (1) the model revealed the possibility that under conditions of Lis1 reduced expression, neurons experience an oscillatory neuron-glial association prior to the multipolar stage; and (2) we hypothesized that observed morphology variations in rats and mice may be explained by a single difference in the way that Lis1 and DCX stimulate bipolar motility. From this we make the following predictions: (1) under reduced Lis1 and enhanced DCX expression, we predict a reduced bipolar migration in rats, and (2) under enhanced DCX expression in mice we predict a normal or a higher bipolar migration. CONCLUSIONS: We present here a system-wide computational model of neuronal migration that integrates theory and data within a precise, testable framework. Our model accounts for a range of observable behaviors and affords a computational framework to study aspects of neuronal migration as a complex process that is driven by a relatively simple molecular program. Analysis of the model generated new hypotheses and yet unobserved phenomena that may guide future experimental studies. This paper thus reports a first step toward a comprehensive in-silico model of neuronal migration.

Learning and Production of Movement Sequences: Behavioral, Neurophysiological, and Modeling Perspectives

A growing wave of behavioral studies, using a wide variety of paradigms that were introduced or greatly refined in recent years, has generated a new wealth of parametric observations about serial order behavior. What was a mere trickle of neurophysiological studies has grown to a more steady stream of probes of neural sites and mechanisms underlying sequential behavior. Moreover, simulation models of serial behavior generation have begun to open a channel to link cellular dynamics with cognitive and behavioral dynamics. Here we summarize the major results from prominent sequence learning and performance tasks, namely immediate serial recall, typing, 2XN, discrete sequence production, and serial reaction time. These populate a continuum from higher to lower degrees of internal control of sequential organization. The main movement classes covered are speech and keypressing, both involving small amplitude movements that are very amenable to parametric study. A brief synopsis of classes of serial order models, vis-à-vis the detailing of major effects found in the behavioral data, leads to a focus on competitive queuing (CQ) models. Recently, the many behavioral predictive successes of CQ models have been joined by successful prediction of distinctively patterend electrophysiological recordings in prefrontal cortex, wherein parallel activation dynamics of multiple neural ensembles strikingly matches the parallel dynamics predicted by CQ theory. An extended CQ simulation model-the N-STREAMS neural network model-is then examined to highlight issues in ongoing attemptes to accomodate a broader range of behavioral and neurophysiological data within a CQ-consistent theory. Important contemporary issues such as the nature of working memory representations for sequential behavior, and the development and role of chunks in hierarchial control are prominent throughout.

A proteome-wide strategy reveals a novel mechanism of control of cell cycle progression through modulation of cyclin mRNA stability

La quantité de données générée dans le cadre d'étude à grande échelle du réseau d'interaction protéine-protéine dépasse notre capacité à les analyser et à comprendre leur sens; d'une part, par leur complexité et leur volume, et d'un autre part, par la qualité du jeu de donnée produit qui semble bondé de faux positifs et de faux négatifs. Cette dissertation décrit une nouvelle méthode de criblage des interactions physique entre protéines à haut débit chez Saccharomyces cerevisiae, la complémentation de fragments protéiques (PCA). Cette approche est accomplie dans des cellules intactes dans les conditions natives des protéines; sous leur promoteur endogène et dans le respect des contextes de modifications post-traductionnelles et de localisations subcellulaires. Une application biologique de cette méthode a permis de démontrer la capacité de ce système rapporteur à répondre aux questions d'adaptation cellulaire à des stress, comme la famine en nutriments et un traitement à une drogue. Dans le premier chapitre de cette dissertation, nous avons présenté un criblage des paires d'interactions entre les protéines résultant des quelques 6000 cadres de lecture de Saccharomyces cerevisiae. Nous avons identifié 2770 interactions entre 1124 protéines. Nous avons estimé la qualité de notre criblage en le comparant à d'autres banques d'interaction. Nous avons réalisé que la majorité de nos interactions sont nouvelles, alors que le chevauchement avec les données des autres méthodes est large. Nous avons pris cette opportunité pour caractériser les facteurs déterminants dans la détection d'une interaction par PCA. Nous avons remarqué que notre approche est sous une contrainte stérique provenant de la nécessité des fragments rapporteurs à pouvoir se rejoindre dans l'espace cellulaire afin de récupérer l'activité observable de la sonde d'interaction. L'intégration de nos résultats aux connaissances des dynamiques de régulations génétiques et des modifications protéiques nous dirigera vers une meilleure compréhension des processus cellulaires complexes orchestrés aux niveaux moléculaires et structuraux dans les cellules vivantes. Nous avons appliqué notre méthode aux réarrangements dynamiques opérant durant l'adaptation de la cellule à des stress, comme la famine en nutriments et le traitement à une drogue. Cette investigation fait le détail de notre second chapitre. Nous avons déterminé de cette manière que l'équilibre entre les formes phosphorylées et déphosphorylées de l'arginine méthyltransférase de Saccharomyces cerevisiae, Hmt1, régulait du même coup sont assemblage en hexamère et son activité enzymatique. L'activité d'Hmt1 a directement un impact dans la progression du cycle cellulaire durant un stress, stabilisant les transcrits de CLB2 et permettant la synthèse de Cln3p. Nous avons utilisé notre criblage afin de déterminer les régulateurs de la phosphorylation d'Hmt1 dans un contexte de traitement à la rapamycin, un inhibiteur de la kinase cible de la rapamycin (TOR). Nous avons identifié la sous-unité catalytique de la phosphatase PP2a, Pph22, activé par l'inhibition de la kinase TOR et la kinase Dbf2, activé durant l'entrée en mitose de la cellule, comme la phosphatase et la kinase responsable de la modification d'Hmt1 et de ses fonctions de régulations dans le cycle cellulaire. Cette approche peut être généralisée afin d'identifier et de lier mécanistiquement les gènes, incluant ceux n'ayant aucune fonction connue, à tout processus cellulaire, comme les mécanismes régulant l'ARNm.

Dinâmica celular e microbiológica do leite de ovelhas Santa Inês acompanhadas durante a lactação

Objetivou-se neste estudo, avaliar a dinâmica da infecção intramamária de ovelhas por meio da avaliação clínica, da contagem de células somáticas e do isolamento de bactérias envolvidas na infecção mamária ao longo de toda a lactação, bem como o perfil de sensibilidade destes isolados frente a antimicrobianos. Foram avaliadas 34 ovelhas da raça Santa Inês criadas em sistema semi-intensivo e submetidas ao mesmo manejo higiênico-sanitário e nutricional acompanhadas antes e durante o período de lactação: aproximadamente 10 dias que precedeu ao parto, 15 dias pós-parto (dpp), 30 dpp, 60 dpp e 90 dpp (secagem). Nestes momentos foi realizado o exame clínico da glândula. A contagem de células somáticas (CCS) e o CMT foram realizados nos momentos seguintes ao parto (15dpp, 30dpp, 60dpp e 90dpp), assim como a análise bacteriológica, realizada além dos momentos citados anteriormente, também no momento que precedeu ao parto. A colheita do leite foi realizada por ordenha manual. Todas as ovelhas foram submetidas à sorologia para lentivírus. Os dados da variável CCS foram submetidos ao teste de normalidade segundo Kolmogorov-Smirnov e por não atender a premissa de normalidade, foram transformados em log de base 10 (Log10). Por conseguinte efetuou-se a análise de variância e contraste de médias pelo teste de Tukey com nível de significância de P<0,05. Foi realizado o estudo descritivo das variáveis empregando-se a distribuição de frequências (%). O valor médio da CCS das glândulas não reagentes ao CMT, ao longo do período de lactação, variou de 387.896,08 células/mL a 620.611,11 células/mL e nas glândulas reagentes, dependendo do escore do CMT, apresentou valores médios que variaram de 2.133.914,19 células/mL a 6.730.514,50 células/mL, sem contudo sofrer influência das diferentes fases da lactação. Os resultados obtidos permitiram concluir que a mastite subclínica representa uma preocupação sanitária na criação de ovelhas Santa Inês, chamando-se atenção para o período que precede o parto devido o alto percentual de isolamento bacteriano em glândulas aparentemente sadias, bem como a elevada frequência de isolamento, particularmente de Staphylococcus coagulase-negativo no primeiro mês de lactação.

A multi-scaled approach for simulating chemical reaction systems

In this paper we give an overview of some very recent work, as well as presenting a new approach, on the stochastic simulation of multi-scaled systems involving chemical reactions. In many biological systems (such as genetic regulation and cellular dynamics) there is a mix between small numbers of key regulatory proteins, and medium and large numbers of molecules. In addition, it is important to be able to follow the trajectories of individual molecules by taking proper account of the randomness inherent in such a system. We describe different types of simulation techniques (including the stochastic simulation algorithm, Poisson Runge–Kutta methods and the balanced Euler method) for treating simulations in the three different reaction regimes: slow, medium and fast. We then review some recent techniques on the treatment of coupled slow and fast reactions for stochastic chemical kinetics and present a new approach which couples the three regimes mentioned above. We then apply this approach to a biologically inspired problem involving the expression and activity of LacZ and LacY proteins in E. coli, and conclude with a discussion on the significance of this work.

Towards a predictive model of Ca²⁺ puffs

We investigate key characteristics of Ca²⁺ puffs in deterministic and stochastic frameworks that all incorporate the cellular morphology of IP[subscript]3 receptor channel clusters. In a first step, we numerically study Ca²⁺ liberation in a three dimensional representation of a cluster environment with reaction-diffusion dynamics in both the cytosol and the lumen. These simulations reveal that Ca²⁺ concentrations at a releasing cluster range from 80 µM to 170 µM and equilibrate almost instantaneously on the time scale of the release duration. These highly elevated Ca²⁺ concentrations eliminate Ca²⁺ oscillations in a deterministic model of an IP[subscript]3R channel cluster at physiological parameter values as revealed by a linear stability analysis. The reason lies in the saturation of all feedback processes in the IP[subscript]3R gating dynamics, so that only fluctuations can restore experimentally observed Ca²⁺ oscillations. In this spirit, we derive master equations that allow us to analytically quantify the onset of Ca²⁺ puffs and hence the stochastic time scale of intracellular Ca²⁺ dynamics. Moving up the spatial scale, we suggest to formulate cellular dynamics in terms of waiting time distribution functions. This approach prevents the state space explosion that is typical for the description of cellular dynamics based on channel states and still contains information on molecular fluctuations. We illustrate this method by studying global Ca²⁺ oscillations.

The dynamics of cellular automata on 2-manifolds is affected by topology

In this paper, we demonstrate that the distribution of Wolfram classes within a cellular automata rule space in the triangular tessellation is not consistent across different topological general. Using a statistical mechanics approach, cellular automata dynamical classes were approximated for cellular automata defined on genus-0, genus-1 and genus-2 2-manifolds. A distribution-free equality test for empirical distributions was applied to identify cases in which Wolfram classes were distributed differently across topologies. This result implies that global structure and local dynamics contribute to the long term evolution of cellular automata.

Infection and cellular defense dynamics in a novel 17beta-estradiol murine model of chronic human group B streptococcus genital tract colonization reveal a role for hemolysin in persistence and neutrophil accumulation

Genital tract carriage of group B streptococcus (GBS) is prevalent among adult women; however, the dynamics of chronic GBS genital tract carriage, including how GBS persists in this immunologically active host niche long term, are not well defined. To our knowledge, in this study, we report the first animal model of chronic GBS genital tract colonization using female mice synchronized into estrus by delivery of 17β-estradiol prior to intravaginal challenge with wild-type GBS 874391. Cervicovaginal swabs, which were used to measure bacterial persistence, showed that GBS colonized the vaginal mucosa of mice at high numbers (106–107 CFU/swab) for at least 90 d. Cellular and histological analyses showed that chronic GBS colonization of the murine genital tract caused significant lymphocyte and PMN cell infiltrates, which were localized to the vaginal mucosal surface. Long-term colonization was independent of regular hormone cycling. Immunological analyses of 23 soluble proteins related to chemotaxis and inflammation showed that the host response to GBS in the genital tract comprised markers of innate immune activation including cytokines such as GM-CSF and TNF-α. A nonhemolytic isogenic mutant of GBS 874391, Δcyle9, was impaired for colonization and was associated with amplified local PMN responses. Induction of DNA neutrophil extracellular traps, which was observed in GBS-infected human PMNs in vitro in a hemolysin-dependent manner, appeared to be part of this response. Overall, this study defines key infection dynamics in a novel murine model of chronic GBS genital tract colonization and establishes previously unknown cellular and soluble defense responses to GBS in the female genital tract.

LANDSCAPE DYNAMICS MODELING THROUGH INTEGRATED MARKOV, FUZZY-AHP AND CELLULAR AUTOMATA

Multi temporal land use information were derived using two decades remote sensing data and simulated for 2012 and 2020 with Cellular Automata (CA) considering scenarios, change probabilities (through Markov chain) and Multi Criteria Evaluation (MCE). Agents and constraints were considered for modeling the urbanization process. Agents were nornmlized through fiizzyfication and priority weights were assigned through Analytical Hierarchical Process (AHP) pairwise comparison for each factor (in MCE) to derive behavior-oriented rules of transition for each land use class. Simulation shows a good agreement with the classified data. Fuzzy and AHP helped in analyzing the effects of agents of growth clearly and CA-Markov proved as a powerful tool in modelling and helped in capturing and visualizing the spatiotemporal patterns of urbanization. This provided rapid land evaluation framework with the essential insights of the urban trajectory for effective sustainable city planning.

Dynamics of filopodium-like protrusion and endothelial cellular motility on onedimensional extracellular matrix fibrils

Endothelial filopodia play key roles in guiding the tubular sprouting during angiogenesis. However, their dynamic morphological characteristics, with the associated implications in cell motility, have been subjected to limited investigations. In this work, the interaction between endothelial cells and extracellular matrix fibrils was recapitulated in vitro, where a specific focus was paid to derive the key morphological parameters to define the dynamics of filopodium-like protrusion during cell motility. Based on one-dimensional gelatin fibrils patterned by near-field electrospinning (NFES), we study the response of endothelial cells (EA.hy926) under normal culture or ROCK inhibition. It is shown that the behaviour of temporal protrusion length versus cell motility can be divided into distinct modes. Persistent migration was found to be one of the modes which permitted cell displacement for over 300 μm at a speed of approximately 1 μm min-1. ROCK inhibition resulted in abnormally long protrusions and diminished the persistent migration, but dramatically increased the speeds of protrusion extension and retraction. Finally, we also report the breakage of protrusion during cell motility, and examine its phenotypic behaviours. © 2014 The Author(s) Published by the Royal Society. All rights reserved.