Uncovering distinct protein-network topologies in heterogeneous cell populations

Background: Cell biology research is fundamentally limited by the number of intracellular components, particularly proteins, that can be co-measured in the same cell. Therefore, cell-to-cell heterogeneity in unmeasured proteins can lead to completely different observed relations between the same measured proteins. Attempts to infer such relations in a heterogeneous cell population can yield uninformative average relations if only one underlying biochemical network is assumed. To address this, we developed a method that recursively couples an iterative unmixing process with a Bayesian analysis of each unmixed subpopulation. Results: Our approach enables to identify the number of distinct cell subpopulations, unmix their corresponding observations and resolve the network structure of each subpopulation. Using simulations of the MAPK pathway upon EGF and NGF stimulations we assess the performance of the method. We demonstrate that the presented method can identify better than clustering approaches the number of subpopulations within a mixture of observations, thus resolving correctly the statistical relations between the proteins. Conclusions: Coupling the unmixing of multiplexed observations with the inference of statistical relations between the measured parameters is essential for the success of both of these processes. Here we present a conceptual and algorithmic solution to achieve such coupling and hence to analyze data obtained from a natural mixture of cell populations. As the technologies and necessity for multiplexed measurements are rising in the systems biology era, this work addresses an important current challenge in the analysis of the derived data.Fil: Wieczorek, Jakob. Universitat Dortmund; AlemaniaFil: Malik Sheriff, Rahuman S.. Institut Max Planck fur Molekulare Physiologie; Alemania. Imperial College London; Reino Unido. European Bioinformatics Institute. European Molecular Biology Laboratory; Reino UnidoFil: Fermin, Yessica. Universitat Dortmund; AlemaniaFil: Grecco, Hernan Edgardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentina. Institut Max Planck fur Molekulare Physiologie; AlemaniaFil: Zamir, Eli. Institut Max Planck fur Molekulare Physiologie; AlemaniaFil: Ickstadt, Katja. Universitat Dortmund; Alemani

Symmetric exchange of multi-protein building blocks between stationary focal adhesions and the cytosol

How can the integrin adhesome get self-assembled locally, rapidly, and correctly as diverse cell-matrix adhesion sites? Here, we investigate this question by exploring the cytosolic state of integrin-adhesome components and their dynamic exchange between adhesion sites and cytosol. Using fluorescence cross-correlation spectroscopy (FCCS) and fluorescence recovery after photobleaching (FRAP) we found that the integrin adhesome is extensively pre-assembled already in the cytosol as multi-protein building blocks for adhesion sites. Stationary focal adhesions release symmetrically the same types of protein complexes that they recruit, thereby keeping the cytosolic pool of building blocks spatiotemporally uniform. We conclude a model in which multi-protein building blocks enable rapid and modular self-assembly of adhesion sites and symmetric exchange of these building blocks preserves their specifications and thus the assembly logic of the system

Propuesta de una guía de estudio sobre el contenido programático de la materia Derecho Tributario II, para facilitar el proceso de enseñanza-aprendizaje de los estudiantes de la carrera Licenciatura en Contaduría Pública, en la Facultad Multidisciplinaria Oriental de la Universidad de El Salvador

La profesión contable como elemento vital e indispensable en la organización y planificación de las actividades de los sectores productivos, guarda estrecha relación con los conocimientos y aplicaciones de las obligaciones formales y sustantivas que contienen las leyes tributarias de nuestro país, es el profesional en ésta área el que debe cuidar la observancia en el cumplimiento de los reglamentos establecidos a efecto de regular la actividad a la cual se dedica el sujeto pasivo. El campo de acción del contador público ha experimentado una creciente diversificación en sus servicios, tales como: la contabilidad de costos, agrícola, de seguros, bancaria, comercial, gubernamental, auditoría interna y externa y recientemente el área de asesoramiento en la materia fiscal, incrementando al mismo tiempo la responsabilidad en mantener su capacidad profesional para responder a cabalidad con las expectativas de la demanda Tomando en cuenta la necesidad de una formación completa en cada ámbito de la formación profesional de un Licenciado en Contaduría Pública nace la idea de elaborar de una guía de estudio que permita facilitar el proceso de enseñanza- aprendizaje de los estudiantes que cursan la materia de Derecho Tributario II, mediante aplicación de ejemplos, un instructivo y teoría legal relaciona al área tributaria, además la guía se convertirá en un apoyo para el docente. Proponer una guía de estudio sobre el contenido programático de la materia DERECHO TRIBUTARIO II, que permita mejorar el proceso de enseñanza- aprendizaje de los estudiantes de la carrera de Licenciatura en Contaduría Pública, en la Facultad Multidisciplinaria Oriental de la Universidad de El Salvador es el objetivo general. La metodología es el instrumento que enlaza el sujeto con el objeto de la investigación, Sin la metodología es casi imposible llegar a la lógica que conduce al conocimiento científicon 12 . Para la realización de nuestra investigación (Propuesta De Una Guía De Estudio Sobre El Contenido Programático De La Materia Derecho Tributario II, Para Facilitar El Proceso De Enseñanza-Aprendizaje De Los Estudiantes De La Carrera De Licenciatura En Contaduría Pública, En La Facultad Multidisciplinaria Oriental De La Universidad De El Salvador), como grupo de investigación y tomando en cuenta las características de nuestra investigación hemos convenido emplear el Método Compresivo - Explicativo, el cual se define como: ―Procedimiento metodológico que consiste en realizar una recolección e interpretación de la información proveniente de fuentes bibliográficas y de campo‖, ya que específicamente los objetivos que nos hemos planteado como grupo son primordialmente conocer cada uno de los temas y sub-temas del contenido programático de la materia de DERECHO TRIBUTARIO II para luego aplicar los artículos que comprenden la Ley del Impuesto a la Transferencia de Bienes Muebles y a la Prestación de Servicios y para finalmente elaborar casos prácticos, aplicando La Ley del Impuesto a la Transferencia de Bienes Muebles y a la Prestación de Servicios y su relación con la contabilida

Statistical modeling of protein-protein interaction networks

Understanding how proteins bind to each other in a cell is the key in molecular biology to determine how experts can repair anomalies in cells. The major challenge in the prediction of protein-protein interactions is the cell-to-cell heterogeneity within a sample, due to genetic and epigenetic variabilities. Most studies about protein-protein interaction carry out their analysis without awareness of the underlying heterogeneity. This situation can lead to the identification of invalid interactions. As part of the solution to this problem, we proposed in this thesis two aspects of analysis, one for snapshot data, where different samples of ten proteins were taken by toponome imaging and another for the analysis of time correlated data that guarantees a better approximation to the prediction of protein-protein interactions. The latter represents an advance in the analysis of data with high temporal resolution, such as that obtained through the quantification technique known as multicolor live cell imaging. The thesis here presented is divided into two parts: The first part called "Revealing relationships among proteins involved in assembling focal adhesions" consists of the development of a methodology based on frequentist methods, such as machine learning and meta-analysis, for the prediction of protein-protein interaction on six different toponome imaging datasets. This methodology presents an advance in the analysis of highly heterogeneous snapshot data. Our aim here focused on the formulation of a single model capable of identifying the relationship among different samples by summing is common results over them concerning their random variation. This methodology leads to a set of common models over the six datasets hierarchized by their predictive power, where the researcher can choose the model according to its accuracy in the prediction or according to its parsimony. The developing of this part is in Chapters 1-7 â this part published in Harizanova et al. (2016). The second part is called "Modelling of temporal networks with a nonparametric mixture of dynamic Bayesian networks". The content of this part contemplates the advance of a Bayesian methodology regarding temporal networks that successfully enables to identify subpopulations in heterogeneous cell populations as well as at the same time reconstructing the protein interaction network associated with each subpopulation. This method extends the nonparametric Bayesian networks (NPBNs) (Ickstadt et al., 2011) for the analysis of time-correlated data by using Gaussian dynamic Bayesian Networks (GDBNs). We evaluate our model based on the variation of specific parameters such as the underlying number of subpopulations, network density, intra-subpopulation variability among others. On the other hand, a comparative analysis with existing clustering methods such as NPBNs and hierarchical agglomerative clustering (Hclust), shows that the inclusion of temporal correlations in the classification of multivariate time series is relevant for an improvement in the classification. The classic Hclust method using the dynamic time warping distances (T-Hclust) was found to be similar in precision to our Bayesian method here proposed. On the other hand, a comparative analysis with the GDBNs shows the lack of adjustment of the GDBNs to reconstruct temporal networks in heterogeneous cell populations through a single model, while our method, as well as the joint use of the T-Hclust classifications with the GDBNs (T-Hclust+), show a high adequacy in the prediction of temporal networks in a mixture. The developing of this part is in Chapters 8-16

Highly Multiplexed Imaging Uncovers Changes in Compositional Noise within Assembling Focal Adhesions.

Integrin adhesome proteins bind each other in alternative manners, forming within the cell diverse cell-matrix adhesion sites with distinct properties. An intriguing question is how such modular assembly of adhesion sites is achieved correctly solely by self-organization of their components. Here we address this question using high-throughput multiplexed imaging of eight proteins and two phosphorylation sites in a large number of single focal adhesions. We found that during the assembly of focal adhesions the variances of protein densities decrease while the correlations between them increase, suggesting reduction in the noise levels within these structures. These changes correlate independently with the area and internal density of focal adhesions, but not with their age or shape. Artificial neural network analysis indicates that a joint consideration of multiple components improves the predictability of paxillin and zyxin levels in internally dense focal adhesions. This suggests that paxillin and zyxin densities in focal adhesions are fine-tuned by integrating the levels of multiple other components, thus averaging-out stochastic fluctuations. Based on these results we propose that increase in internal protein densities facilitates noise suppression in focal adhesions, while noise suppression enables their stable growth and further density increase-hence forming a feedback loop giving rise to a quality-controlled assembly

Density-dependent high-order statistical relations between components in focal adhesions.

<p>(a) The artificial neural network architecture, exemplified for the case of four input proteins. (b) The coefficients of determinations between the predicted and observed densities of the target component as obtained by artificial neural networks versus Random Forests, shown for all possible combinations of input and target proteins. (c) A scatter plot showing the extent of high-order relations identified for the indicated target components. The horizontal axis indicates the average number of input components in the identified high-order relations. The vertical axis indicates the average coefficient of determination between the predicted levels of the target protein, based on the artificial neural network analysis, and its actual levels in the focal adhesions. High-order relations with average coefficient of determination lower than 0.6 are omitted from the plot. The diameter of the circles indicates the number of identified high-order relations (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0160591#sec002" target="_blank">Materials and Methods</a>). (d) A positive feedback model for the emergence of noise suppression in focal adhesions.</p

Noise decrease in focal adhesions is coupled to their size and internal density independently.

<p>(a) The workflow of inferring changes in noise levels between two categories of focal adhesions (e.g. small versus big focal adhesions). (b) Changes in noise levels as a function of focal adhesions area. (c) Changes in noise levels as a function of focal adhesions age. (d) Focal adhesions were sub-categorized according to both their area and age. Changes in noise levels were inferred within each age category as a function of area and vice versa. (e) Same as (d), using eccentricity instead of age. (f) Same as (d), using density instead of age. Error bars indicate standard error of the mean between datasets (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0160591#pone.0160591.s002" target="_blank">S1 Fig</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0160591#pone.0160591.s017" target="_blank">S2 Table</a>).</p

Inferring changes in noise levels in the molecular content of focal adhesions.

<p>(a) An assembly process with competing binding interactions. (b) Higher diversity in the local levels of a recruiting protein leads to a stronger correlation between the recruited proteins, while higher noise causes the opposite. (c) Simulated <i>CV</i> and <i>r</i>² of the densities of the dark-blue and red components as a function of binding noise and diversity in the density of the yellow component among focal adhesions. (d) Inferring changes in noise levels based on Δ<i>CV</i> and Δ<i>r</i>². Changes between focal adhesion categories exemplified in (c) are indicated. The inference approach was validated by systematic screen of diversity and noise levels for competitive and non-competitive assembly processes.</p