Neutral space analysis for a Boolean network model of the fission yeast cell cycle network

BACKGROUND: Interactions between genes and their products give rise to complex circuits known as gene regulatory networks (GRN) that enable cells to process information and respond to external stimuli. Several important processes for life, depend of an accurate and context-specific regulation of gene expression, such as the cell cycle, which can be analyzed through its GRN, where deregulation can lead to cancer in animals or a directed regulation could be applied for biotechnological processes using yeast. An approach to study the robustness of GRN is through the neutral space. In this paper, we explore the neutral space of a Schizosaccharomyces pombe (fission yeast) cell cycle network through an evolution strategy to generate a neutral graph, composed of Boolean regulatory networks that share the same state sequences of the fission yeast cell cycle. RESULTS: Through simulations it was found that in the generated neutral graph, the functional networks that are not in the wildtype connected component have in general a Hamming distance more than 3 with the wildtype, and more than 10 between the other disconnected functional networks. Significant differences were found between the functional networks in the connected component of the wildtype network and the rest of the network, not only at a topological level, but also at the state space level, where significant differences in the distribution of the basin of attraction for the G1 fixed point was found for deterministic updating schemes. CONCLUSIONS: In general, functional networks in the wildtype network connected component, can mutate up to no more than 3 times, then they reach a point of no return where the networks leave the connected component of the wildtype. The proposed method to construct a neutral graph is general and can be used to explore the neutral space of other biologically interesting networks, and also formulate new biological hypotheses studying the functional networks in the wildtype network connected component.

Discovering craniofacial patterns using multivariate cephalometric data for treatment decision making in orthodontics / Descubriendo patrones craneofaciales usando datos cefalométricos multivariados para la toma de decisiones en ortodoncia

The aim was to find craniofacial morphology patterns in a multivariate cephalometric database using a clustering technique. Cephalometric analysis was performed in a sample of 100 teleradiographs collected from Chilean orthodontic patients. Thirty cephalometric measurements were taken from commonly used analysis. The computed variables were used to perform a clustering analysis with the k-means algorithm to identify patterns of craniofacial morphology. The J48 decision tree was used to analyze each cluster, and the ANOVA test to determine the statistical differences between the clusters. Four clusters were found that had significant differences (P<0.001) in 24 of the 30 variables studied, suggesting that they represent different patterns of craniofacial form. Using the decision tree, 8 of the 30 variables appeared to be relevant for describing the clusters. The clustering analysis is effective in identifying different craniofacial patterns based on a multivariate database. The distinct clusters appear to be caused by differences in the compensation process of the facial structure responding to a genetically determined cranial and mandible form. The proposed method can be applied to several databases, creating specific classifications for each one of them.

El objetivo fue encontrar patrones morfológicos craneofaciales, a partir de una base de datos cefalométricos multivariada, utilizando una técnica de clustering. Se realizó un análisis cefalométrico a una muestra de 100 telerradiografías pertenecientes a pacientes chilenos de ortodoncia. Treinta medidas cefalométricas obtenidas de los análisis más utilizados fueron registradas. Las variables computadas se utilizaron para realizar un análisis de clustering con el algoritmo k-medias, para identificar patrones de morfología craneofacial. El árbol de decisión J48 se utilizó para analizar cada cluster, y test de ANOVA para determinar diferencias estadísticamente significativas entre los clusters. Se encontraron cuatro clusters con diferencia estadísticamente significativas (p<0,001) en 24 de las 30 variables estudiadas, lo que sugiere que efectivamente corresponden a diferentes patrones craneofaciales. Utilizando el árbol de decisión, se pudo determinar que 8 de las 30 variables resultaron ser relevantes en la definición de los clusters. El análisis de clustering es efectivo en identificar patrones morfológicos craneofaciales usando una base de datos multivariada. Los distintos cluster encontrados, aparentemente se formarían a partir de diferencias en el proceso de compensación de la estructura facial, en respuesta a la forma mandibular genéticamente determinada. El método propuesto puede ser aplicado a múltiples bases de datos, creando clasificaciones específicas para cada una de ellas.