Graphical modeling for gene set analysis: A critical appraisal

Current demand for understanding the behavior of groups of related genes, combined with the greater availability of data, has led to an increased focus on statistical methods in gene set analysis. In this paper, we aim to perform a critical appraisal of the methodology based on graphical models developed in Massa et al. ( 2010 ) that uses pathway signaling networks as a starting point to develop statistically sound procedures for gene set analysis. We pay attention to the potential of the methodology with respect to the organizational aspects of dealing with such complex but highly informative starting structures, that is pathways. We focus on three themes: the translation of a biological pathway into a graph suitable for modeling, the role of shrinkage when more genes than samples are obtained, the evaluation of respondence of the statistical models to the biological expectations. To study the impact of shrinkage, two simulation studies will be run. To evaluate the biological expectation we will use data from a network with known behavior that offer the possibility of carrying out a realistic check of respondence of the model to changes in the experimental conditions.     

Exploring novel objective functions for simulating muscle coactivation in the neck

Musculoskeletal modeling allows for analysis of individual muscles in various situations. However, current techniques to realistically simulate muscle response when significant amounts of intentional coactivation is required are inadequate. This would include stiffening the neck or spine through muscle coactivation in preparation for perturbations or impacts. Muscle coactivation has been modeled previously in the neck and spine using optimization techniques that seek to maximize the joint stiffness by maximizing total muscle activation or muscle force. These approaches have not sought to replicate human response, but rather to explore the possible effects of active muscle. Coactivation remains a challenging feature to include in musculoskeletal models, and may be improved by extracting optimization objective functions from experimental data. However, the components of such an objective function must be known before fitting to experimental data. This study explores the effect of components in several objective functions, in order to recommend components to be used for fitting to experimental data. Four novel approaches to modeling coactivation through optimization techniques are presented, two of which produce greater levels of stiffness than previous techniques. Simulations were performed using OpenSim and MATLAB cooperatively. Results show that maximizing the moment generated by a particular muscle appears analogous to maximizing joint stiffness. The approach of optimizing for maximum moment generated by individual muscles may be a good candidate for developing objective functions that accurately simulate muscle coactivation in complex joints. This new approach will be the focus of future studies with human subjects.     

Lactate modeling and its application to endurance training

1. 1. In this short review, previous studies regarding the modeling of lactate (La) response to exercise and its application to endurance training have been summarized.

2. 2. Additionally the result of a recent study by the present authors are shown.

3. 3. Several models for La response to step and ramp exercise are already proposed and deductions derived from them are used for practical purposes such as the prediction of race performance in middle-and long-distance runners as well as for construction of their training regimens.

4. 4. Only a limited number of models however have tried to quantify whole body La kinetics to exercise in humans concomitantly with describing physiological mechanisms underlying the observed phenomenon.

5. 5. In a recent study described further in this paper a 2 compartment model was used for the purpose of clarifying the current “La production vs degradation” controversy during La adaptation to training.

6. 6. It was determined from this investigation that the La metabolic clearance rate during recovery is enhanced by the endurance training.

7. 7. This is in accordance with another recent observation of an increased La metabolic clearance rate at high absolute work rates and all relative work rates during exercise.

VoroMQA: Assessment of protein structure quality using interatomic contact areas

In the absence of experimentally determined protein structure many biological questions can be addressed using computational structural models. However, the utility of protein structural models depends on their quality. Therefore, the estimation of the quality of predicted structures is an important problem. One of the approaches to this problem is the use of knowledge‐based statistical potentials. Such methods typically rely on the statistics of distances and angles of residue‐residue or atom‐atom interactions collected from experimentally determined structures. Here, we present VoroMQA (Voronoi tessellation‐based Model Quality Assessment), a new method for the estimation of protein structure quality. Our method combines the idea of statistical potentials with the use of interatomic contact areas instead of distances. Contact areas, derived using Voronoi tessellation of protein structure, are used to describe and seamlessly integrate both explicit interactions between protein atoms and implicit interactions of protein atoms with solvent. VoroMQA produces scores at atomic, residue, and global levels, all in the fixed range from 0 to 1. The method was tested on the CASP data and compared to several other single‐model quality assessment methods. VoroMQA showed strong performance in the recognition of the native structure and in the structural model selection tests, thus demonstrating the efficacy of interatomic contact areas in estimating protein structure quality. The software implementation of VoroMQA is freely available as a standalone application and as a web server at http://bioinformatics.lt/software/voromqa . Proteins 2017; 85:1131–1145. © 2017 Wiley Periodicals, Inc.     

Past,current, and future trends of red spiny lobster based on PCA with MaxEnt model in Galapagos Islands,Ecuador

In order to enhance in terms of accuracy and predict the modeling of the potential distribution of species, the integration of using principal components of environmental variables as input of maximum entropy (MaxEnt) has been proposed in this study. Principal components selected previously from the principal component analysis results performed in ArcGIS in the environmental variables was used as an input data of MaxEnt instead of raw data to model the potential distribution of red spiny lobster from the year 1997 to 2015 and for three different future scenarios 2020, 2050, and 2070. One set of six original environmental variables pertaining to the years 1997–2015 and one set of four variables for future scenarios were transformed independently into a single multiband raster in ArcGIS in order to select the variables whose eigenvalues explains more than 5% of the total variance with the purpose to use in the modeling prediction in MaxEnt. The years 1997 and 1998 were chosen to compare the accuracy of the model, showing better results using principal components instead of raw data in terms of area under the curve and partial receiver operating characteristic as well as better predictions of suitable areas. Using principal components as input of MaxEnt enhances the prediction of good habitat suitability for red spiny lobster; however, future scenarios suggest an adequate management by researches to elaborate appropriate guidelines for the conservation of the habitat for this valuable specie with face to the climate change.     

iPSC technology-Powerful hand for disease modeling and therapeutic screen

Cardiovascular and neurodegenerative diseases are major health threats in many developed countries. Recently, target tissues derived from human embryonic stem (hES) cells and induced pluripotent stem cells (iPSCs), such as cardiomyocytes (CMs) or neurons, have been actively mobilized for drug screening. Knowledge of drug toxicity and efficacy obtained using stem cell-derived tissues could parallel that obtained from human trials. Furthermore, iPSC disease models could be advantageous in the development of personalized medicine in various parts of disease sectors. To obtain the maximum benefit from iPSCs in disease modeling, researchers are now focusing on aging, maturation, and metabolism to recapitulate the pathological features seen in patients. Compared to pediatric disease modeling, adult-onset disease modeling with iPSCs requires proper maturation for full manifestation of pathological features. Herein, the success of iPSC technology, focusing on patient-specific drug treatment, maturation-based disease modeling, and alternative approaches to compensate for the current limitations of patient iPSC modeling, will be further discussed. [BMB Reports 2015; 48(5): 256-265]     

Tropical terrestrial invertebrates—Where to from here?

There are over one million described invertebrate species on Earth, the majority of which are likely to inhabit the highly biodiverse rain forests around the equator. These are some of the most vulnerable ecosystems on Earth due to the pressures of deforestation and climate change with many of their inhabitants at risk of extinction. Invertebrates play a major role in ecosystem functioning from decomposition and nutrient cycling to herbivory and pollination; however, while our understanding of these roles is improving, we are far from being able to predict the consequences of further deforestation, climate change, and biodiversity loss due to the lack of comparative data and the high proportion of species which remain to be discovered. As we move into an era of increased pressure on old-growth habitats and biodiversity, it is imperative that we understand how changes to invertebrate communities, and the extinction of species, affect ecosystems. Innovative and comprehensive methods that approach these issues are needed. Here, we highlight priorities for future tropical terrestrial invertebrate research such as the efficiency of sustainable land management, exploration of innovative methods for better understanding of invertebrate ecology and behavior, and quantifying the role of invertebrates in ecosystem functioning.