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.