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author:("stinson, Anna")
1.  A Systems Biology Approach Identifies Molecular Networks Defining Skeletal Muscle Abnormalities in Chronic Obstructive Pulmonary Disease 
PLoS Computational Biology  2011;7(9):e1002129.
Chronic Obstructive Pulmonary Disease (COPD) is an inflammatory process of the lung inducing persistent airflow limitation. Extensive systemic effects, such as skeletal muscle dysfunction, often characterize these patients and severely limit life expectancy. Despite considerable research efforts, the molecular basis of muscle degeneration in COPD is still a matter of intense debate. In this study, we have applied a network biology approach to model the relationship between muscle molecular and physiological response to training and systemic inflammatory mediators. Our model shows that failure to co-ordinately activate expression of several tissue remodelling and bioenergetics pathways is a specific landmark of COPD diseased muscles. Our findings also suggest that this phenomenon may be linked to an abnormal expression of a number of histone modifiers, which we discovered correlate with oxygen utilization. These observations raised the interesting possibility that cell hypoxia may be a key factor driving skeletal muscle degeneration in COPD patients.
Author Summary
Chronic Obstructive Pulmonary Disease (COPD) is a major life threatening disease of the lungs, characterized by airflow limitation and chronic inflammation. Progressive reduction of the body muscle mass is a condition linked to COPD that significantly decreases quality of life and survival. Physical exercise has been proposed as a therapeutic option but its utility is still a matter of debate. The mechanisms underlying muscle wasting are also still largely unknown. The results presented in this paper show that diseased muscles are largely unable to coordinate the expression of muscle remodelling and bioenergetics pathways and that the cause of this phenomena may be tissue hypoxia. These findings contrast with current hypotheses based on the role of chronic inflammation and show that a mechanism based on an oxygen driven, epigenetic control of these two important functions may be an important disease mechanism.
PMCID: PMC3164707  PMID: 21909251
2.  A systems biology approach sheds new light on Escherichia coli acid resistance 
Nucleic Acids Research  2011;39(17):7512-7528.
In order to develop an infection, diarrhogenic Escherichia coli has to pass through the stomach, where the pH can be as low as 1. Mechanisms that enable E. coli to survive in low pH are thus potentially relevant for pathogenicity. Four acid response systems involved in reducing the concentration of intracellular protons have been identified so far. However, it is still unclear to what extent the regulation of other important cellular functions may be required for survival in acid conditions. Here, we have combined molecular and phenotypic analysis of wild-type and mutant strains with computational network inference to identify molecular pathways underlying E. coli response to mild and strong acid conditions. The interpretative model we have developed led to the hypothesis that a complex transcriptional programme, dependent on the two-component system regulator OmpR and involving a switch between aerobic and anaerobic metabolism, may be key for survival. Experimental validation has shown that the OmpR is responsible for controlling a sizeable component of the transcriptional programme to acid exposure. Moreover, we found that a ΔompR strain was unable to mount any transcriptional response to acid exposure and had one of the strongest acid sensitive phenotype observed.
PMCID: PMC3177180  PMID: 21690099
3.  A computational framework for gene regulatory network inference that combines multiple methods and datasets 
BMC Systems Biology  2011;5:52.
Reverse engineering in systems biology entails inference of gene regulatory networks from observational data. This data typically include gene expression measurements of wild type and mutant cells in response to a given stimulus. It has been shown that when more than one type of experiment is used in the network inference process the accuracy is higher. Therefore the development of generally applicable and effective methodologies that embed multiple sources of information in a single computational framework is a worthwhile objective.
This paper presents a new method for network inference, which uses multi-objective optimisation (MOO) to integrate multiple inference methods and experiments. We illustrate the potential of the methodology by combining ODE and correlation-based network inference procedures as well as time course and gene inactivation experiments. Here we show that our methodology is effective for a wide spectrum of data sets and method integration strategies.
The approach we present in this paper is flexible and can be used in any scenario that benefits from integration of multiple sources of information and modelling procedures in the inference process. Moreover, the application of this method to two case studies representative of bacteria and vertebrate systems has shown potential in identifying key regulators of important biological processes.
PMCID: PMC3098160  PMID: 21489290

Results 1-3 (3)