scholarly journals Complex Systems: From Chemistry to Systems Biology Special Feature: Modeling the temporal interplay of molecular signaling and gene expression by using dynamic nested effects models

2009 ◽  
Vol 106 (16) ◽  
pp. 6447-6452 ◽  
Author(s):  
B. Anchang ◽  
M. J. Sadeh ◽  
J. Jacob ◽  
A. Tresch ◽  
M. O. Vlad ◽  
...  
Keyword(s):  
Gene Expression ◽  
Systems Biology ◽  
Complex Systems ◽  
Feature Modeling ◽  
Molecular Signaling

Co-expressional conservation in virulence and stress related genes of three Gammaproteobacterial species: Escherichia coli, Salmonella enterica and Pseudomonas aeruginosa

2015 ◽  
Vol 11 (11) ◽  
pp. 3137-3148
Author(s):  
Nazanin Hosseinkhan ◽  
Peyman Zarrineh ◽  
Hassan Rokni-Zadeh ◽  
Mohammad Reza Ashouri ◽  
Ali Masoudi-Nejad
Keyword(s):  
Gene Expression ◽  
Escherichia Coli ◽  
Pseudomonas Aeruginosa ◽  
Systems Biology ◽  
Gene Expression Data ◽  
High Throughput ◽  
Expression Data ◽  
P Gene ◽  
Stress Related Genes ◽  
High Throughput Gene Expression

Gene co-expression analysis is one of the main aspects of systems biology that uses high-throughput gene expression data.

scholarly journals Systems Approaches to Treatment Response to Imatinib in Severe Asthma: A Pilot Study

2021 ◽  
Vol 11 (4) ◽  
pp. 240
Author(s):  
Seung Han Baek ◽  
Dinah Foer ◽  
Katherine N. Cahill ◽  
Elliot Israel ◽  
Enrico Maiorino ◽  
...  
Keyword(s):  
Gene Expression ◽  
Clinical Trial ◽  
Systems Biology ◽  
Pilot Study ◽  
Severe Asthma ◽  
Treated Patient ◽  
Expression Patterns ◽  
Airflow Limitation ◽  
Response To Treatment ◽  
Imatinib Treatment

There is an acute need for advances in pharmacologic therapies and a better understanding of novel drug targets for severe asthma. Imatinib, a tyrosine kinase inhibitor, has been shown to improve forced expiratory volume in 1 s (FEV1) in a clinical trial of patients with severe asthma. In a pilot study, we applied systems biology approaches to epithelium gene expression from these clinical trial patients treated with imatinib to better understand lung function response with imatinib treatment. Bronchial brushings from ten imatinib-treated patient samples and 14 placebo-treated patient samples were analyzed. We used personalized perturbation profiles (PEEPs) to characterize gene expression patterns at the individual patient level. We found that strong responders—patients with greater than 20% increase in FEV1—uniquely shared multiple downregulated mitochondrial-related pathways. In comparison, weak responders (5–10% FEV1 increase), and non-responders to imatinib shared none of these pathways. The use of PEEP highlights its potential for application as a systems biology tool to develop individual-level approaches to predicting disease phenotypes and response to treatment in populations needing innovative therapies. These results support a role for mitochondrial pathways in airflow limitation in severe asthma and as potential therapeutic targets in larger clinical trials.

Early response of gene clusters is associated with mouse lung resistance or sensitivity to cigarette smoke

2009 ◽  
Vol 296 (3) ◽  
pp. L418-L429 ◽  
Author(s):  
Eleonora Cavarra ◽  
Paolo Fardin ◽  
Silvia Fineschi ◽  
Annamaria Ricciardi ◽  
Giovanna De Cunto ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cigarette Smoke ◽  
Gene Clusters ◽  
Early Response ◽  
Cigarette Smoke Exposure ◽  
Mouse Lung ◽  
Individual Response ◽  
Molecular Signaling ◽  
Gene Expression Response ◽  
Real Time Quantitative Pcr

We have investigated the effects of cigarette smoke exposure in three different strains of mice. DBA/2 and C57BL/6J are susceptible to smoke and develop different lung changes in response to chronic exposure, whereas ICR mice are resistant to smoke and do not develop emphysema. The present study was carried out to determine early changes in the gene expression profile of mice exposed to cigarette smoke with either a susceptible or resistant phenotype. The three strains of mice were exposed to smoke from three cigarettes per day, 5 days/wk, for 4 wk. Microarray analysis was carried out on total RNA extracted from the lung using the Affymetrix platform. Cigarette smoke modulates several clusters of genes (i.e., proemphysematous, acute phase response, and cell adhesion) in smoke-sensitive DBA/2 or C57BL/6J strains, but the same genes are not altered by smoke in ICR resistant mice. Only a few genes were commonly modulated by smoke in the three strains of mice. This pattern of gene expression suggests that the response to smoke is strain-dependent and may involve different molecular signaling pathways. Real-time quantitative PCR was used to verify the pattern of modulation of selected genes and their potential biological relevance. We conclude that gene expression response to smoke is highly dependent on the mouse genetic background. We speculate that the definition of gene clusters associated, to various degrees, with mouse susceptibility or resistance to smoke may be instrumental in defining the molecular basis of the individual response to smoke-induced lung injury in humans.

scholarly journals A Systems Biology-Based Gene Expression Classifier of Glioblastoma Predicts Survival with Solid Tumors

PLoS ONE ◽  
2009 ◽  
Vol 4 (7) ◽  
pp. e6274 ◽  
Author(s):  
Jing Zhang ◽  
Bing Liu ◽  
Xingpeng Jiang ◽  
Huizhi Zhao ◽  
Ming Fan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Systems Biology ◽  
Solid Tumors ◽  
Gene Expression Classifier

Systems Biology-Based Approaches Applied to Vaccine Development

Data Mining ◽  
2013 ◽  
pp. 1131-1148
Author(s):  
Patricio A. Manque ◽  
Ute Woehlbier
Keyword(s):  
Gene Expression ◽  
Systems Biology ◽  
Vaccine Development ◽  
Cost Effective ◽  
Host Responses ◽  
Effective Vaccine ◽  
Antigen Discovery ◽  
Genomic Technologies ◽  
History Of ◽  
Genomic Revolution

Vaccines represent one of the most cost-effective ways to prevent and treat diseases. The use of vaccines in the control of viral diseases represents an important milestone in the history of medicine. The genomic revolution brought us the possibility to scan genomes in the search of new and more effective vaccine candidates and the advancement of bioinformatics provided the framework for the application of strategies that were focused not only on antigen discovery but also on comparative genomics, and pathogenic factor identification and data mining. In addition, the progress in post-genomic technologies including gene expression technologies such as microarray and proteomics gave us the opportunity to explore the host responses to vaccines leading to a better understanding of immune responses to pathogens and/or to vaccines, assisting in the development of new and better vaccines and adjuvants. This chapter will review how systems biology-based approaches including genomics, gene expression technologies, and bioinformatics have changed the way of thinking about antigen discovery and vaccine development. In addition, the chapter will discuss how the study of the host responses in combination with “in silico” approaches could help predict immunogenicity and improve the efficacy of vaccines.

scholarly journals An Integrative Systems Biology and Experimental Approach Identifies Convergence of Epithelial Plasticity, Metabolism, and Autophagy to Promote Chemoresistance

2019 ◽  
Vol 8 (2) ◽  
pp. 205 ◽  
Author(s):  
Shengnan Xu ◽  
Kathryn Ware ◽  
Yuantong Ding ◽  
So Kim ◽  
Maya Sheth ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Systems Biology ◽  
Gene Expression Data ◽  
Transforming Growth Factor ◽  
Epithelial Mesenchymal Transition ◽  
Phylogenetic Analyses ◽  
Host Immune System ◽  
Expression Data ◽  
Mesenchymal Transition

The evolution of therapeutic resistance is a major cause of death for cancer patients. The development of therapy resistance is shaped by the ecological dynamics within the tumor microenvironment and the selective pressure of the host immune system. These selective forces often lead to evolutionary convergence on pathways or hallmarks that drive progression. Thus, a deeper understanding of the evolutionary convergences that occur could reveal vulnerabilities to treat therapy-resistant cancer. To this end, we combined phylogenetic clustering, systems biology analyses, and molecular experimentation to identify convergences in gene expression data onto common signaling pathways. We applied these methods to derive new insights about the networks at play during transforming growth factor-β (TGF-β)-mediated epithelial–mesenchymal transition in lung cancer. Phylogenetic analyses of gene expression data from TGF-β-treated cells revealed convergence of cells toward amine metabolic pathways and autophagy during TGF-β treatment. Knockdown of the autophagy regulatory, ATG16L1, re-sensitized lung cancer cells to cancer therapies following TGF-β-induced resistance, implicating autophagy as a TGF-β-mediated chemoresistance mechanism. In addition, high ATG16L expression was found to be a poor prognostic marker in multiple cancer types. These analyses reveal the usefulness of combining evolutionary and systems biology methods with experimental validation to illuminate new therapeutic vulnerabilities for cancer.

scholarly journals Towards the integration of computational systems biology and high-throughput data: supporting differential analysis of microarray gene expression data

2008 ◽  
Vol 5 (1) ◽  
pp. 57-71 ◽  
Author(s):  
Nicola Segata ◽  
Enrico Blanzieri ◽  
Corrado Priami
Keyword(s):  
Gene Expression ◽  
Systems Biology ◽  
High Throughput ◽  
System Level ◽  
Expression Data ◽  
Differential Analysis ◽  
Computational Systems Biology ◽  
Microarray Gene Expression ◽  
High Throughput Data ◽  
Computational Systems

Summary The paradigmatic shift occurred in biology that led first to high-throughput experimental techniques and later to computational systems biology must be applied also to the analysis paradigm of the relation between local models and data to obtain an effective prediction tool. In this work we introduce a unifying notational framework for systems biology models and high-throughput data in order to allow new integrations on the systemic scale like the use of in silico predictions to support the mining of gene expression datasets. Using the framework, we propose two applications concerning the use of system level models to support the differential analysis of microarray expression data. We tested the potentialities of the approach with a specific microarray experiment on the phosphate system in Saccharomyces cerevisiae and a computational model of the PHO pathway that supports the systems biology concepts.

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