Title |
Agent-based dynamic knowledge representation of Pseudomonas aeruginosa virulence activation in the stressed gut: Towards characterizing host-pathogen interactions in gut-derived sepsis
|
---|---|
Published in |
Theoretical Biology and Medical Modelling, September 2011
|
DOI | 10.1186/1742-4682-8-33 |
Pubmed ID | |
Authors |
John B Seal, John C Alverdy, Olga Zaborina, Gary An |
Abstract |
There is a growing realization that alterations in host-pathogen interactions (HPI) can generate disease phenotypes without pathogen invasion. The gut represents a prime region where such HPI can arise and manifest. Under normal conditions intestinal microbial communities maintain a stable, mutually beneficial ecosystem. However, host stress can lead to changes in environmental conditions that shift the nature of the host-microbe dialogue, resulting in escalation of virulence expression, immune activation and ultimately systemic disease. Effective modulation of these dynamics requires the ability to characterize the complexity of the HPI, and dynamic computational modeling can aid in this task. Agent-based modeling is a computational method that is suited to representing spatially diverse, dynamical systems. We propose that dynamic knowledge representation of gut HPI with agent-based modeling will aid in the investigation of the pathogenesis of gut-derived sepsis. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 4 | 5% |
Germany | 1 | 1% |
Montenegro | 1 | 1% |
France | 1 | 1% |
Peru | 1 | 1% |
United Kingdom | 1 | 1% |
Unknown | 74 | 89% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 20 | 24% |
Student > Ph. D. Student | 17 | 20% |
Student > Bachelor | 9 | 11% |
Professor > Associate Professor | 8 | 10% |
Student > Master | 6 | 7% |
Other | 14 | 17% |
Unknown | 9 | 11% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 20 | 24% |
Medicine and Dentistry | 14 | 17% |
Computer Science | 10 | 12% |
Engineering | 7 | 8% |
Biochemistry, Genetics and Molecular Biology | 5 | 6% |
Other | 14 | 17% |
Unknown | 13 | 16% |