This article is part of the supplement: The ISIBM International Joint Conferences on Bioinformatics, Systems Biology and Intelligent Computing (IJCBS)
A conceptual cellular interaction model of left ventricular remodelling post-MI: dynamic network with exit-entry competition strategy
1 Department of Electrical and Computer Engineering, University of Texas at San Antonio, San Antonio, USA
2 Department of Mechanical Engineering, University of Texas at San Antonio, San Antonio, USA
3 Center for Research in Biological Systems, University of California at San Diego, La Jolla, California 92093-0043, USA
4 Department of Medicine, University of Texas Health Science Center at San Antonio, San Antonio, USA
BMC Systems Biology 2010, 4(Suppl 1):S5 doi:10.1186/1752-0509-4-S1-S5Published: 28 May 2010
Progressive remodelling of the left ventricle (LV) following myocardial infarction (MI) is an outcome of spatial-temporal cellular interactions among different cell types that leads to heart failure for a significant number of patients. Cellular populations demonstrate temporal profiles of flux post-MI. However, little is known about the relationship between cell populations and the interaction strength among cells post-MI. The objective of this study was to establish a conceptual cellular interaction model based on a recently established graph network to describe the interaction between two types of cells.
We performed stability analysis to investigate the effects of the interaction strengths, the initial status, and the number of links between cells on the cellular population in the dynamic network. Our analysis generated a set of conditions on interaction strength, structure of the network, and initial status of the network to predict the evolutionary profiles of the network. Computer simulations of our conceptual model verified our analysis.
Our study introduces a dynamic network to model cellular interactions between two different cell types which can be used to model the cellular population changes post-MI. The results on stability analysis can be used as a tool to predict the responses of particular cell populations.