A conceptual cellular interaction model of left ventricular remodelling post-MI: dynamic network with exit-entry competition strategy
© Jin et al. 2010
Published: 28 May 2010
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© Jin et al. 2010
Published: 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.
Progressive remodelling of the left ventricle (LV) following myocardial infarction (MI) involves spatial-temporal cellular interactions among different cell types . Apoptosis of myocytes, infiltration of neutrophils, activation of macrophages, activation of endothelial cells, and proliferation of fibroblasts are LV remodelling components [2–5]. These events are accompanied with a temporal flux in the cellular population profiles post-MI [6–13]. Among these cells, macrophages play a pivotal role by coordinating phagocytosis of cellular debris at the MI site and secreting cytokines interleukin-1β, interleukin-6, and tumor necrosis factor α, matrix metalloproteinases (MMPs), tissue inhibitor of metalloproteinases (TIMPs), and growth factors [14–17]. Macrophages are known to undergo a classical activation characterized by pro-inflammatory gene expression in the early stage post-MI. In the later stage post-MI, macrophages undergo an alternative activation characterized by the secretion of factors that promote fibrosis, would healing, neovascularization and granuloma formation. While research has been carried out to investigate the populations of macrophages through different activation schemes , the relationship and interactions between these two activated macrophage cellular populations post-MI remains unclear.
Macrophages are believed to first undergo classical activation, and then proceed through the alternative activation pathway [19, 20]. Macrophages do not die locally in the scar tissue, but emigrate from scar tissue to the lymph node system . Thus, the MI site behaves as a network that regulates the exit and entry of macrophages, and the local cytokine environment determines the populations of classically and alternatively activated macrophages. Accordingly, the purpose of this study was to investigate the mathematical relationship among macrophage populations and interactions in a dynamic network.
The evolution of a dynamic network has been carried out in game theory, social networks, and other biological systems [22–26]. Existing studies have demonstrated that outcomes of tumor growth are determined by the cellular interactions, and these interactions include both cooperation and competition among these cells through a dynamic network . In our research, we have generated stability conditions of a LV network containing two types of macrophages and introduced a new approach to model the temporal activation of macrophages post-MI.
We developed a dynamic network including two types of macrophages based on a previous graphic model published by Nowak and colleagues . To elucidate the underlying mechanisms of the dynamical evolution, theoretical analysis was carried out and conditions for different evolutionary profiles were obtained. Computer simulations illustrated the dynamic evolution of the network with interactions among two types of macrophages.
In this study, an exit-entry strategy was chosen for the conceptual model, since exit-entry is a fundamental cellular migration scheme for cellular interaction post-MI. In the exit-entry strategy, each iterative step in the exit-entry evolutionary process is called a generation. During the evolutionary process, a cell is chosen randomly to exit in each generation. Assuming a vacated vertex caused by cellular exiting will be only replaced with either a new type A or type C cell, a probability of replacing by a type A cell is determined by F A /(F A +F C ), where F A and F C are fitness functions of all adjacent cells linked to the vacated vertex. To be specific, the fitness function contributed by all the neighbouring type A cells connected to the exiting cell, is calculated as , where ω is the intensity of selection, K A is the number of type A cells linked to the exiting cell.
where O(•) denotes higher order terms of a variable.
In the case of weak selection, ω << 1 holds. Therefore, equation (5) represents a fast manifold and equation (2) represents a slow manifold of the dynamics. Our analysis has led to three equilibriums, P A = 0, 1, or of the dynamic network based on the constraints of the interaction strength parameters a, b, c, d, and the number of links k in the situation of weak selection. We summarized the following conditions for the three equilibriums.
Case 1: Stable equilibrium at P A = 1
where P A0 is the initial position of P A .
Based on the theoretical analysis, we predicted three types of evolutionary profiles: 1) population of type A cells (alternatively activated macrophages) dominates the total cellular population, 2) population of type C cells (classically activated macrophages) dominates the total cellular population, or 3) populations of type A and type C cells reach a dynamic balance in the total cellular population. In addition, the simulations also showed that the evolutionary profiles are related with the interaction strengths, the number of links between cells, and the initial status of the cellular population. To verify the prediction, we designed computer simulations based on the conditions given from equations (7-9) to explore the evolutionary profiles of the network.
All the initial conditions, interaction strength, and the number of links listed in the simulations satisfied the condition associated with the specified equilibrium. The simulations verified predictions on the evolutionary profiles of the network based on our theoretical analysis.
We have used a dynamic network model to study the cellular interactions with an exit-entry strategy. Our results demonstrated that evolutionary profiles of a dynamic network could be stabilized at different states by perturbing the interaction strength matrix, the number of links, and initial status of the network. We have quantified conditions for stable states in terms of interaction strengths, the initial status, and the number of links in the network. Our computer simulations verified predictions of our analytic results. While we used an exit-entry strategy presented by game theory [28, 29], our stability analysis provided not only the stability property but also the convergence states of the system, which is broader than the previous evaluations . We extended analytical stability to the current analysis methods that quantify results using graph theory [24, 26, 30, 31].
Here we have two remarks of our methods. First, we only considered an exit-entry strategy in a structured dynamic network. The exit-entry strategy was chosen because it was the most fundamental and logical cellular function for an initial investigation of the interactions between populations of classically and alternatively activated macrophages post-MI. There exist other evolutionary strategies such as entry-exit, mutation, and imitation. These strategies will need to be considered and potentially incorporated in future models. Secondly, the structure of the dynamic network is fixed by assuming a weak selection, ω << 1, and a constant interaction strength matrix. However, stability analysis of the evolutionary strategies with varying structures has more realistic applications to biological systems and has attracted lots of research interest to game theory recently [22, 32]. Stability analysis of dynamic networks with varying structure needs to be included in future research models.
We provide here the first application of a dynamic network model to describe macrophage interactions. We have obtained explicit conditions that determine interaction strength and have established a structure of the network that allows us to predict the stability and equilibrium of the post-MI dynamic network. Our simulation results confirmed the prediction of the stability and the equilibriums of the network.
We used a new approach to model the cellular interactions between macrophage activation types in the post-MI setting. The results on stability analysis can be used as a useful tool to predict the responses of specific cellular populations.
Define the parameters c 0 = (k + 1)a + (k 2 – k − 1)b – c +(k 2 − 1)d
c 1 = (k + 1)(k − 3)a −(2k 2 − 2k − 3)b −(k 2 – k − 3)c −(k + 1)d
and c 2 = −(k + 1)(k − 2)(a − b − c + d) in the case of weak selection(ω << 1), stability of P A is determined by checking the sign of polynomial . There exist three equilibriums for i.e., p A = 0, 1 or .
Stability of the system can be checked with 3 cases based on the position of the third root and sign of the parameter c 2. The relations of the three equilibriums have been shown in Figures (2, 3, 4, 5, 6, 7).
The authors acknowledge grant support from NIH R01 HL75360, AHA Grant-in-Aid 0855119F, and the Morrison Fund (to MLL), grant support from NSF 0644646 and 0602834 (to HCH), and grant support from NSF 0649172 and NIH 1SC2 HL101430 (to YJ).
This article has been published as part of BMC Systems Biology Volume 4 Supplement 1, 2010: Proceedings of the ISIBM International Joint Conferences on Bioinformatics, Systems Biology and Intelligent Computing (IJCBS). The full contents of the supplement are available online at http://www.biomedcentral.com/1752-0509/4?issue=S1.
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