Agent-based modeling of endotoxin-induced acute inflammatory response in human blood leukocytes

Abstract

Background: Inflammation is a highly complex biological response evoked by many stimuli. A persistent challenge in modeling this dynamic process has been the (nonlinear) nature of the response that precludes the single-variable assumption. Systems-based approaches offer a promising possibility for understanding inflammation in its homeostatic context. In order to study the underlying complexity of the acute inflammatory response, an agent-based framework is developed that models the emerging host response as the outcome of orchestrated interactions associated with intricate signaling cascades and intercellular immune system interactions.

Methodology/principal findings: An agent-based modeling (ABM) framework is proposed to study the nonlinear dynamics of acute human inflammation. The model is implemented using NetLogo software. Interacting agents involve either inflammation-specific molecules or cells essential for the propagation of the inflammatory reaction across the system. Spatial orientation of molecule interactions involved in signaling cascades coupled with the cellular heterogeneity are further taken into account. The proposed in silico model is evaluated through its ability to successfully reproduce a self-limited inflammatory response as well as a series of scenarios indicative of the nonlinear dynamics of the response. Such scenarios involve either a persistent (non)infectious response or innate immune tolerance and potentiation effects followed by perturbations in intracellular signaling molecules and cascades.

Conclusions/significance: The ABM framework developed in this study provides insight on the stochastic interactions of the mediators involved in the propagation of endotoxin signaling at the cellular response level. The simulation results are in accordance with our prior research effort associated with the development of deterministic human inflammation models that include transcriptional dynamics, signaling, and physiological components. The hypothetical scenarios explored in this study would potentially improve our understanding of how manipulating the behavior of the molecular species could manifest into emergent behavior of the overall system.

Figure 1. Interacting components/agents involved in the propagation of LPS signaling on macrophages.

Yellow triangles reflect the extracellular signal (LPS) and white circles represent the plasma membrane. Red polygons refer to the endotoxin (LPS) receptor and blue polygons refer to the TNF-a receptor. Light green polygons correspond to IL-4 receptors and dark green polygons reflect the presence of kinase (IKK). Blue+orange squares represent the inactive (bound) NF-kB with its inhibitor, IkBa while the grey area refers to the nucleus.

Figure 2. Schematic illustration of elements and interactions involved in the agent based model of endotoxin induced inflammation.

Figure 3. A self-limited inflammatory response (LPS(0) = 350 units).

Temporal profiles of essential components that constitute the agent based model resolved within 24 hr.

Figure 4. Temporal responses of an unresolved inflammatory response due to high LPS concentration.

A high concentration of LPS (LPS(0) = 750) can cause a malfunction in the dynamics of the host leading to an exacerbated inflammatory response (solid lines). Dashed lines refer to the implications of high concentration of LPS as simulated by our deterministic (ODE) approach. For the purpose of comparing the simulated output between the ABM and the ODE model, all responses are normalized so that numerically they range between (0,1).

Figure 5. Temporal responses in a persistent infectious inflammatory response.

Solid lines correspond to LPS(t = 0 hr) = 1000 which accounts for a prolonged inflammatory activity causing a malfunction in LPS clearance rate. Dashed lines refer to equation-based model predictions for the case of a persistent infectious challenge which can be achieved by manipulating the first order degradation rate of LPS as discussed in the original analysis . The output of both modeling approaches is normalized so that numerically it ranges between (0,1).

Figure 6. Endotoxin tolerance scenario.

Pre-existing infection might cause a profound reduction in cell's capacity (hypo-responsiveness) to respond in the main endotoxin challenge. Solid line: LPS(t = 0 hr) = 750. Dotted line: LPS(t = 0 hr) = 100 & LPS(t = 8 hr) = 650.

Figure 7. Lethal potentiation.

Successive administration of small doses of endotoxin can lead to an unresolved inflammatory response. Solid line: LPS(t = 0 hr) = 350. Dotted line: LPS(t = 0 hr) = 100 & LPS(t = 2 hr) = 250.

Figure 8. Pre-existence of inflammatory mediators (TNF-a) may enhance abnormally the intracellular signaling amplifying IKK activity.

Such response leads to an aberrant inflammatory response which cannot be counter-regulated by the anti-inflammatory arms of the system. Such a mode of dysregulation is simulated by concomitant exposure of the system to TNF-a and bacterial infection (LPS): LPS(t = 0 hr) = 350 & TNF-a(t = 0 hr) = 300.

Figure 9. Exploring the effect of an intervention (anti-inflammatory) strategy that inhibits IKK activity.

Such scenario is simulated by administering LPS(t = 0 hr) = 750 and IKK inhibitors, IKK inhibitors (t = 0 hr) = 400 (green line).