(344a) A Logic-Based Modeling Study of the Immune Response Under High Glucose Conditions in Diabetic Kidney Disease

Introduction: Diabetic kidney disease is a complication in 1 out of 3 patients with type 1 and type 2 diabetes. Aberrant glucose metabolism in diabetes lead to systemic inflammation and immune response, which cause structural and functional damage in the kidney. There are several inflammatory signaling pathways, e.g. PI3K/AKT, TLR, NF-κB, MAPK, which get activated under high glucose conditions. Due to these metabolic derangements, immune cells such as macrophages have a pro-inflammatory phenotype expressed by cytokines and reactive oxygen species (ROS). However, the mechanism underlying macrophage function in diabetic kidney disease is not fully understood. These complex regulatory and signaling networks can be understood through mathematical models. The objective of the present work is to develop a predictive logic-based dynamic model of immune response mediated by stimulants, like glucose, in the diabetic kidney by integrating a literature-based biological network, experimental data, and Boolean transfer functions. This logic-based model will complement the analysis with both a qualitative and quantitative approach, especially when sufficient quantitative information is not available.

Methodology: A time-dependent continuous logic-based model derived from ordinary differential equations (ODEs) has been built using the CNORode library in CellNOptR, an open-source R package. The signaling network (Fig. 1) and supporting biochemical data to formulate the logic-based model have been adapted from published experimental data¹. This preliminary model considers two stimulatory species, glucose and lipopolysaccharide (LPS), that trigger downstream signaling of ROS and pro-inflammatory phenotype of macrophages via cytokines including interleukin-6 (IL-6) and tumor necrosis factor-α (TNF-α). The OFF or ON states of the glucose node correspond to normal glucose levels of 5mM and high glucose level of 25mM respectively. The continuous ordinary differential equations (ODE) are dependent on normalized Hill functions, which are constrained between 0 and 1. The logic-based model is trained over experimental data while optimizing model fitness and model size.

Results and Discussion: The model response of each readout node is recorded for all possible combinations of input node state. The ODE parameters are optimized using an built-in genetic algorithm function to improve model prediction power. By training the logic-based ODE model on experimental data, improved model prediction and minimized number of edges across each node are expected. This literature-derived model setup yields qualitative temporal behavior of the system and provides understanding of cellular response and interaction during inflammation in diabetic kidney disease. The logic-based modelling approach may provide broader understanding of underlying mechanisms and qualitative descriptions to help wet-bench analysis and clinical research in the future.

Reference: 1. Ayala, T.S., Tessaro, F.H.G., Jannuzzi, G.P., Bella, L.M., Ferreira, K. S. and Martins, J. O. High Glucose Environments Interfere with Bone Marrow-Derived Macrophage Inflammatory Mediator Release, the TLR4 Pathway and Glucose Metabolism. Sci Rep 9, 11447 (2019).

Acknowledgment: This work was supported by the National Institutes of Health grant R35GM133763, the National Science Foundation grant 1845117, and the University at Buffalo.