Spatiotemporal modeling and model restructuration approaches in studies of intracellular signalling pathways

The main focus of the research is to understand the complex phenomena of cell transduction pathways and cell biology in a single cell. Mathematical modeling and experimental evaluation are widely used approaches for this kind of research. Firstly, A multiscale framework for protein-protein interaction has been established using Brownian dynamics algorithm. Sit specific feature, steric collision, diffusion, co-localization and complex formation with time and space has been included in this spatial modeling framework. By implementation of the time adaptive feature in this framework, the computation time reduces in an order of magnitude compared with traditional modeling framework. This multiscale Brownian framework has been used for the investigation FcεRI aggregation which is an important signaling pathway for immune cells. Using the spatial modeling framework, FcεRI aggregation in the presence of trivalent antigen showed consistent results with current experimental studies. Secondly, the rule-based modeling approach is an excellent way of performing large biochemical network modeling for a single cell as it considers the site-specific features. However, the major difficulty of rule-based modeling approach is combinatorial complexity. In this study, model restructuring approaches have been applied to overcome this problem for cell signaling pathway modeling. These mechanistic modeling approaches are very effective to model large network of signaling pathways together without compromising the accuracy. Finally, Cell size dependent cellular uptake study carried out using confocal microscopy and flow cytometer. To understand the particle uptake behavior with time and steady state condition, reaction-diffusion and kinetics model has been developed in these work. After a detailed analysis of experimental data and models, it showed that total particle uptake is increasing with cell size, however, particle flux is reducing in larger cells --Abstract, page iv

A Spatio-Temporal Model Reveals Self-Limiting FcɛRI Cross-Linking by Multivalent Antigens

Aggregation of cell surface receptor proteins by multivalent antigens is an essential early step for immune cell signalling. A number of experimental and modelling studies in the past have investigated multivalent ligand-mediated aggregation of IgE receptors (FcɛRI) in the plasma membrane of mast cells. However, understanding of the mechanisms of FcɛRI aggregation remains incomplete. Experimental reports indicate that FcɛRI forms relatively small and finite-sized clusters when stimulated by a multivalent ligand. By contrast, modelling studies have shown that receptor cross-linking by a trivalent ligand may lead to the formation of large receptor superaggregates that may potentially give rise to hyperactive cellular responses. In this work, we have developed a Brownian dynamics-based spatio-temporal model to analyse FcɛRI aggregation by a trivalent antigen. Unlike the existing models, which implemented non-spatial simulation approaches, our model explicitly accounts for the coarse-grained site-specific features of the multivalent species (molecules and complexes). The model incorporates membrane diffusion, steric collisions and sub-nanometre-scale site-specific interaction of the time-evolving species of arbitrary structures. Using the model, we investigated temporal evolution of the species and their diffusivities. Consistent with a recent experimental report, our model predicted sharp decay in species mobility in the plasma membrane in response receptor cross-linking by a multivalent antigen. We show that, due to such decay in the species mobility, post-stimulation receptor aggregation may become self-limiting. Our analysis reveals a potential regulatory mechanism suppressing hyperactivation of immune cells in response to multivalent antigens

A Multiscale Algorithm for Spatiotemporal Modeling of Multivalent Protein-Protein Interaction

This article introduces a multiscale framework for spatiotemporal modeling of protein-protein interaction. Cellular protein molecules represent multivalent species that contain modular features, such as binding domains and phosphorylation motifs. The binding and transformations of these features occur at a small time and spatial scale. On the other hand, space and time involved in protein diffusion, colocalization, and formation of complexes could be relatively large. Here, we present an agent-based framework integrated with a multiscale Brownian Dynamics (BD) simulation algorithm. The framework employs spatial graphs to describe multivalent molecules and complexes with their site-specific details. By implementing a time-adaptive feature, the BD algorithm enables efficient computation while capturing the site-specific interactions of the diffusing species at the sub-nanometer scale. We demonstrate these capabilities by modeling two multivalent molecules, one representing a ligand and the other a receptor, in a two-dimensional plane (cell membrane). Using the model, we show that the algorithm can accelerate computation by orders of magnitudes in both concentrated and dilute regimes. We also show that the algorithm enables robust model predictions against a wide range of selection of time step sizes