ABM of osteoblast's mechanotransduction pathway: time patterns of critical events

TL;DR

This paper models osteoblast mechanotransduction using an agent-based approach to analyze molecular dynamics and critical event patterns, revealing how fluctuations and waiting times reflect system behavior and potential disease mechanisms.

Contribution

It introduces a novel agent-based model of osteoblast mechanotransduction that captures stochastic molecular interactions and analyzes critical event recurrence patterns.

Findings

Large molecular fluctuations contain hidden information beyond baseline variations.

Waiting time distributions serve as signatures of system dynamics.

Parameter changes alter molecular event patterns, highlighting key molecules in the pathway.

Abstract

Background: Mechanotransduction in bone cells plays a pivotal role in osteoblast differentiation and bone remodelling. Mechanotransduction provides the link between modulation of the extracellular matrix and intracellular actions. By controlling the balance between the intracellular and extracellular domains, the mechanotransduction process determines optimal functionality of the skeletal dynamics, and it is one of the possible causes of osteophatological diseases. Results: Mechanotransduction in a single osteoblast under external mechanical perturbations has been modelled in the agent based framework to reproduce the dynamics of the stochastic reaction diffusion process among molecules in the cytoplasm, nuclear and extracellular domains. The amount of molecules and fluctuations of each molecular class has been analysed in terms of recurrences of critical events. A numerical approach has been developed to invert subordination processes and to extract the directing processes from the molecular signals in order to derive the distribution of recurrence of events. Conclusions: We observed large fluctuations enclose information hidden in the noise which is beyond the dynamic variations of molecular baselines. Studying the system under different parametric conditions and stimuli, the results have shown that the waiting time distributions of each molecule are a signature of the system's dynamics. The behaviours of the molecular waiting times change with the changing of parameters presenting the same variation of patterns for similar interacting molecules and identifying specific alterations for key molecules in the mechanotransduction pathway.