Predicting bone formation induced by mechanical loading using agent based models

University Of Washington, Seattle WA

Investigators

Abstract

[unreadable] DESCRIPTION (provided by applicant): Mechanical loading is a potent anabolic stimulus for bone cells. Despite this promise, exercise strategies have proven to be of limited utility in compensating for the loss of bone mass accompanying aging and menopause. The ineffectiveness of exercise has arisen, in part, due to current gaps in knowledge of how mechanotransduction functions within bone. Our long-term goal is to improve understanding of this biological process and to thereby develop tools for the optimized design of exercise capable of successfully counteracting skeletal fragility. Central to our pursuit are experimental observations that bone cells acutely perceive and respond to even brief bouts of mechanical loading. To explore this process, we developed agent based models (ABM) of mechanical loading induced real-time Ca2+ signaling in bone cells - a focal cellular mechanism currently inaccessible in vivo. In preliminary studies, we found that ABM simulations of Ca2+ signaling interactions (order of seconds) could be related to down-stream osteoblastic activation and bone formation with surprising accuracy. In this exploratory R21 application, we will extend our studies and test the general hypothesis that agent based models of real-time Ca2+ signaling in bone cell networks predict surface bone formation induced by mechanical loading. We will test our hypothesis via two specific aims. In the first aim, we will implement three ABMs of progressively increased biological detail and parametric complexity. Following iterative refinement, our efforts will culminate in a multi-scale ABM in which loading induced real-time Ca2+ signaling interactions within the bone cell syncytium guides lining cell and osteoblast function, and overtime, influences bone tissue formation. Model parameters will be estimated via Markov chain Monte Carlo methods such that ABMs simulate bone formation induced in the murine tibia mid-shaft by 10 previously implemented loading protocols. In the second aim, the validity of the ABMs will be challenged by their ability to predict bone formation induced by new loading protocols, and predictions validated via subsequent in vivo experiments. Significantly, our models will describe for the first time, how distinct osteogenic outcomes emerge over vast temporal and spatial scales from bone cell signaling interactions, function and turnover induced by a variety of mechanical stimuli. By exploring bone adaptation at such detail, our study holds potential to generate novel insights into the complex, 'bottom-up" functioning of mechanotransduction within bone. Success of this application will therefore represent a crucial first step towards our long-term goal of developing tools for the optimized design of mechanical loading protocols to augment bone mass and strength. [unreadable] [unreadable] [unreadable]