Toward Mechanical Systems Biology in Bone

Abstract

Cyclic mechanical loading is perhaps the most important physiological factor regulating bone mass and shape in a way which balances optimal strength with minimal weight. This bone adaptation process spans multiple length and time scales. Forces resulting from physiological exercise at the organ scale are sensed at the cellular scale by osteocytes, which reside inside the bone matrix. Via biochemical pathways, osteocytes orchestrate the local remodeling action of osteoblasts (bone formation) and osteoclasts (bone resorption). Together these local adaptive remodeling activities sum up to strengthen bone globally at the organ scale. To resolve the underlying mechanisms it is required to identify and quantify both cause and effect across the different scales. Progress has been made at the different scales experimentally. Computational models of bone adaptation have been developed to piece together various experimental observations at the different scales into coherent and plausible mechanisms. However additional quantitative experimental validation is still required to build upon the insights which have already been achieved. In this review we discuss emerging as well as state of the art experimental and computational techniques and how they might be used in a mechanical systems biology approach to further our understanding of the mechanisms governing load induced bone adaptation, i.e., ways are outlined in which experimental and computational approaches could be coupled, in a quantitative manner to create more reliable multiscale models of bone.

Experimental studies of bone mechanoadaptation: bridging in vitro and in vivo studies with multiscale systems

“…Trü ssel et al [68] recently outlined a mechanical systems biology approach in the study of bone adaptation. Briefly, the authors demonstrate that mCT scans taken at regular intervals of a loading treatment can be registered using advanced imaging software in order to identify local regions of bone adaptation.…”

Mechanical Stimuli in the Local In Vivo Environment in Bone: Computational Approaches Linking Organ-Scale Loads to Cellular Signals

“…Coupling these technologies has revealed great insights into dynamic bone (re)modelling via comparisons between mechanical loading and structural changes in bone tissue (6, 9–11). As these imaging and computational modelling methods have matured, they have become accurate enough to inform techniques such as laser capture microdissection to investigate individual cells within the bone tissue, and to perform “mechanomic” analysis, reconciling genetic responses to mechanical stimuli (12, 13) of the acquired cells (14, 15). The extraction of small populations of cells (16) and the assessment of their molecular and genetic profiles (17) has been combined with computational predictions of mechanical loads within the local in vivo environment (L iv E) of these cells (17), advancing our understanding of how organ-scale loads influence individual cells and the resultant (re)modelling behaviour.…”

Studying osteocytes within their environment

“…To overcome these limitations a novel combination of old and new technologies has recently been proposed (termed microfluidic imaging) which promises to map, quantitatively, and in three dimensions the expression of multiple genes in individual osteocytes. This ‘microfluidic imaging’ approach is reviewed in more detail elsewhere [64] but can be briefly described by the following workflow (Figure 7): 1) Bone formation and resorption are spatially mapped and quantified in a mouse loading model using in vivo μCT [65] and 3D image registration techniques [66]. 2) The micromechanical environment in loaded bone is determined by creating μFE models of the loaded bone from the initial CT image [67, 68].…”

“…4) By registering 2D images of cryosections with registered μCT images and their μFE models osteocyte positions along with their unique expression profiles are mapped back to their original in vivo micro-environments. It is anticipated that the vast amount of data generated using this approach can be used to build, feed and validate computational models of bone which incorporate all of the different length scales, from the organ-level to the cellular-level [64, 72]. By combining computational and experimental approaches in this way it hoped that the move towards a more complete understanding of the osteocyte and bone biology in general will be expedited.…”