**4. Discussion**

Salivary diagnostics is being increasingly touted as a suitable alternative to whole blood assays. However, the study of salivary functions could also be challenging because of the high physiological variability of saliva in comparison with other bodily fluids [38]. However, given that the saliva samples can be obtained non-invasively, patients are much more likely to cooperate for frequent extraction of saliva, for performing assays regularly. We also observed this trend among the volunteers, who were unanimously relieved with the painless approach of collecting saliva samples for our experiments. Nevertheless, to exploit this convenience of salivary analysis, it is also equally important to develop a

suitable automated point-of-care testing methodology that would enable rapid diagnostics to reduce the time and e ffort normally put into laboratory-based assays. The cumulative results of our present work demonstrates that the hybrid integrated optical biosensing principle proposed herein could be readily applied to potassium measurement from whole saliva samples for real-time point-of-care testing applications based on the principle of colorimetry.

The colorimetric optical absorption-based detection principle presented here is simple and can be easily extended to any other fluid sample. While the results of traditional point-of-care colorimetric assays reported to the naked eye (such as pregnancy tests or blood glucose tests), integrating smart-phones [39] for automated testings have lately been gaining huge popularity. But, the implementation of the colorimetric technique would not be e ffective unless uniform, controlled mixing of samples with the appropriate chromogenic agents is achieved. The acoustofluidic actuator thus lends a major advantage to this device for achieving the required homogenous sample mixing. The bulk acoustic wave micromixer configuration described in this work is simple and cost-e ffective to fabricate. For the device setup presented here, the ease of operation and feasibility of seamless hybrid integration with other complementary modules of the biosensor platform lend advantages to the bulk acoustic wave micromixer over the popularly used surface acoustic wave micromixing formats. However, mixing e fficiency could be further improved by adopting simple modifications to the existing setup, such as incorporation of passive microfluidic elements, surface treatment resulting in increased hydrophobicity [40], and using acoustic index matching glues to improve piezoacoustic energy transfer. Our initial experiments showed that the cavitation-based acoustofluidic micromixing can be used with a wide variety of fluids with di fferent physical properties such as miscibility, viscosities, vapor pressures etc. (Supplementary Figure S1B). While the present work focused only on cavitation-enabled fluid mixing, it was also observed during the initial characterization experiments that the cavitation phenomenon also induced other physical e ffects in the fluids. For example, it was possible to take advantage of the energy released from collapse of the cavitation bubbles for cleaning the surface of the microfluidic channels to get rid of non-specifically bound adherent particles [26]. This functionality could be extremely useful for conducting biological assays repeatedly without cross-contamination inside the microfluidic channels.

However, there are a few shortcomings to the present approach. Firstly, the device format in its present state does not enable the realization of cheap, disposable biosensors. But, since the device is sterilizable for repeated use, integrating light sources and sensor units on-chip along with suitable optoelectronic packaging could enable further help with the miniaturization of this device for developing a more cost-e ffective version. Secondly, optical absorption-based detection may hit the limits when conducting assays for measuring extremely low analyte concentrations in the samples. Despite the relatively poor sensitivity of microfluidic absorbance detection compared to fluorescence, the principle of working of optical absorption and its instrumentation simplicity gives it an advantage in applications requiring point-of-use analysis. Also, wherever applicable, it is possible to carry out a comprehensive study of time-dependent biochemical interactions using the optical absorption method. One can also take confidence from the fact that a number of absorbance-based microfluidic point-of-care products are commercially available [41]. Our device design also allows for implementing more than one optical sensing principle on the same platform. In an alternate waveguide arrangement to that used in the present work, the output waveguide was designed at an angle with respect to the inlet waveguide so that light is not directly coupled onto the output waveguide. This configuration, although not used in this work, could be useful for sensitive low absorption measurements or fluorescence-based optical detections where specific emission wavelengths are sensed spectroscopically without being saturated by the excitation wavelengths. Thus, overall our present work clearly demonstrates the feasibility of developing integrated microfluidic optical detection systems for rapid biosensing and real-time diagnostics, which could be incorporated into the next generation point-of-care devices.


**Abstract**

According to the structural and metabolic demands of the body, proportionate and accurate bone quantities are resorbed and formed, establishing what is known as bone remodeling process. This physiological process requires a highly coordinated regulation through a complex interconnected network involving several cells from diverse origins, in addition to various hormones, cytokines, growth factors and signaling pathways. One of the main factors initiating the remodeling process is the mechanotransduction mechanism, through which osteocytes translate the mechanical stimuli subjected to the bone into biochemical signals, generating thereby the activation of osteoclasts and osteoblasts that govern bone resorption and formation. This mechanically-induced behavior of bone tissue has been the target of computational modeling and numerical simulations, to address biomechanical questions and provide information that is not amenable to direct measurements. In this context, the current chapter aims to review the coupling and mechanotransduction mechanisms spearheading the remodeling process, in addition to the main mathematical models developed over recent years and their use in bone numerical simulations based on the finite element method.

**Keywords:** bone remodeling, mechanical stimulus, cell interaction, mechanotransduction, finite element method

#### **1. Introduction**

Bone is a living composite material, providing various mechanical and homeostatic functions and characterized by a constant adaptation of its structure according to the metabolic and physical demands of the body. This self-adaptation property is governed by the complementary activities of resorption and formation, establishing what is known as bone remodeling process. This process requires a highly coordinated regulation over time and space to consistently maintain the proper quality and quantity of bone forming the skeleton. This coordination mainly incorporates two types of cells: bone-resorbing osteoclasts and bone-forming osteoblasts, representing the two major players of the remodeling event and providing a delicate balance between the removed bone amount and the subsequent deposited amount, which is carried out by generating the appropriate number of osteoblasts, a phenomenon known as the coupling mechanism. The coordination between

osteoblast and osteoclast activities also involves other cells of various origins, in addition to several hormones, cytokines and growth factors that tightly connect the osteoblast- and osteoclast-lineages through a complex communication network during the remodeling cycle.

The remodeling process maintains the calcium homeostasis and provides a crucial mechanism for old bone removal, as well as for damage repair and adaptation to physical stress, which helps preserving the mechanical integrity of the skeleton. The remodeling process takes place at an anatomically distinct sites called bone mass units (BMUs), with each BMU operating asynchronously and independently of other BMUs throughout the skeleton. Notably, BMUs in the cortical bone greatly differ from those in the trabecular bone, in terms of structure, but also of the resorption and formation activities. Bone remodeling is initiated in a canopy, defining what is called the bone remodeling compartment (BRC), where intercellular communication occurs among the component bone cells, from vascular and endothelial cells and probably from immune cells reaching the remodeling sites through the blood supply. To better understand bone behavior and biomechanics, several research have been conducted using clinical investigations, as well as numerical modeling.

Computational modeling and numerical simulation of represent an interesting tool to address biomechanical questions, particularly those targeting the biomechanical behavior of bone, allowing to provide information that is not amenable to direct measurements, such as bone strength and joint load. This kind of information is required for several clinical applications, including fracture prevention, implant design, and pathology analysis. In recent years, partition of unity methods, explicitly using finite element (FE) mesh, has become popular due to its easy applicability. The FE method is one of the most widely used numerical analysis techniques based on FE mesh. It provides approximate solutions to a wide range of engineering problems. Recently, the FE method has experienced a phenomenal expansion in the field of bone biomedical engineering, owing to its flexibility and diversity as an analysis tool, but also because it easily manages complex and evolving cellular domains, and can be generalized to multidimensions with little complication. Particularly, the FE method has been widely used to analyze and predict the mechanical behavior of bones, under physiologic and pathologic conditions, based on mathematical models that describe the cellular mechanisms governing the remodeling process. These mathematical models are including more and more factors and actors, to attempt affording a more realistic description of bone cell interactions.

In this context, the current paper provides a review of the processes of bone remodeling and cell interactions, as well as the recent findings about the mechanotransduction mechanisms, in addition to the mechanobiological models targeting bone dynamics and the main generated FE results in recent years.

#### **2. Bone cells and coupling mechanisms**

**Osteoblasts** are cuboidal cells located on the newly synthesized bone interface and have two types of functions: a bone building function, through which they produce matrix proteins, and endocrine functions, through which they release a wide range of regulatory factors that influence energy metabolism, male fertility and cognition [1, 2]. Osteoblasts originate from mesenchymal stromal cells (MSCs) according to two distinct embryonic populations: the first one is when osteoblastlineage cells derive from the neural ectoderm, the mesenchymal progenitors directly differentiate into preosteoblasts and the subsequent mature osteoblasts

#### *Cell Interaction and Mechanobiological Modeling of Bone Remodeling Process DOI: http://dx.doi.org/10.5772/intechopen.95045*

form the calvarian bones and clavicles through intramembranous ossification, whereas the second one is when the MSCs differentiate into perichondral cells and chondrocytes, a subsequent chondrocyte hypertrophy drives the perichondral cell differentiation into preosteoblasts, and mature osteoblasts form bone in the extremities and in the axial skeleton through endochondral ossification [3–5].

**Osteoclasts** are giant multinucleated cells that hydrolyze and solubilize both of the inorganic and organic components of bone, owing to their polarized secretion of acid and proteolytic enzymes. Thus, osteoclasts provide bone with a unique characteristic of being the only tissue in the body able to undergo a self-destruction, fulfilling thereby a crucial physiologic process for bone homeostasis [6]. Osteoclasts originate from the fusion of monocyte–macrophage [7] precursors that derive from hematopoietic stem cells (HSCs) in the marrow [8]. The hematopoietic precursors required for osteoclast formation are provided through the capillary blood supply closely associated with and penetrating the BRC [9], as well as from nearby marrow precursors [10]. Interestingly, the programming of osteoclast formation involve the actions of osteoblast-lineage cells, endothelial cells, and BRC microenvironment [9].

**Osteocytes** are the most abundant and the dominant mechanosensory cells of bone and are easily identified in a bone section, owing to the matrix in which they are completely embedded during skeletal maturation of previous remodeling cycles [1, 11]. These long-living cells are characterized by a stellar/spider shape, due to which they form and extensive interconnected network of long branched cellular processes within a fluid-filled canalicular system [12]. These processes contact each other, and probably other cell populations through gap junctions, providing thereby a cell–cell interaction by an intercellular exchange of small signaling molecules. This network plays a crucial role in regulating bone material turnover, by coordinating bone response to mechanical signals, as well as to endocrine and paracrine biological signals [13, 14]. Osteocytes have both local and systemic effects through responding to the strains generated by mechanical stimuli and translating the load into biochemical signals via a mechanotransduction mechanism [11, 15], which is one of the main factors initiating the remodeling process. Osteocytes emerge from the differentiation of a subset of osteoblasts that are trapped in the newly synthetized osteoid matrix before its mineralization [16], by undergoing a four-stage differentiation process to become mature osteocytes: (i) type I preosteocytes, named osteoblastic osteocytes, (ii) type II preosteocytes, named osteoid-osteocytes, (iii) type III preosteocytes, named young osteocytes, and (iv) old osteocytes [17].

**Reversal cells** were found to belong to osteoblast-lineage cells that seem to particularly be preosteoblasts [18] that progressively mature into bone-forming osteoblasts and play a crucial role in the resorption-to-formation coupling mechanism [19, 20]. The intermediate position of the reversal cells between osteoblasts and osteoclasts suggests their obvious contribution to the osteoblast–osteoclast interplay [21]. Interestingly, the early reversal cells located proximal to osteoclasts are less mature than the late ones located proximal to osteoblasts [18] and appear to be morphologically, ultrastructurally and immunohistochemically quite different from the late ones located next to osteoid surfaces. These differences reflect their diverse cellular interactions, varied functions, and distinct differentiation states [18–20].

 Besides, the lining cells establish physical homotypic connections with osteocytes through gap functions, which suggests that these lining cells form a functional membrane separating bone from interstitial fluids [24]. Particularly during the remodeling cycle, bone lining cells persist over the remodeling sites to isolate osteoblasts and osteoclasts from the bone marrow [25]. Indeed, at the very beginning of a remodeling cycle, the lining cells separate and raise from the underlying bone surface, forming a canopy above the site to be resorbed. This initiating phenomenon might result from osteocyte signaling to surface cells through their canaliculae when they recognize the need for replacing a specific bone area [26]. Subsequent signals could come from osteocyte apoptosis or from the lining cells themselves, generating the release of paracrine factors and chemokines, which attract the precursors of both osteoblasts and osteoclasts, as well as other required vascular elements [27].

#### **3. Coupling mechanism**

The spatial and temporal arrangement of bone cells within the BMU is crucial to the remodeling process, ensuring its distinct and sequential phase coordination, under the control of several hormones, cytokines, and growth factors. The communication between bone cells is led through at least three modes: (i) a communication through a direct cell–cell contact, (ii) a communication through gap junction formation, and (iii) a communication through diffusible paracrine factors.

Indeed, osteoblasts regulate osteoclast formation, differentiation, and maturation. In turn, osteoclasts exert positive and negative regulatory effects on osteoblast activity. Moreover, osteocytes, reversal cells, lining cells and bone matrix are all actors in the osteoblast–osteoclast crosstalk, among others. The interplay between osteoblasts and osteoclasts is also mediated by bidirectional signaling pathways, including Eph/ephrin pathways, Semaphorins/Plexins pathways, as well as RANKL/ W9/RANK pathway. **Figure 1** summarizes the communication network between bone cells.

#### **Figure 1.**

*Intercellular communication pathways within the BMU whereby the remodeling process takes place. (1) mechanical stimulus affecting OCY (osteocytes). (2) stimulatory effects from OCY to act OB (active osteoblasts). (3) bone matrix signals to act OB. (4) signaling within osteoblast-lineage cells. (5) Stimulatory and inhibitory signaling from act OB to OCP (osteoclast precursors), OC (osteoclasts), and act OC (activated osteoclasts). (6) bidirectional signaling between act OB and act OC. (7) Stimulatory and inhibitory signals from OCP to OBP (osteoblast precursors). (8) stimulatory signals from late RvCs (reversal cells) to act OB. (9) Stimulatory signals from early RvCs to act OC.*

#### **4. Mechanotransduction mechanisms**

As mentioned above, osteocytes are the dominant mechanosensory cells in bone, owing to their location in the matrix and their complex dendritic network. However, the lining cells were also suggested to be important mechanosensory cells in

#### *Cell Interaction and Mechanobiological Modeling of Bone Remodeling Process DOI: http://dx.doi.org/10.5772/intechopen.95045*

the adult skeleton, which thereby needs more investigation to clarify their exact role in bone biology. Unlike in vivo loading, where the ideal intensity, frequency, and timing to increase bone mass [28–30] are well characterized, in vitro experiments to identify these parameters and replicate in vivo results remain a major challenge. The fact that in vivo osteocyte gene expression changes under mechanical loading and unloading shows that the load affects osteocyte function [31–34].

Early in vitro experiments were based on hydrostatic pressure and substrate stretching, whereas current ones are based on fluid flow shear stress (FFSS), owing to the higher sensitivity of primary osteocytes to shear stress than to substrate stretching [35, 36].

**Mechanosensation** ability allows osteocytes to orchestrate osteoblast activation and osteoclast partial suppression under increased loading, and osteoblast partial suppression and osteoclast activation under reduced loading [37]. However, the way external forces are transmitted at the cellular and molecular levels is still unclear. Mechanical stressors include hydrostatic pressure, FFSS, and direct cellular deformation [38]. These mechanical stresses are driven by microstrain of bone matrix generated by loading and gravitational forces. Cell responses are also influenced by the specific components of these stressors, such as amplitude, frequency and rate.

Although the calcified bone matrix is a mechanically rigid material, mechanical loading induces poro-elastic interactions and microstrains of the matrix, of up to 0.2% [39, 40]. These microstrains drive the interstitial fluid flow (IFF) within the lacunocanalicular spaces [41, 42]. Indeed, real-time measurement of load-induced solute transport has been demonstrated, which suggested a peak shear stress of 5 Pa on osteocyte processes [43]. Loading of long bones also increases the pressure at the intramedullary cavity and induces IFF at the endosteal surface, as well as within the lacunocanalicular network [41]. Moreover, intramedullary pressurization-derived IFF can induce fluid shear stress-related responses not only in osteocytes but also in osteoblasts and osteoclasts on the endosteal surface, which suggests that osteoblast and osteoclasts are also mechanosensitive [44, 45]. Several research works have shown that osteocytes are connected to the canalicular wall through transverse tethering elements and transmembrane molecules that provide physical connections between the extracellular matrix, the intracellular protein complexes and cytoskeletal structures [46–49].

Several mechanosensors have been identified in bone, including cilia, integrins, calcium channels and G-protein coupled receptors (GPCRs). Integrins, among which αvβ3 is highly expressed in osteocytes, are composed of an α and β dimer and FFSS generates conformational changes in the β-subunit and the cascade signaling activation [46, 47]. The primary cilium is a non-motile structure needed for both mechano- and chemosensation, and represents another cellular moiety required to perceive FFSS. According to physical laws, FFSS occurs around the cell processes and not on the cell body where the primary cilium is located. The latter was proposed to perceive hydrostatic pressure applied on the cell body and not FFSS [50]. TAZ/YAP was also found to be an important signaling pathway for mechanosensation. Furthermore, glycocalyces on the dendritic process surfaces, but not on the cell body, were found to play a crucial role in mechanotransduction, whereas a different mechanosensing mechanism is active on the cell body [51]. Moreover, osteocytes sense load through cilia, which are single flagellar-like structures found on every cell [52, 53] and have specific functions. Particularly, cilia in bone cells induce the PGE2 release [53]. It was also reported that polycystin 1 (PC-1) in osteocytes is important for the anabolic response of bone to load [54]. Indeed, applying 2000 microstrain to bone sample at the macroscopic scale induced over 30,000 microstrain surrounding the osteocyte lacunae [55]. Besides, osteocyte

processes are extremely responsive to mechanical loadings of piconewton-level, which is not the case for their cell body and processes with no local attachments [56].

In osteocytes, among other cells, biophysical stressors are transmitted to the cells by coupling the extracellular matrix to the actin cytoskeleton through focal adhesions. The actin cytoskeleton transmits mechanical forces from a focal adhesion site to a mechanosensing site within the cell and to the neighboring ones. Focal adhesion kinases (FAK) are major components of focal adhesions and are required for the mechanotransduction mechanism by osteocytes. Besides, a structural cytoskeletal protein named spectrin is required for osteoblast-to-osteocyte differentiation and was recently identified as a mechanosensitive element within the osteocyte [11, 57]. Other potential mechanosensors are ephrins, Connexin 43 (Cx43) hemichannels and ion channels, as well as gap junctions. A response of bone to mechanical loading and unloading also requires the action of an intact axis of parathyroid hormone (PTH) related peptide (PTHrP) and its receptor (PTHR), or PTH-PTHrP-PTHR axis [58].

**Mechanotransduction** is the mechanism of transducing the mechanical signal sensed by osteocytes via the mechanosensation mechanism into biological cues. Ca++, ATP, NO, PGE2 and Wnts are the best described mechanically-induced pathways. Deleting one of these molecules inhibits the anabolic response of bone to mechanical stimulation.

Ca++ is an exclusively intracellular signal and the opening of stretch-activated calcium channel (TRPV6) results in a quick increase in intracellular Ca++, subsequently followed by cellular response to mechanical cues. Ca++ is also required for ATP response and ATP concentration rapidly increases upon mechanical forces. However, the exact mechanism by which Ca++ regulates ATP release is still not completely elucidated [49]. ATP can be released from osteocytes in response to whether mechanical stimulation or extracellular calcium [59, 60]. An ATP-gated ion channel, known as P2X7 nucleotide receptor, is expressed in many cell types and significantly influences the mechanosensation mechanisms, and P2X7 receptor is suggested to be essential for PGE2 release in response to mechanical strain [11].

In turn, NO and PGE2 are also expressed in and affect osteoblasts and osteoclasts. In bone, NO suppresses bone resorption and promotes its formation. Indeed, both osteoblasts and osteocytes release NO in response to mechanical strain or FFSS [61], but osteoblasts are less sensitive to FFSS than osteocytes [35, 36]. Under mechanical forces, osteocytes synthetize and release PGE2, which has been known to be one of the earliest responses to loading [62–65]. FFSS promotes gap junctionmediated intercellular communication and stimulates Cx43 expression, which in turn forms hemichannels, allowing thereby to release PGE2 [62]. The latter acts in an autocrine fashion and activates EP2-EP4 receptors expressed on osteocytes, while acting in a paracrine fashion to modulate osteoblast and osteoclast activities. The activation of EP2-EP4 is associated with increased intracellular cAMP and activated protein kinase A (PKA), which regulates the expression of many downstream effectors, including RANKL, Dmp1, and Sost. In vivo, new bone formation is induced by PGE2 and anabolic loading effects are blocked by indomethacin [66]. PGE2 seem to be released in response to shear stress through hemichannels unopposed halves of gap-junction channels [62]. These hemichannels in osteocytes exert multiple functions, including the protection of cell viability and the release of signaling factors [67, 68].

The canonical Wnt-signaling plays a significant role in bone homeostasis and mainly targets osteocytes among bone cells. Wnt activity increases under mechanical loading and decreases during unloading. Most of these effects are, indeed, mediated by sclerostin [49]. Mechanical transduction is also associated with sex hormones that are estrogens and androgens, and a close relationship has been determined between them and skeletal mechanobiology [69–71]. Insulin-like

growth factor 1 (IGF-1) was also found to form another signaling pathway required for proper mechanotransduction [72].