- Email: [email protected]
Dynamic reciprocity revisited
Journal of Theoretical Biology ∎ (∎∎∎∎) ∎∎∎–∎∎∎
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 Q2 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66
Contents lists available at ScienceDirect
Journal of Theoretical Biology journal homepage: www.elsevier.com/locate/yjtbi
Letter to Editor
Dynamic reciprocity revisited
H I G H L I G H T S
In this letter, an amendment to the concept of dynamic reciprocity is proposed. Flow and transport, along with the ECM, play a primary role in dynamic reciprocity. Cells can alter transport, without involving the ECM, through the use of cilia. Ciliary flow plays a key role in morphogenesis. Simulating dynamic reciprocity is crucial to developing biological substitutes ex vivo.
art ic l e i nf o Keywords: Cell Cilia Extracellular matrix Flow-fields Morphogenesis Transport phenomena
a b s t r a c t The cellular microenvironment – which includes the cells, extracellular matrix (ECM), and local transport processes – affects the cell which in turn responds by synthetic or degradative processes causing the composition and the structure of ECM, and the local transport processes, to change which in a coupled manner influence the cell, and so forth. & 2015 Published by Elsevier Ltd.
1. Letter Benedict de Spinoza (1632–1677) in his correspondence with Henry Oldenburg, the first secretary of the Royal Society, opined the existence of dynamic interdependence between various elements within the body. Articulating in a Newtonian style, quite typical of that era, he wrote, “All natural bodies can and ought to be considered in the same way as we have here considered the blood, for all bodies are surrounded by others, and are mutually determined to exist and operate in a fixed and definite proportion, while the relations between motion and rest in the sum total of them, that is, in the whole universe, remain unchanged (Spinoza and Elwes, 1883).” The year was 1663, and the basic unit of life – the cell – was hitherto undiscovered. The gist of Benedict de Spinoza’s afore-quoted expression found its true cellular context in the early nineteenth century when Christian Heinrich Pander, a German Biologist, hypothesised dependence of tissue development on a dynamic interplay between cells and their surrounding microenvironment (Pander, 1817; Wessel, 2010). Pander’s speculation was finally confirmed in 1928 when developmental biologists observed certain regions of hydra and amphibian embryos directing the adjacent group of cells to specific tissue fates (Spemann, 1918; Spemann and Mangold, 1924). This mutually instructional relationship between cells and their immediate environment was conceptualised, in 1982, as dynamic reciprocity: a phrase coined by Bornstein et al. (1982). That same year, Bissell et al. (1982) proposed a model, which suggested that the ECM affects gene expression via
transmembrane receptors that interact with the cytoskeleton to alter the patterns of gene expression. According to Bissell et al. (1982), this interdependence “appears to evolve continually”. As such, “the ECM affects the cell which in turn responds by synthetic and degradative processes causing the composition and the structure of ECM to change which in turn influences the cell and so forth” (Bissell et al., 1982). The discovery and characterisation of integrins validated this model (Schultz et al., 2011). The inability of cells to form functional structures when cultured as monolayers or on two-dimensional substrates (with certain exceptions) also testified to the importance of the cells’ microenvironment to tissue development. The dependence of tissue micro-architecture on the ECM-forming ability of cells further validated this principle (Hansen and Bissell, 2000; Nelson and Bissell, 2006). The principle as well as its current definition, however, limited the microenvironment to the extra-cellular matrix (ECM), or the ‘solid’ phase, surrounding the cells alone. Folkman reported the dependence of histogenesis on mass transport requirements of the growing structure back in the 1970s (Folkman and Hochberg, 1973). Of course, the growing number of cells would inevitably lead to an increase in metabolic demands of the system, which would subsequently necessitate the need for better perfusion conditions; but the increase in transport requirements is not solely a result of the increase in number of cells. A colony of cells may deposit ECM, which is bound to alter the local permeability, in value and (an)isotropy, and in turn affect transport occurring in that area. As adequate perfusion, and thereby transport conditions, are necessary for cells to proliferate, these
http://dx.doi.org/10.1016/j.jtbi.2015.01.016 0022-5193/& 2015 Published by Elsevier Ltd.
Please cite this article as: Kaul, H., Ventikos, Y., Dynamic reciprocity revisited. J. Theor. Biol. (2015), http://dx.doi.org/10.1016/j. jtbi.2015.01.016i
67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132
2
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66
Letter to Editor / Journal of Theoretical Biology ∎ (∎∎∎∎) ∎∎∎–∎∎∎
conditions shape how a system of cells develops in a culture (or inside the body). The proliferating cells, on the other hand, by both aggregating (into a monolayer and then growing in the thirddimension) as well as, consequently, depositing (or degrading) matrix, alter the transport characteristics of their immediate environment and, thus, shape the transport processes that operate in their vicinity, and possibly overall. While the definition proposed in the original article explained this eventuality, it, nevertheless, restricted the role of flow and transport to being secondary artefacts of the interplay between cells and their ECM, ignoring altogether their informational contribution to morphogenesis. The implication being that (i) flow and transport cannot by themselves regulate cellular growth and proliferation and (ii) cells are wholly reliant on the ECM to alter flow and transport around them: conclusions that offer limited explanation to account for the following set of observations. More recently, in the late 1990s, Bejan introduced the constructal law (Bejan and Lorente, 2011), which implicates flow as a major player in design and structural evolution. The concept finds direct relevance in the multiple operations of biological systems, which dynamically rely on flow and transport to govern their spatiotemporal development. This is evident from the impact blood flow (Bishop and Lindahl, 1999; Chen et al., 2012) and interstitial flow (Griffith and Swartz, 2006; Ng et al., 2005) have on the overall tissue development and behaviour, and, eventually, the flow itself (Chen et al., 2012). In fact, the role of blood flow in regulating cerebral vasculature (Chen et al., 2012) and the response of the cardiovascular system to changes in haemodynamics (such as increase in blood pressure and flow/shear, mechanotransduced by the smooth muscle cells and the endothelium) in tissue remodelling (Bishop and Lindahl, 1999; Watton et al., 2011; Weinbaum et al., 2003) underscore the importance of flow to the spatiotemporal evolution of biological systems. Similarly, normal development of heart valves (Hove et al., 2003), blood vessels (Buschmann et al., 2010; Corti et al., 2011), and glomerulus (Serluca et al., 2002) have been observed to be contingent on haemodynamic shear; as is the formation of functional haematopoietic stem cells in embryonic blood vessels and in vitro cultures (Adamo et al., 2009; Pardanaud and Eichmann, 2009). Another topical example (reviewed in great depth elsewhere Peiffer et al., 2013) of the contribution of flow and transport in dynamic reciprocity is the impact of haemodynamic shear on the trans-endothelial transport of low-density lipoproteins, which is considered to initiate atherosclerotic plaque formation, eventually influencing blood flow (Olgac et al., 2008; Vincent et al., 2009). As another example, of gaseous flow and transport, the etiological role of pathological airflow in causing anatomical alterations (Gungor and Turkkahraman, 2009; Hartsook, 1946; Ricketts, 1968) has been extensively documented. Furthermore, an excellent example of the dynamic relationship between gaseous flow/transport and tissue morphology is that of airway remodelling (Bergeron et al., 2009) initiated due to the introduction of allergenic/nonallergenic particulates within airways: a set of symptoms which often manifest themselves as asthma (Kay, 2000; Lukacs, 2001; Pascual and Peters, 2005; Vonk and Boezen, 2006) or chronic obstructive pulmonary disorder (Decramer et al., 2012; Fattahi et al., 2013). These particulates cause the airway cells to respond by increasing the airway wall thickness and minimising its lumen (Bergeron et al., 2009; Pascual and Peters, 2005), thereby reducing the airflow that may have been responsible in exposing the airway to these particulates in the first place. Finally, it must be emphasised that while cells can alter transport and flow indirectly through, as Bissell et al. (1982) discussed in their original article, the ECM, they can also do so directly – and rather dramatically – through the use of cellular components (for example, active cilia Chen et al., 2011; Nonaka et al., 1998). The impact of ciliary flow on morphogenesis is illustrated by the documented role of cilia-mediated flow in the
formation of inner ear and otolith (Colantonio et al., 2009), cardiac morphogenesis (Slough et al., 2008), and migration of neuroblasts following cerebrospinal fluid flow regulated by the beating of ependymal cilia (Sawamoto et al., 2006). Dynamic Reciprocity, as introduced originally, overlooked this primary and direct impact of flow/transport on biological dynamism and informs the reader little about the role of such transport-remodelling interactions. One can sympathise with the omission of flow, and the corresponding transport processes in the original definition, for it is easy to omit the vital role played by transport processes in the evolution of biological systems, in the absence of suitable experimental methodologies that can visualise flowfields and gradients. These, even today, remain mostly inaccessible to direct observation in experiment (certainly in the presence of cells anyway), and can only be simulated computationally. However, the level of complexity associated with dynamic reciprocity, particularly due to (i) the set of multiple, non-linear, complex interactions between cells and their microenvironment (Kaul and Ventikos, 2013), and especially with (ii) the inclusion of flow and the resulting local transport processes, makes it quite difficult to be captured by most numerical models. Current computational approaches and formulations, generally either continuous or discrete, struggle to account for both (i) and (ii). The classical models of the continuum variety, due to the underlying homogeneity condition, treat the entire biomass (cells, matrix, etc.) as a continuum, thereby ignoring the microscopic, heterogeneous details of biological systems. However, unlike biological systems, continua are not dynamic and they do not alter their material properties over time (Semple et al., 2005)—though various formulations incorporating such variations have been proposed. The continuum approach, basically, fails to offer a realistic ontology to capture biological interaction(s). Furthermore, a population-based approach to model cellular behaviour, instead of providing clarity, end up implicating “random or ‘unseen’ mechanisms” as responsible for the underlying mechanics “especially when small number of input cells are used” (Viswanathan and Zandstra, 2003). However, the continuum assumption makes the approach most suited to simulate the bulk aspects of a biological system (such as hydrodynamics, transport, reaction of species, etc.). Discrete models, on the other hand, typically divide a system into discrete entities that are capable of responding to local information based on a rule-set attributed to them at each discrete time-step. As such they consider the relevant microscopic details of the systems they are being employed to simulate. However, they are not recommended to study bulk phenomena due to the high computational overhead—a direct result of their considering the microscopic details of the system. To illustrate this point, consider, for example, the diffusion of an arbitrary solute in 1 μL of water. Solving the relevant equations will take the continuum approach a matter of hours (if not minutes); however, the discrete approach considering all the particles in the system (i.e. water and the solute) will take days (if not weeks) to compute the same process.3 Hybrid models, which incorporate both continuous and discrete features, succeed in capturing both cellular and environmental aspects of a biological system, though only to an extent. This is due to the fact that current hybrid approaches have been limited in capturing the appropriate datastructures (continuous, discrete, binary, spatial) operating in biological systems, explaining the scarcity of models that can capture dynamic reciprocity in appropriate resolution. Nevertheless, for reasons discussed above, the contribution of flow and transport processes towards dynamic reciprocity cannot be neglected, and must also be quantitatively pursued. Yet, this
3
A volume of 1 μL water contains 1019 molecules of water.
Please cite this article as: Kaul, H., Ventikos, Y., Dynamic reciprocity revisited. J. Theor. Biol. (2015), http://dx.doi.org/10.1016/j. jtbi.2015.01.016i
67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132
Letter to Editor / Journal of Theoretical Biology ∎ (∎∎∎∎) ∎∎∎–∎∎∎
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66
contribution was not explicitly addressed until very recently. In order to simulate this dynamic interplay, Kaul et al. (2013) developed a hybrid computational platform composed of two working elements: (a) discrete—employed to model cellular behaviour that can detect spatial information available to them within their immediate surroundings, generated due to prior cellular activity and transport processes operating in the region; and (b) continuum—which simulated these transport processes that vary dynamically as a result of continuous cellular activity, which captured the impact that the variation in local transport can have on cellular proliferation and colonisation, and therefore overall tissue development, as shown in Box 1. Furthermore, and importantly, the authors were able to demonstrate variation in local transport inside the bioreactors that arises due to concurrent cellular activity. This level of cellular decision-making in synergy with its evolving microenvironment was a first attempt at capturing dynamic reciprocity in virtuo. Computational transport phenomena modelling was used to account for the continuous component, and was crucial to the success of the investigation, as the methodology allows for intuitive evaluation of flow-fields, especially in the microscopic recesses that may be harbouring the evolving tissue structure, as well as variations that these flow-fields undergo with the development of these structures: insight that is extremely difficult if not outright impossible to attain experimentally. Moreover, the case shown in Box 1 displays proof-of-principle that the role of flow and transport as primary agents in dynamic reciprocity could, finally, be verified as well as quantified and, therefore, better understood using computational approaches (such as the one cited) over experimental methodologies that are not yet fully optimised to quantify flow within systems containing cells. This, furthermore, provides a unique foray into the governing mechanics of dynamic reciprocity.4 Here, based on the empirical observations citing evidence for the primary role of flow and transport in morphogenesis, we propose a slightly amended definition of dynamic reciprocity that is informed by the computational investigation conducted on capturing dynamic reciprocity in a physical system characterised by continuous flow (Kaul et al., 2013) (as is the case with most biological systems). The cellular microenvironment – which includes the cells, extracellular matrix (ECM), and local transport processes – affects the cell which in turn responds by synthetic or degradative processes causing the composition and the structure of ECM, and the local transport processes, to change which in a coupled manner influence the cell, and so forth. The implication being that flow and transport is integral to the cellular microenvironment, not just as a coupling element between ECM and cells alone, but as a third pillar of comparable importance. This view is supported by the presence of mechano-sensitive cellular components that can transduce flow-related mechanical forces at the cellular level. These include flow-sensitive membrane channels, plasma membrane mechanodetectors, mechanosensitive proteins containing cilia, glycocalyx, as well as adhesive mechanosensory receptors. Despite, however, the knowledge of the identity of (some of) these mechanodetectors, the mechanisms and pathways involved in mechanodetection remain unclear. It is our belief that availability of modelling paradigms, such as the one discussed above, which treats both flow (and transport) as well as cellular behaviour, at individual cell-level, will help elucidate and quantify these mechanisms and pathways. The aforementioned mechanosensitive features as well as certain relevant pathways have been reviewed in detail by Freund et al. (2012).
4 Additional examples of hybrid platforms that in our opinion are most suited to simulating dynamic reciprocity include CompuCell3D (www.compucell3d.org) and Cancer Heart And Soft Tissue Environment (www.cs.ox.ac.uk/chaste).
3
67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 Box 1. Effect of perfusion boundary conditions on cellular output. The figure 97 shows temporal evolution of cell population and oxygen concentration in two 98 bioreactors with the same dimensions and initial conditions, but set at different 99 medium inlet velocities; 0.001 m/s (top) and 0.01 m/s (bottom). The top right side 100 of the bioreactor, separated from the rest of the bioreactor by a column, serves as the inlet for the perfusing medium with the column itself serving as a porous wall 101 (the right side of the column has a porosity of 100%). Cells inside the bioreactors 102 were seeded randomly though same cell co-ordinates were employed in both cases. 103 Both bioreactors initially contained five virtual cells capable of displaying a wide 104 array of cellular behaviours such as oxygen consumption, proliferation, migration, 105 chemotaxis, and apoptosis; with each behaviour being impacted by oxygen concentration and, in case of chemotaxis, its gradient. These virtual cells were 106 being represented by agents (Kaul and Ventikos, 2013) or communicating stream 107 X-machines which possess memory, ability to detect local information, and act at 108 discrete time steps (i.e. display the entire range of behaviours) based on rules 109 attributed to them. As perfusion begins, the bioreactor begins oxygenating as illustrated by the oxygen colour map (displaying oxygen concentration in mol/m3) 110 with cells in the normoxic zones (oxygen concentration 4 0.0672 mol/m3) begin- 111 ning to proliferate and migrate. However, few cells survive in the top bioreactor 112 with greater hypoxic zones due to sub-optimal transport of oxygen resulting from 113 low perfusion rate. The surviving cell proliferates forming a colony of cells. The 114 bioreactor at the bottom ends up with considerably higher number of cells due to superior perfusion of oxygen that enables it to meet the transport/metabolic 115 requirements of the seeded cells. These cells also show a distinct growth pattern 116 than the one on the top; a result of higher number of ‘initial’ cells proliferating 117 coupled with flow characteristics of the bioreactor. Additionally, notice the vast 118 difference in concentration isocontours in the two bioreactor systems informed by the cells’ spatial heterogeneity. Traditional transport phenomena platforms would 119 fail to capture this level of spatial resolution due to their underlying assumption of 120 homogeneity. While the dynamic nature of biological systems, both in vivo or 121 ex vivo, is well observed, this was the first time the dynamic nature of a biological 122 system and the dependence of the spatiotemporal evolution of the system on 123 cellular behaviour coupled with flow and mass transport was computationally captured in 3D. The frames were recorded at 5.5 days. For more information, the 124 reader is directed to the original article (Kaul et al., 2013), where the figure first 125 appeared. Furthermore, we recommend perusing the supplementary video S1 126 provided in the article for observing a visually illustrating depiction of the 127 dependence between transport and cellular behaviour. The figure was reprinted 128 under the Creative Commons Attribution License. 129 130 In conclusion, this letter is an attempt to underscore the sig131 nificant role of flow and transport (along with the ECM) in dynamic 132 reciprocity, highlight the evidence that substantiate their inclusion in
Please cite this article as: Kaul, H., Ventikos, Y., Dynamic reciprocity revisited. J. Theor. Biol. (2015), http://dx.doi.org/10.1016/j. jtbi.2015.01.016i
Letter to Editor / Journal of Theoretical Biology ∎ (∎∎∎∎) ∎∎∎–∎∎∎
4
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66
the principle, and revise the definition. The amended concept remains applicable to most biological systems, both in vivo and ex vivo, such as tumour, uterus, compromised tissue, airway walls, or proliferating in vitro cell colonies. This, in our opinion, timely amendment is required as (a) the view that dynamism is related only to cells and ECM requires modification, for there are other essential elements that need consideration especially as the Tissue Engineering and Regenerative Medicine community tries to harness this interplay ex vivo, (b) flow has not been properly addressed by the concept (as well as the definition) as it currently stands, and (c) capturing dynamic reciprocity computationally is essential if the information from computational models is to be utilised to guide the construction of devices and automated systems in order to achieve (a). We sincerely hope investigators whose seminal and relevant work in this field we were unable to cite will forgive the nature of this “Letter to the Editor” and the associated space constraints. References Adamo, L., Naveiras, O., Wenzel, P.L., McKinney-Freeman, S., Mack, P.J., GraciaSancho, J., Suchy-Dicey, A., Yoshimoto, M., Lensch, M.W., Yoder, M.C., GarciaCardena, G., Daley, G.Q., 2009. Biomechanical forces promote embryonic haematopoiesis. Nature 459, 1131–1135. Bejan, A., Lorente, S., 2011. The constructal law and the evolution of design in nature. Phys. Life Rev. 8, 209–240. Bergeron, C., Al-Ramli, W., Hamid, Q., 2009. Remodeling in asthma. Proc. Am. Thorac. Soc. 6, 301–305. Bishop, J.E., Lindahl, G., 1999. Regulation of cardiovascular collagen synthesis by mechanical load. Cardiovasc. Res. 42, 27–44. Bissell, M.J., Hall, H.G., Parry, G., 1982. How does the extracellular-matrix direct gene-expression. J. Theor. Biol. 99, 31–68. Bornstein, P., McPherson, J., Sage, H., 1982. Synthesis and secretion of structural macromolecules by endothelial cells in culture. In: Nossel, H.L., Vogel, H.J. (Eds.), Pathobiology of the Endothelial Cell. Academic Press, New York, NY, pp. 215–228. Buschmann, I., Pries, A., Styp-Rekowska, B., Hillmeister, P., Loufrani, L., Henrion, D., Shi, Y., Duelsner, A., Hoefer, I., Gatzke, N., Wang, H., Lehmann, K., Ulm, L., Ritter, Z., Hauff, P., Hlushchuk, R., Djonov, V., van Veen, T., Le Noble, F., 2010. Pulsatile shear and Gja5 modulate arterial identity and remodeling events during flow-driven arteriogenesis. Development 137, 2187–2196. Chen, D., Norris, D., Ventikos, Y., 2011. Ciliary behaviour and mechano-transduction in the embryonic node: computational testing of hypotheses. Med. Eng. Phys. 33, 857–867. Chen, Q., Jiang, L., Li, C., Hu, D., Bu, J.W., Cai, D., Du, J.L., 2012. Haemodynamicsdriven developmental pruning of brain vasculature in zebrafish. PLoS Biol., 10. http://dx.doi.org/10.1371/journal.pbio.1001374. Colantonio, J.R., Vermot, J., Wu, D., Langenbacher, A.D., Fraser, S., Chen, J.-N., Hill, K.L., 2009. The dynein regulatory complex is required for ciliary motility and otolith biogenesis in the inner ear. Nature 457, 205–209. Corti, P., Young, S., Chen, C.Y., Patrick, M.J., Rochon, E.R., Pekkan, K., Roman, B.L., 2011. Interaction between alk1 and blood flow in the development of arteriovenous malformations. Development 138, 1573–1582. Decramer, M., Janssens, W., Miravitlles, M., 2012. Chronic obstructive pulmonary disease. Lancet 379, 1341–1351. Fattahi, F., Ten Hacken, N.H.T., Lofdahl, C.G., Hylkema, M.N., Timens, W., Postma, D.S., Vonk, J.M., 2013. Atopy is a risk factor for respiratory symptoms in COPD patients: results from the EUROSCOP study. Respir. Res., 14. Folkman, J., Hochberg, M., 1973. Self-regulation of growth in 3 dimensions. J. Exp. Med. 138, 745–753. Freund, J.B., Goetz, J.G., Hill, K.L., Vermot, J., 2012. Fluid flows and forces in development: functions, features and biophysical principles. Development 139, 1229–1245. Griffith, L.G., Swartz, M.A., 2006. Capturing complex 3D tissue physiology in vitro. Nat. Rev. Mol. Cell Biol. 7, 211–224. Gungor, A.Y., Turkkahraman, H., 2009. Effects of airway problems on maxillary growth: a review. Eur. J. Dent. 3, 250–254. Hansen, R.K., Bissell, M.J., 2000. Tissue architecture and breast cancer: the role of extracellular matrix and steroid hormones. Endocr. Relat. Cancer 7, 95–113. Hartsook, J.T., 1946. Mouth breathing as a primary etiologic factor in the production of malocclusion. J. Dent. Child. 13, 91–94.
67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 Himanshu Kaul 1, Yiannis Ventikos 2,n Q1 Institute of Biomedical Engineering, Department of Engineering 120 Science, University of Oxford, Oxford, UK 121 E-mail address: [email protected] (Y. Ventikos) 122 123 124 125 126 127 128 n Corresponding author. 129 1 Kroto Research Institute, Department of Computer Science, Broad Lane, Shef130 field S3 7HQ, UK. 2 Department of Mechanical Engineering, University College London, Torrington 131 132 Place, London WC1E 7JE, UK.
Hove, J.R., Koster, R.W., Forouhar, A.S., Acevedo-Bolton, G., Fraser, S.E., Gharib, M., 2003. Intracardiac fluid forces are an essential epigenetic factor for embryonic cardiogenesis. Nature 421, 172–177. Kaul, H., Ventikos, Y., 2013. Investigating biocomplexity through the agent-based paradigm. Brief. Bioinf. , http://dx.doi.org/10.1093/bib/bbt077. Kaul, H., Cui, Z., Ventikos, Y., 2013. A multi-paradigm modeling framework to simulate dynamic reciprocity in a bioreactor. PLoS One 8, e59671. http://dx. doi.org/10.1371/journal.pone.0059671. Kay, A.B., 2000. Overview of ‘Allergy and allergic diseases: with a view to the future’. Br. Med. Bull. 56, 843–864. Lukacs, N.W., 2001. Role of chemokines in the pathogenesis of asthma. Nat. Rev. Immunol. 1, 108–116. Nelson, C.M., Bissell, M.J., 2006. Of extracellular matrix, scaffolds, and signaling: tissue architecture regulates development, homeostasis, and cancer. Annu. Rev. Cell Dev. Biol. 22, 287–309. Ng, C.P., Hinz, B., Swartz, M.A., 2005. Interstitial fluid flow induces myofibroblast differentiation and collagen alignment in vitro. J. Cell Sci. 118, 4731–4739. Nonaka, S., Tanaka, Y., Okada, Y., TakedaA, S., Harada, A., Kanai, Y., Kido, M., Hirokawa, N., 1998. Randomization of left-right asymmetry due to loss of nodal cilia generating leftward flow of extraembryonic fluid in mice lacking KIF3B motor protein. Cell 95, 829–837. Olgac, U., Kurtcuoglu, V., Poulikakos, D., 2008. Computational modeling of coupled blood-wall mass transport of LDL: effects of local wall shear stress. Am. J. Physiol. Heart Circ. Physiol. 294, H909–H919. Pander, C.H., 1817. Beiträge zur Entwickelungsgeschichte [Entwicklungsgeschichte] des Hühnchens im Eye L.P, Wurzburg. Pardanaud, L., Eichmann, A., 2009. Stem cells: the stress of forming blood cells. Nature 459, 1068–1069. Pascual, R.M., Peters, S.P., 2005. Airway remodeling contributes to the progressive loss of lung function in asthma: an overview. J. Allergy Clin. Immunol. 116, 477–486. Peiffer, V., Sherwin, S.J., Weinberg, P.D., 2013. Does low and oscillatory wall shear stress correlate spatially with early atherosclerosis? A systematic review. Cardiovasc. Res. 99, 242–250. Ricketts, R.M., 1968. Respiratory obstruction syndrome. Am. J. Orthod. 54 (495–&). Sawamoto, K., Wichterle, H., Gonzalez-Perez, O., Cholfin, J.A., Yamada, M., Spassky, N., Murcia, N.S., Garcia-Verdugo, J.M., Marin, O., Rubenstein, J.L.R., Tessier-Lavigne, M., Okano, H., Alvarez-Buylla, A., 2006. New neurons follow the flow of cerebrospinal fluid in the adult brain. Science 311, 629–632. Schultz, G.S., Davidson, J.M., Kirsner, R.S., Bornstein, P., Herman, I.M., 2011. Dynamic reciprocity in the wound microenvironment. Wound Repair Regen. 19, 134–148. Semple, J.L., Woolridge, N., Lumsden, C.J., 2005. In vitro, in vivo, in silico: computational systems in tissue engineering and regenerative medicine. Tissue Eng. 11, 341–356. Serluca, F.C., Drummond, I.A., Fishman, M.C., 2002. Endothelial signaling in kidney morphogenesis: a role for hemodynamic forces. Curr. Biol. 12, 492–497. Slough, J., Cooney, L., Brueckner, M., 2008. Monocilia in the embryonic mouse heart suggest a direct role for cilia in cardiac morphogenesis. Dev. Dyn. 237, 2304–2314. Spemann, H., 1918. Uber die Determination der ersten Organanlagen des Amphibienembryonen. Zool. Jahrb. (Suppl. 15), 1–48. Spemann, H., Mangold, H., 1924. Uber induktion von Embryonalagen durch Implantation ArtfremderOrganisatoren. Arch. Mikr. Anat. Entw. Mech. 100, 599–638. Spinoza, B.D., Elwes, R.H.M., 1883. The Chief Works of Benedict de Spinoza. G. Bell and Sons, London. Vincent, P.E., Sherwin, S.J., Weinberg, P.D., 2009. The effect of a spatially heterogeneous transmural water flux on concentration polarization of low density lipoprotein in arteries. Biophys. J. 96, 3102–3115. Viswanathan, S., Zandstra, P.W., 2003. Towards predictive models of stem cell fate. Cytotechnology 41, 75–92. Vonk, J.M., Boezen, H.M., 2006. Predicting adult asthma in childhood. Curr. Opin. Pulm. Med. 12, 42–47. Watton, P.N., Selimovic, A., Raberger, N.B., Huang, P., Holzapfel, G.A., Ventikos, Y., 2011. Modelling evolution and the evolving mechanical environment of saccular cerebral aneurysms. Biomech. Model. Mechanobiol. 10, 109–132. Weinbaum, S., Zhang, X.B., Han, Y.F., Vink, H., Cowin, S.C., 2003. Mechanotransduction and flow across the endothelial glycocalyx. Proc. Natl. Acad. Sci. U.S.A. 100, 7988–7995. Wessel, G.M., 2010. Christian Heinrich Pander (1794–1865). Mol. Reprod. Dev., 77.
Please cite this article as: Kaul, H., Ventikos, Y., Dynamic reciprocity revisited. J. Theor. Biol. (2015), http://dx.doi.org/10.1016/j. jtbi.2015.01.016i
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 Q2 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66
Contents lists available at ScienceDirect
Journal of Theoretical Biology journal homepage: www.elsevier.com/locate/yjtbi
Letter to Editor
Dynamic reciprocity revisited
H I G H L I G H T S
In this letter, an amendment to the concept of dynamic reciprocity is proposed. Flow and transport, along with the ECM, play a primary role in dynamic reciprocity. Cells can alter transport, without involving the ECM, through the use of cilia. Ciliary flow plays a key role in morphogenesis. Simulating dynamic reciprocity is crucial to developing biological substitutes ex vivo.
art ic l e i nf o Keywords: Cell Cilia Extracellular matrix Flow-fields Morphogenesis Transport phenomena
a b s t r a c t The cellular microenvironment – which includes the cells, extracellular matrix (ECM), and local transport processes – affects the cell which in turn responds by synthetic or degradative processes causing the composition and the structure of ECM, and the local transport processes, to change which in a coupled manner influence the cell, and so forth. & 2015 Published by Elsevier Ltd.
1. Letter Benedict de Spinoza (1632–1677) in his correspondence with Henry Oldenburg, the first secretary of the Royal Society, opined the existence of dynamic interdependence between various elements within the body. Articulating in a Newtonian style, quite typical of that era, he wrote, “All natural bodies can and ought to be considered in the same way as we have here considered the blood, for all bodies are surrounded by others, and are mutually determined to exist and operate in a fixed and definite proportion, while the relations between motion and rest in the sum total of them, that is, in the whole universe, remain unchanged (Spinoza and Elwes, 1883).” The year was 1663, and the basic unit of life – the cell – was hitherto undiscovered. The gist of Benedict de Spinoza’s afore-quoted expression found its true cellular context in the early nineteenth century when Christian Heinrich Pander, a German Biologist, hypothesised dependence of tissue development on a dynamic interplay between cells and their surrounding microenvironment (Pander, 1817; Wessel, 2010). Pander’s speculation was finally confirmed in 1928 when developmental biologists observed certain regions of hydra and amphibian embryos directing the adjacent group of cells to specific tissue fates (Spemann, 1918; Spemann and Mangold, 1924). This mutually instructional relationship between cells and their immediate environment was conceptualised, in 1982, as dynamic reciprocity: a phrase coined by Bornstein et al. (1982). That same year, Bissell et al. (1982) proposed a model, which suggested that the ECM affects gene expression via
transmembrane receptors that interact with the cytoskeleton to alter the patterns of gene expression. According to Bissell et al. (1982), this interdependence “appears to evolve continually”. As such, “the ECM affects the cell which in turn responds by synthetic and degradative processes causing the composition and the structure of ECM to change which in turn influences the cell and so forth” (Bissell et al., 1982). The discovery and characterisation of integrins validated this model (Schultz et al., 2011). The inability of cells to form functional structures when cultured as monolayers or on two-dimensional substrates (with certain exceptions) also testified to the importance of the cells’ microenvironment to tissue development. The dependence of tissue micro-architecture on the ECM-forming ability of cells further validated this principle (Hansen and Bissell, 2000; Nelson and Bissell, 2006). The principle as well as its current definition, however, limited the microenvironment to the extra-cellular matrix (ECM), or the ‘solid’ phase, surrounding the cells alone. Folkman reported the dependence of histogenesis on mass transport requirements of the growing structure back in the 1970s (Folkman and Hochberg, 1973). Of course, the growing number of cells would inevitably lead to an increase in metabolic demands of the system, which would subsequently necessitate the need for better perfusion conditions; but the increase in transport requirements is not solely a result of the increase in number of cells. A colony of cells may deposit ECM, which is bound to alter the local permeability, in value and (an)isotropy, and in turn affect transport occurring in that area. As adequate perfusion, and thereby transport conditions, are necessary for cells to proliferate, these
http://dx.doi.org/10.1016/j.jtbi.2015.01.016 0022-5193/& 2015 Published by Elsevier Ltd.
Please cite this article as: Kaul, H., Ventikos, Y., Dynamic reciprocity revisited. J. Theor. Biol. (2015), http://dx.doi.org/10.1016/j. jtbi.2015.01.016i
67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132
2
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66
Letter to Editor / Journal of Theoretical Biology ∎ (∎∎∎∎) ∎∎∎–∎∎∎
conditions shape how a system of cells develops in a culture (or inside the body). The proliferating cells, on the other hand, by both aggregating (into a monolayer and then growing in the thirddimension) as well as, consequently, depositing (or degrading) matrix, alter the transport characteristics of their immediate environment and, thus, shape the transport processes that operate in their vicinity, and possibly overall. While the definition proposed in the original article explained this eventuality, it, nevertheless, restricted the role of flow and transport to being secondary artefacts of the interplay between cells and their ECM, ignoring altogether their informational contribution to morphogenesis. The implication being that (i) flow and transport cannot by themselves regulate cellular growth and proliferation and (ii) cells are wholly reliant on the ECM to alter flow and transport around them: conclusions that offer limited explanation to account for the following set of observations. More recently, in the late 1990s, Bejan introduced the constructal law (Bejan and Lorente, 2011), which implicates flow as a major player in design and structural evolution. The concept finds direct relevance in the multiple operations of biological systems, which dynamically rely on flow and transport to govern their spatiotemporal development. This is evident from the impact blood flow (Bishop and Lindahl, 1999; Chen et al., 2012) and interstitial flow (Griffith and Swartz, 2006; Ng et al., 2005) have on the overall tissue development and behaviour, and, eventually, the flow itself (Chen et al., 2012). In fact, the role of blood flow in regulating cerebral vasculature (Chen et al., 2012) and the response of the cardiovascular system to changes in haemodynamics (such as increase in blood pressure and flow/shear, mechanotransduced by the smooth muscle cells and the endothelium) in tissue remodelling (Bishop and Lindahl, 1999; Watton et al., 2011; Weinbaum et al., 2003) underscore the importance of flow to the spatiotemporal evolution of biological systems. Similarly, normal development of heart valves (Hove et al., 2003), blood vessels (Buschmann et al., 2010; Corti et al., 2011), and glomerulus (Serluca et al., 2002) have been observed to be contingent on haemodynamic shear; as is the formation of functional haematopoietic stem cells in embryonic blood vessels and in vitro cultures (Adamo et al., 2009; Pardanaud and Eichmann, 2009). Another topical example (reviewed in great depth elsewhere Peiffer et al., 2013) of the contribution of flow and transport in dynamic reciprocity is the impact of haemodynamic shear on the trans-endothelial transport of low-density lipoproteins, which is considered to initiate atherosclerotic plaque formation, eventually influencing blood flow (Olgac et al., 2008; Vincent et al., 2009). As another example, of gaseous flow and transport, the etiological role of pathological airflow in causing anatomical alterations (Gungor and Turkkahraman, 2009; Hartsook, 1946; Ricketts, 1968) has been extensively documented. Furthermore, an excellent example of the dynamic relationship between gaseous flow/transport and tissue morphology is that of airway remodelling (Bergeron et al., 2009) initiated due to the introduction of allergenic/nonallergenic particulates within airways: a set of symptoms which often manifest themselves as asthma (Kay, 2000; Lukacs, 2001; Pascual and Peters, 2005; Vonk and Boezen, 2006) or chronic obstructive pulmonary disorder (Decramer et al., 2012; Fattahi et al., 2013). These particulates cause the airway cells to respond by increasing the airway wall thickness and minimising its lumen (Bergeron et al., 2009; Pascual and Peters, 2005), thereby reducing the airflow that may have been responsible in exposing the airway to these particulates in the first place. Finally, it must be emphasised that while cells can alter transport and flow indirectly through, as Bissell et al. (1982) discussed in their original article, the ECM, they can also do so directly – and rather dramatically – through the use of cellular components (for example, active cilia Chen et al., 2011; Nonaka et al., 1998). The impact of ciliary flow on morphogenesis is illustrated by the documented role of cilia-mediated flow in the
formation of inner ear and otolith (Colantonio et al., 2009), cardiac morphogenesis (Slough et al., 2008), and migration of neuroblasts following cerebrospinal fluid flow regulated by the beating of ependymal cilia (Sawamoto et al., 2006). Dynamic Reciprocity, as introduced originally, overlooked this primary and direct impact of flow/transport on biological dynamism and informs the reader little about the role of such transport-remodelling interactions. One can sympathise with the omission of flow, and the corresponding transport processes in the original definition, for it is easy to omit the vital role played by transport processes in the evolution of biological systems, in the absence of suitable experimental methodologies that can visualise flowfields and gradients. These, even today, remain mostly inaccessible to direct observation in experiment (certainly in the presence of cells anyway), and can only be simulated computationally. However, the level of complexity associated with dynamic reciprocity, particularly due to (i) the set of multiple, non-linear, complex interactions between cells and their microenvironment (Kaul and Ventikos, 2013), and especially with (ii) the inclusion of flow and the resulting local transport processes, makes it quite difficult to be captured by most numerical models. Current computational approaches and formulations, generally either continuous or discrete, struggle to account for both (i) and (ii). The classical models of the continuum variety, due to the underlying homogeneity condition, treat the entire biomass (cells, matrix, etc.) as a continuum, thereby ignoring the microscopic, heterogeneous details of biological systems. However, unlike biological systems, continua are not dynamic and they do not alter their material properties over time (Semple et al., 2005)—though various formulations incorporating such variations have been proposed. The continuum approach, basically, fails to offer a realistic ontology to capture biological interaction(s). Furthermore, a population-based approach to model cellular behaviour, instead of providing clarity, end up implicating “random or ‘unseen’ mechanisms” as responsible for the underlying mechanics “especially when small number of input cells are used” (Viswanathan and Zandstra, 2003). However, the continuum assumption makes the approach most suited to simulate the bulk aspects of a biological system (such as hydrodynamics, transport, reaction of species, etc.). Discrete models, on the other hand, typically divide a system into discrete entities that are capable of responding to local information based on a rule-set attributed to them at each discrete time-step. As such they consider the relevant microscopic details of the systems they are being employed to simulate. However, they are not recommended to study bulk phenomena due to the high computational overhead—a direct result of their considering the microscopic details of the system. To illustrate this point, consider, for example, the diffusion of an arbitrary solute in 1 μL of water. Solving the relevant equations will take the continuum approach a matter of hours (if not minutes); however, the discrete approach considering all the particles in the system (i.e. water and the solute) will take days (if not weeks) to compute the same process.3 Hybrid models, which incorporate both continuous and discrete features, succeed in capturing both cellular and environmental aspects of a biological system, though only to an extent. This is due to the fact that current hybrid approaches have been limited in capturing the appropriate datastructures (continuous, discrete, binary, spatial) operating in biological systems, explaining the scarcity of models that can capture dynamic reciprocity in appropriate resolution. Nevertheless, for reasons discussed above, the contribution of flow and transport processes towards dynamic reciprocity cannot be neglected, and must also be quantitatively pursued. Yet, this
3
A volume of 1 μL water contains 1019 molecules of water.
Please cite this article as: Kaul, H., Ventikos, Y., Dynamic reciprocity revisited. J. Theor. Biol. (2015), http://dx.doi.org/10.1016/j. jtbi.2015.01.016i
67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132
Letter to Editor / Journal of Theoretical Biology ∎ (∎∎∎∎) ∎∎∎–∎∎∎
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66
contribution was not explicitly addressed until very recently. In order to simulate this dynamic interplay, Kaul et al. (2013) developed a hybrid computational platform composed of two working elements: (a) discrete—employed to model cellular behaviour that can detect spatial information available to them within their immediate surroundings, generated due to prior cellular activity and transport processes operating in the region; and (b) continuum—which simulated these transport processes that vary dynamically as a result of continuous cellular activity, which captured the impact that the variation in local transport can have on cellular proliferation and colonisation, and therefore overall tissue development, as shown in Box 1. Furthermore, and importantly, the authors were able to demonstrate variation in local transport inside the bioreactors that arises due to concurrent cellular activity. This level of cellular decision-making in synergy with its evolving microenvironment was a first attempt at capturing dynamic reciprocity in virtuo. Computational transport phenomena modelling was used to account for the continuous component, and was crucial to the success of the investigation, as the methodology allows for intuitive evaluation of flow-fields, especially in the microscopic recesses that may be harbouring the evolving tissue structure, as well as variations that these flow-fields undergo with the development of these structures: insight that is extremely difficult if not outright impossible to attain experimentally. Moreover, the case shown in Box 1 displays proof-of-principle that the role of flow and transport as primary agents in dynamic reciprocity could, finally, be verified as well as quantified and, therefore, better understood using computational approaches (such as the one cited) over experimental methodologies that are not yet fully optimised to quantify flow within systems containing cells. This, furthermore, provides a unique foray into the governing mechanics of dynamic reciprocity.4 Here, based on the empirical observations citing evidence for the primary role of flow and transport in morphogenesis, we propose a slightly amended definition of dynamic reciprocity that is informed by the computational investigation conducted on capturing dynamic reciprocity in a physical system characterised by continuous flow (Kaul et al., 2013) (as is the case with most biological systems). The cellular microenvironment – which includes the cells, extracellular matrix (ECM), and local transport processes – affects the cell which in turn responds by synthetic or degradative processes causing the composition and the structure of ECM, and the local transport processes, to change which in a coupled manner influence the cell, and so forth. The implication being that flow and transport is integral to the cellular microenvironment, not just as a coupling element between ECM and cells alone, but as a third pillar of comparable importance. This view is supported by the presence of mechano-sensitive cellular components that can transduce flow-related mechanical forces at the cellular level. These include flow-sensitive membrane channels, plasma membrane mechanodetectors, mechanosensitive proteins containing cilia, glycocalyx, as well as adhesive mechanosensory receptors. Despite, however, the knowledge of the identity of (some of) these mechanodetectors, the mechanisms and pathways involved in mechanodetection remain unclear. It is our belief that availability of modelling paradigms, such as the one discussed above, which treats both flow (and transport) as well as cellular behaviour, at individual cell-level, will help elucidate and quantify these mechanisms and pathways. The aforementioned mechanosensitive features as well as certain relevant pathways have been reviewed in detail by Freund et al. (2012).
4 Additional examples of hybrid platforms that in our opinion are most suited to simulating dynamic reciprocity include CompuCell3D (www.compucell3d.org) and Cancer Heart And Soft Tissue Environment (www.cs.ox.ac.uk/chaste).
3
67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 Box 1. Effect of perfusion boundary conditions on cellular output. The figure 97 shows temporal evolution of cell population and oxygen concentration in two 98 bioreactors with the same dimensions and initial conditions, but set at different 99 medium inlet velocities; 0.001 m/s (top) and 0.01 m/s (bottom). The top right side 100 of the bioreactor, separated from the rest of the bioreactor by a column, serves as the inlet for the perfusing medium with the column itself serving as a porous wall 101 (the right side of the column has a porosity of 100%). Cells inside the bioreactors 102 were seeded randomly though same cell co-ordinates were employed in both cases. 103 Both bioreactors initially contained five virtual cells capable of displaying a wide 104 array of cellular behaviours such as oxygen consumption, proliferation, migration, 105 chemotaxis, and apoptosis; with each behaviour being impacted by oxygen concentration and, in case of chemotaxis, its gradient. These virtual cells were 106 being represented by agents (Kaul and Ventikos, 2013) or communicating stream 107 X-machines which possess memory, ability to detect local information, and act at 108 discrete time steps (i.e. display the entire range of behaviours) based on rules 109 attributed to them. As perfusion begins, the bioreactor begins oxygenating as illustrated by the oxygen colour map (displaying oxygen concentration in mol/m3) 110 with cells in the normoxic zones (oxygen concentration 4 0.0672 mol/m3) begin- 111 ning to proliferate and migrate. However, few cells survive in the top bioreactor 112 with greater hypoxic zones due to sub-optimal transport of oxygen resulting from 113 low perfusion rate. The surviving cell proliferates forming a colony of cells. The 114 bioreactor at the bottom ends up with considerably higher number of cells due to superior perfusion of oxygen that enables it to meet the transport/metabolic 115 requirements of the seeded cells. These cells also show a distinct growth pattern 116 than the one on the top; a result of higher number of ‘initial’ cells proliferating 117 coupled with flow characteristics of the bioreactor. Additionally, notice the vast 118 difference in concentration isocontours in the two bioreactor systems informed by the cells’ spatial heterogeneity. Traditional transport phenomena platforms would 119 fail to capture this level of spatial resolution due to their underlying assumption of 120 homogeneity. While the dynamic nature of biological systems, both in vivo or 121 ex vivo, is well observed, this was the first time the dynamic nature of a biological 122 system and the dependence of the spatiotemporal evolution of the system on 123 cellular behaviour coupled with flow and mass transport was computationally captured in 3D. The frames were recorded at 5.5 days. For more information, the 124 reader is directed to the original article (Kaul et al., 2013), where the figure first 125 appeared. Furthermore, we recommend perusing the supplementary video S1 126 provided in the article for observing a visually illustrating depiction of the 127 dependence between transport and cellular behaviour. The figure was reprinted 128 under the Creative Commons Attribution License. 129 130 In conclusion, this letter is an attempt to underscore the sig131 nificant role of flow and transport (along with the ECM) in dynamic 132 reciprocity, highlight the evidence that substantiate their inclusion in
Please cite this article as: Kaul, H., Ventikos, Y., Dynamic reciprocity revisited. J. Theor. Biol. (2015), http://dx.doi.org/10.1016/j. jtbi.2015.01.016i
Letter to Editor / Journal of Theoretical Biology ∎ (∎∎∎∎) ∎∎∎–∎∎∎
4
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66
the principle, and revise the definition. The amended concept remains applicable to most biological systems, both in vivo and ex vivo, such as tumour, uterus, compromised tissue, airway walls, or proliferating in vitro cell colonies. This, in our opinion, timely amendment is required as (a) the view that dynamism is related only to cells and ECM requires modification, for there are other essential elements that need consideration especially as the Tissue Engineering and Regenerative Medicine community tries to harness this interplay ex vivo, (b) flow has not been properly addressed by the concept (as well as the definition) as it currently stands, and (c) capturing dynamic reciprocity computationally is essential if the information from computational models is to be utilised to guide the construction of devices and automated systems in order to achieve (a). We sincerely hope investigators whose seminal and relevant work in this field we were unable to cite will forgive the nature of this “Letter to the Editor” and the associated space constraints. References Adamo, L., Naveiras, O., Wenzel, P.L., McKinney-Freeman, S., Mack, P.J., GraciaSancho, J., Suchy-Dicey, A., Yoshimoto, M., Lensch, M.W., Yoder, M.C., GarciaCardena, G., Daley, G.Q., 2009. Biomechanical forces promote embryonic haematopoiesis. Nature 459, 1131–1135. Bejan, A., Lorente, S., 2011. The constructal law and the evolution of design in nature. Phys. Life Rev. 8, 209–240. Bergeron, C., Al-Ramli, W., Hamid, Q., 2009. Remodeling in asthma. Proc. Am. Thorac. Soc. 6, 301–305. Bishop, J.E., Lindahl, G., 1999. Regulation of cardiovascular collagen synthesis by mechanical load. Cardiovasc. Res. 42, 27–44. Bissell, M.J., Hall, H.G., Parry, G., 1982. How does the extracellular-matrix direct gene-expression. J. Theor. Biol. 99, 31–68. Bornstein, P., McPherson, J., Sage, H., 1982. Synthesis and secretion of structural macromolecules by endothelial cells in culture. In: Nossel, H.L., Vogel, H.J. (Eds.), Pathobiology of the Endothelial Cell. Academic Press, New York, NY, pp. 215–228. Buschmann, I., Pries, A., Styp-Rekowska, B., Hillmeister, P., Loufrani, L., Henrion, D., Shi, Y., Duelsner, A., Hoefer, I., Gatzke, N., Wang, H., Lehmann, K., Ulm, L., Ritter, Z., Hauff, P., Hlushchuk, R., Djonov, V., van Veen, T., Le Noble, F., 2010. Pulsatile shear and Gja5 modulate arterial identity and remodeling events during flow-driven arteriogenesis. Development 137, 2187–2196. Chen, D., Norris, D., Ventikos, Y., 2011. Ciliary behaviour and mechano-transduction in the embryonic node: computational testing of hypotheses. Med. Eng. Phys. 33, 857–867. Chen, Q., Jiang, L., Li, C., Hu, D., Bu, J.W., Cai, D., Du, J.L., 2012. Haemodynamicsdriven developmental pruning of brain vasculature in zebrafish. PLoS Biol., 10. http://dx.doi.org/10.1371/journal.pbio.1001374. Colantonio, J.R., Vermot, J., Wu, D., Langenbacher, A.D., Fraser, S., Chen, J.-N., Hill, K.L., 2009. The dynein regulatory complex is required for ciliary motility and otolith biogenesis in the inner ear. Nature 457, 205–209. Corti, P., Young, S., Chen, C.Y., Patrick, M.J., Rochon, E.R., Pekkan, K., Roman, B.L., 2011. Interaction between alk1 and blood flow in the development of arteriovenous malformations. Development 138, 1573–1582. Decramer, M., Janssens, W., Miravitlles, M., 2012. Chronic obstructive pulmonary disease. Lancet 379, 1341–1351. Fattahi, F., Ten Hacken, N.H.T., Lofdahl, C.G., Hylkema, M.N., Timens, W., Postma, D.S., Vonk, J.M., 2013. Atopy is a risk factor for respiratory symptoms in COPD patients: results from the EUROSCOP study. Respir. Res., 14. Folkman, J., Hochberg, M., 1973. Self-regulation of growth in 3 dimensions. J. Exp. Med. 138, 745–753. Freund, J.B., Goetz, J.G., Hill, K.L., Vermot, J., 2012. Fluid flows and forces in development: functions, features and biophysical principles. Development 139, 1229–1245. Griffith, L.G., Swartz, M.A., 2006. Capturing complex 3D tissue physiology in vitro. Nat. Rev. Mol. Cell Biol. 7, 211–224. Gungor, A.Y., Turkkahraman, H., 2009. Effects of airway problems on maxillary growth: a review. Eur. J. Dent. 3, 250–254. Hansen, R.K., Bissell, M.J., 2000. Tissue architecture and breast cancer: the role of extracellular matrix and steroid hormones. Endocr. Relat. Cancer 7, 95–113. Hartsook, J.T., 1946. Mouth breathing as a primary etiologic factor in the production of malocclusion. J. Dent. Child. 13, 91–94.
67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 Himanshu Kaul 1, Yiannis Ventikos 2,n Q1 Institute of Biomedical Engineering, Department of Engineering 120 Science, University of Oxford, Oxford, UK 121 E-mail address: [email protected] (Y. Ventikos) 122 123 124 125 126 127 128 n Corresponding author. 129 1 Kroto Research Institute, Department of Computer Science, Broad Lane, Shef130 field S3 7HQ, UK. 2 Department of Mechanical Engineering, University College London, Torrington 131 132 Place, London WC1E 7JE, UK.
Hove, J.R., Koster, R.W., Forouhar, A.S., Acevedo-Bolton, G., Fraser, S.E., Gharib, M., 2003. Intracardiac fluid forces are an essential epigenetic factor for embryonic cardiogenesis. Nature 421, 172–177. Kaul, H., Ventikos, Y., 2013. Investigating biocomplexity through the agent-based paradigm. Brief. Bioinf. , http://dx.doi.org/10.1093/bib/bbt077. Kaul, H., Cui, Z., Ventikos, Y., 2013. A multi-paradigm modeling framework to simulate dynamic reciprocity in a bioreactor. PLoS One 8, e59671. http://dx. doi.org/10.1371/journal.pone.0059671. Kay, A.B., 2000. Overview of ‘Allergy and allergic diseases: with a view to the future’. Br. Med. Bull. 56, 843–864. Lukacs, N.W., 2001. Role of chemokines in the pathogenesis of asthma. Nat. Rev. Immunol. 1, 108–116. Nelson, C.M., Bissell, M.J., 2006. Of extracellular matrix, scaffolds, and signaling: tissue architecture regulates development, homeostasis, and cancer. Annu. Rev. Cell Dev. Biol. 22, 287–309. Ng, C.P., Hinz, B., Swartz, M.A., 2005. Interstitial fluid flow induces myofibroblast differentiation and collagen alignment in vitro. J. Cell Sci. 118, 4731–4739. Nonaka, S., Tanaka, Y., Okada, Y., TakedaA, S., Harada, A., Kanai, Y., Kido, M., Hirokawa, N., 1998. Randomization of left-right asymmetry due to loss of nodal cilia generating leftward flow of extraembryonic fluid in mice lacking KIF3B motor protein. Cell 95, 829–837. Olgac, U., Kurtcuoglu, V., Poulikakos, D., 2008. Computational modeling of coupled blood-wall mass transport of LDL: effects of local wall shear stress. Am. J. Physiol. Heart Circ. Physiol. 294, H909–H919. Pander, C.H., 1817. Beiträge zur Entwickelungsgeschichte [Entwicklungsgeschichte] des Hühnchens im Eye L.P, Wurzburg. Pardanaud, L., Eichmann, A., 2009. Stem cells: the stress of forming blood cells. Nature 459, 1068–1069. Pascual, R.M., Peters, S.P., 2005. Airway remodeling contributes to the progressive loss of lung function in asthma: an overview. J. Allergy Clin. Immunol. 116, 477–486. Peiffer, V., Sherwin, S.J., Weinberg, P.D., 2013. Does low and oscillatory wall shear stress correlate spatially with early atherosclerosis? A systematic review. Cardiovasc. Res. 99, 242–250. Ricketts, R.M., 1968. Respiratory obstruction syndrome. Am. J. Orthod. 54 (495–&). Sawamoto, K., Wichterle, H., Gonzalez-Perez, O., Cholfin, J.A., Yamada, M., Spassky, N., Murcia, N.S., Garcia-Verdugo, J.M., Marin, O., Rubenstein, J.L.R., Tessier-Lavigne, M., Okano, H., Alvarez-Buylla, A., 2006. New neurons follow the flow of cerebrospinal fluid in the adult brain. Science 311, 629–632. Schultz, G.S., Davidson, J.M., Kirsner, R.S., Bornstein, P., Herman, I.M., 2011. Dynamic reciprocity in the wound microenvironment. Wound Repair Regen. 19, 134–148. Semple, J.L., Woolridge, N., Lumsden, C.J., 2005. In vitro, in vivo, in silico: computational systems in tissue engineering and regenerative medicine. Tissue Eng. 11, 341–356. Serluca, F.C., Drummond, I.A., Fishman, M.C., 2002. Endothelial signaling in kidney morphogenesis: a role for hemodynamic forces. Curr. Biol. 12, 492–497. Slough, J., Cooney, L., Brueckner, M., 2008. Monocilia in the embryonic mouse heart suggest a direct role for cilia in cardiac morphogenesis. Dev. Dyn. 237, 2304–2314. Spemann, H., 1918. Uber die Determination der ersten Organanlagen des Amphibienembryonen. Zool. Jahrb. (Suppl. 15), 1–48. Spemann, H., Mangold, H., 1924. Uber induktion von Embryonalagen durch Implantation ArtfremderOrganisatoren. Arch. Mikr. Anat. Entw. Mech. 100, 599–638. Spinoza, B.D., Elwes, R.H.M., 1883. The Chief Works of Benedict de Spinoza. G. Bell and Sons, London. Vincent, P.E., Sherwin, S.J., Weinberg, P.D., 2009. The effect of a spatially heterogeneous transmural water flux on concentration polarization of low density lipoprotein in arteries. Biophys. J. 96, 3102–3115. Viswanathan, S., Zandstra, P.W., 2003. Towards predictive models of stem cell fate. Cytotechnology 41, 75–92. Vonk, J.M., Boezen, H.M., 2006. Predicting adult asthma in childhood. Curr. Opin. Pulm. Med. 12, 42–47. Watton, P.N., Selimovic, A., Raberger, N.B., Huang, P., Holzapfel, G.A., Ventikos, Y., 2011. Modelling evolution and the evolving mechanical environment of saccular cerebral aneurysms. Biomech. Model. Mechanobiol. 10, 109–132. Weinbaum, S., Zhang, X.B., Han, Y.F., Vink, H., Cowin, S.C., 2003. Mechanotransduction and flow across the endothelial glycocalyx. Proc. Natl. Acad. Sci. U.S.A. 100, 7988–7995. Wessel, G.M., 2010. Christian Heinrich Pander (1794–1865). Mol. Reprod. Dev., 77.
Please cite this article as: Kaul, H., Ventikos, Y., Dynamic reciprocity revisited. J. Theor. Biol. (2015), http://dx.doi.org/10.1016/j. jtbi.2015.01.016i