Regenerative orthopaedics: in vitro, in vivo … in silico

In silico, defined in analogy to in vitro and in vivo as those studies that are performed on a computer, is an essential step in problem-solving and product development in classical engineering fields. The use of in silico models is now slowly easing its way into medicine. In silico models are already used in orthopaedics for the planning of complicated surgeries, personalised implant design and the analysis of gait measurements. However, these in silico models often lack the simulation of the response of the biological system over time. In silico models focusing on the response of the biological systems are in full development. This review starts with an introduction into in silico models of orthopaedic processes. Special attention is paid to the classification of models according to their spatiotemporal scale (gene/protein to population) and the information they were built on (data vs hypotheses). Subsequently, the review focuses on the in silico models used in regenerative orthopaedics research. Contributions of in silico models to an enhanced understanding and optimisation of four key elements—cells, carriers, culture and clinics—are illustrated. Finally, a number of challenges are identified, related to the computational aspects but also to the integration of in silico tools into clinical practice.

[1]  Liesbet Geris,et al.  Mathematical Modeling in Wound Healing, Bone Regeneration and Tissue Engineering , 2010, Acta biotheoretica.

[2]  J. Schrooten,et al.  Spatial optimization in perfusion bioreactors improves bone tissue‐engineered construct quality attributes , 2014, Biotechnology and bioengineering.

[3]  L. Saiz,et al.  Computational modelling of Smad-mediated negative feedback and crosstalk in the TGF-β superfamily network , 2013, Journal of The Royal Society Interface.

[4]  Johan Lammens,et al.  The Pentaconcept in skeletal tissue engineering. A combined approach for the repair of bone defects. , 2012, Acta orthopaedica Belgica.

[5]  Joshua E S Socolar,et al.  Autonomous Boolean modelling of developmental gene regulatory networks , 2013, Journal of The Royal Society Interface.

[6]  M Bohner,et al.  Theoretical model to determine the effects of geometrical factors on the resorption of calcium phosphate bone substitutes. , 2004, Biomaterials.

[7]  I. Jonkers,et al.  Gait characteristics and lower limb muscle strength in women with early and established knee osteoarthritis. , 2013, Clinical biomechanics.

[8]  Liesbet Geris,et al.  Relating the Chondrocyte Gene Network to Growth Plate Morphology: From Genes to Phenotype , 2012, PloS one.

[9]  F Gelaude,et al.  A custom-made guide for femoral component positioning in hip resurfacing arthroplasty: development and validation study , 2011, Computer aided surgery : official journal of the International Society for Computer Aided Surgery.

[10]  Jos Vander Sloten,et al.  Occurrence and Treatment of Bone Atrophic Non-Unions Investigated by an Integrative Approach , 2010, PLoS Comput. Biol..

[11]  Robert T Tranquillo,et al.  Transmural flow bioreactor for vascular tissue engineering , 2009, Biotechnology and bioengineering.

[12]  Josep A Planell,et al.  Simulation of tissue differentiation in a scaffold as a function of porosity, Young's modulus and dissolution rate: application of mechanobiological models in tissue engineering. , 2007, Biomaterials.

[13]  S. Hollister Porous scaffold design for tissue engineering , 2005, Nature materials.

[14]  D. Lauffenburger,et al.  Multipathway Kinase Signatures of Multipotent Stromal Cells Are Predictive for Osteogenic Differentiation , 2009, Stem cells.

[15]  A. Hadjizadeh,et al.  Computational study of culture conditions and nutrient supply in a hollow membrane sheet bioreactor for large-scale bone tissue engineering , 2014, Journal of Artificial Organs.

[16]  Peter Pivonka,et al.  Role of mathematical modeling in bone fracture healing. , 2012, BoneKEy reports.

[17]  Sundararajan V Madihally,et al.  Modeling of porous scaffold deformation induced by medium perfusion. , 2014, Journal of biomedical materials research. Part B, Applied biomaterials.

[18]  Min Jae Song,et al.  Mechanical modulation of nascent stem cell lineage commitment in tissue engineering scaffolds. , 2013, Biomaterials.

[19]  V. Grantcharova,et al.  Therapeutically Targeting ErbB3: A Key Node in Ligand-Induced Activation of the ErbB Receptor–PI3K Axis , 2009, Science Signaling.

[20]  Manuela T. Raimondi,et al.  A multiphysics 3D model of tissue growth under interstitial perfusion in a tissue-engineering bioreactor , 2013, Biomechanics and modeling in mechanobiology.

[21]  M Viceconti,et al.  The EuroPhysiome, STEP and a roadmap for the virtual physiological human , 2008, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[22]  Ivan Martin,et al.  The influence of the scaffold design on the distribution of adhering cells after perfusion cell seeding. , 2011, Biomaterials.

[23]  M Mengoni,et al.  An enhanced version of a bone-remodelling model based on the continuum damage mechanics theory , 2015, Computer methods in biomechanics and biomedical engineering.

[24]  F. Luyten,et al.  The Effect of Activating Fibroblast Growth Factor Receptor 3 Mutations on Osteogenic Differentiation and Ectopic Bone Formation by Human Periosteal Derived Cells , 2012 .

[25]  Arnold Neumaier,et al.  Mathematical Modeling of the Dynamics of Macroscopic Structural Transformations in Self-Propagating High-Temperature Synthesis , 2004 .

[26]  Peter Pivonka,et al.  Mathematical modeling in bone biology: from intracellular signaling to tissue mechanics. , 2010, Bone.

[27]  Scott J Hollister,et al.  Strut size and surface area effects on long-term in vivo degradation in computer designed poly(L-lactic acid) three-dimensional porous scaffolds. , 2012, Acta biomaterialia.

[28]  J. Schrooten,et al.  Mechanisms of ectopic bone formation by human osteoprogenitor cells on CaP biomaterial carriers. , 2012, Biomaterials.

[29]  J Vander Sloten,et al.  Role of subject-specific musculoskeletal loading on the prediction of bone density distribution in the proximal femur. , 2014, Journal of the mechanical behavior of biomedical materials.

[30]  L. Geris,et al.  A computational model for cell/ECM growth on 3D surfaces using the level set method: a bone tissue engineering case study , 2014, Biomechanics and modeling in mechanobiology.

[31]  Sara Checa,et al.  Effect of cell seeding and mechanical loading on vascularization and tissue formation inside a scaffold: a mechano-biological model using a lattice approach to simulate cell activity. , 2010, Journal of biomechanics.

[32]  Rebekah A. Neal,et al.  Three-dimensional elastomeric scaffolds designed with cardiac-mimetic structural and mechanical features. , 2013, Tissue engineering. Part A.

[33]  Bart Smeets,et al.  Individual-based modelling of the mechanical microenvironment , 2013 .

[34]  Wolf L. Glende The Boeing 777 : A Look Back , 2000 .

[35]  H Van Oosterwyck,et al.  Designing optimal calcium phosphate scaffold-cell combinations using an integrative model-based approach. , 2011, Acta biomaterialia.

[36]  Thimo Rohlf,et al.  Understanding epigenetic changes in aging stem cells – a computational model approach , 2014, Aging cell.

[37]  Liesbet Geris,et al.  A hybrid bioregulatory model of angiogenesis during bone fracture healing , 2011, Biomechanics and modeling in mechanobiology.

[38]  J Vander Sloten,et al.  In silico biology of bone modelling and remodelling: regeneration , 2009, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[39]  Liesbet Geris,et al.  MOSAIC: A Multiscale Model of Osteogenesis and Sprouting Angiogenesis with Lateral Inhibition of Endothelial Cells , 2012, PLoS Comput. Biol..

[40]  M. Taslim,et al.  The use of computational fluid dynamic models for the optimization of cell seeding processes. , 2011, Biomaterials.

[41]  Paul Suetens,et al.  Skull reconstruction planning transfer to the operation room by thin metallic templates: clinical results. , 2008, Journal of cranio-maxillo-facial surgery : official publication of the European Association for Cranio-Maxillo-Facial Surgery.

[42]  Marco Viceconti,et al.  Are spontaneous fractures possible? An example of clinical application for personalised, multiscale neuro-musculo-skeletal modelling. , 2012, Journal of biomechanics.

[43]  J Vander Sloten,et al.  Computer-aided planning of reconstructive surgery of the innominate bone: Automated correction proposals , 2007, Computer aided surgery : official journal of the International Society for Computer Aided Surgery.

[44]  E. Bueno,et al.  Tissue growth modeling in a wavy-walled bioreactor. , 2009, Tissue engineering. Part A.

[45]  C. Evans Advances in regenerative orthopedics. , 2013, Mayo Clinic proceedings.

[46]  M. Reinders,et al.  Predicting the therapeutic efficacy of MSC in bone tissue engineering using the molecular marker CADM1. , 2013, Biomaterials.

[47]  G. Barabino,et al.  Modeling of bioreactor hydrodynamic environment and its effects on tissue growth. , 2012, Methods in molecular biology.

[48]  David M. Umulis,et al.  The Intersection of Theory and Application in Elucidating Pattern Formation in Developmental Biology. , 2009, Mathematical modelling of natural phenomena.

[49]  F. Luyten,et al.  A biomarker-based mathematical model to predict bone-forming potency of human synovial and periosteal mesenchymal stem cells. , 2008, Arthritis and rheumatism.

[50]  S A Riboldi,et al.  Bioreactors in tissue engineering: scientific challenges and clinical perspectives. , 2009, Advances in biochemical engineering/biotechnology.

[51]  Ellen Kuhl,et al.  Computational modeling of bone density profiles in response to gait: a subject-specific approach , 2012, Biomechanics and modeling in mechanobiology.

[52]  C. Cobelli,et al.  In Silico Preclinical Trials: A Proof of Concept in Closed-Loop Control of Type 1 Diabetes , 2009, Journal of diabetes science and technology.

[53]  J M García-Aznar,et al.  On the role of bone damage in calcium homeostasis. , 2008, Journal of theoretical biology.

[54]  J. M. Oliver,et al.  Multiphase modelling of the influence of fluid flow and chemical concentration on tissue growth in a hollow fibre membrane bioreactor. , 2014, Mathematical medicine and biology : a journal of the IMA.

[55]  Frederik Gelaude,et al.  Quantitative Computerized Assessment of the Degree of Acetabular Bone Deficiency: Total radial Acetabular Bone Loss (TrABL) , 2011, Advances in orthopedics.

[56]  G.E. Moore,et al.  Cramming More Components Onto Integrated Circuits , 1998, Proceedings of the IEEE.

[57]  A. Concas,et al.  A novel simulation model for stem cells differentiation. , 2007, Journal of biotechnology.

[58]  P. Pivonka,et al.  A Systems Approach to Understanding Bone Cell Interactions in Health and Disease , 2012 .

[59]  P. Giannoudis,et al.  Fracture healing: the diamond concept. , 2007, Injury.

[60]  Peter Pivonka,et al.  Theoretical investigation of the role of the RANK-RANKL-OPG system in bone remodeling. , 2010, Journal of theoretical biology.

[61]  Margherita Cioffi,et al.  Potential and Bottlenecks of Bioreactors in 3D Cell Culture and Tissue Manufacturing , 2009, Advanced materials.

[62]  C. Hellmich,et al.  Mathematical modeling of postmenopausal osteoporosis and its treatment by the anti-catabolic drug denosumab , 2013, International journal for numerical methods in biomedical engineering.

[63]  M. Viceconti,et al.  Biomechanical robustness of a new proximal epiphyseal hip replacement to patient variability and surgical uncertainties: a FE study. , 2012, Medical engineering & physics.

[64]  Peter J. Hunter,et al.  FieldML, a proposed open standard for the Physiome project for mathematical model representation , 2013, Medical & Biological Engineering & Computing.

[65]  D. Drasdo,et al.  Impact of oxygen environment on mesenchymal stem cell expansion and chondrogenic differentiation , 2009, Cell proliferation.

[66]  Sara Checa,et al.  Simulation of angiogenesis and cell differentiation in a CaP scaffold subjected to compressive strains using a lattice modeling approach. , 2010, Biomaterials.

[67]  L. Greller,et al.  Response to continuous and pulsatile PTH dosing: a mathematical model for parathyroid hormone receptor kinetics. , 2005, Bone.

[68]  Liesbet Geris,et al.  Towards a quantitative understanding of oxygen tension and cell density evolution in fibrin hydrogels. , 2011, Biomaterials.