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See detailUncertainty in biology: a computational modeling approach
Geris, Liesbet ULg; Gomez-Cabrero, David

Book published by Springer (2015)

Computational modeling of biomedical processes is gaining more and more weight in the current research into the etiology of biomedical problems and potential treatment strategies. Computational modeling ... [more ▼]

Computational modeling of biomedical processes is gaining more and more weight in the current research into the etiology of biomedical problems and potential treatment strategies. Computational modeling allows to reduce, refine and replace animal experimentation as well as to translate findings obtained in these experiments to the human background. However these biomedical problems are inherently complex with a myriad of influencing factors, which strongly complicates the model building and validation process. This book wants to address four main issues related to the building and validation of computational models of biomedical processes: 1. Modeling establishment under uncertainty 2. Model selection and parameter fitting 3. Sensitivity analysis and model adaptation 4. Model predictions under uncertainty In each of the abovementioned areas, the book discusses a number of key-techniques by means of a general theoretical description followed by one or more practical examples. This book is intended for graduate students and researchers active in the field of computational modeling of biomedical processes who seek to acquaint themselves with the different ways in which to study the parameter space of their model as well as its overall behavior. [less ▲]

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See detailA semiquantitative framework for gene regulatory networks: increasing the time and quantitative resolution of Boolean networks
Kerkhofs, Johan ULg; Geris, Liesbet ULg

in PLoS ONE (2015), 10(6), 0130033

Boolean models have been instrumental in predicting general features of gene networks and more recently also as explorative tools in specific biological applications. In this study we introduce a basic ... [more ▼]

Boolean models have been instrumental in predicting general features of gene networks and more recently also as explorative tools in specific biological applications. In this study we introduce a basic quantitative and a limited time resolution to a discrete (Boolean) framework. Quantitative resolution is improved through the employ of normalized variables in unison with an additive approach. Increased time resolution stems from the introduction of two distinct priority classes. Through the implementation of a previously published chondrocyte network and T helper cell network, we show that this addition of quantitative and time resolution broadens the scope of biological behaviour that can be captured by the models. Specifically, the quantitative resolution readily allows models to discern qualitative differences in dosage response to growth factors. The limited time resolution, in turn, can influence the reachability of attractors, delineating the likely long term system behaviour. Importantly, the information required for implementation of these features, such as the nature of an interaction, is typically obtainable from the literature. Nonetheless, a trade-off is always present between additional computational cost of this approach and the likelihood of extending the model’s scope. Indeed, in some cases the inclusion of these features does not yield additional insight. This framework, incorporating increased and readily available time and semi-quantitative resolution, can help in substantiating the litmus test of dynamics for gene networks, firstly by excluding unlikely dynamics and secondly by refining falsifiable predictions on qualitative behaviour. [less ▲]

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See detailIn silico screening to predict chondrocyte hypertrophy using a semiquantitative gene network model
Kerkhofs, Johan ULg; Leijten, Jeroen; Luyten, Frank et al

Poster (2015, April 30)

PURPOSE: In development, chondrocyte hypertrophy is a crucial and well-studied step in endochondral ossification. Hypertrophy may also play a role in pathophysiological processes, including osteoarthritis ... [more ▼]

PURPOSE: In development, chondrocyte hypertrophy is a crucial and well-studied step in endochondral ossification. Hypertrophy may also play a role in pathophysiological processes, including osteoarthritis. We employ a computational approach to estimate the effect of individual factors in this complex process. METHODS: We have combined information gleaned from a high number of publications on chondrocyte differentiation into a gene regulatory network of 46 factors and over 150 interactions. This network can estimate the stability of proliferative chondrocytes/permanent cartilage (stable state with SOX9 activity) and hypertrophic chondrocytes (stable state with RUNX2 activity) by employing 2 measures. A first measure is a Monte Carlo analysis that assesses stability in the face of random initial conditions, the second modifies stable states to estimate the sensitivity to perturbation. RESULTS: For each factor, these qualitative measures are calculated in silico under knockout and overexpression conditions and compared to the wild type situation. This enables screening of the effects of all incorporated factors on cartilage homeostasis, differentiation and pathogenesis via the initiation of hypertrophy. Indeed, our gene network analysis indicated multiple candidate genes for the development of osteoarthritis. Factors that amplify the SOX9 attractor basin include TGFβ, PPR, IGF-I, and PKA. The presence of RAS, IHH, GLI2 and FGF is required for the Runx2 stable state. Using a literature study, we corroborated several of the proposed factors. CONCLUSIONS: In silico screening of overexpression and knockout presents a novel strategy to improve bone and cartilage tissue engineering approaches, and can be used to propose a list of putative therapeutic targets for e.g. osteoarthritis. [less ▲]

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See detailA Three-Dimensional Computational Fluid Dynamics Model Of Shear Stress Distribution During Neotissue Growth In A Perfusion Bioreactor.
Guyot, Yann ULg; Luyten, F. P.; Schrooten, J. et al

in Biotechnology and bioengineering (2015)

Bone tissue engineering strategies use flow through perfusion bioreactors to apply mechanical stimuli to cells seeded on porous scaffolds. Cells grow on the scaffold surface but also by bridging the ... [more ▼]

Bone tissue engineering strategies use flow through perfusion bioreactors to apply mechanical stimuli to cells seeded on porous scaffolds. Cells grow on the scaffold surface but also by bridging the scaffold pores leading a fully filled scaffold following the scaffold's geometric characteristics. Current computational fluid dynamic approaches for tissue engineering bioreactor systems have been mostly carried out for empty scaffolds. The effect of 3D cell growth and extracellular matrix formation (termed in this study as neotissue growth), on its surrounding fluid flow field is a challenge yet to be tackled. In this work a combined approach was followed linking curvature driven cell growth to fluid dynamics modeling. The level-set method (LSM) was employed to capture neotissue growth driven by curvature, while the Stokes and Darcy equations, combined in the Brinkman equation, provided information regarding the distribution of the shear stress profile at the neotissue/medium interface and within the neotissue itself during growth. The neotissue was assumed to be micro-porous allowing flow through its structure while at the same time allowing the simulation of complete scaffold filling without numerical convergence issues. The results show a significant difference in the amplitude of shear stress for cells located within the micro-porous neo-tissue or at the neotissue/medium interface, demonstrating the importance of taking along the neotissue in the calculation of the mechanical stimulation of cells during culture.The presented computational framework is used on different scaffold pore geometries demonstrating its potential to be used a design as tool for scaffold architecture taking into account the growing neotissue. This article is protected by copyright. All rights reserved. [less ▲]

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See detailCell based advanced therapeutic medicinal products for bone repair: Keep it simple?
Leijten, J.; Chai, Y. C.; Papantoniou, I. et al

in Advanced drug delivery reviews (2015), 84

The development of cell based advanced therapeutic medicinal products (ATMPs) for bone repair has been expected to revolutionize the health care system for the clinical treatment of bone defects. Despite ... [more ▼]

The development of cell based advanced therapeutic medicinal products (ATMPs) for bone repair has been expected to revolutionize the health care system for the clinical treatment of bone defects. Despite this great promise, the clinical outcomes of the few cell based ATMPs that have been translated into clinical treatments have been far from impressive. In part, the clinical outcomes have been hampered because of the simplicity of the first wave of products. In response the field has set-out and amassed a plethora of complexities to alleviate the simplicity induced limitations. Many of these potential second wave products have remained "stuck" in the development pipeline. This is due to a number of reasons including the lack of a regulatory framework that has been evolving in the last years and the shortage of enabling technologies for industrial manufacturing to deal with these novel complexities. In this review, we reflect on the current ATMPs and give special attention to novel approaches that are able to provide complexity to ATMPs in a straightforward manner. Moreover, we discuss the potential tools able to produce or predict 'goldilocks' ATMPs, which are neither too simple nor too complex. [less ▲]

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See detailComputational modeling under uncertainty: challenges and opportunities
Gomez-Cabrero, David; Geris, Liesbet ULg

in Modeling under uncertainty: a computational modeling approach (2015)

Computational Biology has increasingly become an important tool for biomedical and translational research. In particular, when generating novel hypothesis despite fundamental uncertainties in data and ... [more ▼]

Computational Biology has increasingly become an important tool for biomedical and translational research. In particular, when generating novel hypothesis despite fundamental uncertainties in data and mechanistic understanding of biological processes underpinning diseases. While in the present book, we have reviewed the necessary background and existing novel methodologies that set the basis for dealing with uncertainty, there are still many “grey”, or less well-defined, areas of investigations offering both challenges and opportunities. This final chapter in the book provides some reflections on those areas, namely: (1) the need for novel robust mathematical and statistical methodologies to generate hypothesis under uncertainty; (2) the challenge of aligning those methodologies in a context that requires larger computational resources; (3) the accessibility of modeling tools for less mathematical literate researchers; and (4) the integration of models with –omics data and its application in clinical environments. [less ▲]

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See detailAn introduction to uncertainty in the development of computational models of biological processes
Geris, Liesbet ULg; Gomez-Cabrero, David

in Modeling under uncertainty: a computational modeling approach (2015)

This chapter aims to provide an introduction to the different ways in which uncertainty can be dealt with computational modelling of biological processes. The first step is model establishment under ... [more ▼]

This chapter aims to provide an introduction to the different ways in which uncertainty can be dealt with computational modelling of biological processes. The first step is model establishment under uncertainty. Once models have been established, data can further be used to select which of the proposed models best meets the predefined criteria. Subsequently, parameter values can be optimized for a specific model configuration. Sensitivity analyses allow to assess the influence of the previous choices on the model output. Additionally, model adaptation permits to focus on specific aspects of the model without losing its global predictive capacity. Finally, predictions with the established models should also consider the effect of uncertainty in the model development process. [less ▲]

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See detailOxygen as a critical determinant of bone fracture healing-a multiscale model.
Carlier, Aurelie; Geris, Liesbet ULg; Gastel, Nick Van et al

in Journal of theoretical biology (2015), 365

A timely restoration of the ruptured blood vessel network in order to deliver oxygen and nutrients to the fracture zone is crucial for successful bone healing. Indeed, oxygen plays a key role in the ... [more ▼]

A timely restoration of the ruptured blood vessel network in order to deliver oxygen and nutrients to the fracture zone is crucial for successful bone healing. Indeed, oxygen plays a key role in the aerobic metabolism of cells, in the activity of a myriad of enzymes as well as in the regulation of several (angiogenic) genes. In this paper, a previously developed model of bone fracture healing is further improved with a detailed description of the influence of oxygen on various cellular processes that occur during bone fracture healing. Oxygen ranges of the cell-specific oxygen-dependent processes were established based on the state-of-the art experimental knowledge through a rigorous literature study. The newly developed oxygen model is compared with previously published experimental and in silico results. An extensive sensitivity analysis was also performed on the newly introduced oxygen thresholds, indicating the robustness of the oxygen model. Finally, the oxygen model was applied to the challenging clinical case of a critical sized defect (3mm) where it predicted the formation of a fracture non-union. Further model analyses showed that the harsh hypoxic conditions in the central region of the callus resulted in cell death and disrupted bone healing thereby indicating the importance of a timely vascularization for the successful healing of a large bone defect. In conclusion, this work demonstrates that the oxygen model is a powerful tool to further unravel the complex spatiotemporal interplay of oxygen delivery, diffusion and consumption with the several healing steps, each occurring at distinct, optimal oxygen tensions during the bone repair process. [less ▲]

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See detailBringing computational models of bone regeneration to the clinic.
Carlier, Aurelie; Geris, Liesbet ULg; Lammens, Johan et al

in Wiley interdisciplinary reviews. Systems biology and medicine (2015), 7(4), 183-94

Although the field of bone regeneration has experienced great advancements in the last decades, integrating all the relevant, patient-specific information into a personalized diagnosis and optimal ... [more ▼]

Although the field of bone regeneration has experienced great advancements in the last decades, integrating all the relevant, patient-specific information into a personalized diagnosis and optimal treatment remains a challenging task due to the large number of variables that affect bone regeneration. Computational models have the potential to cope with this complexity and to improve the fundamental understanding of the bone regeneration processes as well as to predict and optimize the patient-specific treatment strategies. However, the current use of computational models in daily orthopedic practice is very limited or inexistent. We have identified three key hurdles that limit the translation of computational models of bone regeneration from bench to bed side. First, there exists a clear mismatch between the scope of the existing and the clinically required models. Second, most computational models are confronted with limited quantitative information of insufficient quality thereby hampering the determination of patient-specific parameter values. Third, current computational models are only corroborated with animal models, whereas a thorough (retrospective and prospective) assessment of the computational model will be crucial to convince the health care providers of the capabilities thereof. These challenges must be addressed so that computational models of bone regeneration can reach their true potential, resulting in the advancement of individualized care and reduction of the associated health care costs. WIREs Syst Biol Med 2015, 7:183-194. doi: 10.1002/wsbm.1299 For further resources related to this article, please visit the WIREs website. CONFLICT OF INTEREST: The authors have declared no conflicts of interest for this article. [less ▲]

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See detailMultifactorial Optimization of Contrast-Enhanced Nanofocus Computed Tomography for Quantitative Analysis of Neo-Tissue Formation in Tissue Engineering Constructs.
Sonnaert, Maarten; Kerckhofs, Greet; Papantoniou, Ioannis et al

in PloS one (2015), 10(6), 0130227

To progress the fields of tissue engineering (TE) and regenerative medicine, development of quantitative methods for non-invasive three dimensional characterization of engineered constructs (i.e. cells ... [more ▼]

To progress the fields of tissue engineering (TE) and regenerative medicine, development of quantitative methods for non-invasive three dimensional characterization of engineered constructs (i.e. cells/tissue combined with scaffolds) becomes essential. In this study, we have defined the most optimal staining conditions for contrast-enhanced nanofocus computed tomography for three dimensional visualization and quantitative analysis of in vitro engineered neo-tissue (i.e. extracellular matrix containing cells) in perfusion bioreactor-developed Ti6Al4V constructs. A fractional factorial 'design of experiments' approach was used to elucidate the influence of the staining time and concentration of two contrast agents (Hexabrix and phosphotungstic acid) and the neo-tissue volume on the image contrast and dataset quality. Additionally, the neo-tissue shrinkage that was induced by phosphotungstic acid staining was quantified to determine the operating window within which this contrast agent can be accurately applied. For Hexabrix the staining concentration was the main parameter influencing image contrast and dataset quality. Using phosphotungstic acid the staining concentration had a significant influence on the image contrast while both staining concentration and neo-tissue volume had an influence on the dataset quality. The use of high concentrations of phosphotungstic acid did however introduce significant shrinkage of the neo-tissue indicating that, despite sub-optimal image contrast, low concentrations of this staining agent should be used to enable quantitative analysis. To conclude, design of experiments allowed us to define the most optimal staining conditions for contrast-enhanced nanofocus computed tomography to be used as a routine screening tool of neo-tissue formation in Ti6Al4V constructs, transforming it into a robust three dimensional quality control methodology. [less ▲]

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See detailInvestigation of shear stress evolution during neotissue growth in a perfusion bioreactor using 3d multiphysics modeling
Guyot, Yann ULg; Papantoniou, Ioannis; Schrooten, Jan et al

Conference (2014, October)

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See detailA Multiphysics model of neotissue growth in a perfu sion bioreactor
Guyot, Yann ULg; Papantoniou, Ioannis; Schrooten, Jan et al

Conference (2014, September)

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See detailModel-guided bone tissue engineering: from bench to bedside via in silico modeling
Geris, Liesbet ULg

Conference (2014, September)

The creation of man-made living implants is the holy grail of tissue engineering (TE). As basic science advances, one of the major challenges in TE is the translation of the increasing biological ... [more ▼]

The creation of man-made living implants is the holy grail of tissue engineering (TE). As basic science advances, one of the major challenges in TE is the translation of the increasing biological knowledge on complex cell and tissue behavior into a predictive and robust engineering process. Mastering this complexity is an essential step towards clinical applications of TE. Computational modeling allows to study the biological complexity in a more integrative and quantitative way. Specifically, computational tools can help in quantifying and optimizing the TE product and process but also in assessing the influence of the in vivo environment on the behavior of the TE product after implantation. In this talk, I will use the example of bone tissue engineering to demonstrate how computational modeling can contribute in all aspects of the TE product development cycle: cells, carriers, culture conditions and clinics (figure 1 and 2). Depending on the specific question that needs to be answered the optimal model systems can vary from single scale to multiscale. Furthermore, depending on the available information, model systems can be purely data-driven or more hypothesis-driven in nature. The talk makes the case for in silico models receiving proper recognition, besides the in vitro and in vivo work in the TE field. Figure 1: overview of the 4 important components in bone tissue engineering: cells, carriers, culture and clinics. Figure 2: overview of in silico contributions to the 4 important components in bone tissue engineering: cells [1], carriers, culture [3] and clinics [4]. Acknowledgements This work presented in this talk is part of Prometheus, the KU Leuven R&D division for skeletal tissue engineering. http://www.kuleuven.be/prometheus. The research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Programme (FP/2007-2013) / ERC Grant Agreements 279100; from the Research Programme of the Research Foundation - Flanders (FWO, grant n. G.0982.11), from the Belgian National Fund for Scientific Research (FNRS) and from the special research fund of the KU Leuven (GOA/13/016) References 1. Kerkhofs J, Roberts SJ, Luyten FP, Van Oosterwyck H, Geris L. Relating the chondrocyte gene network to growth plate morphology: from genes to phenotype. PLoS One. 2012;7(4):e34729. doi: 10.1371/journal.pone.0034729 2. Guyot Y, Papantoniou I, Chai YC, Van Bael S, Schrooten J, Geris L. A computational model for cell/ECM growth on 3D surfaces using the level set method: a bone tissue engineering case study.Biomech Model Mechanobiol. 2014 3. Carlier A, Geris L, Bentley K, Carmeliet G, Carmeliet P, Van Oosterwyck H. MOSAIC: a multiscale model of osteogenesis and sprouting angiogenesis with lateral inhibition of endothelial cells. PLoS Comput Biol. 2012;8(10):e1002724. [less ▲]

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See detail3D MODELING OF SHEAR STRESS DEVELOPMENT DURING NEOTISSUE GROWTH IN A PERFUSION BIOREACTOR
Guyot, Yann ULg; Papantoniou, Ioannis; Schrooten, Jan et al

Conference (2014, July)

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See detailA Multiphycics approach to calculate shear stresses during neotissue growth in perfusion bioreactor
Guyot, Yann ULg; Papantoniou, Ioannis; Schrooten, Jan et al

Conference (2014, July)

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See detailSpatial optimization in perfusion bioreactors improves bone tissue-engineered construct quality attributes
Papantoniou, Ioannis; Guyot, Yann ULg; Sonnaert, Maarten et al

in Biotechnology and Bioengineering (2014)

Perfusion bioreactors have shown great promise for tissue engineering applications providing a homogeneous and consistent distribution of nutrients and flow-induced shear stresses throughout tissue ... [more ▼]

Perfusion bioreactors have shown great promise for tissue engineering applications providing a homogeneous and consistent distribution of nutrients and flow-induced shear stresses throughout tissue-engineered constructs. However, non uniform fluid-flow profiles found in the perfusion chamber entrance region have been shown to affect tissue-engineered construct quality characteristics during culture. In this study a whole perfusion and construct, three dimensional (3D) computational fluid dynamics approach was used in order to optimize a critical design parameter such as the location of the regular pore scaffolds within the perfusion bioreactor chamber. Computational studies were coupled to bioreactor experiments for a case-study flow rate. Two cases were compared in the first instance seeded scaffolds were positioned immediately after the perfusion chamber inlet while a second group was positioned at the computationally determined optimum distance were a steady state flow profile had been reached. Experimental data showed that scaffold location affected significantly cell content and neo-tissue distribution, as determined and quantified by contrast enhanced nanoCT, within the constructs both at 14 and 21 days of culture. However gene expression level of osteopontin and osteocalcin was not affected by the scaffold location. This study demonstrates that the bioreactor chamber environment, incorporating a scaffold and its location within it, affects the flow patterns within the pores throughout the scaffold requiring therefore dedicated optimization that can lead to bone tissue engineered constructs with improved quality attributes [less ▲]

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