Computational Modeling and Reverse Engineering to Reveal Dominant Regulatory Interactions Controlling Osteochondral Differentiation: Potential for Regenerative Medicine

The specialization of cartilage cells, or chondrogenic differentiation, is an intricate and meticulously regulated process that plays a vital role in both bone formation and cartilage regeneration. Understanding the molecular regulation of this process might help to identify key regulatory factors that can serve as potential therapeutic targets, or that might improve the development of qualitative and robust skeletal tissue engineering approaches. However, each gene involved in this process is influenced by a myriad of feedback mechanisms that keep its expression in a desirable range, making the prediction of what will happen if one of these genes defaults or is targeted with drugs, challenging. Computer modeling provides a tool to simulate this intricate interplay from a network perspective. This paper aims to give an overview of the current methodologies employed to analyze cell differentiation in the context of skeletal tissue engineering in general and osteochondral differentiation in particular. In network modeling, a network can either be derived from mechanisms and pathways that have been reported in the literature (knowledge-based approach) or it can be inferred directly from the data (data-driven approach). Combinatory approaches allow further optimization of the network. Once a network is established, several modeling technologies are available to interpret dynamically the relationships that have been put forward in the network graph (implication of the activation or inhibition of certain pathways on the evolution of the system over time) and to simulate the possible outcomes of the established network such as a given cell state. This review provides for each of the aforementioned steps (building, optimizing, and modeling the network) a brief theoretical perspective, followed by a concise overview of published works, focusing solely on applications related to cell fate decisions, cartilage differentiation and growth plate biology. Particular attention is paid to an in-house developed example of gene regulatory network modeling of growth plate chondrocyte differentiation as all the aforementioned steps can be illustrated. In summary, this paper discusses and explores a series of tools that form a first step toward a rigorous and systems-level modeling of osteochondral differentiation in the context of regenerative medicine

Mapping the use of computational modelling and simulation in clinics: A survey

In silico medicine describes the application of computational modelling and simulation (CM&S) to the study, diagnosis, treatment or prevention of a disease. Tremendous research advances have been achieved to facilitate the use of CM&S in clinical applications. Nevertheless, the uptake of CM&S in clinical practice is not always timely and accurately reflected in the literature. A clear view on the current awareness, actual usage and opinions from the clinicians is needed to identify barriers and opportunities for the future of in silico medicine. The aim of this study was capturing the state of CM&S in clinics by means of a survey toward the clinical community. Responses were collected online using the Virtual Physiological Human institute communication channels, engagement with clinical societies, hospitals and individual contacts, between 2020 and 2021. Statistical analyses were done with R. Participants (n = 163) responded from all over the world. Clinicians were mostly aged between 35 and 64 years-old, with heterogeneous levels of experience and areas of expertise (i.e., 48% cardiology, 13% musculoskeletal, 8% general surgery, 5% paediatrics). The CM&S terms “Personalised medicine” and “Patient-specific modelling” were the most well-known within the respondents. “In silico clinical trials” and “Digital Twin” were the least known. The familiarity with different methods depended on the medical specialty. CM&S was used in clinics mostly to plan interventions. To date, the usage frequency is still scarce. A well-recognized benefit associated to CM&S is the increased trust in planning procedures. Overall, the recorded level of trust for CM&S is high and not proportional to awareness level. The main barriers appear to be access to computing resources, perception that CM&S is slow. Importantly, clinicians see a role for CM&S expertise in their team in the future. This survey offers a snapshot of the current situation of CM&S in clinics. Although the sample size and representativity could be increased, the results provide the community with actionable data to build a responsible strategy for accelerating a positive uptake of in silico medicine. New iterations and follow-up activities will track the evolution of responses over time and contribute to strengthen the engagement with the medical community

BioDeg: A finite element software for the simulation ofthe corrosion and biodegradation process in metallicbiomaterials

peer reviewe

An open source crash course on parameter estimation of computational models using a Bayesian optimization approach

peer reviewe

Cancer modeling: From mechanistic to data-driven approaches, and from fundamental insights to clinical applications

peer reviewe

Computational modeling of degradation process of biodegradable magnesium biomaterials

Despite the advantages of using biodegradable metals in implant design, their uncontrolled degradation and release remain a challenge in practical applications. A validated computational model of the degradation process can facilitate the tuning of implant biodegradation by changing design properties. In this study, a physicochemical model was developed by deriving a mathematical description of the chemistry of magnesium biodegradation and implementing it in a 3D computational model. The model parameters were calibrated using the experimental data of hydrogen evolution by performing a Bayesian optimization routine. The model was validated by comparing the predicted change of pH in saline and buffered solutions with the experimentally obtained values from corrosion tests, showing maximum 5% of difference, demonstrating the model's validity to be used for practical cases

From the digital twins in healthcare to the Virtual Human Twin: a moon-shot project for digital health research

The idea of a systematic digital representation of the entire known human pathophysiology, which we could call the Virtual Human Twin, has been around for decades. To date, most research groups focused instead on developing highly specialised, highly focused patient-specific models able to predict specific quantities of clinical relevance. While it has facilitated harvesting the low-hanging fruits, this narrow focus is, in the long run, leaving some significant challenges that slow the adoption of digital twins in healthcare. This position paper lays the conceptual foundations for developing the Virtual Human Twin (VHT). The VHT is intended as a distributed and collaborative infrastructure, a collection of technologies and resources (data, models) that enable it, and a collection of Standard Operating Procedures (SOP) that regulate its use. The VHT infrastructure aims to facilitate academic researchers, public organisations, and the biomedical industry in developing and validating new digital twins in healthcare solutions with the possibility of integrating multiple resources if required by the specific context of use. The VHT infrastructure can also be used by healthcare professionals and patients for clinical decision support or personalised health forecasting. As the European Commission launched the EDITH coordination and support action to develop a roadmap for the development of the Virtual Human Twin, this position paper is intended as a starting point for the consensus process and a call to arms for all stakeholders

Coupling curvature-dependent and shear stress-stimulated neotissue growth in dynamic bioreactor cultures: a 3D computational model of a complete scaffold.

The main challenge in tissue engineering consists in understanding and controlling the growth process of in vitro cultured neotissues toward obtaining functional tissues. Computational models can provide crucial information on appropriate bioreactor and scaffold design but also on the bioprocess environment and culture conditions. In this study, the development of a 3D model using the level set method to capture the growth of a microporous neotissue domain in a dynamic culture environment (perfusion bioreactor) was pursued. In our model, neotissue growth velocity was influenced by scaffold geometry as well as by flow- induced shear stresses. The neotissue was modeled as a homogenous porous medium with a given permeability, and the Brinkman equation was used to calculate the flow profile in both neotissue and void space. Neotissue growth was modeled until the scaffold void volume was filled, thus capturing already established experimental observations, in particular the differences between scaffold filling under different flow regimes. This tool is envisaged as a scaffold shape and bioprocess optimization tool with predictive capacities. It will allow controlling fluid flow during long-term culture, whereby neotissue growth alters flow patterns, in order to provide shear stress profiles and magnitudes across the whole scaffold volume influencing, in turn, the neotissue growth

Modelling towards a more holistic medicine: The Virtual Physiological Human (VPH).

The Virtual Physiological Human (VPH) is a European initiative, rooted in the international Physiome initiative, focusing on establishing a methodological and technological framework, enabling the collaborative investigation of the human body as a single complex system. This collective framework will facilitate the sharing of resources and observations formed by different institutions and organizations, and the creation of disparate but integrated computer models of the mechanical, physical and biochemical functions of a living human body. The VPH initiative has laid the foundation for integrating heterogeneous data sources into mechanistic computer models of most anatomical systems