Cell sources for articular cartilage repair strategies: shifting from mono-cultures to co-cultures

The repair of articular cartilage is challenging due to the sparse native cell population combined with the avascular and aneural nature of the tissue. In recent years cartilage tissue engineering has shown great promise. As with all tissue engineering strategies, the possible therapeutic outcome is intimately linked with the used combination of cells, growth factors and biomaterials. However, the optimal combination has remained a controversial topic and no consensus has been reached. In consequence, much effort has been dedicated to further design, investigate and optimize cartilage repair strategies. Specifically, various research groups have performed intensive investigations attempting to identify the single most optimal cell source for articular cartilage repair strategies. However, recent findings indicate that not the heavily investigated mono cell source, but the less studied combinations of cell sources in co-culture might be more attractive for cartilage repair strategies. This review will give a comprehensive overview on the cell sources that have been investigated for articular cartilage repair strategies. In particular, the advantages and disadvantages of investigated cell sources are comprehensively discussed with emphasis on the potential of co-cultures in which benefits are combined while the disadvantages of single cell sources for cartilage repair are mitigated

A dual flow bioreactor with controlled mechanical stimulation for cartilage tissue engineering

In cartilage tissue engineering bioreactors can create a controlled environment to study chondrocyte behavior under mechanical stimulation or produce chondrogenic grafts of clinically relevant size. Here we present a novel bioreactor, which combines mechanical stimulation with a two compartment system through which nutrients can be supplied solely by diffusion from opposite sides of a tissue engineered construct. This design is based on the hypothesis that creating gradients of nutrients, growth factors and growth factor antagonists can aid in the generation of zonal tissue engineered cartilage. Computational modeling predicted that the design facilitates the creation of a biologically relevant glucose gradient. This was confirmed by quantitative glucose measurements in cartilage explants. In this system it is not only possible to create gradients of nutrients, but also of anabolic or catabolic factors. Therefore, the bioreactor design allows control over nutrient supply and mechanical stimulation useful for in vitro generation of cartilage constructs that can be used for the resurfacing of articulated joints or as a model for studying OA disease progression

Setting Parameters for Biological Models With ANIMO

ANIMO (Analysis of Networks with Interactive MOdeling) is a software for modeling biological networks, such as e.g. signaling, metabolic or gene networks. An ANIMO model is essentially the sum of a network topology and a number of interaction parameters. The topology describes the interactions between biological entities in form of a graph, while the parameters determine the speed of occurrence of such interactions. When a mismatch is observed between the behavior of an ANIMO model and experimental data, we want to update the model so that it explains the new data. In general, the topology of a model can be expanded with new (known or hypothetical) nodes, and enables it to match experimental data. However, the unrestrained addition of new parts to a model causes two problems: models can become too complex too fast, to the point of being intractable, and too many parts marked as "hypothetical" or "not known" make a model unrealistic. Even if changing the topology is normally the easier task, these problems push us to try a better parameter fit as a first step, and resort to modifying the model topology only as a last resource. In this paper we show the support added in ANIMO to ease the task of expanding the knowledge on biological networks, concentrating in particular on the parameter settings