Extracellular matrix (ECM) is an active, critical regulator of cell behavior and the dynamics of extracellular microenvironments. ECM composition and organization play a key role in development, tissue homeostasis and regeneration, but can also underpin disease processes including tumor development, cancer progression, fibrosis and inflammatory disease. Bioengineering approaches have been developed to modulate physicochemical properties of the ECM and to recreate synthetic analogues, which can help define the impact of ECM via cell-matrix interactions. Recent 3D/4D printing technologies attempt to recreate these microenvironments with enhanced spatiotemporal controls. However, analysis of these complex inputs and responses in heterogeneous environments requires systematic approaches. Beyond conventional imaging, non-linear optics, computational methods, and systems biology approaches can define diverse and complex environmental signals that maintain and perturb ECM microenvironments. In this regard, computational and informatics approaches will be exceptionally helpful to process such data that possess inherently multivariate nature. Analysis of big data using machine learning and deep learning technology is an emerging area, and application of these approaches to understanding ECM microenvironments has the potential to deliver transformative information in a broad range of fields.
In this Research Topic, we encourage participation from diverse research groups working on ECM microenvironments with variety of engineering, imaging and computational technologies. We welcome Original Research, Methods, Review, Perspective and Mini Review articles. Primary areas will include tissue engineering, ECM modeling with in vitro, ex vivo and in vivo technology, bioinformatics, advanced imaging technology, biomaterials, deep learning, and stem cell bioengineering. Some of these areas are well-developed and already have shown to be critical to understand tissue homeostasis and disease conditions. Other areas are emerging with the aid of computational power and availability of ample data. This Research Topic provides an opportunity for researchers with interests in engineering and imaging ECM to share their new data, ideas and approaches in this rapidly developing interdisciplinary field.
Extracellular matrix (ECM) is an active, critical regulator of cell behavior and the dynamics of extracellular microenvironments. ECM composition and organization play a key role in development, tissue homeostasis and regeneration, but can also underpin disease processes including tumor development, cancer progression, fibrosis and inflammatory disease. Bioengineering approaches have been developed to modulate physicochemical properties of the ECM and to recreate synthetic analogues, which can help define the impact of ECM via cell-matrix interactions. Recent 3D/4D printing technologies attempt to recreate these microenvironments with enhanced spatiotemporal controls. However, analysis of these complex inputs and responses in heterogeneous environments requires systematic approaches. Beyond conventional imaging, non-linear optics, computational methods, and systems biology approaches can define diverse and complex environmental signals that maintain and perturb ECM microenvironments. In this regard, computational and informatics approaches will be exceptionally helpful to process such data that possess inherently multivariate nature. Analysis of big data using machine learning and deep learning technology is an emerging area, and application of these approaches to understanding ECM microenvironments has the potential to deliver transformative information in a broad range of fields.
In this Research Topic, we encourage participation from diverse research groups working on ECM microenvironments with variety of engineering, imaging and computational technologies. We welcome Original Research, Methods, Review, Perspective and Mini Review articles. Primary areas will include tissue engineering, ECM modeling with in vitro, ex vivo and in vivo technology, bioinformatics, advanced imaging technology, biomaterials, deep learning, and stem cell bioengineering. Some of these areas are well-developed and already have shown to be critical to understand tissue homeostasis and disease conditions. Other areas are emerging with the aid of computational power and availability of ample data. This Research Topic provides an opportunity for researchers with interests in engineering and imaging ECM to share their new data, ideas and approaches in this rapidly developing interdisciplinary field.