ISBE – set out for a Systems Biology Infrastructure for Europe

Systems biology requires the availability, co-ordination and simultaneous interaction of a large number of diverse facilities and activities. These cover an entire spectrum, from mathematical modelling, through biological, biomedical and clinical experiments, to dedicated technology development. The systems biology community needs close cooperation with data-generation groups and bioinformaticians to define a strategy for producing life-science data of sufficiently high quality for model generation. For each medical, biological or biotechnological problem addressed, the optimal combination of facilities and activities is likely to be different. The complexity of biological systems, and the diversity and dynamics of their processes, means that a full analysis is far too complex to be handled by a single entity, industry or country – a variety of specialist expertise and facilities are typically necessary to achieve results suitable for modelling. Systems-level approaches for tackling the complexity of life-science data provide a profound conceptual advance compared to reductionist biological research methods of the past. Rather than focusing on individual laboratories, specialising in a limited number of research technologies, the Infrastructure for Systems Biology in Europe (ISBE) will facilitate the synergistic application of a wide range of research techniques and technologies to problems of major medical and biotechnological importance

The need for standardisation in life science research - an approach to excellence and trust

Today, academic researchers benefit from the changes driven by digital technologies and the enormous growth of knowledge and data, on globalisation, enlargement of the scientific community, and the linkage between different scientific communities and the society. To fully benefit from this development, however, information needs to be shared openly and transparently. Digitalisation plays a major role here because it permeates all areas of business, science and society and is one of the key drivers for innovation and international cooperation. To address the resulting opportunities, the EU promotes the development and use of collaborative ways to produce and share knowledge and data as early as possible in the research process, but also to appropriately secure results with the European strategy for Open Science (OS). It is now widely recognised that making research results more accessible to all societal actors contributes to more effective and efficient science; it also serves as a boost for innovation in the public and private sectors. However for research data to be findable, accessible, interoperable and reusable the use of standards is essential. At the metadata level, considerable efforts in standardisation have already been made (e.g. Data Management Plan and FAIR Principle etc.), whereas in context with the raw data these fundamental efforts are still fragmented and in some cases completely missing. The CHARME consortium, funded by the European Cooperation in Science and Technology (COST) Agency, has identified needs and gaps in the field of standardisation in the life sciences and also discussed potential hurdles for implementation of standards in current practice. Here, the authors suggest four measures in response to current challenges to ensure a high quality of life science research data and their re-usability for research and innovation

Compressive stress-mediated p38 activation required for ER alpha plus phenotype in breast cancer

Breast cancer is now globally the most frequent cancer and leading cause of women's death. Two thirds of breast cancers express the luminal estrogen receptor-positive (ER alpha + ) phenotype that is initially responsive to antihormonal therapies, but drug resistance emerges. A major barrier to the understanding of the ER alpha-pathway biology and therapeutic discoveries is the restricted repertoire of luminal ER alpha + breast cancer models. The ER alpha + phenotype is not stable in cultured cells for reasons not fully understood. We examine 400 patient-derived breast epithelial and breast cancer explant cultures (PDECs) grown in various three-dimensional matrix scaffolds, finding that ER alpha is primarily regulated by the matrix stiffness. Matrix stiffness upregulates the ER alpha signaling via stress-mediated p38 activation and H3K27me3-mediated epigenetic regulation. The finding that the matrix stiffness is a central cue to the ER alpha phenotype reveals a mechanobiological component in breast tissue hormonal signaling and enables the development of novel therapeutic interventions. Subject terms: ER-positive (ER + ), breast cancer, ex vivo model, preclinical model, PDEC, stiffness, p38 SAPK. Reliable luminal estrogen receptor (ER alpha+) breast cancer models are limited. Here, the authors use patient derived breast epithelial and breast cancer explant cultures grown in several extracellular matrix scaffolds and show that ER alpha expression is regulated by matrix stiffness via stress-mediated p38 activation and H3K27me3-mediated epigenetic regulation.Peer reviewe

Compressive stress-mediated p38 activation required for ERα + phenotype in breast cancer

Breast cancer is now globally the most frequent cancer and leading cause of women's death. Two thirds of breast cancers express the luminal estrogen receptor-positive (ER alpha + ) phenotype that is initially responsive to antihormonal therapies, but drug resistance emerges. A major barrier to the understanding of the ER alpha-pathway biology and therapeutic discoveries is the restricted repertoire of luminal ER alpha + breast cancer models. The ER alpha + phenotype is not stable in cultured cells for reasons not fully understood. We examine 400 patient-derived breast epithelial and breast cancer explant cultures (PDECs) grown in various three-dimensional matrix scaffolds, finding that ER alpha is primarily regulated by the matrix stiffness. Matrix stiffness upregulates the ER alpha signaling via stress-mediated p38 activation and H3K27me3-mediated epigenetic regulation. The finding that the matrix stiffness is a central cue to the ER alpha phenotype reveals a mechanobiological component in breast tissue hormonal signaling and enables the development of novel therapeutic interventions. Subject terms: ER-positive (ER + ), breast cancer, ex vivo model, preclinical model, PDEC, stiffness, p38 SAPK.Reliable luminal estrogen receptor (ER alpha+) breast cancer models are limited. Here, the authors use patient derived breast epithelial and breast cancer explant cultures grown in several extracellular matrix scaffolds and show that ER alpha expression is regulated by matrix stiffness via stress-mediated p38 activation and H3K27me3-mediated epigenetic regulation.</p

The human physiome:How standards, software and innovative service infrastructures are providing the building blocks to make it achievable

Reconstructing and understanding the Human Physiome virtually is a complex mathematical problem, and a highly demanding computational challenge. Mathematical models spanning from the molecular level through to whole populations of individuals must be integrated, then personalized. This requires interoperability with multiple disparate and geographically separated data sources, and myriad computational software tools. Extracting and producing knowledge from such sources, even when the databases and software are readily available, is a challenging task. Despite the difficulties, researchers must frequently perform these tasks so that available knowledge can be continually integrated into the common framework required to realize the Human Physiome. Software and infrastructures that support the communities that generate these, together with their underlying standards to format, describe and interlink the corresponding data and computer models, are pivotal to the Human Physiome being realized. They provide the foundations for integrating, exchanging and re-using data and models efficiently, and correctly, while also supporting the dissemination of growing knowledge in these forms. In this paper, we explore the standards, software tooling, repositories and infrastructures that support this work, and detail what makes them vital to realizing the Human Physiome