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Abstract SummaryNetwork biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions and diseases across biological systems and scales. Although the field has been around for two decades, it remains nascent. It has witnessed rapid evolution, accompanied by emerging challenges. These stem from various factors, notably the growing complexity and volume of data together with the increased diversity of data types describing different tiers of biological organization. We discuss prevailing research directions in network biology, focusing on molecular/cellular networks but also on other biological network types such as biomedical knowledge graphs, patient similarity networks, brain networks, and social/contact networks relevant to disease spread. In more detail, we highlight areas of inference and comparison of biological networks, multimodal data integration and heterogeneous networks, higher-order network analysis, machine learning on networks, and network-based personalized medicine. Following the overview of recent breakthroughs across these five areas, we offer a perspective on future directions of network biology. Additionally, we discuss scientific communities, educational initiatives, and the importance of fostering diversity within the field. This article establishes a roadmap for an immediate and long-term vision for network biology. Availability and implementationNot applicable.
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Molecular and Cellular Biomechanics
This book bridges the gap between life sciences and physical sciences by providing several perspectives on cellular and molecular mechanics on a fundamental level. It begins with a general introduction to the scales and terms that are used in the field of cellular and molecular biomechanics and then moves from the molecular scale to the tissue scale. It discusses various tissues or cellular systems through the chapters written by prominent engineers and physicists working in various fields of biomechanics.
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- Award ID(s):
- 0950374
- PAR ID:
- 10037768
- Date Published:
- Journal Name:
- CRC news
- ISSN:
- 1460-7573
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1201/b18093
