The Axes of Life: A Roadmap for Understanding Dynamic Multiscale Systems.

The biological challenges facing humanity are complex, multi-factorial, and are intimately tied to the future of our health, welfare, and stewardship of the Earth. Tackling problems in diverse areas, such as agriculture, ecology, and health care require linking vast datasets that encompass numerous components and spatio-temporal scales. Here, we provide a new framework and a road map for using experiments and computation to understand dynamic biological systems that span multiple scales.

Deep Learning for Reintegrating Biology.

The goal of this vision paper is to investigate the possible role that advanced machine learning techniques, especially deep learning (DL), could play in the reintegration of various biological disciplines. To achieve this goal, a series of operational, but admittedly very simplistic, conceptualizations have been introduced: Life has been taken as a multidimensional phenomenon that inhabits three physical dimensions (time, space, and scale) and biological research as establishing connection between different points in the domain of life. Each of these points hence denotes a position in time, space, and scale at which a life phenomenon of interest takes place.

Biodiversifying bioinspiration.

Bioinspiration-using insights into the function of biological systems for the development of new engineering concepts-is already a successful and rapidly growing field. However, only a small portion of the world's biodiversity has thus far been considered as a potential source for engineering inspiration. This means that vast numbers of biological systems of potentially high value to engineering have likely gone unnoticed.