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Ground-breaking advances in computational sciences and science could provide astrobiology researchers with innovative methods to discover new patterns, test broader hypotheses and explore the possibility space of life. Mathematical applications in theoretical biology offer new ways to explore the origin and nature of life through complexity and systems studies and allow us to challenge our notions of habitability for life writ large. Computer simulations have become more sophisticated and better at representing large scale phenomena, while machine learning and deep learning techniques have become better at discovering patterns in noisy data and at forecasting. Projects that simulate data, where such data is missing, and/or use pattern recognition techniques, unsupervised and supervised learning and categorization, very large datasets and/or aligning data sets and simulated data from various disparate astrobiology related projects could potentially shed new light on problems such as the coevolution of biospheres and universal requirements for life. This session is seeking abstracts that incorporate astrobiological themes related to biosignatures and the search for life in data science and machine learning applications; computational models of biology; evolutionary,
synthetic and artificial biology; and information and network theory. These studies promise to open new possible search patterns and venues for life.
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