Big data approaches: Chemical complexity as a theoretical basis for life detection

Abstract:

Recent advancements in astrobiology have taught us how biased our search for life in the universe can be. Famous examples include searching for oxygen or water as biosignatures on exoplanets, which is now known to have the risk of false positives. What we think to be the sign of life can be biased by our limited understanding of what life is and how diverse planetary processes can be. In order to be as unbiased as possible, it is important to focus on understanding what life is fundamentally doing and what can be the universal properties of life.

To this aim, we take a statistical approach using big data to understand if there are any universal patterns shared across all biological organisms. There are three distinct domains of life: archaea, bacteria, and eukarya. We use one of the biggest databases that catalogues taxa in all domains of life, and analyze the chemical compounds used by various taxa in each of these domains. This allows us to look at what kind of chemical compounds are utilized universally or differently across various life forms and how complex such compounds are. Understanding chemical complexity used by life can be useful to distinguish life versus non-life as we search for life elsewhere. In this work, molecular weight and structural complexity are used as proxies of chemical complexity.

Our work shows that there are universal patterns of how life utilizes molecules with different levels of complexity. The three domains of life share similar distribution patterns of molecular weight of compounds they use. At the same time, slight differences between the domains are observed, especially for eukarya. This work suggests that looking at life’s usage of complex molecules and its statistical patterns can provide a theoretical basis for life detection.