Abstract
One of the most challenging aspect of origins of life research is that we do not know precisely what life is. In recent years, the use of network theory has revolutionized our understanding of living systems by permitting a mathematical framework for understanding life as an emergent, collective property of many interacting entities. So far, complex systems science has seen little direct application to the origins of life, particularly in laboratory science. Yet, networks are important mathematical descriptors in cases where the structure of interactions matters more than counting individual component parts—precisely what we envision happens as matter transitions to life. Here, we review a few notable examples of the use of network theory in prebiotic evolution, and discuss the promise of systems approaches to origins of life. The end goal is to develop a statistical mechanics useful to origins of life—that is, one that deals with interactions of system components (rather than merely counting them) and is therefore equipped to model life as an emergent phenomena.
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- 1.
It is important to point out it is an assumption of our theories for the origin of life that the process started with molecules we would identify as biological. Alternative hypotheses, such as Cairns-Smith’s “clay world” (Cairns-Smith 1986), make different assumptions. It is a reasonable assumption to make, but in the field of origins where we remain largely in the dark about exactly what happened, it is important to be aware of the starting points we adopt to make traction on the problem.
- 2.
The degree distribution is calculated by determining the frequency of the degree for each node, and is often normalized by dividing by the total number of edges in the graph, which can be interpreted as a probability of connection and the resulting distribution interpreted as probability distribution.
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Walker, S.I., Mathis, C. (2018). Network Theory in Prebiotic Evolution. In: Menor-Salván , C. (eds) Prebiotic Chemistry and Chemical Evolution of Nucleic Acids. Nucleic Acids and Molecular Biology, vol 35. Springer, Cham. https://doi.org/10.1007/978-3-319-93584-3_10
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