A cell’s ability to survive, grow, and reproduce depends on its metabolism, a chemical reaction network (CRN) with many intricate chemical reactions. Amazingly, a cell’s metabolism is organized such that all chemicals in the CRN are regenerated by the system. This property, which is called autocatalysis, arises from the fact that, within the cell’s CRN there are many minimal autocatalytic subnetworks (MASs), which are sets of reactions where a few chemicals can replicate themselves, using just food provided by the rest of the CRN. MASs are the smallest parts of the network that can sustain themselves meaning they are crucial to helping us understand how cells persist, grow, and reproduce.
New research from the David Baum Lab at the Wisconsin Institute for Discovery contributes to our understanding of autocatalysis in the context of life’s origins and evolution on Earth. The study introduces a novel mathematical tool to streamline the identification of Minimal Autocatalytic Sets (MASs) in Chemical Reaction Networks (CRNs), marking a significant step forward in the field.
“The mathematical problem we wanted to understand is, given a network of chemical reactions, can we detect which sets of molecules are autocatalytic, or grow together in the presence of each other?” says Praful Gagrani, former graduate student and postdoc with the David Baum group, and now a postdoc at the University of Tokyo.
“Previously, there have been several characterizations of the problem, but typically the approaches were graph theoretic…However, the combinatorics of exhaustively sifting through subgraphs can grow exponentially.”
The Baum Lab’s approach was different: they simplified the problem by focusing on linear algebraic representations of the network of chemical reactions. They further analyzed the makeup of autocatalytic networks and defined some basic terminology for MASs. They also came up with ways to group these different MASs together, based on their similarities, and developed a mathematical framework for how real chemical networks behave.
“We found that small simple networks can have a lot of MASs.” says Gagrani, “Similar to how with the number 1, we can define operations like addition to obtain 2, 3, 4, etc., we want to understand the behavior of a system composed of several MASs.” It’s believed that understanding a network’s dynamics through its constituent MASs could revolutionize the concept of synthesis.
The mathematical framework and computational tools developed within this study have potential applications for any kind of complex CRN that has multiple MASs, which are crucial for connecting biology and chemistry across many scales of analysis from the subcellular to the global. This means that studies of natural ecological communities, like coral reefs and rainforests, can soon benefit from the recent tools and frameworks developed here.
As researchers continue to advance their understanding of the fundamental principles behind the emergence and evolution of life on Earth, they work to bridge the gap between chemical processes and biological systems. The newest preprint from this group explores this connection in detail, presenting an abstract model for the origins of biochemical life—specifically, a polymerization model that serves as a precursor to genetic life. The paper demonstrates the role of MASs within this model and discusses their implications for the origins of life.
–Morgan Ramsey
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