Quantifying the Generality of the Nonenzymatic rTCA Cycle Using High-Throughput Experimentation

• Applicant: Dr. Maciej Piejko, Ph.D.
• Subject Area: Physical Chemistry of Molecules, Liquids and Interfaces, Biophysical Chemistry
• Term: since 2026
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 580368398

A fundamental challenge to explaining the chemical origin of life is to rationalize the transition from single, isolated reactions to the complex reaction networks of biochemical metabolism. One of the most ancient pathways central to all life is the reverse Tricarboxylic Acid (rTCA) cycle. The pathway produces the universal precursors necessary for the biosynthesis of all core metabolites in modern biochemistry, namely sugars, lipids, and amino acids. Yet, why did biology evolve around specifically this pathway? Even though the modern rTCA cycle operates by enzymatic catalysis, nonenzymatic analogs of this pathway have been reported by several groups in drastically different conditions. This suggests that the reactions forming the rTCA cycle are particularly robust and can occur under a wide range of conditions. The goal of this project is to quantify the generality of reactions mimicking the rTCA cycle by combining high-throughput experimentation and data science and to systematically explore how reaction conditions relate to product outcome in nonenzymatic reaction networks. First, I will establish the concept of automated high-throughput screening for investigating prebiotic reaction networks. I will then generate a dataset mapping the reaction space of the reaction network emerging from mixing the keto acids, glyoxylate and pyruvate, as a function of reaction conditions such as additives or catalysts. Methods from NMR metabolomics will be used for tackling the analytical challenge of quantitatively assessing large numbers of samples encompassing dozens of substrates. Finally, I will test the concept of multivariate correlation analysis to quantitatively define the generality of a reaction network, and explore the time-dependent evolution of the reaction network. By integrating high-throughput experimentation, NMR spectroscopy, and advanced data analysis, this project will deliver a workflow for analyzing how simple compounds can give rise to complex reaction networks. By establishing a way to quantifying the generality of a reaction network, this project will thus allow for testing the hypothesis that modern biochemistry results from the most general reaction networks that can operate without enzymes in water.

• DFG Programme: Position