Abstract:
Electron bifurcation, the splitting of electron pairs into high and low energy pools, underpins energy storage and catalysis in living systems. Redox networks that bifurcate electrons do so at very low thermodynamic cost, accomplishing a Maxwell’s Demon like process. The inner workings of electron bifurcating enzymes are poorly understood, especially with respect to how energy dissipating (short-circuiting) reactions are avoided between the high- and low-energy electron transport pathways. A key feature of electron bifurcation networks is that they link three redox pools at very different potentials. I will show how we are modeling electron bifurcation networks and will describe classes of redox energy landscapes that naturally insulate the bifurcation networks from short-circuiting. The correlated many-particle flow in these networks is, in fact, essential for their function. I will review the physical principles that appear to underpin electron bifurcation, will contrast them with single-electron transfer networks, and will explore open questions and opportunities.
Bio:
David is the R.J. Reynolds Professor of Chemistry, and Professor of Biochemistry and of Physics at Duke University. He studies the physical origins of function in molecules and molecular assemblies, especially structures that underpin energy transduction in living systems. David also develops strategies to optimize the properties of functional structures. He earned his BS in Chemistry from Duke University and PhD in Chemistry from the California Institute of Technology. David was an NRC Research Associate and Member of the Technical Staff at Caltech’s Jet Propulsion Laboratory before moving to a faculty position. At Duke, David has served as Department Chair and has directed an NSF Center for Chemical Innovation. He is a Member of the National Academy of Sciences and a Fellow of the ACS, APS, RSC, and AAAS. David was a J.S. Guggenheim Foundation Fellow and has received national and international awards from the ACS and the RSC. His research is supported by NIH, DOE, NSF, AFOSR and the Keck Foundation.
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