Kinetic modeling of in vitro enzymatic reaction networks is vital to understand and control the complex behaviors emerging from the nonlinear interactions inside. However, modeling is severely hampered by the lack of training data. Here, we introduce a methodology that combines an active learning-like approach and flow chemistry to efficiently create optimized datasets for a highly interconnected enzymatic reactions network with multiple sub-pathways.
View Article and Find Full Text PDFAngew Chem Int Ed Engl
October 2018
The reproduction of emergent behaviors in nature using reaction networks is an important objective in synthetic biology and systems chemistry. Herein, the first experimental realization of an enzymatic reaction network capable of an adaptive response is reported. The design is based on the dual activity of trypsin, which activates chymotrypsin while at the same time generating a fluorescent output from a fluorogenic substrate.
View Article and Find Full Text PDFLiving systems rely on complex networks of chemical reactions to control the concentrations of molecules in space and time. Despite the enormous complexity in biological networks, it is possible to identify network motifs that lead to functional outputs such as bistability or oscillations. One of the greatest challenges in chemistry is the creation of such functionality from chemical reactions.
View Article and Find Full Text PDFThe dynamic behavior of the plant red/far-red light photoreceptor phytochrome B (phyB) has been elucidated in natural and synthetic systems. Red light switches phyB from the inactive Pr state to the active Pfr state, a process that is reversed by far-red light. Alongside light signals, phyB activity is constrained by thermal reversion (that is prominent in the dark) and protein-protein interactions between phyB, other phytochrome molecules, and, among others, PHYTOCHROME INTERACTING FACTORs (PIFs).
View Article and Find Full Text PDFBackground: Obtaining accurate estimates of biological or enzymatic reaction rates is critical in understanding the design principles of a network and how biological processes can be experimentally manipulated on demand. In many cases experimental limitations mean that some enzymatic rates cannot be measured directly, requiring mathematical algorithms to estimate them. Here, we describe a methodology that calculates rates at which light-regulated proteins switch between conformational states.
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