1 research outputs found
Ensemble Bayesian Analysis of Bistability in a Synthetic Transcriptional Switch
An overarching goal of synthetic and systems biology
is to engineer and understand complex biochemical systems by rationally
designing and analyzing their basic component interactions. Practically,
the extent to which such reductionist approaches can be applied is
unclear especially as the complexity of the system increases. Toward
gradually increasing the complexity of systematically engineered systems,
programmable synthetic circuits operating in cell-free <i>in
vitro</i> environments offer a valuable testing ground for principles
for the design, characterization, and analysis of complex biochemical
systems. Here we illustrate this approach using <i>in vitro</i> transcriptional circuits (“genelets”) while developing
an activatable transcriptional switch motif and configuring it as
a bistable autoregulatory circuit, using just four synthetic DNA strands
and three essential enzymes, bacteriophage T7 RNA polymerase, <i>Escherichia coli</i> ribonuclease H, and ribonuclease R. Fulfilling
the promise of predictable system design, the thermodynamic and kinetic
constraints prescribed at the sequence level were enough to experimentally
demonstrate intended bistable dynamics for the synthetic autoregulatory
switch. A simple mathematical model was constructed based on the mechanistic
understanding of elementary reactions, and a Monte Carlo Bayesian
inference approach was employed to find parameter sets compatible
with a training set of experimental results; this ensemble of parameter
sets was then used to predict a test set of additional experiments
with reasonable agreement and to provide a rigorous basis for confidence
in the mechanistic model. Our work demonstrates that programmable <i>in vitro</i> biochemical circuits can serve as a testing ground
for evaluating methods for the design and analysis of more complex
biochemical systems such as living cells