A resource dependent protein synthesis model for evaluating synthetic circuits

Abstract

Reliable in-silico design of synthetic gene networks necessitates novel approaches to model the process of protein synthesis under the influence of limited resources. We present such a novel protein synthesis model which originates from the Ribosome Flow Model and among other things describes the movement of RNA-polymerase and Ribosomes on mRNA and DNA templates respectively. By analyzing the convergence properties of this model based upon geometric considerations we present additional insights into the dynamic mechanisms of the process of protein synthesis. Further, we exemplarily show how this model can be used to evaluate the performance of synthetic gene circuits under different loading scenarios.

Analysis of primitive genetic interactions for the design of a genetic signal differentiator

“…In ( 4 ), we verified this structure by simulating the system based on the much more detailed model described in ( 14 ). This detailed model mainly aims at taking the finite amounts of RNAP and ribosomes as well as the time delay of transcription and translation into account, however, chosen parameters only reflected average parameters from literature.…”

“…In particular, it is expected that this cannot be achieved in an exact way, thus it is necessary to determine how inaccuracies in the basic parts influence the behavior of the assembled circuit. For an initial guess for finding such functions, a semi-mechanistic model of transcription and translation ( 14 ) was used in ( 4 ) to conclude that the processes of transcription and translation can approximately be seen as a gain and integrator, respectively, and that a combinatorial promoter may be used to realize the difference of two signals. In the remainder of this section, we briefly recapitulate these deductions.…”

“…In general, however, although the transcription rate f i increases monotonically with DNA concentration D i , it cannot grow arbitrarily large but is subject to saturation effects for large enough DNA or transcription factor concentrations, see e.g. ( 14 , 17 ).…”

Optimal Experiment Design and Leveraging Competition for Shared Resources in Cell-Free Extracts

“…The results in Lemmas 1-2 can be readily leveraged to characterize the set of optimal experiments according to some real-valued summary statistics, e.g., those in (14)- (17). Additionally, these results are equally well-suited for precisely quantifying how competition for shared cellular resources affects parameter estimation.…”

“…As a result, part characterization must include quantification of resource sequestration so that the resulting coupling phenomenon can be predicted when parts are co-expressed [15], [17]. This way unwanted coupling effects can be taken into account during systems-level design, thus ensuring correct behavior upon interconnection [18], [19].…”