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

Abstract

The fact that genes compete for shared cellular resources poses a fundamental challenge when identifying parameters of genetic parts. A recently developed model of gene expression tackles this problem by explicitly accounting for resource competition. In addition to accurately describing experimental data, this model only depends on a small number of easily identifiable parameters with clear physical interpretation. Based on this model, we outline a procedure to select the optimal set of experiments to characterize biomolecular parts in synthetic biology. Additionally, we reveal the role competition for shared resources plays, provide guidelines how to minimize its detrimental effects, and how to leverage this phenomenon to extract the most information about unknown parameters. To illustrate the results, we consider the case of part characterization in cell-free extracts, treat plasmid DNA concentrations as decision variables, and demonstrate the significant performance difference between naïve and optimal experiment design.

Cell-Free Systems: A Proving Ground for Rational Biodesign

“…No regulation: While living cells are actively regulated at multiple levels of organization, from molecular- to network-scale, cell-free systems contain no active regulatory mechanisms. This simplifies the identification and measurement of host-chassis interactions, allowing resource allocation within the cell-free system to be elucidated in detail (Siegal-Gaskins et al, 2014 ; Gyorgy and Murray, 2016 ; Borkowski et al, 2018 ; Halter et al, 2018 ). On the other hand, cell-free systems lose the robustness conferred by homeostasis (Lewis et al, 2014 ).…”

Models for Cell-Free Synthetic Biology: Make Prototyping Easier, Better, and Faster

“…Parametrization of the models using the appropriate RNA polymerase and ribosomes concentrations and binding/unbinding rates allows an accurate description of the resource competition and to fit properly the production of several proteins expressed concurrently in cell-free (Siegal-Gaskins et al, 2014 ; Borkowski et al, 2018 ; Moore et al, 2018 ). Accounting for RNAP and ribosomes sharing between parts can also be leveraged to minimize the number of experiments required to fit parameters and obtain a predictive model (Halter et al, 2018 ). While not the main focus of this review, parameter estimation or identification is a major hurdle of detailed models, and techniques from systems biology (e.g., Lillacci and Khammash, 2010 ) can be used to tackle this issue.…”