Quantifying resource competition and its effects in the TX-TL system

Abstract

Without accounting for the limited availability of shared cellular resources, the standard model of gene expression fails to reliably predict experimental data obtained in vitro. To overcome this limitation, we develop a dynamical model of gene expression explicitly modeling competition for scarce resources. In addition to accurately describing the experimental data, this model only depends on a handful of easily identifiable parameters with clear physical interpretation. Based on this model, we then characterize the combinations of protein concentrations that are simultaneously realizable with shared resources. As application examples, we demonstrate how the results can be used to explain similarities/differences among different in vitro extracts, furthermore, we illustrate that accounting for resource usage is essential in circuit design considering the toggle switch. I. INTRODUCTION One of the fundamental goals of synthetic biology is to engineer complex behaviors both at the cellular and population levels. Unfortunately, parts designed separately often fail to function once interconnected due to context-dependence. Sources of context-dependence include interactions among parts due to spatial co-localization [1], dependence on the host organism and strain [2], growth-dependence [3], enviromental dependence [4], and unwanted couplings due to the composition of modules [5], [6], [7]. As a result, while there are great successes in creating more and more complex circuits, these efforts often involve numerous iterative cycles of building and testing components. While these steps are slow and expensive in vivo, they are significantly faster and cheaper in vitro. As a result, cell-free transcription-translation (TX-TL) systems offer a promising avenue for synthetic biology [8]. Additionally, these systems do not suffer from unwanted coupling between the synthetic parts and the behavior of the host organism, for instance, issues related to cell growth. Unfortunately, however, protein expression requires the availability of resources (RNAP, nucleotides, tRNAs, ribosomes, ATP, etc.) that are shared among genes, and as a result, protein expression levels are coupled even in the absence of regulatory linkages in vitro [9], just as in vivo [10], [11]. To reliably analyze and predict circuit behavior, these couplings need to be accounted for, otherwise circuits need to be continuously redesigned and re-tuned. According to the most commonly used model of gene expression, mRNA m i is transcribed and degraded with rate

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

“…In this paper, we build upon these earlier results in [15] and quantify the information content of candidate experiments in the presence of measurement noise. To this end, we consider parameter estimation from the perspective of both Least Squares Estimation and Maximum Likelihood Estimation.…”

“…This phenomenon causes coupling between virtually any two components that share the same machinery (RNA polymerase, ribosomes, degradation enzymes, etc. ), both in vivo and in vitro [15], [16].…”

“…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].…”

“…To this end, in [15] we developed a mathematical model considering the in vitro experimental data originally obtained in [12]. This mechanistic model explicitly accounts for the limited availability of shared transcriptional/translational resources and reveals how the expression of one gene affects that of another via competition for these shared resources.…”

Bottom-Up Construction of Complex Biomolecular Systems With Cell-Free Synthetic Biology

“…A promising direction to improve predictability when composing synthetic parts, in light of resource problems, is to take the primary resources into account in mathematical models, thereby considering non-regulatory interactions between components through resource sequestration (Gyorgy et al, 2015;Gorochowski et al, 2016;Qian et al, 2017). Gyorgy and Murray (2016) developed a model that used the previous cell-free extract data obtained by Siegal-Gaskins et al to account for resource competition between genes. They were able to successfully predict expression profiles of multiple co-expressed parts, from data where these parts were characterized individually.…”