Vignoni, A. | Search bioRxiv

bioRxiv2026

Host-aware Identification of Intrinsic Gene Expression Biopart Parameters from Combinatorial Libraries

Model-based design in synthetic biology is limited by the lack of quantitative, mechanistically interpretable biopart parameters that remain valid across genetic and physiological contexts. This limitation is particularly acute for transcriptional units, whose expression phenotypes emerge from nonlinear coupling between plasmid copy number, transcription, translation, and host resource allocation. Here we introduce a context- and host-aware framework for the absolute characterisation of gene expression bioparts embedded in combinatorial libraries of constitutive transcriptional units. Our approach leverages a digital twin of Escherichia coli conditioned on experimentally measured growth rate, used as a low-dimensional physiological proxy for cellular state. By embedding this growth-conditioned digital twin in a model-in-the-loop identification strategy, host-circuit interactions are explicitly accounted for and decoupled from intrinsic biopart properties. Using structured combinatorial libraries, we identify biophysically interpretable and transferable parameters for plasmid origins, promoters, and ribosome binding sites. In particular, we uncover an intrinsic translation initiation capacity of ribosome binding sites that remains invariant across genetic contexts and growth conditions, while context-dependent translation rates emerge as physiological projections of this invariant descriptor. This intrinsic parameterisation enables accurate prediction of protein synthesis across diverse host states, supports incremental and patchwork library expansion, and reveals localized failures of modularity that are obscured by phenotype-only characterisation. Together, these results establish a principled link between DNA sequence, intrinsic biopart parameters, and circuit-level phenotypes, providing a scalable and host-aware foundation for predictive design in synthetic biology.

synthetic biology↗

bioRxiv2017

Promoter and transcription factor dynamics tune protein mean and noise strength in a quorum sensing-based feedback synthetic circuit

Gene expression is a fundamental cellular process. Its stochastic fluctuations due to intrinsic and extrinsic sources, known generically as gene expression noise, trigger both beneficial and harmful consequences for the cell behavior.\n\nControlling gene expression noise is of interest in many applications in biotechnology, biomedicine and others. Yet, control of the mean expression level is an equally desirable goal. Here, we analyze a gene synthetic network designed to reduce gene expression noise while achieving a desired mean expression level. The circuit combines a negative feedback loop over the gene of interest, and a cell-to-cell communication mechanism based on quorum sensing. We analyze the ability of the circuit to reduce noise as a function of parameters that can be tuned in the wet-lab, and the role quorum sensing plays. Intrinsic noise is generated by the inherent stochasticity of biochemical reactions. On the other hand, extrinsic noise is due to variability in the cell environment and the amounts of cellular components that affect gene expression. We develop a realistic model of the gene synthetic circuit over the population of cells using mass action kinetics and the stochastic Chemical Langevin Equation to include intrinsic noise, with parameters drawn from a distribution to account for extrinsic noise. Stochastic simulations allow us to quantify the mean expression level and noise strength of all species under different scenarios, showing good agreement with system-wide available experimental data of protein abundance and noise in E. coli. Our in silico experiments reveal significant noise attenuation in gene expression through the interplay between quorum sensing and the negative feedback, allowing control of the mean expression and variance of the protein of interest. These in silico conclusions are validated by preliminary experimental results. This gene network could have important implications as a robust protein production system in industrial biotechnology.\n\nAuthor SummaryControlling gene expression level is of interest in many applications in biotechnology, biomedicine and others. Yet, the stochastic nature of biochemical reactions plays an important role in biological systems, and cannot be disregarded. Gene expression noise resulting from this stochasticity has been studied over the past years both in vivo, and in silico using mathematical models. Nowadays, synthetic biology approaches allow to design novel biological circuits, drawing on principles elucidated from biology and engineering, for the purpose of decoupled control of mean gene expression and its variance. We propose a gene synthetic circuit with these characteristics, using negative feedback and quorum sensing based cell-to-cell communication to induce population consensus. Our in silico analysis using stochastic simulations with a realistic model reveal significant noise attenuation in gene expression through the interplay between quorum sensing and the negative feedback, allowing control of the mean expression and variance of the protein of interest. Preliminary in vivo results fully agree with the computational ones.

synthetic biology↗