Biology subjects

Pico, J.

Publications and source records attributed to Pico, J..

4 recordsLinked to original sources

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↗

bioRxiv2020

Resources allocation explains the differential roles of RBS and promoter strengths in cell mass distribution and optimal protein expression productivity

Design of synthetic genetic circuits without considering the impact of host-circuit interactions results in an inefficient design process and lengthy trial-and-error iterations to appropriately tune the expression levels. Microorganisms have evolved to reach an optimal use of cellular resources. This balance is perturbed by circuit-host interactions resulting from the interaction among the cell environment from which the cell takes substrates, its metabolism, and the needs of exogenous synthetic genetic circuit introduced in the cell host. The resulting competition for common shared cell resources introduces spurious dynamics leading to problems of malfunctioning of the synthetic circuit due to lack of enough cellular resources. Therefore, there is an increasing interest in development of methods for model-based design of synthetic gene circuits considering host-circuit interactions. Here we present a small-size model of gene expression dynamics in bacterial cells accounting for host-circuit interactions. For each gene, the model defines the cellular resources recruitment strength as the key functional coefficient that allows to explain the distribution of resources among the host and the genes of interest and the relationship between the usage of resources and cell growth. This functional coefficient explicitly takes into account the availability of resources and lab-accessible gene characteristics, such as promoter and ribosome binding site (RBS) strengths and capture their interplay with the availability of free cell resources. In spite of its simplicity, the model is able to explain the differential role of promoter and RBS strengths in the distribution of protein mass and the optimal protein expression productivity with remarkable fit to the experimental data from the literature for E. coli. This makes the model amenable for model-based circuit design purposes. Moreover, the model also allows to understand why endogenous ribosomal and non-ribosomal genes have evolved different strategies in the expression space.

synthetic biology↗

bioRxiv2018

Flux-dependent graphs for metabolic networks

Cells adapt their metabolic fluxes in response to changes in the environment. We present a frame-work for the systematic construction of flux-based graphs derived from organism-wide metabolic networks. Our graphs encode the directionality of metabolic fluxes via edges that represent the flow of metabolites from source to target reactions. The methodology can be applied in the absence of a specific biological context by modelling fluxes probabilistically, or can be tailored to different environ-mental conditions by incorporating flux distributions computed through constraint-based approaches such as Flux Balance Analysis. We illustrate our approach on the central carbon metabolism of Escherichia coli and on a metabolic model of human hepatocytes. The flux-dependent graphs under various environmental conditions and genetic perturbations exhibit systemic changes in their topo-logical and community structure, which capture the re-routing of metabolic fluxes and the varying importance of specific reactions and pathways. By integrating constraint-based models and tools from network science, our framework allows the study of context-specific metabolic responses at a system level beyond standard pathway descriptions.

systems 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↗