Modeling and control of gene expression dynamics in yeast

Synthetic biology is a novel research field which aims to engineer new functionalities in living cells with the final goal of controlling cellular behavior for a number of uses, ranging from energy, to environment, to medicine. Here, I contributed to the emerging role of control theory in synthetic biology. Exploring the concept of negative feedback loop, I extended the field of controlling cellular processes by devising novel approaches to model and control gene expression dynamics in a population of living cells

A coarse-grained bacterial cell model for resource-aware analysis and design of synthetic gene circuits

Abstract Within a cell, synthetic and native genes compete for expression machinery, influencing cellular process dynamics through resource couplings. Models that simplify competitive resource binding kinetics can guide the design of strategies for countering these couplings. However, in bacteria resource availability and cell growth rate are interlinked, which complicates resource-aware biocircuit design. Capturing this interdependence requires coarse-grained bacterial cell models that balance accurate representation of metabolic regulation against simplicity and interpretability. We propose a coarse-grained E. coli cell model that combines the ease of simplified resource coupling analysis with appreciation of bacterial growth regulation mechanisms and the processes relevant for biocircuit design. Reliably capturing known growth phenomena, it provides a unifying explanation to disparate empirical relations between growth and synthetic gene expression. Considering a biomolecular controller that makes cell-wide ribosome availability robust to perturbations, we showcase our model’s usefulness in numerically prototyping biocircuits and deriving analytical relations for design guidance

A GPU algorithm for tracking yeast cells in phase-contrast microscopy images

When information and measures obtained from sequences of microscopic images are subject to time constraints, suitable fast algorithms must be implemented to process the whole data set. In this work, we deal with sequences of images obtained from time-lapse microscopy in order to detect single yeast cells in a microfluidics chip over time. The underlying idea consists in determining a minimum cost configuration for each couple of frames, which can be expressed by setting up and solving a linear programming (LP) problem. Laboratories seldom have the opportunity to use HPC hardware for such intent. For this reason, we propose an efficient GPU-parallel software implemented in CUDA and based on the simplex method, a common tool for solving LP problems. Our parallel strategy minimizes the threads divergence and maximizes the device occupancy, in order to maximize the overall throughput. Also, memory transfers between host and device have been minimized to exploit data locality. Experimental results on real images sequences highlight a promising speedup with respect to the CPU version suitable for real-time applications

L'Écho : grand quotidien d'information du Centre Ouest

08 janvier 19391939/01/08 (A68).Appartient à l’ensemble documentaire : PoitouCh

MIRELLA: a mathematical model explains the effect of microRNA-mediated synthetic genes regulation on intracellular resource allocation

Competition for intracellular resources, also known as gene expression burden, induces coupling between independently co-expressed genes, a detrimental effect on predictability and reliability of gene circuits in mammalian cells. We recently showed that microRNA (miRNA)-mediated target downregulation correlates with the upregulation of a co-expressed gene, and by exploiting miRNAs-based incoherent-feed-forward loops (iFFLs) we stabilise a gene of interest against burden. Considering these findings, we speculate that miRNA-mediated gene downregulation causes cellular resource redistribution. Despite the extensive use of miRNA in synthetic circuits regulation, this indirect effect was never reported before. Here we developed a synthetic genetic system that embeds miRNA regulation, and a mathematical model, MIRELLA, to unravel the miRNA (MI) RolE on intracellular resource aLLocAtion. We report that the link between miRNA-gene downregulation and independent genes upregulation is a result of the concerted action of ribosome redistribution and ‘queueing-effect’ on the RNA degradation pathway. Taken together, our results provide for the first time insights into the hidden regulatory interaction of miRNA-based synthetic networks, potentially relevant also in endogenous gene regulation. Our observations allow to define rules for complexity- and context-aware design of genetic circuits, in which transgenes co-expression can be modulated by tuning resource availability via number and location of miRNA target sites.ISSN:1362-4962ISSN:0301-561

MIRELLA

MIRELLA: A mathematical model explains the effect of miRNA (MI) REgulation on intracellular resource aLLocAtio