Synthetic biological circuit

Résumé

Synthetic biological circuits are an application of synthetic biology where biological parts inside a cell are designed to perform logical functions mimicking those observed in electronic circuits. The applications range from simply inducing production to adding a measurable element, like GFP, to an existing natural biological circuit, to implementing completely new systems of many parts. The goal of synthetic biology is to generate an array of tunable and characterized parts, or modules, with which any desirable synthetic biological circuit can be easily designed and implemented. These circuits can serve as a method to modify cellular functions, create cellular responses to environmental conditions, or influence cellular development. By implementing rational, controllable logic elements in cellular systems, researchers can use living systems as engineered "biological machines" to perform a vast range of useful functions. History The first natural g

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Somitogenesis is the rhythmic and sequential formation of somites, which are tissue blocks that give rise to segmented adult body structures including the vertebrae and associated muscle. Somite formation is controlled by the segmentation clock, a population of genetic oscillators that are coordinated by an interplay of cell-intrinsic and -extrinsic spatiotemporal information. Disruption of the segmentation clock can lead to misplaced or defective somite boundaries, and consequently results in deformed adult structures (e.g., congenital scoliosis). Despite decades of research into how the segmentation clock pattern is established, and how it acts to position somite boundaries within the pre-segmental mesoderm(PSM), many open questions remain. The position where the somite boundary is set along the anteroposterior axis of the PSM has been named the "determination front". Question still remain as to the mechanism and location of the determination front, and what spatiotemporal information is instructive.Here I present three studies that tackle this question by advancing imaging and analysis tools such that questions that have persisted for decades can be directly addressed. My work contributed significantly to obtaining a better picture of how somite boundaries are precisely formed.

EPFL2022

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Biochemistry of Aminoacyl tRNA Synthetase and tRNAs and Their Engineering for Cell-Free and Synthetic Cell Applications

Ragunathan Bava Ganesh, Sebastian Maerkl

Cell-free biology is increasingly utilized for engineering biological systems, incorporating novel functionality, and circumventing many of the complications associated with cells. The central dogma describes the information flow in biology consisting of transcription and translation steps to decode genetic information. Aminoacyl tRNA synthetases (AARSs) and tRNAs are key components involved in translation and thus protein synthesis. This review provides information on AARSs and tRNA biochemistry, their role in the translation process, summarizes progress in cell-free engineering of tRNAs and AARSs, and discusses prospects and challenges lying ahead in cell-free engineering.

FRONTIERS MEDIA SA2022

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Inference methods for the study of interacting biological oscillators in single-cells

Colas Noé Droin

Biological oscillators are pervasive in biology, covering all aspects of life from enzyme kinetics reactions to population dynamics. Although their behaviour has been intensively studied in the last decades, the recent advances of high-throughput experimental technologies in the fields of omics and microscopy has called for the development of new analysis methods. Among the many types of models and quantitative analyses, parametric approaches are promising as they enable for a mechanistic or physical explanation of the phenomena under study. In particular, dynamical systems theory seems particularly adapted as the vast majority of oscillators can be modelled through differential equations. Dynamical systems parameters can also be easily opti-mized via maximum likelihood approaches. The validity of the inferred model can then be assessed from the quality of its predictions. We here present three different scientific questions regarding noisy biological oscillators, which are answered using maximum-likelihood inference approaches applied to parametric models. We first take interest in the characterisation of the influence of the cell-cycle over the circadi-an clock in individual mammalian cells. To this end, we develop a method combining a Hidden Markov Model with an Expectation-Maximization algorithm to infer their coupling from single-cell microscopy traces. We show that this coupling predicts multiple phase-locked states exhib-iting different degrees of robustness against molecular fluctuations inherent to cellular scale bio-logical oscillators. We then try to understand how the mammalian transcriptome behaves in the liver. Thence, we use single-cell RNA sequencing (scRNA-seq) along with mixed-models to investigate the interplay between gene regulation in space and time. Categorising mRNA expression profiles using mixed-effect models and smFISH validations, we find that many genes in the liver are both zonated and rhythmic, most of them showing multiplicative space-time effects. Finally, we look more closely at the cell-cycle, as it is one of the main drivers of gene expres-sion cell-to-cell heterogeneity in otherwise homogeneous cell populations. Here, we would like to understand if and how cell-cycle velocity changes depending on the phase of the cycling cells. To that end, we formulate the problem in terms of an autonomous dynamical system and use this to infer consistent dynamics for the cell-cycle from scRNA-seq data. Phase inference being paramount in all of these three studies, a short technical review on the topic is also provided at the end of this thesis, along with Julia scripts for the main inference methods presented. Various computational tools assisting the understanding of the scientific questions at stake are also presented, including a Python Dash web-app, a D3 widget and many Matplotlib animations and widgets.

EPFL2020

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Inference methods for the study of interacting biological oscillators in single-cells

Colas Noé Droin

EPFL2020