Publications by authors named "Olivier Borkowski"

Because they mimic cells while offering an accessible and controllable environment, lysate-based cell-free systems (CFS) have emerged as valuable biotechnology tools for synthetic biology. Historically used to uncover fundamental mechanisms of life, CFS are nowadays used for a multitude of purposes, including protein production and prototyping of synthetic circuits. Despite the conservation of fundamental functions in CFS like transcription and translation, RNAs and certain membrane-embedded or membrane-bound proteins of the host cell are lost when preparing the lysate.

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Lysate-based cell-free systems have become a major platform to study gene expression but batch-to-batch variation makes protein production difficult to predict. Here we describe an active learning approach to explore a combinatorial space of ~4,000,000 cell-free buffer compositions, maximizing protein production and identifying critical parameters involved in cell-free productivity. We also provide a one-step-method to achieve high quality predictions for protein production using minimal experimental effort regardless of the lysate quality.

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Cell-free systems are promising platforms for rapid and high-throughput prototyping of biological parts in metabolic engineering and synthetic biology. One main limitation of cell-free system applications is the low fold repression of transcriptional repressors. Hence, prokaryotic biosensor development, which mostly relies on repressors, is limited.

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Transcriptional biosensors allow screening, selection, or dynamic regulation of metabolic pathways, and are, therefore, an enabling technology for faster prototyping of metabolic engineering and sustainable chemistry. Recent advances have been made, allowing for routine use of heterologous transcription factors, and new strategies such as chimeric protein design allow engineers to tap into the reservoir of metabolite-binding proteins. However, extending the sensing scope of biosensors is only the first step, and computational models can help in fine-tuning properties of biosensors for custom-made behavior.

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Cell-free TX-TL is an increasingly mature and useful platform for prototyping, testing, and engineering biological parts and systems. However, to fully accomplish the promises of synthetic biology, mathematical models are required to facilitate the design and predict the behavior of biological components in cell-free extracts. We review here the latest models accounting for transcription, translation, competition, and depletion of resources as well as genome scale models for lysate-based cell-free TX-TL systems, including their current limitations.

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Translating heterologous proteins places significant burden on host cells, consuming expression resources leading to slower cell growth and productivity. Yet predicting the cost of protein production for any given gene is a major challenge, as multiple processes and factors combine to determine translation efficiency. To enable prediction of the cost of gene expression in bacteria, we describe here a standard cell-free lysate assay that provides a relative measure of resource consumption when a protein coding sequence is expressed.

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Cells use feedback regulation to ensure robust growth despite fluctuating demands for resources and differing environmental conditions. However, the expression of foreign proteins from engineered constructs is an unnatural burden that cells are not adapted for. Here we combined RNA-seq with an in vivo assay to identify the major transcriptional changes that occur in Escherichia coli when inducible synthetic constructs are expressed.

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The predictability and robustness of engineered bacteria depend on the many interactions between synthetic constructs and their host cells. Expression from synthetic constructs is an unnatural load for the host that typically reduces growth, triggers stresses and leads to decrease in performance or failure of engineered cells. Work in systems and synthetic biology has now begun to address this through new tools, methods and strategies that characterise and exploit host-construct interactions in bacteria.

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Complex regulatory programs control cell adaptation to environmental changes by setting condition-specific proteomes. In balanced growth, bacterial protein abundances depend on the dilution rate, transcript abundances and transcript-specific translation efficiencies. We revisited the current theory claiming the invariance of bacterial translation efficiency.

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