Cell-Free Biosensors and AI integration

International audienceCell-free biosensors hold a great potential as alternatives for traditional analytical chemistry methods providing low-cost low resource measurement of specific chemicals. However, their large scale use is limited by the complexity of their development. In this chapter we present a standard methodology based on Computer aided design (CAD) tools that enables fast development of new cell-free biosensors based on target molecule information transduction and reporting through metabolic and genetic layers, respectively. Such systems can then be repurposed to represent complex computational problems, allowing defined multiplex sensing of various inputs and integration of artificial intelligence in synthetic biological systems

Metabolic perceptrons for neural computing in biological systems

Synthetic biological circuits are promising tools for developing sophisticated systems for medical, industrial, and environmental applications. So far, circuit implementations commonly rely on gene expression regulation for information processing using digital logic. Here, we present a different approach for biological computation through metabolic circuits designed by computer-aided tools, implemented in both whole-cell and cell-free systems. We first combine metabolic transducers to build an analog adder, a device that sums up the concentrations of multiple input metabolites. Next, we build a weighted adder where the contributions of the different metabolites to the sum can be adjusted. Using a computational model fitted on experimental data, we finally implement two four-input perceptrons for desired binary classification of metabolite combinations by applying model-predicted weights to the metabolic perceptron. The perceptron-mediated neural computing introduced here lays the groundwork for more advanced metabolic circuits for rapid and scalable multiplex sensing

Large scale active-learning-guided exploration for in vitro protein production optimization

International audienceLysate-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

A large-scale exploration of cell-free compositions to maximize protein production using active learning

International audienc

Metabolic perceptrons for neural computing in biological systems

Synthetic biological circuits are promising tools for developing sophisticated systems for medical, industrial, and environmental applications. So far, circuit implementations commonly rely on gene expression regulation for information processing using digital logic. Here, we present a different approach for biological computation through metabolic circuits designed by computer-aided tools, implemented in both whole-cell and cell-free systems. We first combine metabolic transducers to build an analog adder, a device that sums up the concentrations of multiple input metabolites. Next, we build a weighted adder where the contributions of the different metabolites to the sum can be adjusted. Using a computational model fitted on experimental data, we finally implement two four-input perceptrons for desired binary classification of metabolite combinations by applying model-predicted weights to the metabolic perceptron. The perceptron-mediated neural computing introduced here lays the groundwork for more advanced metabolic circuits for rapid and scalable multiplex sensing

A large-scale exploration of cell-free compositions to maximize protein production using active learning

International audienceLysate-based cell-free systems have become a major platform to study gene expression 1-3 but batch-to-batch variation makes protein production difficult to predict 4. Here we describe an active learning approach 5 to explore a combinatorial space of ~4,000,000 cell-free 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

Le programme DRuIDe : présentation et premiers résultats

National audienc

PeroxiHUB : a modular cell-free biosensing platform using H2O2 as signal integrator

International audienceCell-free systems have great potential for delivering robust, cheap, and field-deployable biosensors. Many cell-free biosensors rely on transcription factors responding to small molecules, but their discovery and implementation still remain challenging. Here we report the engineering of PeroxiHUB, an optimized H 2 O 2-centered sensing platform supporting cellfree detection of different metabolites. H 2 O 2 is a central metabolite and a by-product of numerous enzymatic reactions. PeroxiHUB uses enzymatic transducers to convert metabolites of interest into H 2 O 2 , enabling rapid reprogramming of sensor specificity using alternative transducers. We first screen several transcription factors and optimize OxyR for the transcriptional response to H 2 O 2 in cell-free, highlighting the need for pre-incubation steps to obtain suitable signal-to-noise ratios. We then demonstrate modular detection of metabolites of clinical interest-lactate, sarcosine, and choline-using different transducers mined via a custom retro-synthesis workflow publicly available on the SynBioCAD Galaxy portal. We find that expressing the transducer during the pre-incubation step is crucial for optimal sensor operation. Finally, we show that different reporters can be connected to PeroxiHUB, providing high adaptability for various applications. Given the wide range of enzymatic reactions producing H 2 O 2 , the PeroxiHUB platform will support cell-free detection of a large number of metabolites in a modular and scalable fashion