Biology subjects

Ahavi, P.

Publications and source records attributed to Ahavi, P..

3 recordsLinked to original sources

bioRxiv2026

Engineering growth-coupled metabolic biosensors for disease prognosis and diagnosis using full growth trajectories

Although metabolomics has shown considerable promise for biomarker discovery, and the development of diagnostic and prognostic applications, its translation into routine clinical practice remains limited by analytical complexity, cost, throughput, and standardization challenges. These limitations underscore the need for complementary tools, particularly in resource-limited settings. In this study, we developed a workflow for the engineering and characterization of growth-coupled metabolic sensors capable of disease detection (healthy vs. infected) and outcome prediction (mild vs. severe), which we illustrated using COVID-19 as a proof-of-concept application. We first generated a biomarker-guided library of 34 candidate sensors leveraging both auxotrophic phenotypes and less stringent metabolic dependencies. We then screened the library against patient plasma pools, identifying 19 sensor candidates with diagnostic and/or prognostic potential, including 14 with prognostic potential. Lastly, a selected subset of candidates was further evaluated on a patient cohort using two newly developed analytical frameworks designed to extract additional information from bacterial growth curves. The best-performing sensors achieved a balanced accuracy of 0.88{+/-} 0.06 for prognostic prediction (outer-test AUC = 0.89, 5-fold cross-validation, n = 37) and 1.00 for diagnostic classification (outer-test AUC = 1.00, 5-fold cross-validation, n = 56). Collectively, these findings establish a proof of concept for translating disease-associated plasmatic metabolic signatures into low-cost, growth-coupled biosensors with diagnostic and prognostic capabilities.

synthetic biology↗

bioRxiv2026

dAMN: a genome scale neural-mechanistic hybrid model to predict bacterial growth dynamics

SummaryThis study presents dAMN, a hybrid neural-mechanistic model that integrates neural networks with genome-scale dynamic flux balance analysis (dFBA) to predict bacterial growth curves across diverse nutrient environments. dAMN uses neural networks to infer dynamic behavior from initial metabolite concentrations, while mechanistic constraints ensure stoichiometric and thermodynamic consistency based on genome scale metabolic models. dAMN is trained on E. coli and P. putida experimental growth data from media containing various combinations of sugars, amino acids, and nucleobases, and evaluated on two test sets: one for forecasting over time and another for predicting growth dynamics on unseen media. dAMN achieved high predictive power (R2 [≥] 0.9), successfully reproducing growth and substrate depletion dynamics including acetate overflow and glucose-acetate consumption shift for E. coli. An interesting innovation of dAMN is the treatment of the lag phase, enabling realistic adaptation dynamics absent from standard dFBA models. dAMN stands out for its ability to generalize across combinatorial nutrient inputs and produce full growth-curve predictions from minimal input data. Availability and implementationThe dAMN software, along with the associated models and data, is available at https://github.com/brsynth/dAMN-main-release and via DOI 10.5281/zenodo.17908125

systems biology↗

bioRxiv2024

Reservoir Computing with Bacteria

We introduce a systems-level approach to sensing and computing in which Escherichia coli acts as a living reservoir computer, performing complex information processing through its native growth responses without requiring genetic modification or specialized instrumentation. We validate this framework by accurately classifying early-stage COVID-19 plasma samples (mild vs. severe) using only bacterial growth data, highlighting a diagnostic potential without infrastructure-dependent methods. By controlling nutrient media compositions, we also demonstrate that E. coli growth encodes nonlinear transformations that outperform linear regression, support vector machines, and multilayer perceptrons across diverse regression and classification tasks. Using simulations across genome-scale metabolic models from multiple bacterial species, we establish a strong link between phenotypic diversity and computational capacity, showing that learning capacities scale with the diversity of metabolic phenotypes. These findings position biological reservoir computing as a robust, scalable, and low-cost platform for intelligent biosensing, diagnostics, and hybrid bio-digital computation, while providing new mechanistic insights into the computational capabilities of living systems.

synthetic biology↗