Biology subjects

Shende, A. R.

Publications and source records attributed to Shende, A. R..

3 recordsLinked to original sources

bioRxiv2026

Gene circuit-driven amplified selection enables evolution of fast-growing Escherichia coli

The laboratory Escherichia coli K-12 strain has doubled no faster than [~]20 minutes for decades. This plateau could reflect a biophysical limit or simply the way batch culture selects on growth rate. Here we show it can be broken through amplified selection with a Red Queen gene circuit, which takes advantage of growth rate heterogeneity in monoclonal populations to selectively suppress slow-growing cells and creates a tunable mapping from intrinsic growth rate to survival. After 70 days ([~]1,000 generations) of amplified selection in MG1655+FHr and subsequent removal of the circuit, a top evolved clone (RQ70) reached a maximum specific growth rate of 2.61 h-{superscript 1} in shake-flask culture. This corresponds to a doubling time of 15.9 minutes, to our knowledge the shortest reported for E. coli K-12, against 18.1 minutes for evolved controls and 20.3 minutes for the ancestor. The gain came at the cost of a [~]3-fold increase in lag time, indicating that the 20-minute plateau is a multi-trait optimum under conventional batch selection rather than an absolute constraint. We argue that synthetic gene circuits can therefore reshape the evolutionary process itself, pushing performance beyond apparent physiological limits.

synthetic biology↗

bioRxiv2025

A foundation model for microbial growth dynamics

Microbial growth dynamics contain rich information about microbial populations, which support applications from antibiotic testing to microbiome engineering. However, the high dimensionality of growth data and the scarcity of large, task-specific datasets have limited generalizable modeling analysis across systems. Here, we develop a foundation model for microbial growth dynamics. It is a large-scale, self-supervised representation model trained on [~]370,000 experimental and simulated growth curves spanning diverse microbial species, environmental conditions, and community contexts. The model learns lower-dimensional latent embeddings that capture essential dynamical features of raw growth data and enable accurate reconstruction of these data. The concise representations enhance predictive performance in diverse downstream applications. Using these embedding, we achieve few-shot learning for antibiotic classification and concentration prediction, accurate forecasting of simulated and experimental communities, and inference of total abundance from relative-abundance data. By extracting transferable representations from heterogeneous datasets, our model provides a general framework for analyzing and predicting microbial community dynamics from limited measurements.

synthetic biology↗

bioRxiv2025

Engineering microbial consortia for distributed signal processing

A critical goal in biology is deducing input signals from measurable readouts. Genetic circuits have successfully been designed to respond to specific analytes and produce quantifiable outputs; however, multiplexed signal processing remains challenging. This limitation is partially due to crosstalk, or sensors non-specific responses to unintended signals. While strategies to achieve orthogonality are promising, they are time-intensive and context-dependent. Here, we introduce a new, generalizable approach that leverages microbial consortia to distribute sensory functions and computational methods to disentangle signals. Compartmentalizing sensor circuits within distinct populations simplifies experimental optimization by allowing individual populations to be exchanged without requiring genetic modifications. Our computational pipeline combines mechanistic modeling with machine learning to decode microbial communities unique temporal responses and predict multiple input concentrations. We demonstrated this platforms versatility in a variety of contexts: measuring signals with high crosstalk, detecting antibiotics with natural microbial communities, and quantifying chemicals in hospital sink wastewater. Our findings highlight how combining microbial engineering with computational strategies can produce robust, scalable biosensors for diverse applications.

synthetic biology↗