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<p>A central goal in biology is to infer input signals from measurable readouts. Engineered biosensors are usually built to respond selectively to single inputs, so crosstalk between sensors must be removed through laborious, context-specific orthogonalization. Here we show that multiplexed concentrations can be inferred without eliminating crosstalk. We distribute sensing across a microbial community and decode its time-resolved collective response, which can disambiguate combinations of chemical inputs even when individual sensors show crosstalk or respond indirectly. A computational framework coupling kinetic modeling with machine learning maps these community dynamics to input concentrations. We demonstrate quantitative inference in communities with low or high sensor crosstalk, in communities that respond only indirectly to antibiotic combinations, and in pooled hospital sink water spiked with target analytes. By tolerating non-orthogonal, cross-reactive, and indirect responses, distributed dynamic sensing broadens the range of biological systems usable for multiplexed measurement, wherever input combinations produce reproducible, distinguishable response trajectories.</p>

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Engineering microbial consortia for distributed signal processing

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月18日

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  • Katherine E. Duncker,
  • Ashwini R. Shende,
  • Irida Shyti,
  • Ashley Ruan,
  • Ryan D’Cunha,
  • Harshitha Venugopal-Lavanya,
  • Helena R. Ma,
  • Sizhe Liu,
  • Neil Gottel,
  • Deverick J. Anderson,
  • Claudia Gunsch,
  • Lingchong You

摘要

A central goal in biology is to infer input signals from measurable readouts. Engineered biosensors are usually built to respond selectively to single inputs, so crosstalk between sensors must be removed through laborious, context-specific orthogonalization. Here we show that multiplexed concentrations can be inferred without eliminating crosstalk. We distribute sensing across a microbial community and decode its time-resolved collective response, which can disambiguate combinations of chemical inputs even when individual sensors show crosstalk or respond indirectly. A computational framework coupling kinetic modeling with machine learning maps these community dynamics to input concentrations. We demonstrate quantitative inference in communities with low or high sensor crosstalk, in communities that respond only indirectly to antibiotic combinations, and in pooled hospital sink water spiked with target analytes. By tolerating non-orthogonal, cross-reactive, and indirect responses, distributed dynamic sensing broadens the range of biological systems usable for multiplexed measurement, wherever input combinations produce reproducible, distinguishable response trajectories.