From 9dcd90dc348cc379c8de3eb028716dcfb77ba1f2 Mon Sep 17 00:00:00 2001
From: Fabrizio Angaroni <43064628+ang-one@users.noreply.github.com>
Date: Fri, 14 Feb 2025 16:51:03 +0100
Subject: [PATCH] Update README.md

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 README.md | 2 +-
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 Ecological and evolutionary processes of microbes are characterized by observables like growth rates and biomass yield, inferred from kinetics experiments. 
 Across conditions, these observables map response patterns such as antibiotic growth inhibition and yield dependence on substrate.
-But how do we extract ecological and evolutionary insights from massive datasets of time-resolved microbial data? Here we introduce Kinbiont — an ecosystem of numerical methods combining state-of-the-art solvers for ordinary differential equations, non-linear optimization, signal processing, and interpretable machine learning algorithms.
+But how do we extract ecological and evolutionary insights from massive datasets of time-resolved microbial data? Here, we introduce Kinbiont — an ecosystem of numerical methods combining state-of-the-art solvers for ordinary differential equations, non-linear optimization, signal processing, and interpretable machine learning algorithms.
 Kinbiont provides a comprehensive, model-based analysis pipeline, covering all aspects of microbial kinetics data, from preprocessing to result interpretation. 
 
 # Documentation