Biology subjects

Alexis, E.

Publications and source records attributed to Alexis, E..

9 recordsLinked to original sources

bioRxiv2026

Multichromatic Dynamic Control of Multi-Membered Microbial Consortia Compositions for Chemical Production

Engineered microbial consortia offer a promising strategy for chemical production by distributing specialized functions among microbial strains, reducing metabolic burden, facilitating modular pathway optimization, reducing toxicity, and increasing strain stability. However, differences in growth rates can destabilize population composition, compromising productivity and limiting their applicability. Here, we developed a multichromatic optogenetic Toxin-Antitoxin (optogeneticTA) platform for dynamic control of Escherichia coli consortia of up to four members using blue, red, and near-infrared light and darkness. By varying light intensities or pulses, we precisely program and dynamically modulate the population composition of two-, three-, and four-membered consortia. We further developed a modular mathematical framework that captures and predicts population dynamics of these optogenetically controlled co-cultures. Applying dynamic control to a two-membered engineered consortium increased phenol production by ~69% relative to unregulated consortia. These results establish a programmable platform for stabilizing and dynamically optimizing microbial consortia, with potential applications across microbial biomanufacturing.

synthetic biology↗

bioRxiv2026

Noise analysis of derivative-action biomolecular topologies

Temporal gradient sensing is a fundamental capability observed across diverse natural biological systems, contributing to the coordination of their functions. Harnessing this ability is also of significant interest in synthetic biology, particularly for sensing and control applications. In this work, we focus on a biomolecular topology that exemplifies a broader class of signal-differentiating architectures, while introducing a structural variant of it. We examine their behavior under both nominal and non-ideal conditions, accounting for stochastic noise arising from different sources. Our investigation includes scenarios where these topologies operate independently, as well as when embedded within minimal regulatory architectures based on negative as well as positive feedback. We analyze the stability of the resulting macroscopic dynamics--a prerequisite for practical deployment--and quantify stochastic fluctuations in system output, providing comparisons with the corresponding input/unregulated process. Importantly, our results demonstrate that signal differentiation can be effectively implemented in a biomolecular setting without incurring deleterious noise amplification--a major concern in the utilization of derivative action across disciplines.

synthetic biology↗

bioRxiv2026

Robust Multiplicative Control in Chemical Reaction Networks -Extended Version

Achieving complex multi-species control objectives is essential for engineering advanced autoregulated biomolecular devices. This paper addresses the problem of robust steady-state tracking for outputs defined as multiplicative combinations of biomolecular species concentrations. We first introduce a control architecture realized via chemical reaction networks that steers the product of two target species concentrations in the controlled network to a prescribed value. A robust stability analysis is provided for closed-loop system families with distinct structural characteristics. The proposed framework is also extended to a more general formulation capable of regulating arbitrary monomial outputs involving multiple species. Numerical simulations of representative examples corroborate the theoretical results and illustrate the effectiveness of our approach.

synthetic biology↗

bioRxiv2024

Biochemical implementation of acceleration sensing and PIDA control

Designing dependable, self-regulated biochemical systems has long posed a challenge in the field of Synthetic Biology. Here, we propose a realization of a Proportional-Integral-Derivative-Acceleration (PIDA) control scheme as a Chemical Reaction Network (CRN) governed by mass action kinetics. A constituent element of this architecture is a speed and acceleration biosensing mechanism we introduce and, subsequently, place within a feedback configuration. Our control scheme provides enhanced dynamic performance and robust steady-state tracking. In addition to our theoretical analysis, this is practically highlighted in both the deterministic and stochastic settings by regulating a specific biochemical process in-silico and drawing comparisons with a simpler PID controller.

synthetic biology↗

bioRxiv2024

AC-BioSD : A biomolecular signal differentiator module with enhanced performance

Temporal gradient estimation is a pervasive phenomenon in natural biological systems and holds great promise for synthetic counterparts with broad-reaching applications. Here, we advance the concept of BioSD (Biomolecular Signal Differentiators) by introducing a novel biomolecular topology, termed Autocatalytic-BioSD or AC-BioSD. Its structure allows for insensitivity to input signal changes and high precision in terms of signal differentiation, even when operating far from nominal conditions. Concurrently, disruptive high-frequency signal components are effectively attenuated. In addition, the usefulness of our topology in biological regulation is highlighted via a PID (Proportional-Integral-Derivative) bio-control scheme with set point weighting and filtered derivative action in both the deterministic and stochastic domains.

synthetic biology↗

bioRxiv2022

On the Design of a PID Bio-controller with Set Point Weighting and Filtered Derivative Action

Effective and robust regulation of biomolecular processes is crucial for designing reliable synthetic bio-devices functioning in uncertain and constantly changing biological environments. Proportional-Integral-Derivative (PID) controllers are undeniably the most common way of implementing feed-back control in modern technological applications. Here, we introduce a highly tunable PID bio-controller with set point weighting and filtered derivative action presented as a chemical reaction network with mass action kinetics. To demonstrate its effectiveness, we apply our PID scheme on a simple biological process of two mutually activated species, one of which is assumed to be the output of interest. To highlight its performance advantages we compare it to PI regulation using numerical simulations in both the deterministic and stochastic setting.

synthetic biology↗

bioRxiv2022

Regulation strategies for two-output biomolecular networks

Feedback control theory facilitates the development of self-regulating systems with desired performance which are predictable and insensitive to disturbances. Feedback regulatory topologies are found in many natural systems and have been of key importance in the design of reliable synthetic bio-devices operating in complex biological environments. Here, we study control schemes for biomolecular processes with two outputs of interest, expanding previous traditional concepts describing one-output systems. Regulation of such processes may unlock new design possibilities but can be challenging due to coupling interactions; also potential disturbances applied on one of the outputs may affect both. We therefore propose architectures for robustly manipulating the ratio and linear combinations of the outputs as well as each of the outputs independently. To demonstrate their characteristics, we apply these architectures to a simple process of two mutually activated biomolecular species. We also highlight the potential for experimental implementation by exploring synthetic realizations both in vivo and in vitro. This work presents an important step forward in building bio-devices capable of sophisticated functions.

synthetic biology↗

bioRxiv2021

Dichotomous Feedback: A Signal Sequestration-based Feedback Mechanism for Biocontroller Design

We introduce a new design framework for implementing negative feedback regulation in Synthetic Biology, which we term dichotomous feedback. Our approach is different from current methods, in that it sequesters existing fluxes in the process to be controlled, and in this way takes advantage of the processs architecture to design the control law. This signal sequestration mechanism appears in many natural biological systems and can potentially be easier to realise than molecular sequestration and other comparison motifs that are nowadays common in biomolecular feedback control design. The loop is closed by linking the strength of signal sequestration to the process output. Our feedback regulation mechanism is motivated by two-component signalling systems, where we introduce a second response regulator competing with the natural response regulator thus sequestering kinase activity. Here, dichotomous feedback is established by increasing the concentration of the second response regulator as the level of the output of the natural process increases. Extensive analysis demonstrates how this type of feedback shapes the signal response, attenuates intrinsic noise while increasing robustness and reducing crosstalk.

synthetic biology↗

bioRxiv2021

Biomolecular mechanisms for signal differentiation

Cells can sense temporal changes of molecular signals, allowing them to predict environmental vari-ations and modulate their behaviour. This paper elucidates the underlying biomolecular mechanisms of time derivative computation, facilitating the design of reliable synthetic differentiator devices for a variety of applications, ultimately expanding our understanding of cell behaviour. In particular, we describe and analyse three alternative biomolecular topologies that work as signal differentiators of high accuracy to arbitrary input signals around their nominal operation. We propose strategies to preserve their performance even in the presence of high-frequency input signal components, which are detrimental to the performance of most differentiators. We found that the core of the proposed topologies appears in natural regulatory networks and we further discuss their biological relevance. The simple structure of our designs makes them promising tools for realizing derivative control action in synthetic biology.

synthetic biology↗