Biology subjects

Mayalu, M. N.

Publications and source records attributed to Mayalu, M. N..

4 recordsLinked to original sources

bioRxiv2025

Design of a Modular Inducible Circuit for Pulsatile Population Control of Engineered Bacteria

Synthetic biology enables the precise control of bacterial population dynamics through engineered genetic circuits, paving the way for innovative therapeutic and biosensing applications. Here, we present genetic circuit design that leverages AHL (acyl-homoserine lactone)-based quorum sensing and a transcriptional cascade regulated by an external inducer to drive synchronized proliferation and lysis events. In this system, the absence of the inducer arabinose prevents CinR mediated repression, enabling activation of a transcriptional cascade that drives the expression of the lysis protein, fluorescent protein, and a drug resistance gene. The accumulation of AHL acts as a proxy for population density and modulates gene expression through a quorum-sensing mechanism. This inducible circuit provides tunable and pulsatile control over bacterial population dynamics, enabling cycles of population growth and collapse. Such behavior prevents premature population clearance while ensuring functional bacterial densities for extended durations. Our system represents a foundational step toward implementing more complex multi-stable architectures for self-regulation of bacterial population. This dynamic and modular framework offers significant advantages for therapeutic interventions, such as self-regulating drug delivery, inflammation-responsive probiotic therapies, and targeted bacterial therapy.

synthetic biology↗

bioRxiv2020

Synthetic mammalian signaling circuits for robust cell population control

In multicellular organisms, cells actively sense and control their own population density. Synthetic mammalian quorum sensing circuits could provide insight into principles of population control and extend cell therapies. However, a key challenge is reducing their inherent sensitivity to "cheater" mutations that evade control. Here, we repurposed the plant hormone auxin to enable orthogonal mammalian cell-cell communication and quorum sensing. We designed a paradoxical population control circuit, termed Paradaux, in which auxin stimulates and inhibits net cell growth at different concentrations. This circuit limited population size over extended timescales, of up to 42 days of continuous culture. By contrast, when operating in a non-paradoxical regime, the same cells limited population growth, but were more susceptible to mutational escape. These results establish auxin as a versatile "private" communication system, and demonstrate that paradoxical circuit architectures can provide robust population control.

synthetic biology↗

bioRxiv2020

Theoretical Design of Paradoxical Signaling-Based Synthetic Population Control Circuit in E. coli

We have developed a mathematical framework to analyze the cooperative control of cell population homeostasis via paradoxical signaling in synthetic contexts. Paradoxical signaling functions through quorum sensing (where cells produce and release a chemical signal as a function of cell density). Precisely, the same quorum sensing signal provides both positive (proliferation) and negative (death) feedback in different signal concentration regimes. As a consequence, the relationship between intercellular quorum sensing signal concentration and net growth rate (cell proliferation minus death rates) can be non-monotonic. This relationship is a condition for robustness to certain cell mutational overgrowths and allows for increased stability in the presence of environmental perturbations. Here, we explore stability and robustness of a conceptualized synthetic circuit. Furthermore, we asses possible design principles that could exist among a subset of paradoxical circuit implementations. This analysis sparks the development a bio-molecular control theory to identify ideal underlying characteristics for paradoxical signaling control systems.

systems biology↗

bioRxiv2019

Multi-Cell ECM compaction is predictable via superposition of nonlinear cell dynamics linearized in augmented state space

Cells interacting through an extracellular matrix (ECM) exhibit emergent behaviors resulting from collective intercellular interaction. In wound healing and tissue development, characteristic compaction of ECM gel is induced by multiple cells that generate tensions in the ECM fibers and coordinate their actions with other cells. Computational prediction of collective cell-ECM interaction based on first principles is highly complex especially as the number of cells increase. Here, we introduce a computationally-efficient method for predicting nonlinear behaviors of multiple cells interacting mechanically through a 3-D ECM fiber network. The key enabling technique is superposition of single cell computational models to predict multicellular behaviors. While cell-ECM interactions are highly nonlinear, they can be linearized accurately with a unique method, termed Dual-Faceted Linearization. This method recasts the original nonlinear dynamics in an augmented space where the system behaves more linearly. The independent state variables are augmented by combining auxiliary variables that inform nonlinear elements involved in the system. This computational method involves a) expressing the original nonlinear state equations with two sets of linear dynamic equations b) reducing the order of the augmented linear system via principal component analysis and c) superposing individual single cell-ECM dynamics to predict collective behaviors of multiple cells. The method is computationally efficient compared to original nonlinear dynamic simulation and accurate compared to traditional Taylor expansion linearization. Furthermore, we reproduce reported experimental results of multi-cell induced ECM compaction.\n\nAuthor summaryCollective behaviors of multiple cells interacting through an ECM are prohibitively complex to predict with a mechanistic computational model due to its highly nonlinear dynamics and high dimensional space. We introduce a methodology where nonlinear dynamics of single cells are superposed to predict collective multi-cellular behaviors through a developed linearization method. We represent nonlinear single cell dynamics with linear state equations by augmenting the independent state variables with a set of auxiliary variables. We then transform the linear augmented state equations to a low-dimensional latent model and superpose the linear latent models of individual cells to predict collective behaviors that emerge from multi-cellular interactions. The method successfully reproduced experimental results of cell-induced ECM compaction.

systems biology↗