Biology subjects

Gutierrez, M. E.

Publications and source records attributed to Gutierrez, M. E..

5 recordsLinked to original sources

bioRxiv2026

Collective chemotactic localization emerges from interaction-driven phase transitions under temporally correlated noise

Chemotactic microorganisms operate in environments where signals are not only noisy but temporally structured, with finite correlation times that can severely impair gradient sensing and target localization. While previous models have extensively characterized the effects of fluctuating environments on individual chemotaxis, most theoretical frameworks treat agents as non-interacting, leaving unresolved how inter-bacterial interactions reshape collective robustness under temporally correlated noise. Here, we introduce a two-dimensional agent-based model of interacting run-and-tumble bacteria navigating noisy chemotactic landscapes. We show that short-range isotropic cohesion induces a two-stage collective response: interactions first stabilize population connectivity and above a finite interaction threshold, this structural cohesion translates into robust target localization even in regimes where individual chemotaxis fails. The resulting transition reveals an intermediate phase of cohesive but weakly localized states, demonstrating that structural condensation and functional targeting are distinct collective observables. We further demonstrate that selective heterotypic interactions in binary populations produce a structurally distinct collective regime characterized by dynamically maintained red-blue contact networks composed of transient mixed dimers and local heterotypic motifs. Unlike isotropic cohesion, selective interactions reorganize local contact topology without generating macroscopic condensation. These structures are quantitatively characterized through bond density, mixing statistics, and anisotropy metrics, and are governed primarily by interaction specificity rather than by environmental noise persistence. Together, these results establish that collective chemotactic behavior is controlled by the interplay between temporal signal correlations and interaction topology. More broadly, in this work we identify collective localization and internal organization as partially independent emergent properties of interacting active matter under fluctuating environments.

bioengineering↗

bioRxiv2026

Temporal Structure of Environmental Noise Controls the Localization and Tracking of Populations of Chemotactic Microorganisms

The ability of chemotactic populations to localize and track targets in fluctuating environments depends critically on the temporal structure of environmental signals. Using a minimal agent-based framework of non-interacting run-and-tumble cells implementing an E. coli-inspired temporal sensing strategy, populations are exposed to static and moving chemoattractant fields perturbed by noise with controlled temporal structure, spanning white, pink (1/f), and correlated Ornstein-Uhlenbeck processes. Chemotactic populations are found to act as temporal filters, robustly suppressing fast fluctuations while remaining highly sensitive to slowly varying perturbations. As a consequence, chemotactic performance is governed not by noise amplitude, but by its temporal correlations. By continuously varying the noise correlation time, a critical regime emerges at{tau} c [~]{tau} run, where aggregates lose stability, tracking errors increase sharply, and spatial dispersion rises. Power spectral analysis further shows that the low-frequency power fraction of the signal provides a strong predictor of failure, outperforming total signal variance and establishing a direct link between environmental noise spectra and collective behavior. Introducing external flow reveals that advective transport amplifies noise-induced destabilization when it overlaps the chemotactic capture region, defining a combined spatiotemporal constraint on robustness. Together, these results identify temporal correlations and spectral structure as fundamental control parameters for chemotactic organization and provide a quantitative framework for predicting and designing collective behavior in fluctuating environments.

bioengineering↗

bioRxiv2024

Voronoi spatial pattern construction through bacterial colony simulation

Synthetic multicellular systems provide a controllable framework for studying how local interactions generate robust spatial organization in growing populations. Pattern formation is approached here as a programmable design problem, in which signaling, growth, and commitment must be coordinated to produce stable collective structure rather than treated as a secondary outcome. Voronoi-like tilings represent a useful class of organizations in which domains remain locally exclusive, collectively cover space, and often converge toward efficient hexagonal geometries, yet they have rarely been examined as emergent collective states governed by explicit local rules and tunable spatial control parameters. Here, a spatial-resolution-based framework is presented to generate stable Voronoi-like partitions in simulated bacterial colonies by combining quorum sensing, leader-follower role assignment, band-detection logic, and CRISPR-Cas9-mediated irreversible commitment. The system assigns local node identities, stabilizes spatial domains over time, and produces Voronoi-like partitions that emerge from distributed interactions rather than centralized control. The framework is implemented in the gro agent-based simulator and analyzed across parameter sweeps that vary leader-signal thresholds, node-size control, and signal propagation features. Robust spatial organization is found to depend on irreversible commitment, appropriate threshold separation, and controlled diffusion and degradation, revealing distinct robustness regimes that separate clean Voronoi-like partitions from deformed or mixed-identity patterns. These results position Voronoi-like spatial partitioning as a representative problem for programmable microbial active matter and support the use of synthetic gene circuits as minimal platforms for studying and exploiting spatial self-organization in microbial populations.

bioengineering↗

bioRxiv2020

A framework for implementing metaheuristic algorithms using intercellular communication

Metaheuristic procedures (MH) have been a trend driving Artificial Intelligence (AI) researchers for the past 50 years. A variety of tools and applications (not only in Computer Science) stem from these techniques. Also, MH frequently rely on evolution, a trademark process involved in cell colony growth. Generally, MH are used to approximate the solution to difficult problems but require a large amount of computational resources. Cell colonies harboring synthetic distributed circuits using intercell communication offer a direction for tackling this problem, as they process information in a massively parallel fashion. In this work, we propose a framework that maps MH elements to synthetic circuits in growing cell colonies. The framework relies on cell-cell communication mechanisms such as quorum sensing (QS) and bacterial conjugation. As a proof-of-concept, we also implemented the workflow associated to the framework, and tested the execution of two specific MH (Genetic Algorithms and Simulated Annealing) encoded as synthetic circuits on the gro simulator. Furthermore, we show an example of how our framework can be extended by implementing another kind of computational model: The Cellular Automaton. This work seeks to lay the foundations of mappings for implementing AI algorithms in a general manner using Synthetic Biology constructs in cell colonies.

synthetic biology↗

bioRxiv2016

A new improved and extended version of the multicell bacterial simulator gro

gro is a cell programming language developed in Klavins Lab for simulating colony growth and cell-cell communication. It is used as a synthetic biology prototyping tool for simulating multicellular biocircuits. In this work, we present several extensions made to gro that improve the performance of the simulator, make it easier to use and provide new functionalities. The new version of gro is between one and two orders of magnitude faster than the original version. It is able to grow microbial colonies with up to 105 cells in less than 20 minutes. A new library, CellEngine, accelerates the resolution of spatial physical interactions between growing and dividing cells by implementing a new shoving algorithm. A genetic library, CellPro, based on Probabilistic Timed Automata, simulates gene expression dynamics using simplified and easy to compute digital proteins. We also propose a more convenient language specification layer, ProSpec, based on the idea that proteins drive cell behavior. CellNutrient, another library, implements Monod-based growth and nutrient uptake functionalities. The intercellular signaling management was improved and extended in a library called CellSignals. Finally, bacterial conjugation, another local cell-cell communication process, was added to the simulator. To show the versatility and potential outreach of this version of gro, we provide studies and novel examples ranging from synthetic biology to evolutionary microbiology. We believe that the upgrades implemented for gro have made it into a powerful and fast prototyping tool capable of simulating a large variety of systems and synthetic biology designs.

synthetic biology↗