Biology subjects

Pla-Mauri, J.

Publications and source records attributed to Pla-Mauri, J..

6 recordsLinked to original sources

bioRxiv2026

Associative Learning and Memory Erasure in a Synthetic Multicellular System

Learning is a basic form of adaptation that organisms display from neural systems to single cells. In associative learning, the pairing of two stimuli lets one of them evoke the response to the other. Engineering this behavior in a living cell is difficult, because coincidence detection, memory, and conditional readout must be wired into a single regulatory network. Here we build a consortium of five E. coli strains that distributes the operations of associative learning and communicates through one diffusible quorum-sensing signal. The same signal acts as the memory trace. One strain detects the coincidence of the two inputs, a second maintains the memory through positive feedback in the shared medium, a third reads it out on the conditioned stimulus, a fourth reports the unconditioned response, and a fifth erases the memory on command. We show experimentally that the assembled consortium learns the association, since a stimulus that is silent before training evokes a strong response after pairing. Memory is retained through a medium exchange in which both stimuli are removed, and then the conditioned stimulus alone evokes the response. The reporter is not produced without its input. An erase command returns the response to the background. A population model of the consortium accounts for the sequence of responses and gives design rules for writing, retention, and erasure, and a trade-off in storing memory in a shared extracellular signal. Distributing the operations over strains makes each of them separately tunable and testable and provides a route to microbial communities that learn from their history.

synthetic biology↗

bioRxiv2026

Self-organized criticality in a mammalian RNA-interference circuit

Living systems have been argued to operate near critical points, but their relevance at the cellular level is poorly understood. Critical states allow for a high sensitivity to external inputs and maximal information transfer and display fluctuations at all scales. Critical points are very special, and require the fine tuning of a control parameter, which is hard to reconcile with the robustness of living cells. Self-organized criticality (SOC) proposes that feedback, rather than tuning, brings a system to its critical point. Here we show that RNA interference, in which messenger RNAs compete for shared silencing machinery and so queue for service, together with negative feedback gives rise to SOC in mammalian cells. In a synthetic circuit, a protein repressor whose mRNA competes with the mRNA of a fluorescent reporter that it represses senses the congestion of the queue, closing the feedback loop. Theory and stochastic simulations show that the loop holds the queue near its critical point despite changes in its parameters, and that expression becomes intermittent, with scale-free bursts. Measurements in human cell lines show the predicted power-law distributions of single-cell fluorescence. RNA interference could thus support engineered SOC in animal cells and may also contribute to SOC in natural systems.

synthetic biology↗

bioRxiv2026

Animals or plants? Evolutionary branching of sessile versus mobile cognitive agents in noisy environments

The evolution of complex cognition has been tied to movement and the need to locate resources in uncertain environments. Plants present a striking contrast: despite sophisticated environmental responses, their sessile lifestyle involves predictable energy capture and growth-mediated foraging. What ecological conditions could separate these alternative strategies? We address this question using spatially explicit individual-based simulations and Adaptive Dynamics models in which evolving agents allocate resources between harvesting a stable energy source (light) and exploiting patchy, fluctuating resources through costly sensing and movement. Starting from intermediate generalists, evolution repeatedly produces two contrasting specialist lineages: sessile, light-dependent agents that abandon sensing and locomotion, and mobile foragers that sacrifice light harvesting while investing in active resource search. Spatial gradients promote divergence through niche partitioning, but specialization also emerges in homogeneous environments through frequency-dependent ecological feedbacks, showing that environmental heterogeneity is not required. Under linear or convex returns, intermediate strategies can be disadvantaged because they bear the costs of both resource-acquisition systems without fully exploiting either; under sufficiently concave returns, intermediate strategies may instead be favored. These results suggest that plant- and animal-based organizations can emerge as alternative evolutionary solutions to a common foraging problem. More broadly, they support the idea that the evolution of costly cognitive machinery depends critically on the statistical structure of the resources that organisms must find and exploit.

evolutionary biology↗

bioRxiv2026

Ecological constraints to mirror life

Our biosphere exhibits remarkable diversity yet is constrained by universal organizational principles, including molecular homochirality. Advances in synthetic biology have raised the possibility of engineering alternative life forms based on mirror-image biomolecules, prompting both technological interest and biosecurity concerns. While current discussions of mirror life largely emphasize molecular feasibility and cellular function, its potential establishment in natural environments remains poorly understood. Here, we develop a theoretical framework to assess the invasion potential of mirror organisms within existing ecosystems. Using population-level models that incorporate resource competition, metabolic constraints, and ecological network interactions, we show that mirror life might face severe limitations arising from both nutrient incompatibility and competitive exclusion by established biota. In particular, the reliance on rare or achiral substrates and the asymmetry of interactions with natural organisms constrain growth and persistence across a broad range of ecological conditions. These results highlight the importance of ecological constraints in evaluating the risks and feasibility of synthetic life.

ecology↗

bioRxiv2025

Engineering Basal Cognition: Minimal Genetic Circuits for Habituation, Sensitization, and Massed-Spaced Learning

Cognition is often associated with complex brains, yet many forms of learning--such as habituation, sensitization, and even spacing effects--have been observed in single cells and aneural organisms. These simple cognitive abilities, despite their cost, offer evolutionary advantages by allowing organisms to reduce environmental uncertainty and improve survival. Recent studies have confirmed early claims of learning-like behavior in protists and slime molds, pointing to the presence of basal cognitive functions long before the emergence of nervous systems. In this work, we adopt a synthetic biology approach to explore how minimal genetic circuits can implement non-associative learning in unicellular and multicellular systems. Building on theoretical models and using well-characterized regulatory elements, we design and simulate synthetic circuits capable of reproducing habituation, sensitization, and the massed-spaced learning effect. Our designs incorporate activators, repressors, fluorescent reporters, and quorum-sensing molecules, offering a platform for experimental validation. By examining the structural and dynamical constraints of these circuits, we highlight the distinct temporal dynamics of gene-based learning systems compared to neural counterparts and provide insights into the evolutionary and engineering challenges of building synthetic cognitive behavior at the cellular level.

synthetic biology↗

bioRxiv2025

A Minimal Genetic Circuit for Cellular Anticipation

Living systems have evolved cognitive complexity to reduce environmental uncertainty, enabling them to predict and prepare for future conditions. Anticipation, distinct from simple prediction, involves active adaptation before an event occurs and is a key feature of both neural and aneural biological agents. Building on the moving average convergence-divergence principle from financial trend analysis, we propose an implementation of anticipation through synthetic biology by designing and evaluating experimentally testable minimal genetic circuits capable of anticipating environmental trends. Through deterministic and stochastic analyses, we demonstrate that these motifs achieve robust anticipatory responses under a wide range of conditions. Our findings suggest that simple genetic circuits could be naturally exploited by cells to prepare for future events, providing a foundation for engineering predictive biological systems.

synthetic biology↗