Biology subjects

Britto Bisso, F.

Publications and source records attributed to Britto Bisso, F..

6 recordsLinked to original sources

bioRxiv2025

CRISPR-based neuromorphic computing for solving regression and classification

The CRISPR-dCas9 system has emerged as a versatile platform for programmable gene regulation, offering unique advantages in modularity and orthogonality for constructing synthetic genetic circuits. Here, we present a novel architecture for biomolecular neural networks based on dCas9, guide RNAs, and antisense RNA sequestration. Through mathematical modeling and steady-state analysis, we demonstrate that this system functions as a molecular perceptron with a threshold activation function analogous to a saturated rectified linear unit (ReLU). However, a critical challenge in scaling these circuits is competition for the finite dCas9 pool, whose expression must remain low to avoid cytotoxicity. We address this constraint by developing a resource-aware design framework and characterizing how shared dCas9 availability affects network performance. Our results show that for classification tasks, decision boundaries remain invariant under resource competition, while for regression tasks, node thresholds are preserved despite sensitivity in output magnitude under heterogeneous binding conditions. We demonstrate the computational capabilities of this platform through both linear and nonlinear classification problems, as well the approximation of a band-pass function as a proof-of-concept regression task. This work expands the repertoire of molecular mechanisms capable of computation and establishes design principles for implementing CRISPR-based neuromorphic circuits that can execute complex computational tasks within the biochemical constraints of living cells.

systems biology↗

bioRxiv2025

Sequestration-based Protein Neural Networks Tolerate the Effects of Shared Translational Resources

Biomolecular neural networks (BNNs) offer a promising framework for implementing advanced computation in living cells, but their performance in vivo is fundamentally constrained by competition for cellular resources. In this work, we develop a mathematical and computational framework to analyze how shared translational resources (i.e., competition for ribosomes) affect protein neural networks implemented via molecular sequestration. Focusing on classification tasks, we show that ribosome competition primarily induces a rescaling of the neural networks effective weights, while preserving the shape of the decision boundary under identical mRNA-ribosome affinities. However, when these affinities are heterogeneous, limited resources lead to a bounded bending of the decision boundary, generating a well-defined uncertainty region. Importantly, classification remains reliable outside this region. Then, we extend our analysis from a perceptron to a multi-layer architectures (MLP), and illustrate that robustness to resource competition is maintained for an MLP with 2 nodes in the hidden layer. To our knowledge, this is the first protein-level neural-network circuit design shown to tolerate competition for translational resources without auxiliary insulation or feedback control.

systems biology↗

bioRxiv2025

Design principles of neuromorphic computing using genetic circuits

Cells have evolved to sense a wide range of input combinations and integrate those signals through signaling pathways to produce context-specific responses, such as differentiation, cell-type specification, and patterning. To replicate this information-processing capacity, synthetic biology has developed large-scale circuitry inspired by the fundamental principles of computer science. Within this framework, neuromorphic computing implemented using genetic circuits offers the opportunity to significantly enhance the computational capabilities of single cells. In this work, we establish design principles for implementing neuromorphic computing in living cells by identifying the key feature that enables a chemical reaction network to function as a perceptron: an input-output mapping with a tunable threshold. We demonstrate that four ubiquitous chemical reaction networks, namely molecular sequestration, catalytic degradation, competitive binding, and activation/deactivation cycles, all satisfy this requirement and can be engineered as perceptrons. By layering these perceptrons into multi-layer architectures, we then show how to construct both linear and nonlinear decision boundaries through rational tuning of production rates that encode network weights. As proof of principle, we apply this framework to design neural networks capable of discriminating between healthy and cancer cells based on gene expression data from 19 tissue types. Together, this work formalizes the design principles for engineering genetic circuits as neural networks and establishes a foundation for implementing next-generation cellular computation.

synthetic biology↗

bioRxiv2025

Sequestration-Based Neural Networks That Operate Out of Equilibrium

Classification of high-dimensional information is a ubiquitous computing paradigm across diverse biological systems, including organs such as the brain, down to signaling between individual cells. Inspired by the success of artificial neural networks in machine learning, the idea of engineering genetic circuits that operate as neural networks emerges as a strategy to expand the classification capabilities of living systems. In this work, we design these biomolecular neural networks (BNNs) based on the molecular sequestration reaction, and experimentally characterize their behavior as linear classifiers for increasing levels of complexity. Initially, we demonstrate that a static, DNA-based system can effectively prototype a linear classifier, though we also identify its limitations to easily tune the slope of the decision boundary it generates. We then propose and experimentally validate a BNN at the protein level using a cell-free transcription-translation (TXTL) system, which overcomes the DNA-based systems limitation and behaves as a linear classifier even before it reaches its steady state (or, equivalently, out-of-equilibrium). Ultimately, we test a CRISPR-based design and its out-of-equilibrium behavior in a biological context by successfully constructing a linear classifier within mammalian cells. Overall, by leveraging mathematical modeling and experimental automation, we establish molecular sequestration as a universal scheme for implementing neural networks within living systems, paving the way for transformative advances in synthetic biology and programmable biocomputing systems.

synthetic biology↗

bioRxiv2024

Engineering cell fate with adaptive feedback control

Engineering cell fate is fundamental for optimizing stem cell-based therapies aimed at replacing cells in patients suffering from trauma or disease. By timely administering molecular regulators--such as transcription factors, RNAs, or small molecules--in a process that mimics in vivo embryonic development, stem cell differentiation can be guided toward a specific cell fate. A significant challenge in scaling up these therapies is that such differentiation strategies often result in mixed cellular populations. While synthetic biology approaches have been proposed to increase the yield of desired cell types, designing gene circuits that effectively redirect cell fate decisions requires mechanistic insight into the dynamics of endogenous regulatory networks that govern decision-making. In this work, we present a biomolecular adaptive controller based on an Incoherent Feedforward Loop (IFFL)-like topology designed to favor a specific cell fate. This controller requires minimal knowledge of the endogenous network as it exhibits adaptive, non-reference-based behavior. The synthetic circuit operates through a sequestration mechanism and a delay introduced by an intermediate species, producing an output that asymptotically approximates a discrete temporal derivative of its input, provided there is a sufficiently fast sequestration rate. By allowing the controller to actuate over a target species involved in the decision-making process, a tunable, synthetic bias is created that favors the production of the desired species with minimal alteration to the overall equilibrium landscape of the endogenous network. Through theoretical and computational analysis, we provide design guidelines for the controllers optimal operation, evaluate its performance under parametric perturbations, and extend its applicability to various examples of common multistable systems in biology.

synthetic biology↗

bioRxiv2024

Design of a biomolecular adaptive controller to restore sustained periodic behavior

Periodic behavior is a widespread biological phenomenon occurring across various spatiotemporal scales, where upstream stimuli are encoded into dynamic intracellular signals such as oscillations, varying in duration, amplitude, and frequency. Disruptions to this periodicity can lead to a range of pathologies, for which we propose an adaptive feedback controller with an Incoherent Feedforward Loop (IFFL)-like topology, based on chemical reactions, designed to restore sustained oscillations in systems that have lost their periodicity. By approximating the controllers dynamics, we defined the design requirements for the first implementation of a biomolecular adaptive controller and tested its applicability for destabilizing the steady-state behavior of a self-inhibiting gene. Numerical simulations illustrate the adaptive behavior of the controller and its ability to tune both the amplitude and the period of the resulting oscillations.

synthetic biology↗