Biology subjects

Cuba Samaniego, C.

Publications and source records attributed to Cuba Samaniego, C..

16 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↗

bioRxiv2024

Engineering sequestration-based biomolecular classifiers with shared resources

Constructing molecular classifiers that enable cells to recognize linear and non-linear input patterns would expand the biocomputational capabilities of engineered cells, thereby unlocking their potential in diagnostics and therapeutic applications. While several biomolecular classifier schemes have been designed, the effect of biological constraints such as resource limitation and competitive binding on the function of those classifiers has been left unexplored. Here, we first demonstrate the design of a sigma factor-based perceptron as a molecular classifier working on the principles of molecular sequestration between the sigma factor and its anti-sigma molecule. We then investigate how the output of the biomolecular perceptron, i.e., its response pattern or decision boundary, is affected by the competitive binding of sigma factors to a pool of shared and limited resources of core RNA polymerase. Finally, we reveal the influence of sharing limited resources on multi-layer perceptron neural networks and outline design principles that enable the construction of non-linear classifiers using sigma-based biomolecular neural networks in the presence of competitive resource-sharing effects.

synthetic biology↗

bioRxiv2024

Design of a sequestration-based network with tunable pulsing dynamics

Incoherent feedforward networks exhibit the ability to generate temporal pulse behavior. However, exerting control over specific dynamic properties, such as amplitude and rise time, poses a challenge and is intricately tied to the networks implementation. In this study, we focus on analyzing sequestration-based networks capable of exhibiting pulse behavior. By employing time-scale separation in the fast sequestration regime, we approximate the temporal dynamics of these networks. This approach allows us to establish a mapping that elucidates the impact of varying the kinetic rates and pulse specifications, including amplitude and rise time. Furthermore, we introduce a positive feedback mechanism to regulate the amplitude of the pulsing response.

synthetic biology↗

bioRxiv2024

Neural networks built from enzymatic reactions can operate as linear and nonlinear classifiers

The engineering of molecular programs capable of processing patterns of multi-input biomarkers holds great potential in applications ranging from in vitro diagnostics (e.g., viral detection, including COVID-19) to therapeutic interventions (e.g., discriminating cancer cells from normal cells). For this reason, mechanisms to design molecular networks for pattern recognition are highly sought after. In this work, we explore how enzymatic networks can be used for both linear and nonlinear classification tasks. By leveraging steady-state analysis and showing global stability, we demonstrate that these networks can function as molecular perceptrons, fundamental units of artificial neural networks--capable of processing multiple inputs associated with positive and negative weights to achieve linear classification. Furthermore, by composing orthogonal enzymatic reactions, we show that multi-layer networks can be constructed to achieve nonlinear classification.

synthetic biology↗

bioRxiv2023

Building subtraction operators and controllers via molecular sequestration

We show how subtraction can be performed via a simple chemical reaction network that includes molecular sequestration. The network computes the difference between the production rate parameters of the two mutually sequestering species. We benefit from introducing a simple change of variables, that facilitates the derivation of an approximate solution for the differential equations modeling the chemical reaction network, under a time scale separation assumption that is valid when the sequestration rate parameter is sufficiently fast. Our main result is that we provide simple expressions confirming that temporal subtraction occurs when the inputs are constant or time varying. Through simulations, we discuss two sequestration-based architectures for feedback control in light of the subtraction operations they perform.

synthetic biology↗

bioRxiv2023

A portable regulatory RNA array design enables tunable and complex regulation across diverse bacteria

A lack of composable and tunable gene regulators has hindered efforts to engineer non-model bacteria and consortia. To address this, we explore the broad-host potential of small transcription activating RNA (STAR) and propose a novel design strategy to achieve tunable gene control. First, we demonstrate that STARs optimized for E. coli function across different Gram-negative species and can actuate using phage RNA polymerase, suggesting that RNA systems acting at the level of transcription are portable. Second, we explore a novel RNA design strategy that uses arrays of tandem and transcriptionally fused RNA regulators to precisely alter regulator concentration from 1 to 8 copies. This provides a simple means to predictably tune output gain across species and does not require access to large regulatory part libraries. Finally, we show RNA arrays can be used to achieve tunable cascading and multiplexing circuits across species, analogous to the motifs used in artificial neural networks.

synthetic biology↗

bioRxiv2022

Building molecular band-pass filters via molecular sequestration

Engineered genetic circuits with tailored functions that mimic how cells process information in changing environments (e.g. cell fate decision, chemotaxis, immune response) have great applications in biomedicine and synthetic biology. Although there is a lot of progress toward the design of gene circuits yielding desired steady states (e.g. logic-based networks), building synthetic circuits for dynamic signal processing (e.g. filters, frequency modulation, and controllers) is still challenging. Here, we provide a model-based approach to build gene networks that can operate as band-pass filters by taking advantage of molecular sequestration. By suitably approximating the dynamics of molecular sequestration, we analyze an Incoherent Feed-Forward Loop (IFFL) and a Negative Feedback (NF) circuit and illustrate how they can achieve band-pass filter behavior. Computational analysis shows that a circuit that incorporates both IFFL and NF motifs improves the filter performance. Our approach facilitates the design of sequestration-based filters, and may support the synthesis of molecular controllers with desired specifications.

synthetic biology↗

bioRxiv2021

Computational characterization of recombinase circuits for periodic behaviors

In nature, recombinases are site-specific proteins capable of rearranging DNA, and they are expanding the repertoire of gene editing tools used in synthetic biology. The on/off response of recombinases, achieved by inverting the direction of a promoter, makes them suitable for Boolean logic computation; however, recombinase-based logic gate circuits are single-use due to the irreversibility of the DNA rearrangement, and it is still unclear how a dynamical circuit, such as an oscillator, could be engineered using recombinases. Preliminary work has demonstrated that recombinase-based circuits can yield periodic behaviors in a deterministic setting. However, since a few molecules of recombinase are enough to perform the inverting function, it is crucial to assess how the inherent stochasticity at low copy number affects the periodic behavior. Here, we propose six different circuit designs for recombinase-based oscillators. We model them in a stochastic setting, leveraging the Gillespie algorithm for extensive simulations, and we show that they can yield periodic behaviors. To evaluate the incoherence of oscillations, we use a metric based on the statistical properties of auto-correlation functions. The main core of our design consists of two self-inhibitory, recombinase-based modules coupled by a common promoter. Since each recombinase inverts its own promoter, the overall circuit can give rise to switching behavior characterized by a regular period. We introduce different molecular mechanisms (transcriptional regulation, degradation, sequestration) to tighten the control of recombinase levels, which slows down the response timescale of the system and thus improves the coherence of oscillations. Our results support the experimental realization of recombinase-based oscillators and, more generally, the use of recombinases to generate dynamic behaviors in synthetic biology.

synthetic biology↗

bioRxiv2021

RNA compensation: A positive feedback insulation strategy for RNA-based networks

The lack of signalling modularity of biomolecular systems poses major challenges toward engineering complex networks. An important problem is posed by the consumption of signaling molecules upon circuit interconnection, which makes it possible to control a downstream circuit but compromises the performance of the upstream circuit. This issue has been previously addressed with insulation strategies including high-gain negative feedback and phosphorylation-dephosphorylation reaction cycle. In this paper, we focus on RNA-based circuits and propose a new positive-feedback insulation strategy to mitigate signal consumption. An RNA input is added in tandem with transcription output to compensate the RNA consumption, leading to concentration robustness of the input RNA molecule regardless of the amount of downstream modules. We term this strategy RNA compensation, and it can be applied to systems that have a stringent input-output gain, such as Small Transcription Activating RNAs (STARs). Our analysis shows that RNA compensation not only eliminates the signaling consumption in individual STAR-based regulators, but also improves the composability of STAR cascades and the modularity of RNA bistable systems.

synthetic biology↗

bioRxiv2020

Signaling-based neural networks for cellular computation

Cellular signaling pathways are responsible for decision making that sustains life. Most signaling pathways include post-translational modification cycles, that process multiple inputs and are tightly interconnected. Here we consider a model for phosphorylation/dephosphorylation cycles, and we show that under some assumptions they can operate as molecular neurons or perceptrons, that generate sigmoidal-like activation functions by processing sums of inputs with positive and negative weights. We carry out a steady-state and structural stability analysis for single molecular perceptrons as well as for feedforward interconnections, concluding that interconnected phosphorylation/dephosphorylation cycles may work as multi-layer biomolecular neural networks (BNNs) with the capacity to perform a variety of computations. As an application, we design signaling networks that behave as linear and non-linear classifiers.

synthetic biology↗

bioRxiv2018

Ultrasensitive molecular controllers for quasi-integral feedback

Feedback control has enabled the success of automated technologies by mitigating the effects of variability, unknown disturbances, and noise. Similarly, feedback loops in biology reduce the impact of noise and help shape kinetic responses, but it is still unclear how to rationally design molecular controllers that approach the performance of controllers in traditional engineering applications, in particular the performance of integral controllers. Here, we describe a strategy to build molecular quasi-integral controllers by following two design principles: (1) a highly ultrasensitive response, which guarantees a small steady-state error, and (2) a tunable ultrasensitivity threshold, which determines the system equilibrium point (reference). We describe a molecular reaction network, which we name Brink motif, that satisfies these requirements by combining sequestration and an activation/deactivation cycle. We show that if ultrasensitivity conditions are satisfied, this motif operates as a quasi-integral controller and promotes homeostatic behavior of the closed-loop system (robust tracking of the input reference while rejecting disturbances). We propose potential biological implementations of Brink controllers and we illustrate different example applications with computational models.

synthetic biology↗