Biology subjects

Belta, C.

Publications and source records attributed to Belta, C..

5 recordsLinked to original sources

bioRxiv2026

Generative Neuromorphic Programming of Mammalian Cells

Cells make decisions through densely interconnected regulatory networks that compute graded, analog responses. Synthetic biology has made cells programmable, but the field's dominant abstractions poorly match the analog nature of cells. Designing analog circuits that work in cells is challenging: predicting how components combine remains difficult, and the vast combinatorial space of parts, weights and topologies exceeds what experiments can explore. Here we establish generative programming of mammalian cells through a neuromorphic framework that compiles desired behavior into DNA. Composable regulatory devices built from RNA-targeting endoribonucleases (ERNs) supply signed weights and nonlinear activation, enabling multi-layer circuits with diverse multi-input analog responses. To predict and design such circuits, we develop biomorphic neural networks (BMNs): a compositional architecture that mirrors biological interactions, in which each basic process (transcription, translation, cleavage) is captured by a reusable neural block learned from the behavior of whole circuits that contain it. Recomposed into architectures absent from training, these models predict responses with errors often comparable to variability between experimental repeats. Inverting this process, our software package, the biocompiler, jointly optimizes topology, parts, and weights to realize specified target behaviors. Challenged with three target behaviors, the biocompiler proposed three previously unseen architectures; we built all three, and each reproduced its target behavior in mammalian cells in a single design pass without manual tuning. Generative neuromorphic programming thus offers a systematic route to engineering the analog computation native to living cells.

synthetic biology↗

bioRxiv2025

Magnetized Cellbots to Spatiotemporally Control Differentiation of Human-Induced Pluripotent Stem Cells

Precise spatiotemporal control of gene expression and cellular differentiation is essential for engineering native-like multicellular structures. Current cell differentiation approaches typically rely on externally provided inputs whose effects are not targeted to distinct cells in the appropriate state and hence cannot spatially organize and mature tissue structures as needed. Our work introduces a magnetically controlled microrobot (MR) platform for guiding mammalian cells to desired locations that, combined with synthetic biology, delivers biological signals at precise locations and times, enabling spatiotemporal control of cell-fate decision-making. We use synNotch, a cell-cell contact-based biological signaling that induces relevant gene expression in receivers when the receiver cells contact sender cells through ligand-receptor binding. Magnetically driven MRs are then allowed to be internalized by sender cells, resulting in magnetized sender cellbots. Using a 3-pair orthogonal Helmholtz coil system, we guided magnetized sender cellbots to precise locations in a receiver cell culture, activating desired fluorescent protein expression in target Chinese Hamster Ovary (CHO) receiver cells. Next, we engineered Human-Induced Pluripotent Stem Cells (hiPSC) to function as receivers that can be instructed by senders to differentiate into endothelial cells (ECs) via overexpression of ETV2 (ETS variant transcription factor 2), a master transcriptional regulator of endothelial cell development. Using our magnetic platform, we guided multiple sender cellbots to target locations on a monolayer of hiPSC receivers, resulting in differentiation of receivers into ECs and possible onset of vascular formation. Our approach provides a foundation for the engineering spatial patterns by activating conditional triggers based on MR location and cell state at multiple time points, enabling several applications such as control of organoid architecture.

synthetic biology↗

bioRxiv2024

Learning Biomolecular Models using Signal Temporal Logic

Modeling dynamical biological systems is key for understanding, predicting, and controlling complex biological behaviors. Traditional methods for identifying governing equations, such as ordinary differential equations (ODEs), typically require extensive quantitative data, which is often scarce in biological systems due to experimental limitations. To address this challenge, we introduce an approach that determines biomolecular models from qualitative system behaviors expressed as Signal Temporal Logic (STL) statements, which are naturally suited to translate expert knowledge into computationally tractable specifications. Our method represents the biological network as a graph, where edges represent interactions between species, and uses a genetic algorithm to identify the graph. To infer the parameters of the ODEs modeling the interactions, we propose a gradient-based algorithm. On a numerical example, we evaluate two loss functions using STL robustness and analyze different initialization techniques to improve the convergence of the approach.

bioengineering↗

bioRxiv2024

Janus Mazebots and Cellbots Navigating Obstacles in Dense Mammalian Environment

The field of microrobotics has immensely grown in the last few decades, exhibiting several challenges as new features such as shapes, sizes, and actuation mechanisms are explored. Two of the biggest challenges faced in microrobotics are the development of a control system suited for precise microrobotic manipulation, and the ability to navigate microrobots in densely populated environments. In this paper, we fabricate the Mazebot microrobots using silica spheres and ferromagnetic coating, and we use them to create cellbots with genetically modified Chinese Hamster Ovary (CHO) cells. Subsequently, we navigate both the Mazebots and the cellbots through a dense environment populated by CHO cells. The Mazebots navigation is done with a control system that allows the Mazebots to swim on their own, or guide a specific cell from a given origin to a target location while avoiding cell obstacles. The control system operates in open and closed-loop modes, where the first one allows the microrobot to reorient the cell using self-induced fluid vortices, and the second one closely follows a predefined trajectory along the origin and destination. On the other hand, the cellbots navigation is done in closed-loop operation. This enables cell manipulation for potential applications in cell and tissue engineering when in a confined space. Biocompatibility of the Mazebots is confirmed through the exposure of CHO cells to the robots for 24 hours. Experimental results demonstrate the functionality of our algorithm and its potential for biomedical applications, showcasing our system as a powerful and efficient solution for precise cellular manipulation.

bioengineering↗

bioRxiv2019

Genetic design automation for autonomous formation of multicellular shapes from a single cell progenitor

AO_SCPLOWBSTRACTC_SCPLOWMulti-cellular organisms originate from a single cell, ultimately giving rise to mature organisms of heterogeneous cell type composition in complex structures. Recent work in the areas of stem cell biology and tissue engineering have laid major groundwork in the ability to convert certain types of cells into other types, but there has been limited progress in the ability to control the morphology of cellular masses as they grow. Contemporary approaches to this problem have included the use of artificial scaffolds, 3D bioprinting, and complex media formulations, however, there are no existing approaches to controlling this process purely through genetics and from a single-cell starting point. Here we describe a computer-aided design approach for designing recombinase-based genetic circuits for controlling the formation of multi-cellular masses into arbitrary shapes in human cells.

synthetic biology↗