1. N. Nolan, E. Peterman, K. E. Galloway, I. Incer, E. D. Sontag, and D. Del Vecchio. Guaranteed multistability in a microRNA-based genetic network by formal methods. In Proc. 64th IEEE Conference on Decision and Control (CDC), 2025. Note: To appear.Keyword(s): biomolecular systems, systems biology, synthetic biology, microRNA, toggle switch. Abstract:

   The development of genetic memory devices in synthetic biology is a challenging process that requires extensive analysis and characterization. In mammalian systems, this complexity is compounded by the need for a small DNA payload for efficient delivery into the cell. Previous genetic memory devices have relied exclusively on protein-based regulation, which are limited by their large size; in this paper, we propose a microRNA-based multistable network, which effectively halves the payload size for more efficient delivery. We demonstrate that the system can be multistable, and use formal methods to characterize constraints on design parameters that guarantee multistability. Our results provide a new genetic network topology that can achieve multistability and demonstrate the use of formal methods in the design of sophisticated genetic network architectures against non-convex top-level specifications.




2. J. Wang, E.D. Sontag, and D. Del Vecchio. Modular Machine Learning with Applications to Genetic Circuit Composition. In 2026 American Control Conference (ACC), 2025. Note: Submitted. Preprint in arXiv 2509.19601. [PDF] Keyword(s): biomolecular systems, machine learning, nonlinear systems identification. Abstract:

   In several applications, including synthetic biology, one often has input/output data on a system composed of many modules, and although the modules’ input/output functions and signals may be unknown, knowledge of the composition architecture can significantly reduce the amount of training data required to learn the system’s input/output mapping. Learning the modules’ input/output functions is also necessary for designing new systems from different composition architectures. Here, we propose a modular learning framework that incorporates prior knowledge of the system’s compositional structure to (a) identify the composing modules’ input/output functions from the system’s input/output data and (b) achieve this using a reduced amount of data compared to what would be required without knowledge of the compositional structure. To achieve this, we introduce the notion of modular identifiability, which allows recovery of modules’ input/output functions from a subset of the system’s input/output data, and we provide theoretical guarantees on a class of systems motivated by genetic circuits. We demonstrate the theory in computational studies showing that a neural network (NNET) that accounts for the compositional structure can learn the composing modules’ input/output functions and predict the system’s output on inputs outside of the training set distribution. By reducing the need for experimental data and enabling modules’ identification, this framework offers the potential to ease the design of synthetic biological circuits and of multi-module systems more generally.

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