Context-aware design of genetic circuits
Our current ability of designing synthetic genetic circuits from the composition of simpler functional units is limited by context-dependence. Our research aims at establishing context-aware models and design frameworks to make design outcomes more predictable. This work is based on a systems theory framework that augments module descriptions with unwanted inputs and outputs that represent the effect of (and onto) the environment of the module. We use these to obtain parameter spaces against top-level specifications. We experimentally test our approaches on bacterial whole cell biosensors, which perform multiplexed biosensing of multiple analyses concurrently, and on mammalian bistable genetic circuits for cell fate control.
Isocost lines describe the cellular economy genetic circuits
A. Gyorgy et al. (June 2015. )
Incoherent merger network for robust ratiometric gene expression...
Ukjin Kwon et al. (Jan 2023)
Feedforward growth rate control mitigates gene activation burden
C. Barajas et al. (October 2022)
Resource Competition Shapes the Response of Genetic Circuits
Y. Qian et al. (March 2017)
dCas9 regulator to neutralize competition in CRISPRi circuits
H-H. Huang* et al. (Jan. 2021)
Context-aware synthetic biology by controller design:...
N. Shakiba et al. (May 2021)
An endoribonuclease-based feedforward controller for decoupling...
R. D. Jones et al. (August 2020)
Robust and tunable signal processing in mammalian cells via...
R.D. Jones et al. (February 2022)
Resource competition shapes CRISPR-mediated gene activation
K. Aravind-Manoj and D. Del Vecchio (December 2025)
Guaranteed multistability in a microRNA-based genetic network by...
Nicholas Nolan et al. (August 2025)
Guaranteeing System-level Properties in Genetic Circuits Subject...
Inigo X. Incer et al. (August 2024)
Modular machine learning for genetic circuit composition
Identifying unwanted interactions among genetic circuits is critical to achieve context-aware model that produce satisfactory design outcomes. We study how learning of these interactions can be achieved from data obtained from isolated modules behavior. Although physics-based models of genetic networks can be expanded to account for context, there will always be some amount of uncertainty on modules interactions. Here, we study the structural properties of the physical models of these interactions and of the neural network models used to learn them, which allow learning of these interactions from data where each module operates in "isolation". This can allow learning unwanted interactions from a small set of experiments instead of requiring a combinatorial number of experiments, which is unpractical.
Machine learning for synthetic gene circuit engineering
S. Palacios et al. (January 2025)
Learning Genetic Circuit Modules with Neural Networks
Jichi Wang et al. (May 2026)
Feedback control to mitigate endogenous silencing
Cell fate (re)programming and biomanufacturing often rely on forced expression of proteins, which become endogenously silenced over time. We investigate how feedback control of chromatin state can mitigate this problem. Although physical insulation of externally introduced genetic cassettes has been proven useful to mitigate silencing, it is alone insufficient to prevent transgender silencing especially in hiPSCs and during differentiation. Here, we leverage our recently engineered editors of the chromatin state to create feedback control architectures that modify the dynamics of chromatin modifications and destabilize the heterochromatin silenced gene state.
Stochastic analysis of genetic feedback controllers to reprogram...
Simone Bruno et al. (January 2019)
Epigenetic OCT4 regulatory network: stochastic analysis of...
S. Bruno et al. (November 2023)
Epigenetic cell memory: The gene’s inner chromatin...
S. Bruno et al. (February 2022)
Analog epigenetic memory revealed by targeted chromatin editing
S. Palacios et al. (Sept 2025)
Engineering cellular memories in mammalian cells
We are re-wiring the chromatin modification circuitry of mammalian cells in order to control memory formation, and in particular analog memory, a new mode of memory that we recently discovered. Although traditionally epigenetic memory has been regarded as binary, we recently discovered that cells can store analog information in the chromatin state, therefore enabling what we call analog epigenetic memory. We are currently developing foundational engineering techniques, guided by our mathematical models of chromatin modifications, in order to enable precise control of analog memory states. With this capability, we will enable the creation of sophisticated multi-cellular systems with permanent, yet reconfigurable, gradients of cell fates, with future applications to tissue engineering and organoids.
Epigenetic cell memory: The gene’s inner chromatin...
S. Bruno et al. (February 2022)
Mathematical analysis of the limiting behaviors of a chromatin...
S. Bruno et al. (Jan 2023)
Stochastic modeling of epigenetic memory
S. Bruno and D. Del Vecchio (March 2026)
Analog epigenetic memory revealed by targeted chromatin editing
S. Palacios et al. (Sept 2025)
Epigenetic memory: The role of the crosstalk between DNA...
Domitilla Del Vecchio (September, 2025)