Eduardo D. Sontag

This paper combines mechanistic and machine learning models to address resource competition in synthetic biology, demonstrating improved predictions and physical consistency.

This paper develops a mathematical model to assess how incorporating toxicity feedback affects adaptive cancer therapy, finding that toxicity-induced treatment breaks can extend time to progression.

The paper develops a dynamical system model to analyze how Notch signaling and epigenetic modifications influence melanoma cell fate transitions during metastasis.

This paper improves heuristic logic synthesis by using a database of optimal 4-input Boolean implementations to reduce gate count in larger circuits, achieving average improvements of 5.16% and 4.54% for 5- and 6-input circuits respectively.

The paper demonstrates that increasing activator expression can reduce target gene expression by displacing repressor from decoy sites, explaining paradoxical gene regulation through competition for genomic binding sites.

This paper models and minimizes dCas9 competition in CRISPRi-based genetic circuits to improve their performance and dynamic range.

The paper develops a quantitative pharmacology model to analyze how TCE drug-target trimer formation and biodistribution depend on binding kinetics and design characteristics.

The paper introduces cumulative dose response (cDR) and shows that incoherent feedforward loops always produce monotonic cDR despite being capable of non-monotonic dose responses.

This paper develops a mathematical model of drug-induced resistance that fits experimental data and identifies optimal dosing strategies to minimize tumor cell volume.

This study creates a synthetic genetic toggle switch that exhibits population bistability and generates spatial differentiation patterns in bacterial colonies.

This paper classifies resource competition structures in synthetic gene circuits and shows how their response to competitors and interventions can distinguish between different competition mechanisms.

This paper designs a synthetic genetic multi-bit counter using repressor-based circuits that can count input pulses modulo 2^N.

The paper develops a method to derive analytic stationary distributions for biochemical reaction networks by transforming them to be complex balanced, and introduces a computational tool for this purpose.

This study analyzes epidemic models under social distancing to find optimal timing and duration for minimizing infected population peaks while reducing social distancing time.

The paper demonstrates that a bell-shaped dose response can occur in a system without incoherent feedforward loops (IFFLs) and analyzes conditions for this in two-dimensional systems.

The paper introduces a mathematical model that integrates DNA methylation with gene regulatory networks to quantify its role in gene silencing stability.

The paper develops a mathematical model to explain immune recruitment and drug resistance in metronomic chemotherapy using a mouse glioma model and validates it across different drug regimens.

This paper develops a method to synthesize distributed Boolean circuits using diffusible small molecules, enabling scalable computation beyond single-cell limitations.

This paper introduces a computational framework to analyze biochemical reaction networks for structural attractiveness using Robust Lyapunov functions, demonstrating its application to various biological systems.

This paper develops mathematical models of fast protease-based biosensors capable of Boolean logic computation using reaction conditions as tuning parameters.

The paper designs and validates an in vitro synthetic gene circuit that uses integral feedback to maintain gene expression at a target level despite disturbances.

The paper introduces effective stoichiometric space (ESS) to infer biochemical reaction network structure from single-cell multiplex data using toric systems theory, identifying differences in drug-treated cells.

The paper analyzes the periodic gain of the Ribosome Flow Model (RFM) and derives conditions for its optimality in translation elongation.

The paper shows that simple biochemical systems can exhibit chaotic dynamics under periodic forcing, revealing previously unrecognized complexity in molecular biology.

The paper investigates how nucleotide triphosphates (NTPs) and magnesium (Mg2+) affect gene expression in a cell-free system, finding that high NTP concentrations directly inhibit translation.

This paper connects dynamical compensation to system equivalence and parameter identifiability, providing tests to verify DC in mathematical models.

The paper clarifies the relationship between dynamical compensation and parameter identifiability by defining DC in terms of parameter identifiability across different system states and measurements.

The paper speculates that incoherent feedforward loops function as change detectors in immune responses, potentially enabling logarithmic sensing and scale invariance.