Stochastic chemical reaction networks (CRNs) provide a fundamental framework for modeling stochastic dynamics in systems biology, population dynamics, and chemistry. A stationary distribution of stochastic CRN describes its long-term behavior. An analytic formula for a stationary distribution can be obtained for only in limited cases, linear or finite-state systems.
Interestingly, the...
A bang–bang control in a reaction system refers to rapid switching between two extreme behavioral regimes of a species in order to optimize an objective, such as population growth. Because such transitions occur abruptly, intrinsic stochasticity typically induces large fluctuations in the system. We have recently identified a class of bacteria that employs a remarkable strategy to suppress...
Chemical reaction networks (CRNs) are foundational models for describing complex biochemical processes. We study noncompetitive CRNs, a class of networks whose static states are rate-independent, and that can implement ReLU neural networks. A central contribution of this work is that noncompetitive CRNs are special instances of Abelian networks (ANs)—a well-established framework for...
We investigate a class of chemical reaction networks models associated to a phosphorylation mechanism with three steps. This is an important mechanism in many biological cells. A chemical species, the substrat has three possible configurations: ${\mathcal S}_1$, ${\mathcal S}_2$ and ${\mathcal S}_3$. There are transformations by two types of chemical species (enzymes) ${\mathcal A}$ and...
A Chemical Reaction Network (CRN) is a set of possible chemical species, along with a finite set of chemical transformations that the species can undergo. These models are frequently used in biochemistry both for investigating existing systems and for designing new synthetic biological circuits, and also see use in other areas of biology like ecology and epidemiology. There is a rich history...
