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Xiaowei Zeng

Publications and source records attributed to Xiaowei Zeng.

2 recordsLinked to original sources

Multistability of small zero-one reaction networks

Zero-one biochemical reaction networks play key roles in cell signalling such as signalling pathways regulated by protein phosphorylation. Multistability of reaction networks is a crucial dynamics feature enabling decision-making in cells. It is well known that multistability can be lifted from a "subnetwork" (a network with less species and fewer reactions) to large networks. So, we aim to explore the multistability problem of small zero-one networks. In this work, we prove the following main results: 1. any zero-one network with a one-dimensional stoichiometric subspace admits at most one positive steady state (it must be stable), and all the one-dimensional zero-one networks can be classified according to if they indeed admit a stable positive steady state or not; 2. any two-dimensional zero-one network with up to three species either admits only degenerate positive steady states, or admits at most one positive steady state (it must be stable); 3. the smallest zero-one networks (here, by "smallest", we mean these networks contain species as few as possible) that admit nondegenerate multistationarity/multistability contain three species and five/six reactions, and they are three dimensional. In these proofs, we use the theorems based on the Brouwer degree theory and the theory of real algebraic geometry. Moreover, applying the tools of computational real algebraic geometry, we provide a systematical way for detecting the networks that admit nondegenerate multistationarity/multistability.

math.DS

Inverse Design of Nonlinear Mechanics of Bio-inspired Materials Through Interface Engineering and Bayesian Optimization

In many biological materials such as nacre and bone, the material structure consists of hard grains and soft interfaces, with the interfaces playing a significant role in the material's mechanical behavior. This type of structures has been utilized in the design of various bio-inspired composite materials. Such applications often require the materials to exhibit a specified nonlinear stress-strain relationship. A key challenge lies in identifying appropriate interface properties from an infinite search space to achieve a given target stress-strain curve. This study introduces a Bayesian optimization (BO) framework specifically tailored for the inverse design of interfaces in bio-inspired composites. As a notable advantage, this method is capable of expanding the design space, allowing the discovery of optimal solutions even when the target curve deviates significantly from the initial dataset. Furthermore, our results show that BO can identify distinct interface designs that produce similar target stress-strain responses, yet differ in their deformation and failure mechanisms. These findings highlight the potential of the proposed BO framework to address a wide range of inverse design challenges in nonlinear mechanics problems.

cond-mat.mtrl-sci