arXiv subjects

Renlei Jiang

Publications and source records attributed to Renlei Jiang.

5 recordsLinked to original sources

arXiv2026

Input-to-state stability of chemical reaction networks with application to molecular computation

Input-to-state stability (ISS) provides a useful tool for analyzing the robustness of time-varying chemical reaction networks (CRNs). This paper investigates the ISS property for CRNs in two cases: one is for weakly reversible CRNs with nonzero deficiency and multiple linkage classes, which is beyond the limit of existing results requesting a weakly reversible CRN to be zero-deficiency and single-linkage-class; the other is for non-weakly reversible CRNs by leveraging the notion of linear conjugacy. These results, on the one hand enrich the studies of the ISS property of broader classes of CRNs, on the other hand serve for designing CRN-based molecular computing systems. The latter application suggests the ISS theory can justify the parallel mechanism of biomolecular computations. We use some CRNs of practical relevance to demonstrate our results, including the p53 signaling and dimerization network, the p21-activated kinase 1 network, etc.

math.DS↗

arXiv2025

Structure-conditioned input-to-state stability for layer-by-layer molecular computations in parallel chemical reaction networks

Molecular computation in chemical reaction networks (CRNs) now constitutes a foundational framework for designing programmable biological systems. However, prevailing design methodologies primarily treat parallelism of chemical reactions as a liability, consequently motivating researchers to redirect research focus toward leveraging parallelism to implement layer-by-layer computations of composite functions in coupled mass-action systems (MASs). MASs exhibiting this property are termed composable. Present composability verification for MASs mainly depends on input-to-state stability (ISS) conditions, with structural characteristics of networks remaining underexplored. This paper investigates the structural conditions under which two MASs are composable. By leveraging ISS-Lyapunov functions, we identify a class of CRN architectures, whose reduced systems have zero deficiency, that guarantee composability with other networks. We also extend our conclusions to encompass some CRN architectures possessing nonzero deficiency. Some examples are presented to demonstrate the validity of our theoretical results. Finally, we employ our methods to devise an algorithm for constructing MASs capable of executing specified molecular computations.

q-bio.MN↗

arXiv2025

Structure and input-to-state stability for composable computations in chemical reaction networks

In the field of molecular computation based on chemical reaction networks (CRNs), leveraging parallelism to enable coupled mass-action systems (MASs) to retain predefined computational functionality has been a research focus. MASs exhibiting this property are termed composable. This paper investigates the structural conditions under which two MASs are composable. By leveraging input-to-state stability (ISS) property, we identify a specific class of CRN architectures that guarantee composability with other networks. A concrete example demonstrates the validity of this conclusion and illustrates the application of composability in computing composite functions.

math.DS↗

arXiv2025

Input-to-state stability-based chemical reaction networks composition for molecular computations

Molecular computation based on chemical reaction networks (CRNs) has emerged as a promising paradigm for designing programmable biochemical systems. However, the implementation of complex computations still requires excessively large and intricate network structures, largely due to the limited understanding of composability, that is, how multiple subsystems can be coupled while preserving computational functionality. Existing composability frameworks primarily focus on rate-independent CRNs, whose computational capabilities are severely restricted. This article aims to establish a systematic framework for composable CRNs governed by mass-action kinetics, a common type of rate-dependent CRNs. Drawing upon the concepts of composable rate-independent CRNs, we introduce the notions of mass-action chemical reaction computers (msCRCs), dynamic computation and dynamic composability to establish a rigorous mathematical framework for composing two or more msCRCs to achieve layer-by-layer computation of composite functions. Further, we derive several sufficient conditions based on the notions of input-to-state stability (ISS) to characterize msCRCs that can be composed to implement desired molecular computations, thereby providing theoretical support for this framework. Some examples are presented to illustrate the efficiency of our method. Finally, comparative results demonstrate that the proposed method exhibits notable advantages in both computational ability and accuracy over the state-of-the-art methods.

q-bio.MN↗

arXiv2025

Disentangled Graph Autoencoder for Treatment Effect Estimation

Treatment effect estimation from observational data has attracted significant attention across various research fields. However, many widely used methods rely on the unconfoundedness assumption, which is often unrealistic due to the inability to observe all confounders, thereby overlooking the influence of latent confounders. To address this limitation, recent approaches have utilized auxiliary network information to infer latent confounders, relaxing this assumption. However, these methods often treat observed variables and networks as proxies only for latent confounders, which can result in inaccuracies when certain variables influence treatment without affecting outcomes, or vice versa. This conflation of distinct latent factors undermines the precision of treatment effect estimation. To overcome this challenge, we propose a novel disentangled variational graph autoencoder for treatment effect estimation on networked observational data. Our graph encoder disentangles latent factors into instrumental, confounding, adjustment, and noisy factors, while enforcing factor independence using the Hilbert-Schmidt Independence Criterion. Extensive experiments on multiple networked datasets demonstrate that our method outperforms state-of-the-art approaches.

cs.LG↗