Biology subjects

Kueken, A.

Publications and source records attributed to Kueken, A..

5 recordsLinked to original sources

bioRxiv2026

COCOA.jl: A Julia package for high-performance analysis of concordance and kinetic modules in biochemical networks

SummaryRecent advances in analysis of biochemical networks have contributed the identification of their modular structure based on the concept of multi reaction dependencies and kinetic coupling of reaction rates (Kuken et al., 2022; Langary et al., 2025). Existing implementations of the algorithms to study modular structure do not scale well with the size of the networks, prohibiting their application with genome-scale networks. Here, we introduce COCOA.jl, a multithreaded Julia package for identification of concordant and kinetic modules, with applications in the study of concentration robustness. Availability and implementationCOCOA.jl is implemented in Julia 1.12.2 and is freely available under the MIT license at https://github.com/antoniofranky/COCOA.jl. It runs on Linux, macOS, and Windows; installation is supported via the Julia package manager. COCOA.jl can be called from Python via JuliaCall. Contactantonschaf@posteo.de; ankueken@uni-potsdam.de

systems biology↗

bioRxiv2021

Nonstoichiometric balanced complexes: Implications on the effective deficiency of the underlying metabolic network

The deficiency of a (bio)chemical reaction network can be conceptually interpreted as a measure of its ability to support exotic dynamical behavior and/or multistationarity. The classical definition of deficiency relates to the capacity of a network to permit variations of the complex formation rate vector at steady state, irrespective of the network kinetics. However, the deficiency is by definition completely insensitive to the fine details of the directionality of reactions as well as bounds on reaction fluxes. While the classical definition of deficiency can be readily applied in the analysis of unconstrained, weakly reversible networks, it only provides an upper bound in the cases where relevant constraints on reaction fluxes are imposed. Here we propose the concept of effective deficiency, which provides a more accurate assessment of the networks capacity to permit steady state variations at the complex level for constrained networks of any reversibility patterns. The effective deficiency relies on the concept of nonstoichiometric balanced complexes, which we have already shown to be present in real-world biochemical networks operating under flux constraints. Our results demonstrate that the effective deficiency of real-world biochemical networks is smaller than the classical deficiency, indicating the effects of reaction directionality and flux bounds on the variation of the complex formation rate vector at steady state.

systems biology↗

bioRxiv2021

The unraveling of balanced complexes in metabolic networks

Balanced complexes in biochemical networks are at core of several theoretical and computational approaches that make statements about the properties of the steady states supported by the network. Recent computational approaches have employed balanced complexes to reduce metabolic networks, while ensuring preservation of particular steady-state properties; however, the underlying factors leading to the formation of balanced complexes have not been studied, yet. Here, we present a number of factorizations providing insights in mechanisms that lead to the origins of the corresponding balanced complexes. The proposed factorizations enable us to categorize balanced complexes into four distinct classes, each with specific origins and characteristics. They also provide the means to efficiently determine if a balanced complex in large-scale networks belongs to a particular class from the categorization. The results are obtained under very general conditions and irrespective of the network kinetics, rendering them broadly applicable across variety of network models. Application of the categorization shows that all classes of balanced complexes are present in large-scale metabolic models across all kingdoms of life, therefore paving the way to study their relevance with respect to different properties of steady states supported by these networks. HighlightsO_LIBalanced complexes are ubiquitous in real-world networks, and facilitate insights in steady state flux phenotypes in large-scale metabolic networks. C_LIO_LINovel factorizations are proposed that explain the formation of balanced complexes and enable their categorization. C_LIO_LIThe results also provide a computationally-efficient tool for the identification of balanced complexes in large-scale networks. C_LIO_LIExamination of metabolic network models across all kingdoms of life shows that all categories naturally arise in this type of networks. C_LI

bioinformatics↗

bioRxiv2021

A structural property for reduction of biochemical networks

Large-scale biochemical models are of increasing sizes due to the consideration of interacting organisms and tissues. Model reduction approaches that preserve the flux phenotypes can simplify the analysis and predictions of steady-state metabolic phenotypes. However, existing approaches either restrict functionality of reduced models or do not lead to significant decreases in the number of modelled metabolites. Here, we introduce an approach for model reduction based on the structural property of balancing of complexes that preserves the steady-state fluxes supported by the network and can be efficiently determined at genome scale. Using two large-scale mass-action kinetic models of Escherichia coli, we show that our approach results in a substantial reduction of 99% of metabolites. Applications to genome-scale metabolic models across kingdoms of life result in up to 55% and 85% reduction in the number of metabolites when arbitrary and mass-action kinetics is assumed, respectively. We also show that predictions of the specific growth rate from the reduced models match those based on the original models. Since steady-state flux phenotypes from the original model are preserved in the reduced, the approach paves the way for analysing other metabolic phenotypes in large-scale biochemical networks.

bioinformatics↗

bioRxiv2018

Cellular determinants of metabolite concentration ranges

Cellular functions are shaped by reaction networks whose dynamics are determined by the concentrations of underlying components. However, cellular mechanisms ensuring that a components concentration resides in a given range remain elusive. We present network properties which suffice to identify components whose concentration ranges can be efficiently computed in mass-action metabolic networks. We show that the derived ranges are in excellent agreement with simulations from a detailed kinetic metabolic model of Escherichia coli. We demonstrate that the approach can be used with genome-scale metabolic models to arrive at predictions concordant with measurements from Escherichia coli under different growth scenarios. By application to 14 genome-scale metabolic models from diverse species, our approach specifies the cellular determinants of concentration ranges that can be effectively employed to make predictions for a variety of biotechnological and medical applications.\n\nAuthor SummaryWe present a computational approach for inferring concentration ranges from genome-scale metabolic models. The approach specifies a determinant and molecular mechanism underling facile control of concentration ranges for components in large-scale cellular networks. Most importantly, the predictions about concentration ranges do not require knowledge of kinetic parameters (which are difficult to specify at a genome scale), provided measurements of concentrations in a reference state. The approach assumes that reaction rates follow the mass action law used in the derivations of other types of kinetics. We apply the approach with large-scale kinetic and stoichiometric metabolic models of organisms from different kingdoms of life to show that we can identify a proportion of metabolites to which our approach is applicable. By challenging the predictions of concentration ranges in the genome-scale metabolic network of E. coli with real-world data sets, we further demonstrate the prediction power and limitations of the approach.

bioinformatics↗