English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

The integration of various types of genomic data into predictive models ofbiological networks is one of the main challenges currently faced bycomputational biology. Constraint-based models in particular play a key role inthe attempt to obtain a quantitative understanding of cellular metabolism atgenome scale. In essence, their goal is to frame the metabolic capabilities ofan organism based on minimal assumptions that describe the steady states of theunderlying reaction network via suitable stoichiometric constraints,specifically mass balance and energy balance (i.e. thermodynamic feasibility).The implementation of these requirements to generate viable configurations ofreaction fluxes and/or to test given flux profiles for thermodynamicfeasibility can however prove to be computationally intensive. We propose herea fast and scalable stoichiometry-based method to explore the Gibbs energylandscape of a biochemical network at steady state. The method is applied tothe problem of reconstructing the Gibbs energy landscape underlying metabolicactivity in the human red blood cell, and to that of identifying and removingthermodynamically infeasible reaction cycles in the Escherichia coli metabolicnetwork (iAF1260). In the former case, we produce consistent predictions forchemical potentials (or log-concentrations) of intracellular metabolites; inthe latter, we identify a restricted set of loops (23 in total) in theperiplasmic and cytoplasmic core as the origin of thermodynamic infeasibilityin a large sample ($10^6$) of flux configurations generated randomly andcompatibly with the prior information available on reaction reversibility.

A scalable algorithm to explore the Gibbs energy landscape of genome-scale metabolic networks