Network translation has recently been used to establish steady-state properties of mass action systems by corresponding the given system to a generalized one which is either dynamically or steady-state equivalent. In this work, we further use network translation to identify network structures which give rise to the well-studied property of absolute concentration robustness in the corresponding mass action systems. In addition to establishing the capacity for absolute concentration robustness, we show that network translation can often provide a method for deriving the steady-state value of the robust species. We furthermore present a MILP algorithm for the identification of translated chemical reaction networks that improves on previous approaches, allowing for easier application of the theory.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1007/s11538-018-0458-7 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Pulmonary hypertension (PH) stands as a tumor paradigm cardiovascular disease marked by hyperproliferation of cells and vascular remodeling, culminating in heart failure. Complex genetic and epigenetic mechanisms collectively contribute to the disruption of pulmonary vascular homeostasis. In recent years, advancements in research technology have identified numerous gene deletions and mutations, in addition to , that are closely associated with the vascular remodeling process in PH.
View Article and Find Full Text PDFExploring functional connectivity in clinical and data-driven groups of preterm and term adults.
Brain Commun
February 2025
Department of Early Life Imaging, School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EH, UK.
Adults born very preterm (i.e. at <33 weeks' gestation) are more susceptible to long-lasting structural and functional brain alterations and cognitive and socio-emotional difficulties, compared with full-term controls.
View Article and Find Full Text PDFIndividualized PEEP can improve both pulmonary hemodynamics and lung function in acute lung injury.
Crit Care
March 2025
Keenan Centre for Biomedical Research, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Toronto, Canada.
Rationale: There are several approaches to select the optimal positive end-expiratory pressure (PEEP), resulting in different PEEP levels. The impact of different PEEP settings may extend beyond respiratory mechanics, affecting pulmonary hemodynamics.
Objectives: To compare PEEP levels obtained with three titration strategies-(i) highest respiratory system compliance (C), (ii) electrical impedance tomography (EIT) crossing point; (iii) positive end-expiratory transpulmonary pressure (P)-in terms of regional respiratory mechanics and pulmonary hemodynamics.
Accelerating the inference of string generation-based chemical reaction models for industrial applications.
J Cheminform
March 2025
Machine Learning Research, Pfizer Research and Development, Berlin, Germany.
Transformer-based, template-free SMILES-to-SMILES translation models for reaction prediction and single-step retrosynthesis are of interest to computer-aided synthesis planning systems, as they offer state-of-the-art accuracy. However, their slow inference speed limits their practical utility in such applications. To address this challenge, we propose speculative decoding with a simple chemically specific drafting strategy and apply it to the Molecular Transformer, an encoder-decoder transformer for conditional SMILES generation.
View Article and Find Full Text PDFArtificial intelligence-driven translational medicine: a machine learning framework for predicting disease outcomes and optimizing patient-centric care.
J Transl Med
March 2025
Unit for Data Science and Computing, North-West University, 11 Hofman Street, Potchefstroom, 2520, South Africa.
Article Synopsis
- Advancements in AI and ML are transforming the medical field, enhancing patient care and disease modeling, but challenges like data variability and class imbalance hinder optimal predictive performance.
- A new AI framework combining Gradient Boosting Machines and Deep Neural Networks was tested on two datasets, showing better results in accuracy metrics compared to traditional models, including achieving an AUROC of 0.96 on the UK Biobank dataset.
- The framework not only demonstrated superior accuracy but also trained quickly, making it well-suited for real-time clinical applications, with future enhancements aimed at improving scalability and interpretability for broader use.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!