ReviewBeilstein journal of organic chemistry2017
Grip on complexity in chemical reaction networks.
Review in Beilstein journal of organic chemistry, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed, 44 citations in OpenAlex.
- Selective epimerization of GlcNAc to GalNAc through steady-state tuning under kinetic network control.Nature chemistry · 2026Article
- The Role of Chemistry Across Disciplines From Humanities to Life Sciences in Understanding Complexity and Emergence.Angewandte Chemie (International ed. in English) · 2026Review
- Comprehensive Mapping of Compositional Dynamics in the Formose Reaction Network.Angewandte Chemie (International ed. in English) · 2026Article
- Experimentally Guided Iterative Parameter Estimation for Predictive Chemical Oscillator Models.Angewandte Chemie (International ed. in English) · 2025Article
- Deep Learning Reaction Framework (DLRN) for kinetic modeling of time-resolved data.Communications chemistry · 2025Article
- Metabolism-inspired chemical reaction networks for chemically driven dissipative oligoesterification.Angewandte Chemie (International ed. in English) · 2025Article
- The sugar cube: Network control and emergence in stereoediting reactions.Science (New York, N.Y.) · 2024Article
- Exploring Emergent Properties in Enzymatic Reaction Networks: Design and Control of Dynamic Functional Systems.Chemical reviews · 2024Review
- Interlinking spatial dimensions and kinetic processes in dissipative materials to create synthetic systems with lifelike functionality.Nature nanotechnology · 2024Review
- Perpetuating enzymatically induced spatiotemporal pH and catalytic heterogeneity of a hydrogel by nanoparticles.Chemical science · 2022Article
- A Bayesian Approach to Extracting Kinetic Information from Artificial Enzymatic Networks.Analytical chemistry · 2022Article
- Self-reproducing catalytic micelles as nanoscopic protocell precursors.Nature reviews. Chemistry · 2021Review
- On the use of catalysis to bias reaction pathways in out-of-equilibrium systems.Chemical science · 2021Article
- Controlling Mirror Symmetry Breaking and Network Formation in Liquid Crystalline Cubic, Isotropic Liquid and Crystalline Phases of Benzil-Based Polycatenars.Chemistry (Weinheim an der Bergstrasse, Germany) · 2020Article
- Engineering multilayer chemical networks.Chemical science · 2019Article
- Spatiotemporal control of DNA-based chemical reaction network via electrochemical activation in microfluidics.Scientific reports · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A new discipline of "systems chemistry" is emerging, which aims to capture the complexity observed in natural systems within a synthetic chemical framework. Living systems rely on complex networks of chemical reactions to control the concentration of molecules in space and time. Despite the enormous complexity in biological networks, it is possible to identify network motifs that lead to functional outputs such as bistability or oscillations. To truly understand how living systems function, we need a complete understanding of how chemical reaction networks (CRNs) create function. We propose the development of a bottom-up approach to design and construct CRNs where we can follow the influence of single chemical entities on the properties of the network as a whole. Ultimately, this approach should allow us to not only understand such complex networks but also to guide and control their behavior.
Indexed as
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.