ArticleBMC systems biology2014
Morphisms of reaction networks that couple structure to function.
Article in BMC systems biology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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
13 citing papers in PubMed.
- Generative design of synthetic gene circuits for functional and evolutionary properties.NPJ systems biology and applications · 2026Article
- Reducing Boolean networks with backward equivalence.BMC bioinformatics · 2023Article
- The role of memory in non-genetic inheritance and its impact on cancer treatment resistance.PLoS computational biology · 2021Article
- Evolution of opposing regulatory interactions underlies the emergence of eukaryotic cell cycle checkpoints.Scientific reports · 2021Article
- Multi-scale stochastic organization-oriented coarse-graining exemplified on the human mitotic checkpoint.Scientific reports · 2019Article
- Synthesizing and tuning stochastic chemical reaction networks with specified behaviours.Journal of the Royal Society, Interface · 2018Article
- Single molecules can operate as primitive biological sensors, switches and oscillators.BMC systems biology · 2018Article
- Memory functions reveal structural properties of gene regulatory networks.PLoS computational biology · 2018Article
- Chemical reaction network designs for asynchronous logic circuits.Natural computing · 2018Article
- Computing with biological switches and clocks.Natural computing · 2018Article
- Efficient Switches in Biology and Computer Science.PLoS computational biology · 2017Article
- Stochasticity in the Genotype-Phenotype Map: Implications for the Robustness and Persistence of Bet-Hedging.Genetics · 2016Article
- Noise Reduction in Complex Biological Switches.Scientific reports · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe mechanisms underlying complex biological systems are routinely represented as networks. Network kinetics is widely studied, and so is the connection between network structure and behavior. However, similarity of mechanism is better revealed by relationships between network structures.
resultsWe define morphisms (mappings) between reaction networks that establish structural connections between them. Some morphisms imply kinetic similarity, and yet their properties can be checked statically on the structure of the networks. In particular we can determine statically that a complex network will emulate a simpler network: it will reproduce its kinetics for all corresponding choices of reaction rates and initial conditions. We use this property to relate the kinetics of many common biological networks of different sizes, also relating them to a fundamental population algorithm.
conclusionsStructural similarity between reaction networks can be revealed by network morphisms, elucidating mechanistic and functional aspects of complex networks in terms of simpler networks.
Indexed as
Identifiers
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.