ArticlePLoS computational biology2022
Quantifying biochemical reaction rates from static population variability within incompletely observed complex networks.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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.
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Who cites it
5 citing papers in PubMed.
- An Analysis of the Governance, Ethical, Legal, and Social Implications of Biocomputing.Science and engineering ethics · 2026Article
- Rigorous validation of ecological models against empirical time series.Nature ecology & evolution · 2025Article
- Determining interaction directionality in complex biochemical networks from stationary measurements.Scientific reports · 2025Article
- A mathematically rigorous algorithm to define, compute and assess relevance of the probable dissociation constants in characterizing a biochemical network.Scientific reports · 2024Article
- ReDirection: an R-package to compute the probable dissociation constant for every reaction of a user-defined biochemical network.Frontiers in molecular biosciences · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Quantifying biochemical reaction rates within complex cellular processes remains a key challenge of systems biology even as high-throughput single-cell data have become available to characterize snapshots of population variability. That is because complex systems with stochastic and non-linear interactions are difficult to analyze when not all components can be observed simultaneously and systems cannot be followed over time. Instead of using descriptive statistical models, we show that incompletely specified mechanistic models can be used to translate qualitative knowledge of interactions into reaction rate functions from covariability data between pairs of components. This promises to turn a globally intractable problem into a sequence of solvable inference problems to quantify complex interaction networks from incomplete snapshots of their stochastic fluctuations.
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