ArticlePLoS computational biology2013
Maximizing the information content of experiments in systems biology.
Article in PLoS computational biology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 72 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
72 citing papers in PubMed.
- Parameter identifiability of a neuroendocrine-inflammatory model across experimental designs.ArXiv · 2026Article
- Optimization of experimental designs for biological rhythm discovery.PLoS computational biology · 2025Article
- Data-driven model discovery and model selection for noisy biological systems.PLoS computational biology · 2025Article
- Creating cell-specific computational models of stem cell-derived cardiomyocytes using optical experiments.PLoS computational biology · 2024Article
- Identifying Bayesian optimal experiments for uncertain biochemical pathway models.Scientific reports · 2024Article
- Achieving Occam's razor: Deep learning for optimal model reduction.PLoS computational biology · 2024Article
- Article
- Creating cell-specific computational models of stem cell-derived cardiomyocytes using optical experiments.bioRxiv : the preprint server for biology · 2024Article
- Integrating a tailored recurrent neural network with Bayesian experimental design to optimize microbial community functions.PLoS computational biology · 2023Article
- Structural identifiability of biomolecular controller motifs with and without flow measurements as model output.PLoS computational biology · 2023Article
- Predictive power of non-identifiable models.Scientific reports · 2023Article
- Single-cell CaJournal of the Royal Society, Interface · 2023Article
- Deep reinforcement learning for optimal experimental design in biology.PLoS computational biology · 2022Article
- Article
- Parameter estimation and uncertainty quantification using information geometry.Journal of the Royal Society, Interface · 2022Review
- Optimal Experimental Design Based on Two-Dimensional Likelihood Profiles.Frontiers in molecular biosciences · 2022Article
- Robust dynamic experiments for the precise estimation of respiration and fermentation parameters of fruit and vegetables.PLoS computational biology · 2022Article
- Investigating Active Learning and Meta-Learning for Iterative Peptide Design.Journal of chemical information and modeling · 2021Article
- Optimal experimental design for mathematical models of haematopoiesis.Journal of the Royal Society, Interface · 2021Article
- Identifiability analysis for stochastic differential equation models in systems biology.Journal of the Royal Society, Interface · 2020Article
12 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Our understanding of most biological systems is in its infancy. Learning their structure and intricacies is fraught with challenges, and often side-stepped in favour of studying the function of different gene products in isolation from their physiological context. Constructing and inferring global mathematical models from experimental data is, however, central to systems biology. Different experimental setups provide different insights into such systems. Here we show how we can combine concepts from Bayesian inference and information theory in order to identify experiments that maximize the information content of the resulting data. This approach allows us to incorporate preliminary information; it is global and not constrained to some local neighbourhood in parameter space and it readily yields information on parameter robustness and confidence. Here we develop the theoretical framework and apply it to a range of exemplary problems that highlight how we can improve experimental investigations into the structure and dynamics of biological systems and their behavior.
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.