ArticlePLoS computational biology2014
Modular composition of gene transcription networks.
Article in PLoS computational biology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 2 citations in OpenAlex.
- Competition and evolutionary selection among core regulatory motifs in gene expression control.Nature communications · 2023Article
- A blueprint for a synthetic genetic feedback optimizer.Nature communications · 2023Article
- A synthetic synthesis to explore animal evolution and development.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2022Review
- Context-Dependent Stability and Robustness of Genetic Toggle Switches with Leaky Promoters.Life (Basel, Switzerland) · 2021Article
- A modular approach for modeling the cell cycle based on functional response curves.PLoS computational biology · 2021Article
- A Practical Step-by-Step Guide for Quantifying Retroactivity in Gene Networks.Methods in molecular biology (Clifton, N.J.) · 2021Article
- Modularity in Biological Networks.Frontiers in genetics · 2021Review
- Article
- Comparison between Effects of Retroactivity and Resource Competition upon Change in Downstream Reporter Genes of Synthetic Genetic Circuits.Life (Basel, Switzerland) · 2019Article
- Computational Analysis of Altering Cell Fate.Methods in molecular biology (Clifton, N.J.) · 2019Article
- Recursively constructing analytic expressions for equilibrium distributions of stochastic biochemical reaction networks.Journal of the Royal Society, Interface · 2017Article
- Switch-like Transitions Insulate Network Motifs to Modularize Biological Networks.Cell systems · 2016Article
- Simplified mechanistic models of gene regulation for analysis and design.Journal of the Royal Society, Interface · 2015Article
- Quantification of Interactions between Dynamic Cellular Network Functionalities by Cascaded Layering.PLoS computational biology · 2015Article
- Synthesizing Configurable Biochemical Implementation of Linear Systems from Their Transfer Function Specifications.PloS one · 2015Article
- Retroactivity in the Context of Modularly Structured Biomolecular Systems.Frontiers in bioengineering and biotechnology · 2015Review
- A load driver device for engineering modularity in biological networks.Nature biotechnology · 2014Article
- CRISPR transcriptional repression devices and layered circuits in mammalian cells.Nature methods · 2014Article
- Effects of downstream genes on synthetic genetic circuits.BMC systems biology · 2014Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Predicting the dynamic behavior of a large network from that of the composing modules is a central problem in systems and synthetic biology. Yet, this predictive ability is still largely missing because modules display context-dependent behavior. One cause of context-dependence is retroactivity, a phenomenon similar to loading that influences in non-trivial ways the dynamic performance of a module upon connection to other modules. Here, we establish an analysis framework for gene transcription networks that explicitly accounts for retroactivity. Specifically, a module's key properties are encoded by three retroactivity matrices: internal, scaling, and mixing retroactivity. All of them have a physical interpretation and can be computed from macroscopic parameters (dissociation constants and promoter concentrations) and from the modules' topology. The internal retroactivity quantifies the effect of intramodular connections on an isolated module's dynamics. The scaling and mixing retroactivity establish how intermodular connections change the dynamics of connected modules. Based on these matrices and on the dynamics of modules in isolation, we can accurately predict how loading will affect the behavior of an arbitrary interconnection of modules. We illustrate implications of internal, scaling, and mixing retroactivity on the performance of recurrent network motifs, including negative autoregulation, combinatorial regulation, two-gene clocks, the toggle switch, and the single-input motif. We further provide a quantitative metric that determines how robust the dynamic behavior of a module is to interconnection with other modules. This metric can be employed both to evaluate the extent of modularity of natural networks and to establish concrete design guidelines to minimize retroactivity between modules in synthetic systems.
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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.