ArticlePhilosophical transactions. Series A, Mathematical, physical, and engineering sciences2013
Using topology to tame the complex biochemistry of genetic networks.
Article in Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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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
4 citing papers in PubMed.
- A window into lysogeny: revealing temperate phage biology with transcriptomics.Microbial genomics · 2020Article
- Logical-continuous modelling of post-translationally regulated bistability of curli fiber expression in Escherichia coli.BMC systems biology · 2015Article
- Inference for the physical sciences.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2013Article
- Breast cancer causes and treatment: where are we going wrong?Breast cancer (Dove Medical Press) · 2013Article
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Abstract
Living cells are controlled by networks of interacting genes, proteins and biochemicals. Cells use the emergent collective dynamics of these networks to probe their surroundings, perform computations and generate appropriate responses. Here, we consider genetic networks, interacting sets of genes that regulate one another's expression. It is possible to infer the interaction topology of genetic networks from high-throughput experimental measurements. However, such experiments rarely provide information on the detailed nature of each interaction. We show that topological approaches provide powerful means of dealing with the missing biochemical data. We first discuss the biochemical basis of gene regulation, and describe how genes can be connected into networks. We then show that, given weak constraints on the underlying biochemistry, topology alone determines the emergent properties of certain simple networks. Finally, we apply these approaches to the realistic example of quorum-sensing networks: chemical communication systems that coordinate the responses of bacterial populations.
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