Interdependence of Cell Growth and Gene Expression: Origins and Consequences

By M. Scott, Carl W. Gunderson, E. Mateescu, Zhongge Zhang, Terence Hwa. Science, 2010

1,404 citations recorded by the source corpus.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/2ef197ce74969f0112b2d7b7b5f79908d8333993

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

1. Jonathan S. Weissman
2. Carol A. Gross
3. Gene‐Wei Li
4. D. Burkhardt
5. Uri Alon
6. Xavier Pennec

In Molecular Biology

1. Carol A. Gross
2. D. Burkhardt
3. Gene‐Wei Li
4. Jonathan S. Weissman
5. Jean M. Macklaim
6. G. Gloor
7. Uri Alon
8. Vincent Danos

Citations in the corpus

• Unifying the analysis of high-throughput sequencing datasets: characterizing RNA-seq, 16S rRNA gene sequencing and selective growth experiments by compositional data analysis (2014, background): “What is not often appreciated is that gene expression is itself a limited resource in the cell, meaning that the transcript abundance should also be modeled as proportional [39].”
• A mechanistic link between cellular trade-offs, gene expression and growth (2015, positive): “coli that demonstrate the two different types of linear dependence of ribosomal mass fraction on growth rate [2].”
• Genome-scale models of metabolism and gene expression extend and refine growth phenotype prediction (2013, neutral): “…20 amino acids per second is imposed as a constraint in the MEModel, the model predicts a linear growth rate-dependent RNA-to-protein ratio (Figure 1C), consistent with the previous measurements (Scott et al, 2010); however, the predicted RNA content does not quantitatively match measured values.”
• Evolutionary Trade-Offs, Pareto Optimality, and the Geometry of Phenotype Space (2012, result): “Similar analysis may explain low-dimensional patterns in gene expression measurements in bacteria ( 19 ) and cancer cells ( 20 ).”
• Quantifying absolute protein synthesis rates reveals principles underlying allocation of cellular resources. (2014, methodology): “The cost of producing excess proteins, independent of their function, comes from competition for ribosomes—an effect that has been widely studied for E. coli (Dekel and Alon, 2005; Dong et al., 1995; Scott et al., 2010).”