Interdependence of Cell Growth and Gene Expression: Origins and Consequences

Scott, Matthew P.; Gunderson, Carl W.; Mateescu, Eduard M.; Zhang, Zhongge; Hwa, Terence

doi:10.1126/science.1192588

Cited by 1,331 publications

(2,432 citation statements)

References 24 publications

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“…If one of these resources is limiting, then a cell that invests more resources in one metabolic process must invest fewer resources in other processes. Recent studies support the existence of such competitive resource conflicts (Dekel and Alon, 2005;Scott et al, 2010), but the specific intracellular resources that were under competition were not identified. Below we discuss two that might be of relevance.…”

Section: Biochemical Conflicts That Could Promote Metabolic Specializmentioning

confidence: 99%

Metabolic specialization and the assembly of microbial communities

et al. 2012

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Metabolic specialization is a general biological principle that shapes the assembly of microbial communities. Individual cell types rarely metabolize a wide range of substrates within their environment. Instead, different cell types often specialize at metabolizing only subsets of the available substrates. What is the advantage of metabolizing subsets of the available substrates rather than all of them? In this perspective piece, we argue that biochemical conflicts between different metabolic processes can promote metabolic specialization and that a better understanding of these conflicts is therefore important for revealing the general principles and rules that govern the assembly of microbial communities. We first discuss three types of biochemical conflicts that could promote metabolic specialization. Next, we demonstrate how knowledge about the consequences of biochemical conflicts can be used to predict whether different metabolic processes are likely to be performed by the same cell type or by different cell types. We then discuss the major challenges in identifying and assessing biochemical conflicts between different metabolic processes and propose several approaches for their measurement. Finally, we argue that a deeper understanding of the biochemical causes of metabolic specialization could serve as a foundation for the field of synthetic ecology, where the objective would be to rationally engineer the assembly of a microbial community to perform a desired biotransformation.

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Section: Biochemical Conflicts That Could Promote Metabolic Specializmentioning

confidence: 99%

Metabolic specialization and the assembly of microbial communities

et al. 2012

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“…Indeed, for complex network systems (such as a leaf), such observations may provide insights that are not afforded by simple mutation of an endogenous system in which internal feedback loops within the network may act to dampen any response. In a manner analogous to computational approaches, in which parameter space can be explored to identify factors that robustly influence a particular feature of a system, by artificially manipulating growth it is possible to characterize the spatial and temporal limits within which this parameter acts to influence the system (Scott et al, 2010). Having characterized these limits, it may be possible to 'reverse engineer' the system to discover the endogenous pathways that regulate that parameter.…”

Section: Approaches To Manipulating and Understanding Leaf Formmentioning

confidence: 99%

Targeted manipulation of leaf form via local growth repression

Malinowski

Kasprzewska²,

Fleming³

2011

The Plant Journal

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SUMMARYA classical view is that leaf shape is the result of local promotion of growth linked to cell proliferation. However, an alternative hypothesis is that leaf form is the result of local repression of growth in an otherwise growing system. Here we show that leaf form can indeed be manipulated in a directed fashion by local repression of growth. We show that targeting expression of an inhibitor of a cyclin-dependent kinase (KRP1) to the sinus area of developing leaves of Arabidopsis leads to local growth repression and the formation of organs with extreme lobing, including generation of leaflet-like organs. Directing KRP1 expression to other regions of the leaf using an miRNA target sequence tagging approach also leads to predictable novel leaf forms, and repression of growth in the leaf margin blocks the outgrowth of lobes, leading to a smoother perimeter. In addition, we show that decreased growth around the perimeter and across the leaf abaxial surface leads to a change in 3D form, as predicted by mechanical models of leaf growth. Our analysis provides experimental evidence that local repression of growth influences leaf shape, suggesting that it could be part of the mechanism of morphogenesis in plants in the context of an otherwise growing system.

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“…Much of our understanding of bacterial and yeast life cycles stems from monitoring their proliferation in time and the most routine way of doing so is using optical density (OD) measurements. The applications of such measurements range from routine checks during different cloning techniques 1 ; through studying cellular physiology and metabolism 2,3 ; to determining the growth rate for antibiotic dosage 4,5 ; and monitoring of biomass accumulation during bio-industrial fermentation 6 . Here we introduce a set of calibration techniques that take into account the relevant parameters affecting OD measurements, including at high culture densities, in a range of conditions commonly used by researchers.…”

mentioning

confidence: 99%

General calibration of microbial growth in microplate readers

Stevenson

McVey

Clark

et al. 2016

Preprint

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Optical density (OD) measurements of microbial growth are one of the most common techniques used in microbiology, with applications ranging from studies of antibiotic efficacy to investigations of growth under different nutritional or stress environments, to characterization of different mutant strains, including those harbouring synthetic circuits. OD measurements are performed under the assumption that the OD value obtained is proportional to the cell number, i.e. the concentration of the sample. However, the assumption holds true in a limited range of conditions, and calibration techniques that determine that range are currently missing. Here we present a set of calibration procedures and considerations that are necessary to successfully estimate the cell concentration from OD measurements.Bacteria and yeast are widely studied microorganisms of great economic, medical and societal interest. Much of our understanding of bacterial and yeast life cycles stems from monitoring their proliferation in time and the most routine way of doing so is using optical density (OD) measurements. The applications of such measurements range from routine checks during different cloning techniques 1 ; through studying cellular physiology and metabolism 2,3 ; to determining the growth rate for antibiotic dosage 4,5 ; and monitoring of biomass accumulation during bio-industrial fermentation 6 . Here we introduce a set of calibration techniques that take into account the relevant parameters affecting OD measurements, including at high culture densities, in a range of conditions commonly used by researchers.OD measurements have become synonymous with measurements of bacterial number (N) or concentration (C), in accordance with the Beer-Lambert law. However, OD measurements are turbidity measurements 7,8 , thus the Beer-Lambert law can be applied, with some considerations, only for microbial cultures of low densities. OD measurements in plate readers, increasingly used for high-throughput estimates of microbial growth, operate predominantly at higher culture densities where OD is expected to have a parabolic dependency on N 8 . Additionally, the proportionality constants (either in low or high density regimes) strongly depend on several parameters, for example cell size, which need to be included in robust calibration techniques. Yet, these techniques, essential when using OD measurements for quantitative studies of microbial growth, including growth rates, lag times and cell yields, have thus far not been established.The Beer-Lambert law (Supplementary Note 1) assumes that light is only absorbed to derive OD ~ C, which is true if the light received by the detector of a typical spectrophotometer is the light that did not interact with the sample in any way 7,9 . In general, when microbial cells are well dispersed in the solution (for the cases where N is small, i.e. single scattering regime) and the geometry of the spectrophotometer is suitable, the Beer-Lambert law is a good approximation for turbidity measurements, and N (or C) is ~...

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Interdependence of Cell Growth and Gene Expression: Origins and Consequences

Cited by 1,331 publications

References 24 publications

Metabolic specialization and the assembly of microbial communities

Metabolic specialization and the assembly of microbial communities

Targeted manipulation of leaf form via local growth repression

General calibration of microbial growth in microplate readers

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