Populations to single cells Daniel Siegal-Gaskins

ISBN: 9780549744337

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92 pages


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Populations to single cells  by  Daniel Siegal-Gaskins

Populations to single cells by Daniel Siegal-Gaskins
| NOOKstudy eTextbook | PDF, EPUB, FB2, DjVu, audiobook, mp3, RTF | 92 pages | ISBN: 9780549744337 | 5.50 Mb

The search for underlying physical laws of biology requires detailed investigations at all length scales in the biological hierarchy, from the molecular to the scale of populations. Although technological advances have made studies at even theMoreThe search for underlying physical laws of biology requires detailed investigations at all length scales in the biological hierarchy, from the molecular to the scale of populations.

Although technological advances have made studies at even the smallest biological length scales feasible, much of what is known at the cellular level has been inferred from population measurements. Unfortunately, population data is often a poor indicator of individual cell behavior- single-cell experiments have revealed remarkable variability between even isogenic cells and yielded insights into complex system behavior masked by population-wide measurements.

Experiments at the single-cell level continue to be necessary to complete our understanding of biological system behavior.-This Dissertation is a summary of my work addressing single-cell vs. population behavior in the model system Caulobacter crescentus. To this end I discuss a number of results that have come out of cell growth experiments in a simple microfluidic device of my own construction.

Specifically, I show that a previously-uncharacterized photosensory two-component signaling system in Caulobacter is involved in bacterial cell attachment, that Caulobacter exhibit highly-regulated division control that is both coupled to cell development and epigenetically-inherited from mother cells to daughters, and that the cellular response to toxin expression is unexpectedly heterogeneous with some cells exhibiting accelerated plasmid loss.

Furthermore, I show how, with only a few well-motivated assumptions, one can extract average single-cell information from population data and thus predict gene expression in single cells.



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