Many cell types stochastically switch phenotypes under some conditions, so that genetically identical sister cells may behave quite differently in a common environment. This non-genetic variability likely arises from noise in gene expression, which can be co-opted to allow random fate determination. This thesis examines both phenomena from experimental and theoretical perspectives, starting with a phenotypic switch. Cells of Bacillus subtilis grow either as individual, motile cells, or as connected groups of sessile cells called chains. We constructed an array of microfluidic channels in which we could capture and observe single cells in a constant environment over hundreds of generations of growth. These conditions allow unperturbed observation of decision-making driven only by factors internal to the cell. We observe that switching is asymmetric: transitions from motility to chaining occur with constant probability (memorylessly), but the reverse transition is tightly timed (exhibits memory). These properties are explained by dissecting the genetic circuit underlying switching, which can be quantitatively separated into components responsible for initiation and maintenance of the state. We propose that memory enables transgenerational cooperation between a cell founding a biofilm and its progeny, and that a stochastic sequestration mechanism is the source of random switching. Next, we introduce an exact framework for analyzing noise in gene networks that phrases results in terms of compounded parameters with simple interpretations. We uncover a basic identity that relates fluctuations in the production and degradation rates of one component to those of any other component within the cell. Since the result is exact, it applies to whole classes of gene networks. We identify basic constraints on the ability of negative feedback to suppress noise, and show that suppressing noise in one species generally requires introducing it elsewhere. When applied to the most common model of gene expression, the identity reveals a simple connection between the statistics of proteins and their cognate mRNAs. We reanalyze a recent experimental study of stochastic gene expression and show that the data are inconsistent with this prediction. Thus in contrast to early studies of single genes, there is currently discord between models and measurements of stochastic gene expression.
| Identifer | oai:union.ndltd.org:harvard.edu/oai:dash.harvard.edu:1/11744461 |
| Date | January 2013 |
| Creators | Norman, Thomas Maxwell |
| Contributors | Losick, Richard M., Paulsson, Johan Martin |
| Publisher | Harvard University |
| Source Sets | Harvard University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis or Dissertation |
| Rights | closed access |
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