In practical applications, typical approaches to inferring genetic regulation are based on
macroscopic properties of cells in culture. That is, the culture is turned into lysate, whose
properties are then studied by a variety of techniques, including high-throughput methods
such as microarrays. To the extent that this knowledge of bulk system properties could be
converted to yield some understanding of individual cell organization – such approaches
may be productively used towards inferring the underlying genetic regulatory structure
and function of biological organisms. Alternatively, the development of experimental
methods with single-cell resolution, such as flow-cytometry and high-magnification
microscopy, has allowed for observation of molecular processes at microscopic scale.
There, dynamics of gene expression and regulation may be substantially different from
those that could be observed and/or inferred at macroscopic scale. These differences not
only underscore the importance of accurate modeling and quantitative analysis of genetic
regulatory networks at different scales, but also selecting the appropriate modeling tools
for each application.
We have been developing techniques for utilizing the results of either macroscopic- or
microscopic-scale measurements toward gaining a better understanding of structure and
function in genetic regulatory networks. This talk will discuss two such topics: biological
network identification from bulk-scale microarray data; and certain non-intuitive effects that may arise in gene expression dynamics within individual cells at molecular scales.
Audio (MP3 File, Podcast Ready)
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