Protein-protein and protein-nucleic acid interactions are fundamental to all biological
processes, and comprehensive determination of such interactions in an organism provides a
framework for understanding it as an integrated system. Posttranscriptional regulatory
networks are the current frontier in systems biology: the proteins mediating RNA transactions
comprise only 3-11% of proteomes, yet macromolecular complexes acting on RNA constitute
highly interconnected nodes in the scale-free cellular functional networks, as evidenced by
high-throughput functional mapping of cellular pathways and direct analyses of interactomes.
These networks are presently best understood in Saccharomyces cerevisiae. Yet despite its
well-known advantages and densely populated interactome datasets, the power of yeast as a
model only extends only so far, partly because this system differs from more complex
eukaryotes in a number of key attributes of gene expression, e.g. it lacks the small RNA
pathways.
processes, and comprehensive determination of such interactions in an organism provides a
framework for understanding it as an integrated system. Posttranscriptional regulatory
networks are the current frontier in systems biology: the proteins mediating RNA transactions
comprise only 3-11% of proteomes, yet macromolecular complexes acting on RNA constitute
highly interconnected nodes in the scale-free cellular functional networks, as evidenced by
high-throughput functional mapping of cellular pathways and direct analyses of interactomes.
These networks are presently best understood in Saccharomyces cerevisiae. Yet despite its
well-known advantages and densely populated interactome datasets, the power of yeast as a
model only extends only so far, partly because this system differs from more complex
eukaryotes in a number of key attributes of gene expression, e.g. it lacks the small RNA
pathways.
In Arabidopsis, the situation is essentially opposite, i.e. its gene expression pathways are
rich in biological complexity but its molecular interactions space is grossly undersampled.
Particularly striking is near-absence of biochemically defined macromolecular complexes,
which stands in contrast with high density and quality of genetic data as well as
exponentially growing transcriptome data. The systems-level comprehension of Arabidopsis
biology is greatly limited by such lopsidedness. Therefore, this project seeks to develop and
deploy the genome-enabled tools for identifying the composition and the targets of the key
macromolecular complexes, particularly those acting on RNA. The awesome power of
system biology lies in the iterative application of a cycle of acquiring comprehensive
information about the system's elements, followed by modeling its behavior and generation
and testing the hypotheses explaining it. Integrating the results of experimental probing of
the system’s components with the outcomes of hypotheses testing then allows for synthesis
of new concepts about the system’s behavior and emergent properties, leading to another
round of testable hypotheses. Characterization of the system’s elements empowers this
cycle. Targeting macromolecular complexes is a highly effective way to do so, because such
complexes capture and define network topology as well as its functionality. The
overwhelming degree of chemical and structural diversity of native macromolecular
complexes and their constituents represents a formidable problem. However, it can be
bypassed via genetically engineered affinity tags. Recent advances in affinity tag-assisted
purifications, mass spectrometry, microarray and deep sequencing technologies enable the
development of pipelines for large-scale, high-definition studies of macromolecular
complexes. However, affinity-tagging tools remain suboptimal in plants, representing a major
obstacle to further progress of plant systems biology. Building on our prior success in
affinity tagging in E.coli, Saccharomyces and Arabidopsis we intend to optimize and deliver
such tools for the Arabidopsis community (see Tags link).
rich in biological complexity but its molecular interactions space is grossly undersampled.
Particularly striking is near-absence of biochemically defined macromolecular complexes,
which stands in contrast with high density and quality of genetic data as well as
exponentially growing transcriptome data. The systems-level comprehension of Arabidopsis
biology is greatly limited by such lopsidedness. Therefore, this project seeks to develop and
deploy the genome-enabled tools for identifying the composition and the targets of the key
macromolecular complexes, particularly those acting on RNA. The awesome power of
system biology lies in the iterative application of a cycle of acquiring comprehensive
information about the system's elements, followed by modeling its behavior and generation
and testing the hypotheses explaining it. Integrating the results of experimental probing of
the system’s components with the outcomes of hypotheses testing then allows for synthesis
of new concepts about the system’s behavior and emergent properties, leading to another
round of testable hypotheses. Characterization of the system’s elements empowers this
cycle. Targeting macromolecular complexes is a highly effective way to do so, because such
complexes capture and define network topology as well as its functionality. The
overwhelming degree of chemical and structural diversity of native macromolecular
complexes and their constituents represents a formidable problem. However, it can be
bypassed via genetically engineered affinity tags. Recent advances in affinity tag-assisted
purifications, mass spectrometry, microarray and deep sequencing technologies enable the
development of pipelines for large-scale, high-definition studies of macromolecular
complexes. However, affinity-tagging tools remain suboptimal in plants, representing a major
obstacle to further progress of plant systems biology. Building on our prior success in
affinity tagging in E.coli, Saccharomyces and Arabidopsis we intend to optimize and deliver
such tools for the Arabidopsis community (see Tags link).
Arabidopsis 2010 Program:
Supported by the National Science Foundation
Supported by the National Science Foundation
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