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Revision as of 05:06, 17 January 2014
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==Contact Info==
==Contact Info==
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[[Image:
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|thumb|right|Manu
(an artistic interpretation)
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[[Image:
Manu
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*Manu
*Manu
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I am interested in understanding how cell fate - the future identity of a cell - is specified during development. Many molecular processes, such as transcriptional regulation, intracellular signaling, chromatin modification, and RNA regulation, are known to be involved in cell-fate specification. In many developmental systems, such as ''Drosophila'' segmentation and mammalian hematopoiesis, cell-fate specification is largely governed by networks of cross-regulating transcription factors (TFs). This simplification makes the analysis of such developmental systems more tractable than others.
I am interested in understanding how cell fate - the future identity of a cell - is specified during development. Many molecular processes, such as transcriptional regulation, intracellular signaling, chromatin modification, and RNA regulation, are known to be involved in cell-fate specification. In many developmental systems, such as ''Drosophila'' segmentation and mammalian hematopoiesis, cell-fate specification is largely governed by networks of cross-regulating transcription factors (TFs). This simplification makes the analysis of such developmental systems more tractable than others.
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[[Image:Gapexpression.png|thumb|right|Gene expression patterns of the "gap" segmentation genes in the ''Drosophila'' embryo. Source: [http://dx.doi.org/10.1371/journal.pcbi.1000303 Manu ''et al''.] ]]
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[[Image:Hematopoiesis.png|thumb|left|link=http://en.wikipedia.org/wiki/Haematopoiesis|Hematopoiesis. Source: [http://en.wikipedia.org/wiki/File:Hematopoiesis_simple.svg Wikipedia] ]]
Transcriptional networks have a multi-level organization. At the network level, each TF can potentially activate or repress many others in the network. At the level of DNA sequence, the gene expression of each TF is driven by many ''cis''-regulatory modules (CRMs) - 500-2000bp sequences - that cause the gene to be expressed in particular tissues or at specific times. The CRMs themselves have sub-structure - each is composed of several TF binding sites. Understanding cell-fate specification thus requires that we understand the complex behavior of transcriptional networks at each level of detail.
Transcriptional networks have a multi-level organization. At the network level, each TF can potentially activate or repress many others in the network. At the level of DNA sequence, the gene expression of each TF is driven by many ''cis''-regulatory modules (CRMs) - 500-2000bp sequences - that cause the gene to be expressed in particular tissues or at specific times. The CRMs themselves have sub-structure - each is composed of several TF binding sites. Understanding cell-fate specification thus requires that we understand the complex behavior of transcriptional networks at each level of detail.
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[[Image:Multilevel.png|thumb|middle|Multi-level organization of transcriptional networks. ]]
Data-driven mathematical modeling is a powerful tool for gaining insight into how transcriptional networks control cell-fate specification. Most of the underlying biophysical or biochemical parameters of models of gene regulation are hard to measure. To be data driven means that we constrain these parameters from quantitative gene expression data using global nonlinear optimization techniques on a parallel computer. Models inferred in this manner can then be further analyzed or used to simulate conditions not part of the training data to gain insight into network behavior. Below are brief descriptions of my past research and current work.
Data-driven mathematical modeling is a powerful tool for gaining insight into how transcriptional networks control cell-fate specification. Most of the underlying biophysical or biochemical parameters of models of gene regulation are hard to measure. To be data driven means that we constrain these parameters from quantitative gene expression data using global nonlinear optimization techniques on a parallel computer. Models inferred in this manner can then be further analyzed or used to simulate conditions not part of the training data to gain insight into network behavior. Below are brief descriptions of my past research and current work.
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In order to gain a deeper understanding of how these feedback loops worked, we used non-linear systems theory to show that gene expression variation was reduced over time because the developmental trajectory of each cell was attracted to stable steady states or a stable trajectory encoded by the feedback loops. These results provided a theoretical insight into cell type specification in the segmentation system. The complex process of the initial specification of about 60 cells fated to be part of the future thorax and abdomen, involving seven genes and approximately 30 regulatory interactions could be understood in terms of just three dynamical mechanisms: selection of stable states, a bifurcation, and a stable trajectory <cite>Manu09b Gursky11</cite>.
In order to gain a deeper understanding of how these feedback loops worked, we used non-linear systems theory to show that gene expression variation was reduced over time because the developmental trajectory of each cell was attracted to stable steady states or a stable trajectory encoded by the feedback loops. These results provided a theoretical insight into cell type specification in the segmentation system. The complex process of the initial specification of about 60 cells fated to be part of the future thorax and abdomen, involving seven genes and approximately 30 regulatory interactions could be understood in terms of just three dynamical mechanisms: selection of stable states, a bifurcation, and a stable trajectory <cite>Manu09b Gursky11</cite>.
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[[Image:Phaseportrait.png|thumb|left|Phase portrait of the gap gene network in a nucleus of the ''Drosophila'' embryo. Source: [http://dx.doi.org/10.1371/journal.pcbi.1000303 Manu ''et al''.] ]]
'''Stability conferred by ''cis''-regulatory sequences and dosage compensation'''
'''Stability conferred by ''cis''-regulatory sequences and dosage compensation'''
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As a result of the ability to track the secondary effects of the mutation on ''eve''’s targets and viability and the ability to make precise measurements, we made a surprising discovery. We found that ''eve'', an autosomal gene, is expressed in a sex-specific manner even though segmentation itself is not sex specific. Using genetic analysis, we were able to show that this difference arises from the incomplete dosage compensation of an X-linked transcription factor. This work <cite>Manu1305</cite> implies that the upregulation of transcription on the X chromosome is not sufficient for complete dosage compensation, and that additional pathway-specific autosomal regulation is necessary.
As a result of the ability to track the secondary effects of the mutation on ''eve''’s targets and viability and the ability to make precise measurements, we made a surprising discovery. We found that ''eve'', an autosomal gene, is expressed in a sex-specific manner even though segmentation itself is not sex specific. Using genetic analysis, we were able to show that this difference arises from the incomplete dosage compensation of an X-linked transcription factor. This work <cite>Manu1305</cite> implies that the upregulation of transcription on the X chromosome is not sufficient for complete dosage compensation, and that additional pathway-specific autosomal regulation is necessary.
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[[Image:Example.jpg]]
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'''Reverse engineering gene regulation from high-throughput data'''
'''Reverse engineering gene regulation from high-throughput data'''