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  • Manu Manu
Manu Manu

Manu Manu

  • Associate Professor, Biology

Contact Info

  • Email: manu.manu@UND.edu
  • Office: 701.777.4671
  • Dept: 701.777.2621

Office Address

Starcher Hall Room 209
10 Cornell St Stop 9019
Grand Forks, ND 58202-9019

Websites

  • Visit Website

Biol 315          Genetics, Fall Semester

Biol 418/590   Systems Biology, Fall Semester (even years)

We are interested in understanding how cell fate—the future identity of a cell—is specified during development. Many molecular processes, such as transcriptional regulation, epigenetics, intracellular signaling, and RNA regulation, are known to be involved in cell-fate specification. Furthermore, cell-fate specification is governed by complex interconnected gene networks. How such complex networks lead to the apparently simple and reliable phenomenon of cell-fate choice is an important problem in biology. Our long-term goal is to determine the rules governing cell-fate specification at the level of the mechanistic details of the regulation of individual genes and, more broadly, at the level of the organization of complex gene networks.

Blood-Cell Development as Model System

Our current work is focussed on cell-fate specification in mouse hematopoiesis. During hematopoiesis, the hematopoietic stem cell, through a series of intermediates with progressively restricted fate potential, gives rise to all the major types of cells found in blood. Hematopoiesis is one of the premier model systems for the study of both gene regulation and epigenetics as well as complex gene regulatory networks (GRNs).

Approaches

We are a highly collaborative and multidisciplinary team developing novel combinations of empirical and computational approaches for understanding gene regulation and GRNs. We utilize functional genomics approaches such as RNA-Seq, ATAC-Seq, Hi-C, including single-cell versions, as well as genome editing to characterize chromatin states and gene expression during differentiation. We define and test the rules of cell-fate specification rigorously by coupling these empirical techniques with predictive computational modeling and machine learning approaches.

Please see our website for more information.

Please see our website for the list of publications.

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