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Ezzat El-Sherif

Assistant Professor

Department of Biology

University of Texas Rio Grande Valley (UTRGV)

Office: ESCNE 1.328

Lab: ESCNE 4.514





Assistant Professor

Department of Biology


Group Leader

Division of Developmental Biology


Postdoc, Mike Levine Lab

University of California Berkeley; USA

University of Texas Rio Grande Valley; USA

Friedrich-Alexander-Universität Erlangen-Nürnberg; Germany.



PhD in Genetics

Kansas State University; USA


MSc in Electronics and Communications Engineering

Cairo University; Egypt


BSc in Electronics and Communications Engineering

Cairo University; Egypt

Extended Biography

Bachelor and Masters @ Cairo University

El-Sherif did his Bachelor and Master studies in Electronics Engineering at Cairo University, Egypt, focusing on computer vision and machine learning. In his master thesis, he used machine learning to recognize handwritten characters (El-Sherif & Abdelazeem, AIPR 2007; Abdelazeem & El-Sherif IJDAR 2008). In addition, he built a system to adaptively find the simplest machine learning classifier cascades to solve computer vision problems, saving computational time (El-Sherif et al, ICPR 2008).

PhD @ Kansas State University

Fascinated, however, with the application of physical and engineering principles in biology, he joined the Genetics PhD program in Kansas State University, United States, where he used both experiments and computational modeling to study embryonic patterning in insects. There he discovered that the division of the anterior-posterior axis of the beetle Tribolium into repeating units (segments) is mediated by a molecular clock (El-Sherif et al., Development 2011), surprisingly more similar to the mode of segmentation found in vertebrates than the more closely related fruit fly Drosophila. This discovery introduced a dramatic shift in the understanding of early embryogenesis in insects. Furthermore, using both modeling and experiments, he demonstrated that the temporal oscillations of the segmentation clock in Tribolium is translated into spatially periodic waves of gene expression by a molecular ’frequency gradient,’ (El-Sherif et al, PLoS Genetics 2013). This constituted a clear demonstration of the utilization of a frequency gradient in embryonic patterning.

Postdoc @ Berkeley

During my postdoctoral studies in Michael Levine’s lab in UC Berkeley, I used state-of-the-art live imaging techniques to dissect the cis-regulatory principles of regulating dynamic gene expressions in the Drosophila embryo. I discovered that the dynamic shifts of segmentation gene expressions in Drosophila are mediated by two different enhancers per gene (El-Sherif and Levine, Current Biology 2016). Mechanistically, this discovery demonstrated that the synergy of multiple enhancers acting at the same time mediates complex gene regulatory computations, rather than (as suggested in previous works) serving as mere redundant ’shadow’ elements. In this work, I carried out the molecular work, live imaging, and built myself all the computational analysis tools required to analyze live imaging data (e.g. cell tracking, signal acquisition, and extraction and analysis of gene expression domains).

Leading my own group in the Division of Developmental Biology in Erlangen, Germany, we further discovered, using an integrated computational and experimental approach, the genetic basis of how drastically different embryonic patterning paradigms like that of segmentation in Drosophila and that of the clock-based mechanism found in ancestral insects and vertebrates could be reconciled  using single computational model that can be easily tuned to account for different modes of development (Zhu, ..., El-Sherif, PNAS 2017; Boos,…, El-Sherif eLife 2018; Rudolf, Zellner, El-Sherif, Dev Bio 2019). Furthermore, we suggested a multi-enhancer regulatory model that explains how waves of gene expression (a common phenomenon in development) are generated, supported by genetic evidence in Tribolium and Drosophila. In this work, most of the computational modeling effort was carried out in my group. We also contributed recently in building a genomic and computational framework to discover and test enhancers in Tribolium (Lai et al., Development 2018) and expanded it using ATAC-seq data, enhancer prediction using machine learning, and sequence conservation for large-scale enhancer discovery in the early Tribolium embryo.

Recently, we have built an imaging-based framework for assaying eRNA for whole gene loci. Coupling this framework with super-resolution imaging of protein condensates, we are working towards an imaging-based equivalent of ChIP-seq (unpublished data; preliminary results will be presented in the full application).

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