SHOVAL LAGZIEL

Ph.D. candidate

ABOUT

I am a Ph.D. candidate in the Computer Science Faculty at the Technion (Haifa, Israel), under the supervision of Prof. Tomer Shlomi. In my research, I develop computational methods for studying cancer metabolic alterations for early diagnosis and treatment, utilizing data science and machine learning techniques. Towards this end, I collaborate with analytical chemists and biologists in the lab.

During my Ph.D. studies, I did an internship at Core Data Science, Facebook Research, and was a teaching assistant in several graduate courses: Database Systems, Algorithms in Computational Biology, and Internet Networking. Prior to my Ph.D. studies, I did a B.Sc. in Applied Mathematics, graduating at 18 years of age (Bar Ilan University, Mathematically Gifted Youth program), and served as a software developer at the IDF intelligence corps.

I am passionate about solving complex and impactful real-world problems. Learn more about my work by checking out my current projects and past publications.

My Research

Publications

MY RESEARCH

Unlocking Cancer’s Mysteries

Inferring cancer metabolic dependencies

Cancer cells reprogram their metabolism to survive and propagate. Thus, targeting metabolic rewiring in tumors is a promising therapeutic strategy. Genome-wide RNAi and CRISPR screens are powerful tools for identifying genes essential for cancer cell proliferation and survival. Integrating loss-of-function genetic screens with genomics and transcriptomics datasets reveals molecular mechanisms that underlie cancer cell dependence on specific genes; though explaining cell line-specific essentiality of metabolic genes was recently shown to be especially challenging.

Mind your media

Considering that metabolic activity is highly dependent on nutrient availability, analyzing publicly available omics datasets, we have shown that utilizing different media types for culturing cancer cell lines has a major effect on intracellular metabolite levels and metabolic gene dependencies – calling for future analyses of published omics datasets such as that of the CCLE to account for this confounding effect.

Fast and sensitive population wide diagnosis

Early diagnosis of cancer greatly increases the chances for successful treatment of cancer. Cancer cells alter their metabolic activity, therefore, leaving traces that can be utilized for diagnostic purposes. We developed rapid mass-spectrometry based metabolomics methods, enabling a reproducible detection and quantitation of thousands of metabolites within less than one minute per sample. We applied the developed metabolomics method to hundreds of serum samples from cancer patients and healthy controls, obtained from our collaborators, to develop diagnostic models utilizing machine learning techniques.

Exploring drug mechanism of actions

Using an integrated experimental-computational approach, we are working on understanding the metabolic phenotype of cancer cells following drug treatments.

PUBLICATIONS

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Tumor reliance on cytosolic versus mitochondrial one-carbon flux depends on folate availability

Won Dong Lee*, Anna Chiara Pirona*, Boris Sarvin*, Alon Stern, Keren Nevo-Dinur, Elazar Besser, Nikita Sarvin, Shoval Lagziel, Dzmitry Mukha, Shachar Raz, Elina Aizenshtein, Tomer Shlomi,
*contributed equally to this work.

Cell metabolism.

December 15, 2020

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