Chaimaa Tarzi is a Lecturer in Computing & Games at Teesside University, within the Department of Computing & Games. She is also a final-year PhD student in Computer Science at Teesside University, working at the intersection between community metabolic modeling, computational biology, and machine learning.

Previously, Chaimaa Tarzi worked as a Part-Time Lecturer (PTL) in Computer Science at Teesside University from January 2023 to February 2024. She holds a Master's degree in Software Engineering awarded in 2019 in Rabat, Morocco. Her initial background is in Mathematics and Physics, having completed the "Classe Preparatoire scientifique aux Grandes Ecoles" in Nimes, France, in 2014.

Her research activity, carried out in collaboration with Complement Genomics Ltd, focuses on the intersection of community metabolic modeling, computational biology, and machine learning. Chaimaa's work focuses on metabolic modeling of biofilms in mixed populations, aiming to reconstruct pairwise genome-scale metabolic (GEM) models using metagenome-assembled genomes (MAGs) data.

To address the complexity of community ecosystems, Chaimaa employs various techniques for community-level modeling, refining metabolic networks and exploring pairwise interactions in health and disease states. She also develops machine learning models to enhance predictive performance and data coverage of GEMs, interpret metabolic model output, and improve GEM reconstruction.

In addition to her research on community metabolic modeling, Chaimaa is involved in unraveling the pathophysiological roles of complex proteins in cancer research using Molecular Docking tools and reconstructing protein 3D structures.

Chaimaa's research merges her background in software engineering and mathematical models to propose innovative computational biology and machine learning methodologies. Her work has already been applied to COVID-19 patients and holds promise for other diseases and cancer research.

Overall, Chaimaa Tarzi's interdisciplinary research contributes to the advancement of knowledge in computational biology and metabolic modeling.