Ali Salehzadeh-Yazdi

Phone: +49 421 200-3549
Mail: asalehzadehyazdi (at) constructor.university
Position: Post-Doctoral Researcher

CV

Ali studied biophysics and received his Ph.D. from University of Tehran in 2014. During his Ph.D., he explored network reconstruction, analysis, and comprehension of complex behavior in biological systems with a focus on metabolic traits.

In 2016 he moved to Germany and as a computational biologist he has been involved in various multidisciplinary projects. Those projects have allowed him to define his main research interest, namely the study of mathematical modeling, data analysis and integration.

Research

During his research career he has acquired extensive experience in

  • Different mathematical modeling approaches; graph theory, constraint-based modeling, logic-based modeling, and dynamical analysis
  • Omica data integration into biological networks
  • Statistical models that are at the basis of the quantitative analysis

By working within two European consortia he has acquired the ability to collaborate with multidisciplinary groups and to coordinate younger researchers.

Recent Publications

Assessing the impact of sampling bias on node centralities in synthetic and biological networks
Ali Salehzadeh-Yazdi , Marc-Thorsten Hütt
npj Systems Biology and Applications, 2025.
Cite

Cite

                    @article{salehzadeh-yazdiAssessingImpactSampling2025,
 abstract = {Centrality measures are crucial for network analysis, offering insights into node importance within complex networks. However, their accuracy is often affected by observational errors and incomplete data. This study investigates how sampling biases systematically impact centrality measures. We simulate six types of biased down-sampling, transitioning networks from dense to sparse states, using the initial network as the ‘ground truth.’ Changes in centrality values reveal the robustness of these measures under various sampling scenarios across synthetic and biological networks. Our results show that in synthetic networks, some sampling methods consistently exhibit higher robustness, particularly in scale-free networks. For biological networks, protein interaction networks are the most robust, followed by metabolite, gene regulatory, and reaction networks. Local centrality measures generally show greater robustness, while global measures are more heterogeneous and less reliable. This study highlights the limitations of centrality measures under sampling biases and informs the development of more robust methodologies.},
 author = {Salehzadeh-Yazdi, Ali and Hütt, Marc-Thorsten},
 copyright = {2025 The Author(s)},
 doi = {10.1038/s41540-025-00526-w},
 file = {Full Text PDF:/home/johannes/.mozilla/firefox/5ah0aj87.default/zotero/storage/PNED283K/Salehzadeh-Yazdi und Hütt - 2025 - Assessing the impact of sampling bias on node cent.pdf:application/pdf},
 issn = {2056-7189},
 journal = {npj Systems Biology and Applications},
 keywords = {Complexity, Computer modelling, Systems analysis},
 language = {en},
 month = {May},
 note = {Publisher: Nature Publishing Group},
 number = {1},
 pages = {1--11},
 title = {Assessing the impact of sampling bias on node centralities in synthetic and biological networks},
 url = {https://www.nature.com/articles/s41540-025-00526-w},
 urldate = {2025-05-20},
 volume = {11},
 year = {2025}
}