Author

M. Mofrad

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Biophysics· 2 Bioengineering· 1 Microbiology· 1

Signature Assays, Organisms & Methods

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Research Focus:cell signaling & trafficking· 2 Approach:deep learning· 2 Organism:bacteria (other)· 1 Assay:bulk RNA-seq· 1 Research Focus:differential expression· 1 Organism:human· 1 Approach:machine learning (classical)· 1 Approach:mechanistic / dynamical model· 1

Frequent Co-Authors

A. Buganza Tepole· 2 shared A. A. Ashkarran· 1 shared A. Barroso· 1 shared A. Bremges· 1 shared A. C. McHardy· 1 shared A. Guha· 1 shared A. Khaledi· 1 shared A. Kola· 1 shared

Activity by Year

2019-2024 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 14 mutual co-author links

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2 shared papersBiophysics

A. Buganza Tepole

Mutual co-authors in cluster

Author profile →

4 papers

Recent·Most cited

Deep Plasma Proteome Profiling by Modulating Single Nanoparticle Protein Corona with Small Molecules

A. A. Ashkarran, H. Gharibi, S. A. Sadeghi, S. M. Modaresi, Q. Wang, T.-J. Lin, G. Yerima, A. Tamadon, M. Sayadi, M. Jafari, Z. Lin, D. Ritz, D. Kakhniashvili, A. Guha, M. Mofrad, , M. Mahmoudi

This paper demonstrates that adding small molecules to plasma enhances nanoparticle protein corona diversity, significantly increasing the number of detectable proteins in plasma proteome profiling.

bioRxiv · Mar 6, 2024 · PreprintBioengineering

Integrin Mechanosensing relies on Pivot-clip Mechanism to Reinforce Cell Adhesion

A. Montes, A. Barroso, W. Wang, G. O'Connell, A. Buganza Tepole, M. Mofrad

This paper identifies a pivot-clip mechanism that enhances 5β1 integrin-fibronectin binding dynamics, reinforcing cell adhesion through conformational changes induced by mechanical forces.

bioRxiv · Dec 13, 2023Biophysics

Multiscale Computational Framework to Investigate Integrin Mechanosensing and Cell Adhesion

A. R. Montes, G. Gutierrez, A. Buganza Tepole, M. Mofrad

This study presents a multiscale computational framework to investigate integrin mechanosensing and cell adhesion by combining molecular dynamics and finite element methods.

bioRxiv · Mar 24, 2023Biophysics

Fighting antimicrobial resistance in Pseudomonas aeruginosa with machine learning-enabled molecular diagnostics

A. Khaledi, A. Weimann, M. Schniederjans, E. Asgari, T.-H. Kuo, A. Oliver, G. Cabot, A. Kola, P. Gastmeier, M. Hogardt, D. Jonas, M. Mofrad, A. Bremges, A. C. McHardy, S. Haeussler

This study uses machine learning on genomic and transcriptomic data to predict antimicrobial resistance in Pseudomonas aeruginosa, achieving high sensitivity for four antibiotics.

bioRxiv · May 24, 2019Microbiology