Author

F. Clarelli

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

Biology Disciplines

Click to filter author papers

Pharmacology & Toxicology· 2 Biochemistry· 1

Signature Assays, Organisms & Methods

Click to filter · or open tag

Research Focus:drug response / drug discovery· 3 Approach:statistical model· 2 Organism:bacteria (other)· 1 Assay:bulk RNA-seq· 1 Research Focus:differential expression· 1 Organism:E. coli· 1 Approach:mechanistic / dynamical model· 1 Research Focus:variant effect prediction· 1

Frequent Co-Authors

P. Abel zur Wiesch· 3 shared T. Cohen· 2 shared A. C. Palmer· 1 shared A. Palmer· 1 shared B. Singh· 1 shared C. Hemez· 1 shared G. Forsdahl· 1 shared H. Weiss· 1 shared

Activity by Year

2019-2024 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 21 mutual co-author links

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

3 shared papersPharmacology & Toxicology

P. Abel zur Wiesch

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

A Mechanistic Approach to Optimize Combination Antibiotic Therapy

F. Clarelli, P. Ankomah, H. Weiss, J. M. Conway, G. Forsdahl, P. Abel zur Wiesch

This paper introduces a mechanistic model to optimize antibiotic combinations by incorporating drug mechanisms and testing synergy in Escherichia coli.

bioRxiv · Jun 10, 2024Pharmacology & Toxicology

Mechanisms of antibiotic action shape the fitness landscapes of resistance mutations

C. Hemez, F. Clarelli, A. C. Palmer, L. Chindelevitch, T. Cohen, P. Abel zur Wiesch

This study models how antibiotic mechanisms affect resistance mutation fitness, finding that drug type has little impact on resistance selection but that heterogeneous binding enables secondary mutations under certain conditions.

bioRxiv · Jun 1, 2020Pharmacology & Toxicology

Drug-target binding quantitatively predicts optimal antibiotic dose levels

F. Clarelli, A. Palmer, B. Singh, M. Storflor, S. Lauksund, T. Cohen, S. Abel, P. Abel zur Wiesch

This paper develops a computational model to predict antibiotic dose-response relationships from drug-target binding parameters and validates it with clinical isolates.

bioRxiv · Jun 27, 2019Biochemistry