Author

Alexander G. Fletcher

Primary ORCID profile (0000-0003-0525-4336 · 1 paper)Unclaimed ORCID profile · follow to get notified of new preprints

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

Same name, identified by ORCID

Alexander G. FletcheriD1 paper

Biology Disciplines

Click to filter author papers

Systems Biology· 2 Biophysics· 1

Signature Assays, Organisms & Methods

Click to filter · or open tag

Approach:experimental assay· 2 Research Focus:benchmarking· 1 Approach:statistical model· 1

Frequent Co-Authors

David J. GavaghaniD· 1 shared Fergus R. CooperiD· 1 shared James M. OsborneiD· 1 shared Jochen KursaweiD· 1 shared Joseph M. Pitt-Francis· 1 shared Philip K. Maini· 1 shared Ruth E. BakeriD· 1 shared Ruth E. Baker· 1 shared

Activity by Year

2016-2017 · peak 2/yr

Co-Author Collaboration Network

8 top collaborators · 8 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

1 shared paperSystems Biology

David J. GavaghanORCID

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Impact of implementation choices on quantitative predictions of cell-based computational models

Jochen Kursawe, Ruth E. Baker, Alexander G. Fletcher

The paper examines how implementation choices in vertex models affect quantitative predictions of tissue dynamics, finding sensitivity to parameters like time step size and cell rearrangement thresholds.

bioRxiv · Apr 6, 2017 · PreprintBiophysics

Comparing individual-based approaches to modelling the self-organization of multicellular tissues

James M. Osborne, Alexander G. Fletcher, Joseph M. Pitt-Francis, Philip K. Maini, David J. Gavaghan

The paper compares different cell-based modelling approaches for simulating multicellular tissue self-organization using a unified framework.

bioRxiv · Sep 9, 2016Systems Biology

Numerical Analysis of the Immersed Boundary Method for Cell-Based Simulation

Fergus R. Cooper, Ruth E. Baker, Alexander G. Fletcher

This paper presents an efficient, open-source implementation of the immersed boundary method for simulating cell population dynamics, providing scaling relationships for accurate parameterization.

bioRxiv · Aug 24, 2016 · PreprintSystems Biology