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@ARTICLE{Huang:860242,
      author       = {Huang, Yunfei and Schell, Christoph and Huber, Tobias B.
                      and Şimşek, Ahmet Nihat and Hersch, Nils and Merkel,
                      Rudolf and Gompper, Gerhard and Sabass, Benedikt},
      title        = {{T}raction force microscopy with optimized regularization
                      and automated {B}ayesian parameter selection for comparing
                      cells},
      journal      = {Scientific reports},
      volume       = {9},
      number       = {1},
      issn         = {2045-2322},
      address      = {[London]},
      publisher    = {Macmillan Publishers Limited, part of Springer Nature},
      reportid     = {FZJ-2019-01026},
      pages        = {539},
      year         = {2019},
      abstract     = {Adherent cells exert traction forces on to their
                      environment which allows them to migrate, to maintain tissue
                      integrity, and to form complex multicellular structures
                      during developmental morphogenesis. Traction force
                      microscopy (TFM) enables the measurement of traction forces
                      on an elastic substrate and thereby provides quantitative
                      information on cellular mechanics in a perturbation-free
                      fashion. In TFM, traction is usually calculated via the
                      solution of a linear system, which is complicated by
                      undersampled input data, acquisition noise, and large
                      condition numbers for some methods. Therefore, standard TFM
                      algorithms either employ data filtering or regularization.
                      However, these approaches require a manual selection of
                      filter- or regularization parameters and consequently
                      exhibit a substantial degree of subjectiveness. This
                      shortcoming is particularly serious when cells in different
                      conditions are to be compared because optimal noise
                      suppression needs to be adapted for every situation, which
                      invariably results in systematic errors. Here, we
                      systematically test the performance of new methods from
                      computer vision and Bayesian inference for solving the
                      inverse problem in TFM. We compare two classical schemes,
                      L1- and L2-regularization, with three previously untested
                      schemes, namely Elastic Net regularization, Proximal
                      Gradient Lasso, and Proximal Gradient Elastic Net. Overall,
                      we find that Elastic Net regularization, which combines L1
                      and L2 regularization, outperforms all other methods with
                      regard to accuracy of traction reconstruction. Next, we
                      develop two methods, Bayesian L2 regularization and Advanced
                      Bayesian L2 regularization, for automatic, optimal L2
                      regularization. Using artificial data and experimental data,
                      we show that these methods enable robust reconstruction of
                      traction without requiring a difficult selection of
                      regularization parameters specifically for each data set.
                      Thus, Bayesian methods can mitigate the considerable
                      uncertainty inherent in comparing cellular tractions in
                      different conditions.},
      cin          = {ICS-7 / ICS-2},
      ddc          = {600},
      cid          = {I:(DE-Juel1)ICS-7-20110106 / I:(DE-Juel1)ICS-2-20110106},
      pnm          = {552 - Engineering Cell Function (POF3-552)},
      pid          = {G:(DE-HGF)POF3-552},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:30679578},
      UT           = {WOS:000456553400104},
      doi          = {10.1038/s41598-018-36896-x},
      url          = {https://juser.fz-juelich.de/record/860242},
}