@article{HaberfehlnervandeVenvanderBurgetal.2023, author = {Haberfehlner, Helga and van de Ven, Shankara S. and van der Burg, Sven A. and Huber, Florian and Georgievska, Sonja and Aleo, Ignazio and Harlaar, Jaap and Bonouvri{\´e}, Laura A. and van der Krogt, Marjolein M. and Buizer, Annemieke I.}, title = {Towards automated video-based assessment of dystonia in dyskinetic cerebral palsy: A novel approach using markerless motion tracking and machine learning}, series = {Frontiers in Robotics and AI}, volume = {10}, journal = {Frontiers in Robotics and AI}, publisher = {Frontiers}, issn = {2296-9144}, doi = {10.3389/frobt.2023.1108114}, url = {http://nbn-resolving.de/urn:nbn:de:hbz:due62-opus-40592}, year = {2023}, language = {en} } @article{StuhrmannHuberKaes2011, author = {Stuhrmann, Bj{\"o}rn and Huber, Florian and K{\"a}s, Josef}, title = {Robust organizational principles of protrusive biopolymer networks in migrating living cells}, series = {Plos One}, volume = {6}, journal = {Plos One}, number = {1}, publisher = {Public Library of Science (PLoS)}, issn = {1932-6203}, doi = {10.1371/journal.pone.0014471}, url = {http://nbn-resolving.de/urn:nbn:de:hbz:due62-opus-34901}, year = {2011}, abstract = {Cell migration is associated with the dynamic protrusion of a thin actin-based cytoskeletal extension at the cell front, which has been shown to consist of two different substructures, the leading lamellipodium and the subsequent lamellum. While the formation of the lamellipodium is increasingly well understood, organizational principles underlying the emergence of the lamellum are just beginning to be unraveled. We report here on a 1D mathematical model which describes the reaction-diffusion processes of a polarized actin network in steady state, and reproduces essential characteristics of the lamellipodium-lamellum system. We observe a steep gradient in filament lengths at the protruding edge, a local depolymerization maximum a few microns behind the edge, as well as a differential dominance of the network destabilizer ADF/cofilin and the stabilizer tropomyosin. We identify simple and robust organizational principles giving rise to the derived network characteristics, uncoupled from the specifics of any molecular implementation, and thus plausibly valid across cell types. An analysis of network length dependence on physico-chemical system parameters implies that to limit array treadmilling to cellular dimensions, network growth has to be truncated by mechanisms other than aging-induced depolymerization, e.g., by myosin-associated network dissociation at the transition to the cell body. Our work contributes to the analytical understanding of the cytoskeletal extension's bisection into lamellipodium and lamellum and sheds light on how cells organize their molecular machinery to achieve motility.}, language = {en} } @article{PreciadoLopezHuberGrigorievetal.2014, author = {Preciado L{\´o}pez, Magdalena and Huber, Florian and Grigoriev, Ilya and Steinmetz, Michel O. and Akhmanova, Anna and Koenderink, Gijsje H. and Dogterom, Marileen}, title = {Actin-microtubule coordination at growing microtubule ends}, series = {Nature Communications}, volume = {5}, journal = {Nature Communications}, publisher = {Springer Nature}, issn = {2041-1723}, doi = {10.1038/ncomms5778}, url = {http://nbn-resolving.de/urn:nbn:de:hbz:due62-opus-34878}, pages = {4778}, year = {2014}, abstract = {To power dynamic processes in cells, the actin and microtubule cytoskeletons organize into complex structures. Although it is known that cytoskeletal coordination is vital for cell function, the mechanisms by which cross-linking proteins coordinate actin and microtubule activities remain poorly understood. In particular, it is unknown how the distinct mechanical properties of different actin architectures modulate the outcome of actin-microtubule interactions. To address this question, we engineered the protein TipAct, which links growing microtubule ends via end-binding proteins to actin filaments. We show that growing microtubules can be captured and guided by stiff actin bundles, leading to global actin-microtubule alignment. Conversely, growing microtubule ends can transport, stretch and bundle individual actin filaments, thereby globally defining actin filament organization. Our results provide a physical basis to understand actin-microtubule cross-talk, and reveal that a simple cross-linker can enable a mechanical feedback between actin and microtubule organization that is relevant to diverse biological contexts.}, language = {en} } @article{HuberVerhoevenMeijeretal.2020, author = {Huber, Florian and Verhoeven, Stefan and Meijer, Christiaan and Spreeuw, Hanno and Castilla, Efra{\´i}n and Geng, Cunliang and van der Hooft, Justin J. J. and Rogers, Simon and Belloum, Adam and Diblen, Faruk and Spaaks, Jurriaan H.}, title = {matchms - processing and similarity evaluation of mass spectrometry data}, series = {Journal of Open Source Software}, volume = {5}, journal = {Journal of Open Source Software}, number = {52}, publisher = {Cold Spring Harbor Laboratory}, issn = {2475-9066}, doi = {10.21105/joss.02411}, url = {http://nbn-resolving.de/urn:nbn:de:hbz:due62-opus-34856}, pages = {2411}, year = {2020}, language = {en} } @article{HubervanderBurgvanderHooftetal.2021, author = {Huber, Florian and van der Burg, Sven and van der Hooft, Justin J. J. and Ridder, Lars}, title = {MS2DeepScore: a novel deep learning similarity measure to compare tandem mass spectra}, series = {Journal of Cheminformatics}, volume = {13}, journal = {Journal of Cheminformatics}, number = {1}, publisher = {Cold Spring Harbor Laboratory}, issn = {1758-2946}, doi = {10.1186/s13321-021-00558-4}, url = {http://nbn-resolving.de/urn:nbn:de:hbz:due62-opus-34847}, pages = {84}, year = {2021}, abstract = {Mass spectrometry data is one of the key sources of information in many workflows in medicine and across the life sciences. Mass fragmentation spectra are generally considered to be characteristic signatures of the chemical compound they originate from, yet the chemical structure itself usually cannot be easily deduced from the spectrum. Often, spectral similarity measures are used as a proxy for structural similarity but this approach is strongly limited by a generally poor correlation between both metrics. Here, we propose MS2DeepScore: a novel Siamese neural network to predict the structural similarity between two chemical structures solely based on their MS/MS fragmentation spectra. Using a cleaned dataset of > 100,000 mass spectra of about 15,000 unique known compounds, we trained MS2DeepScore to predict structural similarity scores for spectrum pairs with high accuracy. In addition, sampling different model varieties through Monte-Carlo Dropout is used to further improve the predictions and assess the model's prediction uncertainty. On 3600 spectra of 500 unseen compounds, MS2DeepScore is able to identify highly-reliable structural matches and to predict Tanimoto scores for pairs of molecules based on their fragment spectra with a root mean squared error of about 0.15. Furthermore, the prediction uncertainty estimate can be used to select a subset of predictions with a root mean squared error of about 0.1. Furthermore, we demonstrate that MS2DeepScore outperforms classical spectral similarity measures in retrieving chemically related compound pairs from large mass spectral datasets, thereby illustrating its potential for spectral library matching. Finally, MS2DeepScore can also be used to create chemically meaningful mass spectral embeddings that could be used to cluster large numbers of spectra. Added to the recently introduced unsupervised Spec2Vec metric, we believe that machine learning-supported mass spectral similarity measures have great potential for a range of metabolomics data processing pipelines.}, language = {en} } @article{HuberStrehleSchnaussetal.2015, author = {Huber, Florian and Strehle, Dan and Schnauß, J{\"o}rg and K{\"a}s, Josef}, title = {Formation of regularly spaced networks as a general feature of actin bundle condensation by entropic forces}, series = {New Journal of Physics}, volume = {17}, journal = {New Journal of Physics}, number = {4}, publisher = {IOP Publishing}, issn = {1367-2630}, doi = {10.1088/1367-2630/17/4/043029}, url = {http://nbn-resolving.de/urn:nbn:de:hbz:due62-opus-34833}, year = {2015}, language = {en} } @article{HuberSchnaussRoenickeetal.2013, author = {Huber, Florian and Schnauß, J{\"o}rg and R{\"o}nicke, S. and Rauch, P. and M{\"u}ller, K. and F{\"u}tterer, C. and K{\"a}s, Josef}, title = {Emergent complexity of the cytoskeleton: from single filaments to tissue}, series = {Advances in Physics}, volume = {62}, journal = {Advances in Physics}, number = {1}, publisher = {Taylor \& Francis}, issn = {1460-6976}, doi = {10.1080/00018732.2013.771509}, url = {http://nbn-resolving.de/urn:nbn:de:hbz:due62-opus-34812}, pages = {1 -- 112}, year = {2013}, abstract = {Despite their overwhelming complexity, living cells display a high degree of internal mechanical and functional organization which can largely be attributed to the intracellular biopolymer scaffold, the cytoskeleton. Being a very complex system far from thermodynamic equilibrium, the cytoskeleton's ability to organize is at the same time challenging and fascinating. The extensive amounts of frequently interacting cellular building blocks and their inherent multifunctionality permits highly adaptive behavior and obstructs a purely reductionist approach. Nevertheless (and despite the field's relative novelty), the physics approach has already proved to be extremely successful in revealing very fundamental concepts of cytoskeleton organization and behavior. This review aims at introducing the physics of the cytoskeleton ranging from single biopolymer filaments to multicellular organisms. Throughout this wide range of phenomena, the focus is set on the intertwined nature of the different physical scales (levels of complexity) that give rise to numerous emergent properties by means of self-organization or self-assembly.}, language = {en} } @article{BeniddirKangGentaJouveetal.2021, author = {Beniddir, Mehdi A. and Kang, Kyo Bin and Genta-Jouve, Gr{\´e}gory and Huber, Florian and Rogers, Simon and van der Hooft, Justin J. J.}, title = {Advances in decomposing complex metabolite mixtures using substructure- and network-based computational metabolomics approaches}, series = {Natural Product Reports}, volume = {38}, journal = {Natural Product Reports}, number = {11}, publisher = {The Royal Society of Chemistry}, issn = {1460-4752}, doi = {10.1039/D1NP00023C}, url = {http://nbn-resolving.de/urn:nbn:de:hbz:due62-opus-34772}, pages = {1967 -- 1993}, year = {2021}, language = {en} } @article{SchornVerhoevenRidderetal.2021, author = {Schorn, Michelle A. and Verhoeven, Stefan and Ridder, Lars and Huber, Florian and Acharya, Deepa D. and Aksenov, Alexander A. and Aleti, Gajender and Moghaddam, Jamshid Amiri and Aron, Allegra T. and Aziz, Saefuddin and Bauermeister, Anelize and Bauman, Katherine D. and Baunach, Martin and Beemelmanns, Christine and Beman, J. Michael and Berlanga-Clavero, Mar{\´i}a Victoria and Blacutt, Alex A. and Bode, Helge B. and Boullie, Anne and Brejnrod, Asker and Bugni, Tim S. and Calteau, Alexandra and Cao, Liu and Carri{\´o}n, V{\´i}ctor J. and Castelo-Branco, Raquel and Chanana, Shaurya and Chase, Alexander B. and Chevrette, Marc G. and Costa-Lotufo, Leticia V. and Crawford, Jason M. and Currie, Cameron R. and Cuypers, Bart and Dang, Tam and de Rond, Tristan and Demko, Alyssa M. and Dittmann, Elke and Du, Chao and Drozd, Christopher and Dujardin, Jean-Claude and Dutton, Rachel J. and Edlund, Anna and Fewer, David P. and Garg, Neha and Gauglitz, Julia M. and Gentry, Emily C. and Gerwick, Lena and Glukhov, Evgenia and Gross, Harald and Gugger, Muriel and Guill{\´e}n Matus, Dulce G. and Helfrich, Eric J. N. and Hempel, Benjamin-Florian and Hur, Jae-Seoun and Iorio, Marianna and Jensen, Paul R. and Kang, Kyo Bin and Kaysser, Leonard and Kelleher, Neil L. and Kim, Chung Sub and Kim, Ki Hyun and Koester, Irina and K{\"o}nig, Gabriele M. and Leao, Tiago and Lee, Seoung Rak and Lee, Yi-Yuan and Li, Xuanji and Little, Jessica C. and Maloney, Katherine N. and M{\"a}nnle, Daniel and Martin H, Christian and McAvoy, Andrew C. and Metcalf, Willam W. and Mohimani, Hosein and Molina-Santiago, Carlos and Moore, Bradley S. and Mullowney, Michael W. and Muskat, Mitchell and Nothias, Louis-F{\´e}lix and O'Neill, Ellis C. and Parkinson, Elizabeth I. and Petras, Daniel and Piel, J{\"o}rn and Pierce, Emily C. and Pires, Karine and Reher, Raphael and Romero, Diego and Roper, M. Caroline and Rust, Michael and Saad, Hamada and Saenz, Carmen and Sanchez, Laura M. and S{\o}rensen, S{\o}ren Johannes and Sosio, Margherita and S{\"u}ssmuth, Roderich D. and Sweeney, Douglas and Tahlan, Kapil and Thomson, Regan J. and Tobias, Nicholas J. and Trindade-Silva, Amaro E. and van Wezel, Gilles P. and Wang, Mingxun and Weldon, Kelly C. and Zhang, Fan and Ziemert, Nadine and Duncan, Katherine R. and Cr{\"u}semann, Max and Rogers, Simon and Dorrestein, Pieter C. and Medema, Marnix H. and van der Hooft, Justin J. J.}, title = {A community resource for paired genomic and metabolomic data mining}, series = {Nature Chemical Biology}, volume = {17}, journal = {Nature Chemical Biology}, number = {4}, publisher = {Nature}, issn = {1552-4469}, doi = {10.1038/s41589-020-00724-z}, url = {http://nbn-resolving.de/urn:nbn:de:hbz:due62-opus-34708}, pages = {363 -- 368}, year = {2021}, language = {en} } @article{HuberRidderVerhoevenetal.2021, author = {Huber, Florian and Ridder, Lars and Verhoeven, Stefan and Spaaks, Jurriaan H. and Diblen, Faruk and Rogers, Simon and van der Hooft, Justin J. J.}, title = {Spec2Vec: Improved mass spectral similarity scoring through learning of structural relationships}, series = {PLOS Computational Biology}, volume = {17}, journal = {PLOS Computational Biology}, number = {2}, publisher = {Cold Spring Harbor Laboratory}, organization = {PLOS}, issn = {1553-7358}, doi = {10.1371/journal.pcbi.1008724}, url = {http://nbn-resolving.de/urn:nbn:de:hbz:due62-opus-34687}, year = {2021}, abstract = {Spectral similarity is used as a proxy for structural similarity in many tandem mass spectrometry (MS/MS) based metabolomics analyses such as library matching and molecular networking. Although weaknesses in the relationship between spectral similarity scores and the true structural similarities have been described, little development of alternative scores has been undertaken. Here, we introduce Spec2Vec, a novel spectral similarity score inspired by a natural language processing algorithm-Word2Vec. Spec2Vec learns fragmental relationships within a large set of spectral data to derive abstract spectral embeddings that can be used to assess spectral similarities. Using data derived from GNPS MS/MS libraries including spectra for nearly 13,000 unique molecules, we show how Spec2Vec scores correlate better with structural similarity than cosine-based scores. We demonstrate the advantages of Spec2Vec in library matching and molecular networking. Spec2Vec is computationally more scalable allowing structural analogue searches in large databases within seconds.}, language = {en} }