@article{5858, abstract = {Spatial patterns are ubiquitous on the subcellular, cellular and tissue level, and can be studied using imaging techniques such as light and fluorescence microscopy. Imaging data provide quantitative information about biological systems; however, mechanisms causing spatial patterning often remain elusive. In recent years, spatio-temporal mathematical modelling has helped to overcome this problem. Yet, outliers and structured noise limit modelling of whole imaging data, and models often consider spatial summary statistics. Here, we introduce an integrated data-driven modelling approach that can cope with measurement artefacts and whole imaging data. Our approach combines mechanistic models of the biological processes with robust statistical models of the measurement process. The parameters of the integrated model are calibrated using a maximum-likelihood approach. We used this integrated modelling approach to study in vivo gradients of the chemokine (C-C motif) ligand 21 (CCL21). CCL21 gradients guide dendritic cells and are important in the adaptive immune response. Using artificial data, we verified that the integrated modelling approach provides reliable parameter estimates in the presence of measurement noise and that bias and variance of these estimates are reduced compared to conventional approaches. The application to experimental data allowed the parametrization and subsequent refinement of the model using additional mechanisms. Among other results, model-based hypothesis testing predicted lymphatic vessel-dependent concentration of heparan sulfate, the binding partner of CCL21. The selected model provided an accurate description of the experimental data and was partially validated using published data. Our findings demonstrate that integrated statistical modelling of whole imaging data is computationally feasible and can provide novel biological insights.}, author = {Hross, Sabrina and Theis, Fabian J. and Sixt, Michael K and Hasenauer, Jan}, issn = {17425689}, journal = {Journal of the Royal Society Interface}, number = {149}, publisher = {Royal Society Publishing}, title = {{Mechanistic description of spatial processes using integrative modelling of noise-corrupted imaging data}}, doi = {10.1098/rsif.2018.0600}, volume = {15}, year = {2018}, } @article{16, abstract = {We report quantitative evidence of mixing-layer elastic instability in a viscoelastic fluid flow between two widely spaced obstacles hindering a channel flow at Re 1 and Wi 1. Two mixing layers with nonuniform shear velocity profiles are formed in the region between the obstacles. The mixing-layer instability arises in the vicinity of an inflection point on the shear velocity profile with a steep variation in the elastic stress. The instability results in an intermittent appearance of small vortices in the mixing layers and an amplification of spatiotemporal averaged vorticity in the elastic turbulence regime. The latter is characterized through scaling of friction factor with Wi and both pressure and velocity spectra. Furthermore, the observations reported provide improved understanding of the stability of the mixing layer in a viscoelastic fluid at large elasticity, i.e., Wi 1 and Re 1 and oppose the current view of suppression of vorticity solely by polymer additives.}, author = {Varshney, Atul and Steinberg, Victor}, journal = {Physical Review Fluids}, number = {10}, publisher = {American Physical Society}, title = {{Mixing layer instability and vorticity amplification in a creeping viscoelastic flow}}, doi = {10.1103/PhysRevFluids.3.103303}, volume = {3}, year = {2018}, } @article{43, abstract = {The initial amount of pathogens required to start an infection within a susceptible host is called the infective dose and is known to vary to a large extent between different pathogen species. We investigate the hypothesis that the differences in infective doses are explained by the mode of action in the underlying mechanism of pathogenesis: Pathogens with locally acting mechanisms tend to have smaller infective doses than pathogens with distantly acting mechanisms. While empirical evidence tends to support the hypothesis, a formal theoretical explanation has been lacking. We give simple analytical models to gain insight into this phenomenon and also investigate a stochastic, spatially explicit, mechanistic within-host model for toxin-dependent bacterial infections. The model shows that pathogens secreting locally acting toxins have smaller infective doses than pathogens secreting diffusive toxins, as hypothesized. While local pathogenetic mechanisms require smaller infective doses, pathogens with distantly acting toxins tend to spread faster and may cause more damage to the host. The proposed model can serve as a basis for the spatially explicit analysis of various virulence factors also in the context of other problems in infection dynamics.}, author = {Rybicki, Joel and Kisdi, Eva and Anttila, Jani}, journal = {PNAS}, number = {42}, pages = {10690 -- 10695}, publisher = {National Academy of Sciences}, title = {{Model of bacterial toxin-dependent pathogenesis explains infective dose}}, doi = {10.1073/pnas.1721061115}, volume = {115}, year = {2018}, } @article{13, abstract = {We propose a new method for fabricating digital objects through reusable silicone molds. Molds are generated by casting liquid silicone into custom 3D printed containers called metamolds. Metamolds automatically define the cuts that are needed to extract the cast object from the silicone mold. The shape of metamolds is designed through a novel segmentation technique, which takes into account both geometric and topological constraints involved in the process of mold casting. Our technique is simple, does not require changing the shape or topology of the input objects, and only requires off-the- shelf materials and technologies. We successfully tested our method on a set of challenging examples with complex shapes and rich geometric detail. © 2018 Association for Computing Machinery.}, author = {Alderighi, Thomas and Malomo, Luigi and Giorgi, Daniela and Pietroni, Nico and Bickel, Bernd and Cignoni, Paolo}, journal = {ACM Trans. Graph.}, number = {4}, publisher = {ACM}, title = {{Metamolds: Computational design of silicone molds}}, doi = {10.1145/3197517.3201381}, volume = {37}, year = {2018}, } @article{137, abstract = {Fluorescent sensors are an essential part of the experimental toolbox of the life sciences, where they are used ubiquitously to visualize intra- and extracellular signaling. In the brain, optical neurotransmitter sensors can shed light on temporal and spatial aspects of signal transmission by directly observing, for instance, neurotransmitter release and spread. Here we report the development and application of the first optical sensor for the amino acid glycine, which is both an inhibitory neurotransmitter and a co-agonist of the N-methyl-d-aspartate receptors (NMDARs) involved in synaptic plasticity. Computational design of a glycine-specific binding protein allowed us to produce the optical glycine FRET sensor (GlyFS), which can be used with single and two-photon excitation fluorescence microscopy. We took advantage of this newly developed sensor to test predictions about the uneven spatial distribution of glycine in extracellular space and to demonstrate that extracellular glycine levels are controlled by plasticity-inducing stimuli.}, author = {Zhang, William and Herde, Michel and Mitchell, Joshua and Whitfield, Jason and Wulff, Andreas and Vongsouthi, Vanessa and Sanchez Romero, Inmaculada and Gulakova, Polina and Minge, Daniel and Breithausen, Björn and Schoch, Susanne and Janovjak, Harald L and Jackson, Colin and Henneberger, Christian}, journal = {Nature Chemical Biology}, number = {9}, pages = {861 -- 869}, publisher = {Nature Publishing Group}, title = {{Monitoring hippocampal glycine with the computationally designed optical sensor GlyFS}}, doi = {10.1038/s41589-018-0108-2}, volume = {14}, year = {2018}, }