Merging Modular Molecular Design with High Throughput Screening of Cell Adhesion on Antimicrobial Supramolecular Biomaterials.

A polymer microarray based on the supramolecular ureido-pyrimidinone (UPy) moiety is fabricated to screen antimicrobial materials for their ability to support cell adhesion. UPy-functionalized additives, either cell-adhesive, antimicrobial or control peptides, are used, and investigated in different combinations at different concentrations, resulting in a library of 194 spots. These are characterized on composition and morphology to evaluate the microarray fabrication. Normal human dermal fibroblasts are cultured on the microarrays and cell adhesion to the spots is systematically analyzed. Results demonstrate enhanced cell adhesion on spots with combinations including the antimicrobial peptides. This study clearly proves the power of the high throughput approach in combination with supramolecular molecules, to screen additive libraries for desired biological response.

Efficient reconstruction of biological networks via transitive reduction on general purpose graphics processors.

BACKGROUND: Techniques for reconstruction of biological networks which are based on perturbation experiments often predict direct interactions between nodes that do not exist. Transitive reduction removes such relations if they can be explained by an indirect path of influences. The existing algorithms for transitive reduction are sequential and might suffer from too long run times for large networks. They also exhibit the anomaly that some existing direct interactions are also removed. RESULTS: We develop efficient scalable parallel algorithms for transitive reduction on general purpose graphics processing units for both standard (unweighted) and weighted graphs. Edge weights are regarded as uncertainties of interactions. A direct interaction is removed only if there exists an indirect interaction path between the same nodes which is strictly more certain than the direct one. This is a refinement of the removal condition for the unweighted graphs and avoids to a great extent the erroneous elimination of direct edges. CONCLUSIONS: Parallel implementations of these algorithms can achieve speed-ups of two orders of magnitude compared to their sequential counterparts. Our experiments show that: i) taking into account the edge weights improves the reconstruction quality compared to the unweighted case; ii) it is advantageous not to distinguish between positive and negative interactions since this lowers the complexity of the algorithms from NP-complete to polynomial without loss of quality.

Tens of images can suffice to train neural networks for malignant leukocyte detection.

Convolutional neural networks (CNNs) excel as powerful tools for biomedical image classification. It is commonly assumed that training CNNs requires large amounts of annotated data. This is a bottleneck in many medical applications where annotation relies on expert knowledge. Here, we analyze the binary classification performance of a CNN on two independent cytomorphology datasets as a function of training set size. Specifically, we train a sequential model to discriminate non-malignant leukocytes from blast cells, whose appearance in the peripheral blood is a hallmark of leukemia. We systematically vary training set size, finding that tens of training images suffice for a binary classification with an ROC-AUC over 90%. Saliency maps and layer-wise relevance propagation visualizations suggest that the network learns to increasingly focus on nuclear structures of leukocytes as the number of training images is increased. A low dimensional tSNE representation reveals that while the two classes are separated already for a few training images, the distinction between the classes becomes clearer when more training images are used. To evaluate the performance in a multi-class problem, we annotated single-cell images from a acute lymphoblastic leukemia dataset into six different hematopoietic classes. Multi-class prediction suggests that also here few single-cell images suffice if differences between morphological classes are large enough. The incorporation of deep learning algorithms into clinical practice has the potential to reduce variability and cost, democratize usage of expertise, and allow for early detection of disease onset and relapse. Our approach evaluates the performance of a deep learning based cytology classifier with respect to size and complexity of the training data and the classification task.

Model checking: recent improvements and applications.

Model checking (Baier and Katoen in Principles of model checking, MIT Press, Cambridge, 2008; Clarke et al. in Model checking, MIT Press, Cambridge, 2001) is an automatic technique to formally verify that a given specification of a concurrent system meets given functional properties. Its use has been demonstrated many times over the years. Key characteristics that make the method so appealing are its level of automaticity, its ability to determine the absence of errors in the system (contrary to testing techniques) and the fact that it produces counter-examples when errors are detected, that clearly demonstrate not only that an error is present, but also how the error can be produced. The main drawback of model checking is its limited scalability, and for this reason, research on reducing the computational effort has received much attention over the last decades. Besides the verification of qualitative functional properties, the model checking technique can also be applied for other types of analyses, such as planning and the verification of quantitative properties. We briefly discuss several contributions in the model checking field that address both its scalability and its applicability to perform planning and quantitative analysis. In particular, we introduce six papers selected from the 23rd International SPIN Symposium on Model Checking Software (SPIN 2016).

Reconn: a cytoscape plug-in for exploring and visualizing reactome.

Interface and visualization tools usually provide static representations of biological pathways, which can be a severe limitation: fixed pathway boundaries are used without consensus about the elements that should be included in a particular pathway; one cannot generate new pathways or produce selective views of existing pathways. Also, the tools are not capable of integrating multiple levels that conceptually can be distinguished in biological systems. We present ReConn, an interface and visualization tool for a flexible analysis of large data at multiple biological levels. ReConn (Reactome Connector) is an open source extension to Cytoscape which allows user friendly interaction with the Reactome database. ReConn can use both predefined Reactome pathways as well as generate new pathways. A pathway can be derived by starting from any given metabolite and existing pathways can be extended by adding related reactions. The tool can also retrieve alternative routes between elements of a biological network. Such an option is potentially applicable in the design and analysis of knockout experiments. ReConn displays information about multiple levels of the system in one view. With these dynamic features ReConn addresses all of the above mentioned limitations of the interface tools.