Controlling and reshaping biological reaction-diffusion

Pattern formation by reaction-diffusion mechanisms is of crucial importance for the development and sustenance of all living beings. However, biological model systems so far lack the tools and versatility of the established chemical models. In this thesis, we set out to develop and expand the Min system of Escherichia coli towards becoming a universal model for biological reaction-diffusion in an in vitro setting. To this end, we firstly developed a strategy to control the Min reaction in situ. This was facilitated by incorporating a chemically synthesized azobenzene-moiety into a peptide derived from MinE. This MinE-peptide is capable of stimulating hydrolysis of ATP by MinD. Photoswitching the azobenzene crosslinker allows to also switch alpha-helicity of the peptide and therefore its activity. By periodically activating this peptide photoswitch we found resonance phenomena in the Min reaction. The photoswitch described here could thus be used in many synthetic biology scenarios, but also to learn about Min and biological reaction-diffusion systems. Secondly, we discovered that the Min system can form stationary patterns, which greatly expands the pattern diversity and therefore the phenomena which the Min model can help us understand. Especially when it comes to important decisions in development, such as cell fate or macroscopically visible effects such as fur patterns, stationary patterns are much more prominent than oscillations and waves. The discovery of these patterns also creates many opportunities for applications, especially when combined with the newly found ability of Min proteins to position arbitrary membrane-bound factors. Thirdly, this thesis shows that the Min system's complexity can be reduced even more by substituting MinE with small peptides. A combined theory-experiment approach outlines how pattern forming capabilities are restored in a small MinE-derived peptide either by adding membrane binding or by dimerizing it. This study further highlights how peptides and proteins excel as model morphogens due to their modularity and mutability. Lastly, protocols and resources are more easily available due to a combined method-paper and video that was published in open access. In conclusion, by adding tools and versatility, this thesis introduces great progress towards establishing the in vitro Min system as the ideal model for biological reaction-diffusion

Controlling and reshaping biological reaction-diffusion

Pattern formation by reaction-diffusion mechanisms is of crucial importance for the development and sustenance of all living beings. However, biological model systems so far lack the tools and versatility of the established chemical models. In this thesis, we set out to develop and expand the Min system of Escherichia coli towards becoming a universal model for biological reaction-diffusion in an in vitro setting. To this end, we firstly developed a strategy to control the Min reaction in situ. This was facilitated by incorporating a chemically synthesized azobenzene-moiety into a peptide derived from MinE. This MinE-peptide is capable of stimulating hydrolysis of ATP by MinD. Photoswitching the azobenzene crosslinker allows to also switch alpha-helicity of the peptide and therefore its activity. By periodically activating this peptide photoswitch we found resonance phenomena in the Min reaction. The photoswitch described here could thus be used in many synthetic biology scenarios, but also to learn about Min and biological reaction-diffusion systems. Secondly, we discovered that the Min system can form stationary patterns, which greatly expands the pattern diversity and therefore the phenomena which the Min model can help us understand. Especially when it comes to important decisions in development, such as cell fate or macroscopically visible effects such as fur patterns, stationary patterns are much more prominent than oscillations and waves. The discovery of these patterns also creates many opportunities for applications, especially when combined with the newly found ability of Min proteins to position arbitrary membrane-bound factors. Thirdly, this thesis shows that the Min system's complexity can be reduced even more by substituting MinE with small peptides. A combined theory-experiment approach outlines how pattern forming capabilities are restored in a small MinE-derived peptide either by adding membrane binding or by dimerizing it. This study further highlights how peptides and proteins excel as model morphogens due to their modularity and mutability. Lastly, protocols and resources are more easily available due to a combined method-paper and video that was published in open access. In conclusion, by adding tools and versatility, this thesis introduces great progress towards establishing the in vitro Min system as the ideal model for biological reaction-diffusion

Evaluating the benefits of key-value databases for scientific applications

The convergence of Big Data applications with High-Performance Computing requires new methodologies to store, manage and process large amounts of information. Traditional storage solutions are unable to scale and that results in complex coding strategies. For example, the brain atlas of the Human Brain Project has the challenge to process large amounts of high-resolution brain images. Given the computing needs, we study the effects of replacing a traditional storage system with a distributed Key-Value database on a cell segmentation application. The original code uses HDF5 files on GPFS through an intricate interface, imposing synchronizations. On the other hand, by using Apache Cassandra or ScyllaDB through Hecuba, the application code is greatly simplified. Thanks to the Key-Value data model, the number of synchronizations is reduced and the time dedicated to I/O scales when increasing the number of nodes.This project/research has received funding from the European Unions Horizon 2020 Framework Programme for Research and Innovation under the Speci c Grant Agreement No. 720270 (Human Brain Project SGA1) and the Speci c Grant Agreement No. 785907 (Human Brain Project SGA2). This work has also been supported by the Spanish Government (SEV2015-0493), by the Spanish Ministry of Science and Innovation (contract TIN2015-65316-P), and by Generalitat de Catalunya (contract 2017-SGR-1414).Postprint (author's final draft

Design of biochemical pattern forming systems from minimal motifs

Although molecular self-organization and pattern formation are key features of life, only very few pattern-forming biochemical systems have been identified that can be reconstituted and studied in vitro under defined conditions. A systematic understanding of the underlying mechanisms is often hampered by multiple interactions, conformational flexibility and other complex features of the pattern forming proteins. Because of its compositional simplicity of only two proteins and a membrane, the MinDE system from Escherichia coli has in the past years been invaluable for deciphering the mechanisms of spatiotemporal self-organization in cells. Here, we explored the potential of reducing the complexity of this system even further, by identifying key functional motifs in the effector MinE that could be used to design pattern formation from scratch. In a combined approach of experiment and quantitative modeling, we show that starting from a minimal MinE-MinD interaction motif, pattern formation can be obtained by adding either dimerization or membrane-binding motifs. Moreover, we show that the pathways underlying pattern formation are recruitment-driven cytosolic cycling of MinE and recombination of membrane-bound MinE, and that these differ in their in vivo phenomenology

Numerical Analysis of the Effects of Engineering Measures on the Sediment Transport and Morphodynamics on the Lower Danube

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchive

Some Advice for Psychologists Who Want to Work With Computer Scientists on Big Data

This article is based on conversations from the project “Big Data in Psychological Assessment” (BDPA) funded by the European Union, which was initiated because of the advances in data science and artificial intelligence that offer tremendous opportunities for personnel assessment practice in handling and interpreting this kind of data. We argue that psychologists and computer scientists can benefit from interdisciplinary collaboration. This article aims to inform psychologists who are interested in working with computer scientists about the potentials of interdisciplinary collaboration, as well as the challenges such as differing terminologies, foci of interest, data quality standards, approaches to data analyses, and diverging publication practices. Finally, we provide recommendations preparing psychologists who want to engage in collaborations with computer scientists. We argue that psychologists should proactively approach computer scientists, learn computer scientific fundamentals, appreciate that research interests are likely to converge, and prepare novice psychologists for a data-oriented scientific future

Some Advice for Psychologists Who Want to Work with Computer Scientists on Big Data

This article is based on conversations from the project “Big Data in Psychological Assessment” (BDPA) funded by the European Union, which was initiated because of the advances in data science and artificial intelligence that offer tremendous opportunities for personnel assessment practice in handling and interpreting this kind of data. We argue that psychologists and computer scientists can benefit from interdiscip

Design and Evaluation of an SVM Framework for Scientific Data Applications

Support vector machines (SVMs) are a popular classification method due totheir good accuracy and broad usage domains in scientific applications. Thecomputational complexity is between O(n2) and O(n3) for the number of n trainingsamples. The scalability for larger data sets is therefore a problem of SVMs. Withthe increasing number of large data problems, this disadvantage becomes moreand more significant. In order to overcome these scalability issues, this thesisdesigns and implements a parallel and scalable framework that realizes the cascadeSVM approach including specific improvements. A fundamental speed up andincreased scalability is gained by splitting up the data set into several sub setsthat can be worked on in parallel. The framework is designed to run in modernHigh Performance Computing (HPC) environments, that provide the necessarymassively parallel resources (e.g. large clusters with good node interconnects) tosolve large data problems. The framework however also works on a simple computerfor smaller problems if needed. To keep the interface usable for non-technical savvydomain scientists, Python is used.The standard cascade SVM approach is improved with a standardized file formatand parallel I/O is introduced that both improve the I/O performance, whichbesides computing is also often observed to be a bottleneck for large problems. Inorder to enable enhanced training speed up as well as a better accuracy furtherimprovements such as distance filters and cross-feedback options are realized andevaluated. The resulting improved cascade SVM approach and parallel and scalableframework design is then evaluated on a real world remote sensing data set andcompared to another parallel implementation called pi-SVM. The parallelizationstrategies of these two implementations are different whereby the cascade SVM is adata processing approach, pi-SVM follows primarily an algorithmic-driven approach