Time filter

Source Type

News Article | November 28, 2016
Site: www.eurekalert.org

The transcription factor Nanog plays a crucial role in the self-renewal of embryonic stem cells. Previously unclear was how its protein abundance is regulated in the cells. Researchers at the Helmholtz Zentrum München and the Technical University of Munich, working in collaboration with colleagues from ETH Zürich, now report in Cell Systems that the more Nanog there is on hand, the less reproduction there is. Every stem cell researcher knows the protein Nanog* because it ensures that these all-rounders continue to renew. A controversial debate revolved around how the quantity of Nanog protein in the cell is regulated. "So far it was often assumed that Nanog activates itself in order to preserve the pluripotency in embryonic stem cells," explains Dr. Carsten Marr. He heads the Quantitative Single Cell Dynamics research group at the Institute of Computational Biology (ICB) of the Helmholtz Zentrum München. Together with colleagues from ETH Zürich, he and his team have developed an algorithm called STILT (Stochastic Inference on Lineage Trees) that now rebuts this assumption. Using STILT, the scientists evaluated time-resolved protein expression data (already collected in 2015) from individual cells in which Nanog could be detected through fusion with a fluorescence protein. "We compared the Nanog dynamics that were measured in this way with three different models. One of the challenges here was the quantitative comparison of the models, and another was taking stem cell divisions into account in the algorithm," reports first author Dr. Justin Feigelman, who had moved from the Helmholtz Zentrum München to ETH Zürich as a postdoc. "The results show that Nanog is regulated by a so-called negative feedback loop, which means that the more Nanog there is in the cells, the less reproduction there will be." Carried over from the computer to the petri dish In order to check these results, the scientists calculated what would happen if there were an artificial increase in Nanog protein levels. "We then actually succeeded in confirming the hypothesis put forward by STILT in an additional single-cell experiment with increased Nanog," explains study leader Marr. Thanks to their research, the scientists promise a better understanding for stem cell renewal and hope that this knowledge might be useful for medical applications in the future. "We will also be applying STILT to other time-resolved single cell data in the future, which will give us insight into the underlying molecular gene regulation mechanisms," Marr explains. The STILT software is freely available to other scientists on the Internet: http://www. * The name is derived from Tír na nÓg, which is the "Land of Eternal Youth" according to an Irish legend. Among other jobs, Nanog is responsible for pluripotency, which is the ability that stem cells have to develop into almost any other cell type. Background: The data were collected in the framework of an already published study from 2015 that used thousands of individual cells (Filipczyk et al., 2015, Nature Cell Biology). At that time, the ICB scientists were already able to draw unexpected conclusions regarding the regulatory network involving Nanog. http://www. The Helmholtz Zentrum München, the German Research Center for Environmental Health, pursues the goal of developing personalized medical approaches for the prevention and therapy of major common diseases such as diabetes and lung diseases. To achieve this, it investigates the interaction of genetics, environmental factors and lifestyle. The Helmholtz Zentrum München is headquartered in Neuherberg in the north of Munich and has about 2,300 staff members. It is a member of the Helmholtz Association, a community of 18 scientific-technical and medical-biological research centers with a total of about 37,000 staff members. http://www. The Institute of Computational Biology (ICB) develops and applies methods for the model-based description of biological systems, using a data-driven approach by integrating information on multiple scales ranging from single-cell time series to large-scale omics. Given the fast technological advances in molecular biology, the aim is to provide and collaboratively apply innovative tools with experimental groups in order to jointly advance the understanding and treatment of common human diseases. http://www. Technical University of Munich (TUM) is one of Europe's leading research universities, with more than 500 professors, around 10,000 academic and non-academic staff, and 40,000 students. Its focus areas are the engineering sciences, natural sciences, life sciences and medicine, com-bined with economic and social sciences. TUM acts as an entrepreneurial university that promotes talents and creates value for society. In that it profits from having strong partners in science and industry. It is represented worldwide with a campus in Singapore as well as offices in Beijing, Brussels, Cairo, Mumbai, San Francisco, and São Paulo. Nobel Prize winners and inventors such as Rudolf Diesel, Carl von Linde, and Rudolf Mößbauer have done research at TUM. In 2006 and 2012 it won recognition as a German "Excellence University." In international rankings, TUM regularly places among the best universities in Germany. http://www. Freedom and individual responsibility, entrepreneurial spirit and open-mindedness: ETH Zurich stands on a bedrock of true Swiss values. Our university for science and technology dates back to the year 1855, when the founders of modern-day Switzerland created it as a centre of innovation and knowledge. At ETH Zurich, students discover an ideal environment for independent thinking, researchers a climate which inspires top performance. Situated in the heart of Europe, yet forging connections all over the world, ETH Zurich is pioneering effective solutions to the global challenges of today and tomorrow. Some 500 professors teach around 20,000 students - including 4,000 doctoral students - from over 120 countries. Their collective research embraces many disciplines: natural sciences and engineering sciences, architecture, mathematics, system-oriented natural sciences, as well as management and social sciences. The results and innovations produced by ETH researchers are channelled into some of Switzerland's most high-tech sectors: from computer science through to micro- and nanotechnology and cutting-edge medicine. Every year ETH registers around 90 patents and 200 inventions on average. Since 1996, the university has produced a total of 330 commercial spin-offs. ETH also has an excellent reputation in scientific circles: 21 Nobel laureates have studied, taught or researched here, and in international league tables ETH Zurich regularly ranks as one of the world's top universities. http://www. Contact for the media: Department of Communication, Helmholtz Zentrum München - German Research Center for Environmental Health, Ingolstädter Landstr. 1, 85764 Neuherberg - Tel. +49 89 3187 2238 - Fax: +49 89 3187 3324 - E-mail: presse@helmholtz-muenchen.de


News Article | November 7, 2016
Site: www.eurekalert.org

Certain proteins in the blood of children can predict incipient type 1 diabetes, even before the first symptoms appear. A team of scientists at the Helmholtz Zentrum München, partners in the German Center for Diabetes Research (DZD), reported these findings in the Diabetologia journal. The work was based on two large studies that are intended to explain the mechanisms behind the development of type 1 diabetes (BABYDIAB and BABYDIET*). The study participants are children who have a first-degree relative with type 1 diabetes and who consequently have an increased risk of developing the disease due to the familial predisposition. This autoimmune process does not develop from one day to the next, however: Often the young patients go through longer asymptomatic preliminary stages that see the formation of the first antibodies against the child's own insulin-producing cells in the pancreas; these are the so-called autoantibodies. Biomarkers that indicate whether and when this is the case and how quickly the clinical symptoms will appear could significantly improve the treatment of patients at-risk. A team of scientists, led by Dr. Stefanie Hauck, head of the Research Unit Protein Science and the Core Facility Proteomics, and Prof. Dr. Anette-G. Ziegler, Director of the Institute of Diabetes Research (IDF) at the Helmholtz Zentrum München, analyzed blood samples from 30 children with autoantibodies who had developed type 1 diabetes either very rapidly or with a very long delay. The researchers compared the data with data on children who displayed neither autoantibodies nor diabetes symptoms. In a second step with samples from another 140 children, the researchers confirmed the protein composition differences that they found in this approach. "Altogether, we were able to identify 41 peptides** from 26 proteins that distinguish children with autoantibodies from those without," reports Dr. Christine von Toerne, a scientist in the Research Unit Protein Science who shared first authorship of the work with Michael Laimighofer, a doctoral candidate in Jan Krumsiek's junior research group at the Institute of Computational Biology. Striking in their evaluations: A large number of these proteins are associated with lipid metabolism. "Two peptides -- from the proteins apolipoprotein M and apolipoprotein C-IV -- were particularly conspicuous and were especially differently expressed in the two groups," von Toerne adds. In autoantibody-positive children, it was furthermore possible to reach a better estimate of the speed of the diabetes development using the peptide concentrations of three proteins (hepatocyte growth factor activator, complement factor H and ceruloplasmin) in combination with the age of the particular child. The researchers are confident that the protein signatures they have discovered will be helpful as biomarkers for future diagnostics. "The progression of type 1 diabetes into a clinical disease takes place over a period of time that varies from individual to individual and that at this time is insufficiently predictable," explains Prof. Ziegler. "The biomarkers that we have identified allow a more precise classification of this presymptomatic stage and they are relatively simple to acquire from blood samples." * The BABYDIAB study, which was established in 1989 as the world's first diabetes prospective birth cohort, is a pioneering study in the field of type 1 diabetes pathogenesis research. More than 1650 children of parents with type 1 diabetes have been observed since their birth, or for a period of 25 years. The objective of the BABYDIAB study is to determine when islet autoantibodies first appear, which genetic factors and environmental factors influence their development, and which characteristics of the autoantibodies are most strongly associated with the development of type 1 diabetes. The participants in the study are reexamined every three years by means of blood samples and questionnaires. The BABYDIET is examining the influence of food containing gluten on the development of type 1 diabetes. Of the 2,441 children included in the two studies, so far 124 have developed a precursor to diabetes. 82 of these meanwhile display a clinical disease (as of November 2014). ** Peptides are molecules that, like proteins, are constructed from amino acids. However, they are smaller and to some extent result as fragments during protein breakdown. The transition is therefore relatively fluid. The study was financed by the Juvenile Diabetes Research Foundation (JDRF), which has headquarters in the USA. The number of new cases of type 1 diabetes each year continues to rise. New immunotherapeutic approaches aim at stopping this development. A precise assessment of the individual stage of disease development is an important criterion for the targeted use of new treatments. The described study shows that children already display proteomic changes in the blood during the presymptomatic stage. This information allows a better assessment of the time until clinical manifestation of the disease. Recently scientists in the Protein Science Research Unit were also able to identify biomarkers for the precursor to type 2 diabetes: https:/ Von Toerne, C. & Laimighofer, M. et al. (2016): Peptide serum markers in islet autoantibody-positive children. Diabetologia, doi: 10.1007/s00125-016-4150-x http://link. The presence of certain proteins in blood samples can predict incipient type 1 diabetes. The researchers identify these in their measurements using so-called peptide peaks (see selection in red). Source: Helmholtz Zentrum München The Helmholtz Zentrum München, the German Research Center for Environmental Health, pursues the goal of developing personalized medical approaches for the prevention and therapy of major common diseases such as diabetes and lung diseases. To achieve this, it investigates the interaction of genetics, environmental factors and lifestyle. The Helmholtz Zentrum München is headquartered in Neuherberg in the north of Munich and has about 2,300 staff members. It is a member of the Helmholtz Association, a community of 18 scientific-technical and medical-biological research centers with a total of about 37,000 staff members. http://www. The independent Research Unit Protein Science (PROT) investigates the composition of protein complexes and their integration into cellular processes and protein networks. One focus is the analysis of the interaction of genetic variance and environmental factors in neurodegenerative and metabolic diseases. The aim of this research is to identify biological systems and disease-associated disorders on a systemic level, thus contributing to a molecular understanding of diseases. http://www. The Core Facility Proteomics is an instrumental analysis platform at the Helmholtz Zentrum München. It provides interested research groups with access to comprehensive proteome analyses conducted with highly sensitive mass spectrometers. The portfolio ranges from technical and scientific consultation during project design and sample preparation to the development of optimized analysis methods to actual sample measurement and data evaluation. The Institute of Diabetes Research (IDF) focuses on the pathogenesis and prevention of type 1 diabetes and type 2 diabetes and the long-term effects of gestational diabetes. A major project is the development of an insulin vaccination against type 1 diabetes. The IDF conducts long-term studies to examine the link between genes, environmental factors and the immune system for the pathogenesis of type 1 diabetes. Findings of the BABYDIAB study, which was established in 1989 as the world's first prospective birth cohort study, identified risk genes and antibody profiles. These permit predictions to be made about the pathogenesis and onset of type 1 diabetes and will lead to changes in the classification and the time of diagnosis. The IDF is part of the Helmholtz Diabetes Center (HDC). http://www. The German Center for Diabetes Research (DZD) is a national association that brings together experts in the field of diabetes research and combines basic research, translational research, epidemiology and clinical applications. The aim is to develop novel strategies for personalized prevention and treatment of diabetes. Members are Helmholtz Zentrum München - German Research Center for Environmental Health, the German Diabetes Center in Düsseldorf, the German Institute of Human Nutrition in Potsdam-Rehbrücke, the Paul Langerhans Institute Dresden of the Helmholtz Zentrum München at the University Medical Center Carl Gustav Carus of the TU Dresden and the Institute for Diabetes Research and Metabolic Diseases of the Helmholtz Zentrum München at the Eberhard-Karls-University of Tuebingen together with associated partners at the Universities in Heidelberg, Cologne, Leipzig, Lübeck and Munich. https:/


News Article | November 8, 2016
Site: www.sciencedaily.com

Certain proteins in the blood of children can predict incipient type 1 diabetes, even before the first symptoms appear. A team of scientists at the Helmholtz Zentrum München, partners in the German Center for Diabetes Research (DZD), reported these findings in the Diabetologia journal. The work was based on two large studies that are intended to explain the mechanisms behind the development of type 1 diabetes (BABYDIAB and BABYDIET*). The study participants are children who have a first-degree relative with type 1 diabetes and who consequently have an increased risk of developing the disease due to the familial predisposition. This autoimmune process does not develop from one day to the next, however: Often the young patients go through longer asymptomatic preliminary stages that see the formation of the first antibodies against the child's own insulin-producing cells in the pancreas; these are the so-called autoantibodies. Biomarkers that indicate whether and when this is the case and how quickly the clinical symptoms will appear could significantly improve the treatment of patients at-risk. A team of scientists, led by Dr. Stefanie Hauck, head of the Research Unit Protein Science and the Core Facility Proteomics, and Prof. Dr. Anette-G. Ziegler, Director of the Institute of Diabetes Research (IDF) at the Helmholtz Zentrum München, analyzed blood samples from 30 children with autoantibodies who had developed type 1 diabetes either very rapidly or with a very long delay. The researchers compared the data with data on children who displayed neither autoantibodies nor diabetes symptoms. In a second step with samples from another 140 children, the researchers confirmed the protein composition differences that they found in this approach. "Altogether, we were able to identify 41 peptides from 26 proteins that distinguish children with autoantibodies from those without," reports Dr. Christine von Toerne, a scientist in the Research Unit Protein Science who shared first authorship of the work with Michael Laimighofer, a doctoral candidate in Jan Krumsiek's junior research group at the Institute of Computational Biology. Striking in their evaluations: A large number of these proteins are associated with lipid metabolism. "Two peptides -- from the proteins apolipoprotein M and apolipoprotein C-IV -- were particularly conspicuous and were especially differently expressed in the two groups," von Toerne adds. In autoantibody-positive children, it was furthermore possible to reach a better estimate of the speed of the diabetes development using the peptide concentrations of three proteins (hepatocyte growth factor activator, complement factor H and ceruloplasmin) in combination with the age of the particular child. The researchers are confident that the protein signatures they have discovered will be helpful as biomarkers for future diagnostics. "The progression of type 1 diabetes into a clinical disease takes place over a period of time that varies from individual to individual and that at this time is insufficiently predictable," explains Prof. Ziegler. "The biomarkers that we have identified allow a more precise classification of this presymptomatic stage and they are relatively simple to acquire from blood samples."


News Article | February 21, 2017
Site: www.eurekalert.org

Today, cell biology is no longer limited to static states but also attempts to understand the dynamic development of cell populations. One example is the generation of different types of blood cells from their precursors, the hematopoietic stem cells. "A hematopoietic stem cell's decision to become a certain cell type cannot be observed. At this time, it is only possible to verify the decision retrospectively with cell surface markers," explains Dr. Carsten Marr, head of the Quantitative Single Cell Dynamics Research Group at the Helmholtz Zentrum München's Institute of Computational Biology (ICB). He and his team have now developed an algorithm that can predict the decision in advance. So-called Deep Learning is the key. "Deep Neural Networks play a major role in our method," says Marr. "Our algorithm classifies light microscopic images and videos of individual cells by comparing these data with past experience from the development of such cells. In this way, the algorithm 'learns' how certain cells behave." Specifically, the researchers examined hematopoietic stem cells that were filmed under the microscope in the lab of Timm Schroeder at ETH Zurich.* Using the information on appearance and speed, the software was able to 'memorize' the corresponding behaviour patterns and then make its prediction. "Compared to conventional methods, such as fluorescent antibodies against certain surface proteins, we know how the cells will decide three cell generations earlier," reports ICB scientist Dr. Felix Buggenthin, joint first author of the study together with Dr. Florian Büttner. But what is the benefit of this look into the future? As study leader Marr explains, "Since we now know which cells will develop in which way, we can isolate them earlier than before and examine how they differ at a molecular level. We want to use this information to understand how the choices are made for particular developmental traits." In the future, the focus will expand beyond hematopoietic stem cells. "We are using Deep Learning for very different problems with sufficiently large data records," explains Prof. Dr. Dr. Fabian Theis, ICB director and holder of the Mathematical Modelling of Biological Systems Chair at the TUM, who led the study together with Carsten Marr. "For example, we use very similar algorithms to analyse disease-associated patterns in the genome and identify biomarkers in clinical cell screens." * The study described here is the latest result of a close cooperation between the ICB scientists and Prof. Dr. Timm Schroeder from the Department of Biosystems Science and Engineering at ETH Zurich in Basel, who previously worked at the Helmholtz Zentrum in Munich. In July 2016, the scientists jointly introduced a software in 'Nature Biotechnolgy' that allows to observe individual cells over many days and simultaneously measure their molecular properties. They also published a study in 'Nature' that already dealt with the development of hematopoietic stem cells. Using time-lapse microscopy, the researchers were able to observe the maturation of living hematopoietic stem cells with high precision while also quantifying certain proteins. Deep Learning algorithms simulate the learning processes in people using artificial neural networks. The principle functions particularly well when large quantities of data (Big Data) are available for training. Image recognition is one of Deep Learning's strengths. More decision layers are placed between the input (here, the cell image data) and the output (here, the prediction of the cell development) than usually found in neuronal networks, which is why the term "deep" is used. Buggenthin, F. et al. (2017): Prospective identification of hematopoietic lineage choice by deep learning. Nature Methods, DOI: 10.1038/nmeth.4182 The Helmholtz Zentrum München, the German Research Center for Environmental Health, pursues the goal of developing personalized medical approaches for the prevention and therapy of major common diseases such as diabetes and lung diseases. To achieve this, it investigates the interaction of genetics, environmental factors and lifestyle. The Helmholtz Zentrum München is headquartered in Neuherberg in the north of Munich and has about 2,300 staff members. It is a member of the Helmholtz Association, a community of 18 scientific-technical and medical-biological research centers with a total of about 37,000 staff members. http://www. The Institute of Computational Biology (ICB) develops and applies methods for the model-based description of biological systems, using a data-driven approach by integrating information on multiple scales ranging from single-cell time series to large-scale omics. Given the fast technological advances in molecular biology, the aim is to provide and collaboratively apply innovative tools with experimental groups in order to jointly advance the understanding and treatment of common human diseases. http://www. Technical University of Munich (TUM) is one of Europe's leading research universities, with more than 500 professors, around 10,000 academic and non-academic staff, and 40,000 students. Its focus areas are the engineering sciences, natural sciences, life sciences and medicine, com-bined with economic and social sciences. TUM acts as an entrepreneurial university that promotes talents and creates value for society. In that it profits from having strong partners in science and industry. It is represented worldwide with a campus in Singapore as well as offices in Beijing, Brussels, Cairo, Mumbai, San Francisco, and São Paulo. Nobel Prize winners and inventors such as Rudolf Diesel, Carl von Linde, and Rudolf Mößbauer have done research at TUM. In 2006 and 2012 it won recognition as a German "Excellence University." In international rankings, TUM regularly places among the best universities in Germany. http://www. Freedom and individual responsibility, entrepreneurial spirit and open-mindedness: ETH Zurich stands on a bedrock of true Swiss values. Our university for science and technology dates back to the year 1855, when the founders of modern-day Switzerland created it as a centre of innovation and knowledge. At ETH Zurich, students discover an ideal environment for independent thinking, researchers a climate which inspires top performance. Situated in the heart of Europe, yet forging connections all over the world, ETH Zurich is pioneering effective solutions to the global challenges of today and tomorrow. Some 500 professors teach around 20,000 students - including 4,000 doctoral students - from over 120 countries. Their collective research embraces many disciplines: natural sciences and engineering sciences, architecture, mathematics, system-oriented natural sciences, as well as management and social sciences. The results and innovations produced by ETH researchers are channelled into some of Switzerland's most high-tech sectors: from computer science through to micro- and nanotechnology and cutting-edge medicine. Every year ETH registers around 90 patents and 200 inventions on average. Since 1996, the university has produced a total of 330 commercial spin-offs. ETH also has an excellent reputation in scientific circles: 21 Nobel laureates have studied, taught or researched here, and in international league tables ETH Zurich regularly ranks as one of the world's top universities. http://www.


News Article | February 21, 2017
Site: phys.org

What are they going to be? Hematopoietic stem cells under the microscope: New methods are helping the Helmholtz scientists to predict how they will develop. Credit: Helmholtz Zentrum München Autonomous driving, automatic speech recognition, and the game Go: Deep Learning is generating more and more public awareness. Scientists at the Helmholtz Zentrum München and their partners at ETH Zurich and the Technical University of Munich (TUM) have now used it to determine the development of hematopoietic stem cells in advance. In 'Nature Methods' they describe how their software predicts the future cell type based on microscopy images. Today, cell biology is no longer limited to static states but also attempts to understand the dynamic development of cell populations. One example is the generation of different types of blood cells from their precursors, the hematopoietic stem cells. "A hematopoietic stem cell's decision to become a certain cell type cannot be observed. At this time, it is only possible to verify the decision retrospectively with cell surface markers," explains Dr. Carsten Marr, head of the Quantitative Single Cell Dynamics Research Group at the Helmholtz Zentrum München's Institute of Computational Biology (ICB). He and his team have now developed an algorithm that can predict the decision in advance. So-called Deep Learning is the key. "Deep Neural Networks play a major role in our method," says Marr. "Our algorithm classifies light microscopic images and videos of individual cells by comparing these data with past experience from the development of such cells. In this way, the algorithm 'learns' how certain cells behave." Specifically, the researchers examined hematopoietic stem cells that were filmed under the microscope in the lab of Timm Schroeder at ETH Zurich. Using the information on appearance and speed, the software was able to 'memorize' the corresponding behaviour patterns and then make its prediction. "Compared to conventional methods, such as fluorescent antibodies against certain surface proteins, we know how the cells will decide three cell generations earlier," reports ICB scientist Dr. Felix Buggenthin, joint first author of the study together with Dr. Florian Büttner. But what is the benefit of this look into the future? As study leader Marr explains, "Since we now know which cells will develop in which way, we can isolate them earlier than before and examine how they differ at a molecular level. We want to use this information to understand how the choices are made for particular developmental traits." In the future, the focus will expand beyond hematopoietic stem cells. "We are using Deep Learning for very different problems with sufficiently large data records," explains Prof. Dr. Dr. Fabian Theis, ICB director and holder of the Mathematical Modelling of Biological Systems Chair at the TUM, who led the study together with Carsten Marr. "For example, we use very similar algorithms to analyse disease-associated patterns in the genome and identify biomarkers in clinical cell screens." Explore further: Enough is enough—stem cell factor Nanog knows when to slow down More information: Buggenthin, F. et al. (2017): Prospective identification of hematopoietic lineage choice by deep learning. Nature Methods, DOI: 10.1038/nmeth.4182


Hiepe P.,Friedrich - Schiller University of Jena | Herrmann K.-H.,Friedrich - Schiller University of Jena | Gullmar D.,Friedrich - Schiller University of Jena | Ros C.,Friedrich - Schiller University of Jena | And 5 more authors.
NMR in Biomedicine | Year: 2014

In the past, spin-echo (SE) echo planar imaging(EPI)-based diffusion tensor imaging (DTI) has been widely used to study the fiber structure of skeletal muscles in vivo. However, this sequence has several shortcomings when measuring restricted diffusion in small animals, such as its sensitivity to susceptibility-related distortions and a relatively short applicable diffusion time. To address these limitations, in the current work, a stimulated echo acquisition mode (STEAM) MRI technique, in combination with fast low-angle shot (FLASH) readout (turbo-STEAM MRI), was implemented and adjusted for DTI in skeletal muscles. Signal preparation using stimulated echoes enables longer effective diffusion times, and thus the detection of restricted diffusion within muscular tissue with intracellular distances up to 100μm. Furthermore, it has a reduced penalty for fast T2 muscle signal decay, but at the expense of 50% signal loss compared with a SE preparation. Turbo-STEAM MRI facilitates high-resolution DTI of skeletal muscle without introducing susceptibility-related distortions. To demonstrate its applicability, we carried out rabbit in vivo measurements on a human whole-body 3 T scanner. DTI parameters of the shank muscles were extracted, including the apparent diffusion coefficient, fractional anisotropy, eigenvalues and eigenvectors. Eigenvectors were used to calculate maps of structural parameters, such as the planar index and the polar coordinates θ and φ{symbol} of the largest eigenvector. These parameters were compared between three muscles. θ and φ{symbol} showed clear differences between the three muscles, reflecting different pennation angles of the underlying fiber structures. Fiber tractography was performed to visualize and analyze the architecture of skeletal pennate muscles. Optimization of tracking parameters and utilization of T2-weighted images for improved muscle boundary detection enabled the determination of additional parameters, such as the mean fiber length. The presented results support the applicability of turbo-STEAM MRI as a promising method for quantitative DTI analysis and fiber tractography in skeletal muscles. © 2013 John Wiley & Sons, Ltd.


Jayadeepa R.M.,Institute of Computational Biology | Sharma S.,Ganpat University
Current Computer-Aided Drug Design | Year: 2011

Taking into consideration the high importance of the drug target 5-α-reductase (5αR) in prostate cancer in this work we are going first to review previous works and discuss works related to the computer aided drug design of 5αR inhibitors. We report new results in the in silico screening of natural 5αR inhibitors. Traditionally, drugs were discovered by testing compounds synthesized in time consuming multi-step processes against a battery of in vivo biological screens. Promising compounds were then further studied in development, where their pharmacokinetic properties, metabolism and potential toxicity were investigated. Here we present a study on herbal lead compounds and their potential binding affinity to the effectors molecules of major disease like Prostate Cancer. Clinical studies demonstrate a positive correlation between the extent of 5αR type 2 (5αR2) and malignant progression of precancerous lesions in prostate. Therefore, identification of effective, well-tolerated 5αR inhibitors represents a rational chemo preventive strategy. This study has investigated the effects of naturally occurring non-protein compounds berberine and monocaffeyltartaric acid that inhibits 5αR type2. Our results reveal that these compounds use less energy to bind to 5αR and inhibit its activity. Their high ligand binding affinity to 5αR introduce the prospect for their use in chemopreventive applications; in addition they are freely available natural compounds that can be safely used to prevent prostate cancer. © 2011 Bentham Science Publishers.


Hahn K.,Institute of Computational Biology | Massopust P.R.,TU Munich | Prigarin S.,Russian Academy of Sciences
BMC Bioinformatics | Year: 2016

Background: Networks or graphs play an important role in the biological sciences. Protein interaction networks and metabolic networks support the understanding of basic cellular mechanisms. In the human brain, networks of functional or structural connectivity model the information-flow between cortex regions. In this context, measures of network properties are needed. We propose a new measure, Ndim, estimating the complexity of arbitrary networks. This measure is based on a fractal dimension, which is similar to recently introduced box-covering dimensions. However, box-covering dimensions are only applicable to fractal networks. The construction of these network-dimensions relies on concepts proposed to measure fractality or complexity of irregular sets in n Results: The network measure Ndim grows with the proliferation of increasing network connectivity and is essentially determined by the cardinality of a maximum k-clique, where k is the characteristic path length of the network. Numerical applications to lattice-graphs and to fractal and non-fractal graph models, together with formal proofs show, that Ndim estimates a dimension of complexity for arbitrary graphs. Box-covering dimensions for fractal graphs rely on a linear log-log plot of minimum numbers of covering subgraph boxes versus the box sizes. We demonstrate the affinity between Ndim and the fractal box-covering dimensions but also that Ndim extends the concept of a fractal dimension to networks with non-linear log-log plots. Comparisons of Ndim with topological measures of complexity (cost and efficiency) show that Ndim has larger informative power. Three different methods to apply Ndim to weighted networks are finally presented and exemplified by comparisons of functional brain connectivity of healthy and depressed subjects. Conclusion: We introduce a new measure of complexity for networks. We show that Ndim has the properties of a dimension and overcomes several limitations of presently used topological and fractal complexity-measures. It allows the comparison of the complexity of networks of different type, e.g., between fractal graphs characterized by hub repulsion and small world graphs with strong hub attraction. The large informative power and a convenient computational CPU-time for moderately sized networks may make Ndim a valuable tool for the analysis of biological networks. © 2016 Hahn et al.


News Article | November 25, 2016
Site: phys.org

STILT generates simulated protein expression of dividing cells based on measured data and a dynamic model. Credit: Helmholtz Zentrum München The transcription factor Nanog plays a crucial role in the self-renewal of embryonic stem cells. Previously unclear was how its protein abundance is regulated in the cells. Researchers at the Helmholtz Zentrum München and the Technical University of Munich, working in collaboration with colleagues from ETH Zürich, now report in Cell Systems that the more Nanog there is on hand, the less reproduction there is. Every stem cell researcher knows the protein Nanog because it ensures that these all-rounders continue to renew. A controversial debate revolved around how the quantity of Nanog protein in the cell is regulated. "So far it was often assumed that Nanog activates itself in order to preserve the pluripotency in embryonic stem cells," explains Dr. Carsten Marr. He heads the Quantitative Single Cell Dynamics research group at the Institute of Computational Biology (ICB) of the Helmholtz Zentrum München. Together with colleagues from ETH Zürich, he and his team have developed an algorithm called STILT (Stochastic Inference on Lineage Trees) that now rebuts this assumption. Using STILT, the scientists evaluated time-resolved protein expression data (already collected in 2015) from individual cells in which Nanog could be detected through fusion with a fluorescence protein. "We compared the Nanog dynamics that were measured in this way with three different models. One of the challenges here was the quantitative comparison of the models, and another was taking stem cell divisions into account in the algorithm," reports first author Dr. Justin Feigelman, who had moved from the Helmholtz Zentrum München to ETH Zürich as a postdoc. "The results show that Nanog is regulated by a so-called negative feedback loop, which means that the more Nanog there is in the cells, the less reproduction there will be." Carried over from the computer to the petri dish In order to check these results, the scientists calculated what would happen if there were an artificial increase in Nanog protein levels. "We then actually succeeded in confirming the hypothesis put forward by STILT in an additional single-cell experiment with increased Nanog," explains study leader Marr. Thanks to their research, the scientists promise a better understanding for stem cell renewal and hope that this knowledge might be useful for medical applications in the future. "We will also be applying STILT to other time-resolved single cell data in the future, which will give us insight into the underlying molecular gene regulation mechanisms," Marr explains. The STILT software is freely available to other scientists on the Internet. Explore further: Researchers challenge long-held assumption of gene expression in embryonic stem cells More information: Justin Feigelman et al. Analysis of Cell Lineage Trees by Exact Bayesian Inference Identifies Negative Autoregulation of Nanog in Mouse Embryonic Stem Cells, Cell Systems (2016). DOI: 10.1016/j.cels.2016.11.001


News Article | August 31, 2016
Site: phys.org

Today, cell biology no longer focuses only on static states, but rather seeks to understand the dynamic development of cells. One example for this is the formation of various types of blood cells, such as red blood cells or endothelial cells from their precursors, the blood stem cells. To understand how this process is genetically controlled, scientists analyze which genes are expressed by means of transcriptome analysis. "To me, it's still amazing that we are now even able to determine the transcriptome of single cells," said lead author Laleh Haghverdi, "especially when one realizes that a typical cell contains only a few picograms of RNA." The availability of these data is now beginning to revolutionize many fields of research, but new statistical methods are required to interpret these correctly. "For example, all cells of a sample never start their development synchronously, and their development takes different lengths of time. Therefore, we are always dealing with a dynamic mixture," added Haghverdi, doctoral student at the Institute of Computational Biology (ICB) at Helmholtz Zentrum München. "It is immensely difficult to construct multiple steps of a process from this, especially since the cells are only available for one measurement." Welcome to the era of pseudotime To decrypt developmental processes from the measurement of a single time point, quasi a snapshot measurement, the researchers led by ICB Director Prof. Dr. Dr. Fabian Theis developed an algorithm called diffusion pseudotime to interpret single cell sequencing data. This algorithm orders cells on a virtual timeline – the pseudotime – along which they show continuous changes in the transcriptome. Thus, it can be reconstructed which genes are expressed sequentially. By means of this method, researchers can graphically display the branching lineages of the developmental paths of different cell types. "For example, we can show how a relatively uniform cluster of blood stem cells develops into different cell types," said study leader Theis. "While some become red blood cells, others differentiate into endothelial cells. We can trace these fates based on the transcriptome data of the single cells." In addition, the scientists obtain information about which gene switches underlie the developments. The relatively diffuse mixture of cells which were found to be at different stages of their development can be disentangled on the computer and, after the analysis, provides a clear picture of the ongoing individual steps. However, this is only the beginning for the researchers because the processes of blood formation are relatively well understood. They served only as a test object to determine how well the method works. "In the future we want to focus on processes that have remained elusive until now or which may not have been discovered at all," said Theis.

Loading Institute of Computational Biology collaborators
Loading Institute of Computational Biology collaborators