Author: "Szymon Stoma" / Search Limiters: Full Text - Searchworks@Jio Institute Digital Library Search Results

Your search keyword '"Szymon Stoma"' showing total 24 results

Start Over Author "Szymon Stoma" Search Limiters Full Text

24 results on '"Szymon Stoma"'

1. Quantitative spatial analysis of haematopoiesis-regulating stromal cells in the bone marrow microenvironment by 3D microscopy

Author: Alvaro Gomariz, Patrick M. Helbling, Stephan Isringhausen, Ute Suessbier, Anton Becker, Andreas Boss, Takashi Nagasawa, Grégory Paul, Orcun Goksel, Gábor Székely, Szymon Stoma, Simon F. Nørrelykke, Markus G. Manz, and César Nombela-Arrieta
Subjects: Science
Abstract: The bone marrow microenvironment modulates haematopoiesis, stem cell maintenance and differentiation. Here, the authors use 3D microscopy to map the topography of haematopoietic stem cell niche stromal components.
Published: 2018
Full Text: View/download PDF

2. Modeling dynamics of cell-to-cell variability in TRAIL-induced apoptosis explains fractional killing and predicts reversible resistance.

Author: François Bertaux, Szymon Stoma, Dirk Drasdo, and Gregory Batt
Subjects: Biology (General), QH301-705.5
Abstract: Isogenic cells sensing identical external signals can take markedly different decisions. Such decisions often correlate with pre-existing cell-to-cell differences in protein levels. When not neglected in signal transduction models, these differences are accounted for in a static manner, by assuming randomly distributed initial protein levels. However, this approach ignores the a priori non-trivial interplay between signal transduction and the source of this cell-to-cell variability: temporal fluctuations of protein levels in individual cells, driven by noisy synthesis and degradation. Thus, modeling protein fluctuations, rather than their consequences on the initial population heterogeneity, would set the quantitative analysis of signal transduction on firmer grounds. Adopting this dynamical view on cell-to-cell differences amounts to recast extrinsic variability into intrinsic noise. Here, we propose a generic approach to merge, in a systematic and principled manner, signal transduction models with stochastic protein turnover models. When applied to an established kinetic model of TRAIL-induced apoptosis, our approach markedly increased model prediction capabilities. One obtains a mechanistic explanation of yet-unexplained observations on fractional killing and non-trivial robust predictions of the temporal evolution of cell resistance to TRAIL in HeLa cells. Our results provide an alternative explanation to survival via induction of survival pathways since no TRAIL-induced regulations are needed and suggest that short-lived anti-apoptotic protein Mcl1 exhibit large and rare fluctuations. More generally, our results highlight the importance of accounting for stochastic protein turnover to quantitatively understand signal transduction over extended durations, and imply that fluctuations of short-lived proteins deserve particular attention.
Published: 2014
Full Text: View/download PDF

3. STL-based analysis of TRAIL-induced apoptosis challenges the notion of type I/type II cell line classification.

Author: Szymon Stoma, Alexandre Donzé, François Bertaux, Oded Maler, and Gregory Batt
Subjects: Biology (General), QH301-705.5
Abstract: Extrinsic apoptosis is a programmed cell death triggered by external ligands, such as the TNF-related apoptosis inducing ligand (TRAIL). Depending on the cell line, the specific molecular mechanisms leading to cell death may significantly differ. Precise characterization of these differences is crucial for understanding and exploiting extrinsic apoptosis. Cells show distinct behaviors on several aspects of apoptosis, including (i) the relative order of caspases activation, (ii) the necessity of mitochondria outer membrane permeabilization (MOMP) for effector caspase activation, and (iii) the survival of cell lines overexpressing Bcl2. These differences are attributed to the activation of one of two pathways, leading to classification of cell lines into two groups: type I and type II. In this work we challenge this type I/type II cell line classification. We encode the three aforementioned distinguishing behaviors in a formal language, called signal temporal logic (STL), and use it to extensively test the validity of a previously-proposed model of TRAIL-induced apoptosis with respect to experimental observations made on different cell lines. After having solved a few inconsistencies using STL-guided parameter search, we show that these three criteria do not define consistent cell line classifications in type I or type II, and suggest mutants that are predicted to exhibit ambivalent behaviors. In particular, this finding sheds light on the role of a feedback loop between caspases, and reconciliates two apparently-conflicting views regarding the importance of either upstream or downstream processes for cell-type determination. More generally, our work suggests that these three distinguishing behaviors should be merely considered as type I/II features rather than cell-type defining criteria. On the methodological side, this work illustrates the biological relevance of STL-diagrams, STL population data, and STL-guided parameter search implemented in the tool Breach. Such tools are well-adapted to the ever-increasing availability of heterogeneous knowledge on complex signal transduction pathways.
Published: 2013
Full Text: View/download PDF

4. Flux-based transport enhancement as a plausible unifying mechanism for auxin transport in meristem development.

Author: Szymon Stoma, Mikael Lucas, Jérôme Chopard, Marianne Schaedel, Jan Traas, and Christophe Godin
Subjects: Biology (General), QH301-705.5
Abstract: Plants continuously generate new organs through the activity of populations of stem cells called meristems. The shoot apical meristem initiates leaves, flowers, and lateral meristems in highly ordered, spiralled, or whorled patterns via a process called phyllotaxis. It is commonly accepted that the active transport of the plant hormone auxin plays a major role in this process. Current hypotheses propose that cellular hormone transporters of the PIN family would create local auxin maxima at precise positions, which in turn would lead to organ initiation. To explain how auxin transporters could create hormone fluxes to distinct regions within the plant, different concepts have been proposed. A major hypothesis, canalization, proposes that the auxin transporters act by amplifying and stabilizing existing fluxes, which could be initiated, for example, by local diffusion. This convincingly explains the organised auxin fluxes during vein formation, but for the shoot apical meristem a second hypothesis was proposed, where the hormone would be systematically transported towards the areas with the highest concentrations. This implies the coexistence of two radically different mechanisms for PIN allocation in the membrane, one based on flux sensing and the other on local concentration sensing. Because these patterning processes require the interaction of hundreds of cells, it is impossible to estimate on a purely intuitive basis if a particular scenario is plausible or not. Therefore, computational modelling provides a powerful means to test this type of complex hypothesis. Here, using a dedicated computer simulation tool, we show that a flux-based polarization hypothesis is able to explain auxin transport at the shoot meristem as well, thus providing a unifying concept for the control of auxin distribution in the plant. Further experiments are now required to distinguish between flux-based polarization and other hypotheses.
Published: 2008
Full Text: View/download PDF

5. Supplementary Information from In Vivo Fluorescence Imaging of the Activity of CEA TCB, a Novel T-Cell Bispecific Antibody, Reveals Highly Specific Tumor Targeting and Fast Induction of T-Cell–Mediated Tumor Killing

Author: Marina Bacac, Christian Gerdes, Pablo Umana, Christian Klein, Markus Rudin, Szymon Stoma, Jörg Zielonka, Anne Freimoser-Grundschober, Teilo Schaller, Linda Fahrni, Tanja Fauti, Sara Colombetti, Johannes Sam, Hans-Peter Grimm, Ramanil Perera, and Steffi Lehmann
Abstract: Supplementary Methods and Figure Legends.
Published: 2023

6. Data from In Vivo Fluorescence Imaging of the Activity of CEA TCB, a Novel T-Cell Bispecific Antibody, Reveals Highly Specific Tumor Targeting and Fast Induction of T-Cell–Mediated Tumor Killing

Author: Marina Bacac, Christian Gerdes, Pablo Umana, Christian Klein, Markus Rudin, Szymon Stoma, Jörg Zielonka, Anne Freimoser-Grundschober, Teilo Schaller, Linda Fahrni, Tanja Fauti, Sara Colombetti, Johannes Sam, Hans-Peter Grimm, Ramanil Perera, and Steffi Lehmann
Abstract: Purpose: CEA TCB (RG7802, RO6958688) is a novel T-cell bispecific antibody, engaging CD3ϵ upon binding to carcinoembryonic antigen (CEA) on tumor cells. Containing an engineered Fc region, conferring an extended blood half-life while preventing side effects due to activation of innate effector cells, CEA TCB potently induces tumor lysis in mouse tumors. Here we aimed to characterize the pharmacokinetic profile, the biodistribution, and the mode of action of CEA TCB by combining in vitro and in vivo fluorescence imaging readouts.Experimental Design: CEA-expressing tumor cells (LS174T) and human peripheral blood mononuclear cells (PBMC) were cocultured in vitro or cografted into immunocompromised mice. Fluorescence reflectance imaging and intravital 2-photon (2P) microscopy were employed to analyze in vivo tumor targeting while in vitro confocal and intravital time-lapse imaging were used to assess the mode of action of CEA TCB.Results: Fluorescence reflectance imaging revealed increased ratios of extravascular to vascular fluorescence signals in tumors after treatment with CEA TCB compared with control antibody, suggesting specific targeting, which was confirmed by intravital microscopy. Confocal and intravital 2P microscopy showed CEA TCB to accelerate T-cell–dependent tumor cell lysis by inducing a local increase of effector to tumor cell ratios and stable crosslinking of multiple T cells to individual tumor cells.Conclusions: Using optical imaging, we demonstrate specific tumor targeting and characterize the mode of CEA TCB–mediated target cell lysis in a mouse tumor model, which supports further clinical evaluation of CEA TCB. Clin Cancer Res; 22(17); 4417–27. ©2016 AACR.See related commentary by Teijeira et al., p. 4277
Published: 2023

7. Video 1 from In Vivo Fluorescence Imaging of the Activity of CEA TCB, a Novel T-Cell Bispecific Antibody, Reveals Highly Specific Tumor Targeting and Fast Induction of T-Cell–Mediated Tumor Killing

Author: Marina Bacac, Christian Gerdes, Pablo Umana, Christian Klein, Markus Rudin, Szymon Stoma, Jörg Zielonka, Anne Freimoser-Grundschober, Teilo Schaller, Linda Fahrni, Tanja Fauti, Sara Colombetti, Johannes Sam, Hans-Peter Grimm, Ramanil Perera, and Steffi Lehmann
Abstract: In vitro live cell confocal imaging of LS174T RFP cells co-cultured with CFSE-labeled T-cells (E:T = 3:1) in the presence of CEA or a control TCB, respectively.
Published: 2023

8. Supplementary Figures from In Vivo Fluorescence Imaging of the Activity of CEA TCB, a Novel T-Cell Bispecific Antibody, Reveals Highly Specific Tumor Targeting and Fast Induction of T-Cell–Mediated Tumor Killing

Author: Marina Bacac, Christian Gerdes, Pablo Umana, Christian Klein, Markus Rudin, Szymon Stoma, Jörg Zielonka, Anne Freimoser-Grundschober, Teilo Schaller, Linda Fahrni, Tanja Fauti, Sara Colombetti, Johannes Sam, Hans-Peter Grimm, Ramanil Perera, and Steffi Lehmann
Abstract: Supplementary Figure S1. CEA TCB leads stimulates the expression of CD25, a T-cell activation marker, on both T-helper (CD4+) and cytotoxic T-cells (CD8+) after incubation with LS174T tumor cells. Supplementary Figure S2. CEA TCB induces efficient MKN45 tumor cell lysis and T-cell activation in the presence of these tumor cells. Supplementary Figure S3. Both CD4+ and CD8+ T-cells contribute to CEA-TCB mediated killing. Supplementary Figure S4. Ex vivo analysis of in vivo tumor targeting. Supplementary Figure S5. Analysis of T-cell migratory persistence index.
Published: 2023

9. Video 5 from In Vivo Fluorescence Imaging of the Activity of CEA TCB, a Novel T-Cell Bispecific Antibody, Reveals Highly Specific Tumor Targeting and Fast Induction of T-Cell–Mediated Tumor Killing

Author: Marina Bacac, Christian Gerdes, Pablo Umana, Christian Klein, Markus Rudin, Szymon Stoma, Jörg Zielonka, Anne Freimoser-Grundschober, Teilo Schaller, Linda Fahrni, Tanja Fauti, Sara Colombetti, Johannes Sam, Hans-Peter Grimm, Ramanil Perera, and Steffi Lehmann
Abstract: 2P time-lapse acquisition of LS174T / human PBMC cografts on day 4 after implantation (baseline), 24 h after control and 24 h after CEA TCB treatment. LS174T RFP tumor cells (red), CFSE labeled T-cells (green). Scale bar indicates 50 ÃŽÂ¼m.
Published: 2023

10. Video 4 from In Vivo Fluorescence Imaging of the Activity of CEA TCB, a Novel T-Cell Bispecific Antibody, Reveals Highly Specific Tumor Targeting and Fast Induction of T-Cell–Mediated Tumor Killing

Author: Marina Bacac, Christian Gerdes, Pablo Umana, Christian Klein, Markus Rudin, Szymon Stoma, Jörg Zielonka, Anne Freimoser-Grundschober, Teilo Schaller, Linda Fahrni, Tanja Fauti, Sara Colombetti, Johannes Sam, Hans-Peter Grimm, Ramanil Perera, and Steffi Lehmann
Abstract: Ex vivo analysis of the CEA TCB (magenta) mediated cross-linking of CFSE labeled T-cells (green) to tumor cells. 3D reconstructions of confocal z-stacks acquired on tumor sections obtained from huPBMC (CFSE labeled T-cells)/LS174T RFP co-grafts, 24 h after intravenous A647 labeled CEA TCB injections are shown. Bar, 10 ÃŽÂ¼m. Cell nuclei (DAPI) are shown in blue.
Published: 2023

11. Posttranscriptional Regulation of the Human LDL Receptor by the U2-Spliceosome

Author: Joel T. Haas, Jan Albert Kuivenhoven, Justina C. Wolters, Andrzej J. Rzepiela, Mathilde Varret, Ann Verhaegen, Valérie Carreau, Szymon Stoma, Anne Philippi, Alaa Othman, Jerome Robert, N. Dalila, Belle V. van Rosmalen, An Verrijken, Arnold von Eckardstein, Bart van de Sluis, Silvija Radosavljevic, Paolo Zanoni, Simon F. Norrelykke, Roger Meier, M. Yalcinkaya, Bart Staels, Andreas Geier, Lucia Rohrer, Michael Stebler, Michele Visentin, Antoine Rimbert, Catherine Boileau, Antonio Gallo, Melinde Wijers, Nicolette C. A. Huijkman, Steve E. Humphries, Jonas Weyler, Freerk van Dijk, Michaela Keel, Srividya Velagapudi, Jean-Pierre Rabès, Marieke Smit, Anne Tybjærg-Hansen, Adriaan van der Graaf, Luisa Vonghia, Yara Abou-Khalil, Sven Francque, Grigorios Panteloglou, Marta Futema, Luc Van Gaal, University hospital of Zurich [Zurich], Universität Zürich [Zürich] = University of Zurich (UZH), Institute for Molecular Systems Biology [ETH Zurich] (IMSB), Department of Biology [ETH Zürich] (D-BIOL), Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich)- Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich), Récepteurs Nucléaires, Maladies Métaboliques et Cardiovasculaires - U1011 (RNMCD), Institut Pasteur de Lille, Réseau International des Instituts Pasteur (RIIP)-Réseau International des Instituts Pasteur (RIIP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille), CHU Lille, Scientific Center for Optical and Electron Microscopy (ScopeM), Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich), University of Groningen [Groningen], unité de recherche de l'institut du thorax UMR1087 UMR6291 (ITX), Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Nantes Université - UFR de Médecine et des Techniques Médicales (Nantes Univ - UFR MEDECINE), Nantes Université - pôle Santé, Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ)-Nantes Université - pôle Santé, Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ), St George's, University of London, Laboratoire de Recherche Vasculaire Translationnelle (LVTS (UMR_S_1148 / U1148)), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité)-Université Sorbonne Paris Nord, AP-HP - Hôpital Bichat - Claude Bernard [Paris], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Université Saint-Joseph de Beyrouth (USJ), University of Copenhagen = Københavns Universitet (UCPH), CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Institut Cochin (IC UM3 (UMR 8104 / U1016)), Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), AP-HP. Université Paris Saclay, Université de Versailles Saint-Quentin-en-Yvelines - UFR Sciences de la santé Simone Veil (UVSQ Santé), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Antwerp University Hospital [Edegem] (UZA), University of Antwerp (UA), University of Amsterdam [Amsterdam] (UvA), Columbia University [New York], University Hospital of Würzburg, University College of London [London] (UCL), 603091, ANR-10-LABX-46, 2015T068, CVON2017-2020, AOM06024, 2014/267, Pfizer: 24052829, European Molecular Biology Organization, EMBO: ALTF277-2014, Seventh Framework Programme, FP7, Sixth Framework Programme, FP6: LSHM-CT-2005-018734, Fondation Maladies Rares, FMR, International Atherosclerosis Society, IAS, British Heart Foundation, BHF, European Commission, EC, European Research Council, ERC: 694717, ANR 16-RHUS-0006, Agence Nationale de la Recherche, ANR: ANR-16-RHUS-0007, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, SNF: 310030-185109, 31003A-160126, Fonds De La Recherche Scientifique - FNRS, FNRS, Fonds Wetenschappelijk Onderzoek, FWO: 1802154 N, PG008/08, RG3008, Nederlandse Organisatie voor Wetenschappelijk Onderzoek, NWO: 184.021.007, Universität Zürich, UZH: FK-20-037, We acknowledge the use of data from BIOS-consortium ( http://wiki.bbmri.nl/wiki/BIOS_bios ) which is funded by BBMRI-NL (NWO project 184.021.007). Flow cytometry was performed with equipment of the flow cytometry facility, University of Zurich., This work was conducted as part of the TransCard project of the seventh Framework Program (FP7) granted by the European Commission, to J. Albert Kuivenhoven, A. Tybjaerg-Hansen, and A. von Eckardstein (number 603091) as well as partially the FP7 RESOLVE project (to J.T. Haas, B. Staels, A. Verhaegen, S. Francque, L. Van Gaal, and A. von Eckardstein) and the European Genomic Institute for Diabetes (EGID, ANR-10-LABX-46 to B. Staels). Additional work by A. von Eckardstein’s team was funded by the Swiss National Science Foundation (31003A-160126, 310030-185109) and the Swiss Systems X program (2014/267 [Medical Research and Development (MRD)] HDL-X). P. Zanoni received funding awards from the Swiss Atherosclerosis Society (Arbeitsgruppe Lipide und Atherosklerose [AGLA] and the DACH Society for Prevention of Cardiovascular Diseases). G. Panteloglou received funding from the University of Zurich (Forschungskredit, grant no. FK-20-037). J. Albert Kuivenhoven is an Established Investigator from the Dutch Heart Foundation (2015T068). J. Albert Kuivenhoven was also supported by GeniusII (CVON2017-2020). The Laboratory for Vascular Translational Science (L.V.T.S.) team is supported by Fondation Maladies Rares, Programme Hospitalier de Recherche Clinique (PHRC) (AOM06024), and the national project CHOPIN (CHolesterol Personalized Innovation), granted by the Agence Nationale de la Recherche (ANR-16-RHUS-0007). Y. Abou Khalil is supported by a grant from Ministère de l’Education Nationale et de la Technologie (France). J.T. Haas was supported by an EMBO Long Term Fellowship (ALTF277-2014). B. Staels is a recipient of an ERC Advanced Grant (no. 694717). Both are also supported by PreciNASH (ANR 16-RHUS-0006). Research at the Antwerp University Hospital was supported by the European Union: FP6 (HEPADIP Contract LSHM-CT-2005-018734). S. Francque has a senior clinical research fellowship from the Fund for Scientific Research (FWO) Flanders (1802154 N). S.E. Humphries received grants RG3008 and PG008/08 from the British Heart Foundation, and the support of the UCLH NIHR BRC. S.E. Humphries directs the UK Children’s FH Register which has been supported by a grant from Pfizer (24052829) given by the International Atherosclerosis Society., This work was conducted as part of the TransCard project of the seventh Framework Program (FP7) granted by the European Commission, to J. Albert Kuivenhoven, A. Tybjaerg-Hansen, and A. von Eckardstein (number 603091) as well as partially the FP7 RESOLVE project (to J.T. Haas, B. Staels, A. Verhaegen, S. Francque, L. Van Gaal, and A. von Eckardstein) and the European Genomic Institute for Diabetes (EGID, ANR-10-LABX-46 to B. Staels). Additional work by A. von Eckardstein's team was funded by the Swiss National Science Foundation (31003A-160126, 310030-185109) and the Swiss Systems X program (2014/267 [Medical Research and Development (MRD)] HDL-X). P. Zanoni received funding awards from the Swiss Atherosclerosis Society (Arbeitsgruppe Lipide und Atherosklerose [AGLA] and the DACH Society for Prevention of Cardiovascular Diseases). G. Panteloglou received funding from the University of Zurich (Forschungskredit, grant no. FK-20-037). J. Albert Kuivenhoven is an Established Investigator from the Dutch Heart Foundation (2015T068). J. Albert Kuivenhoven was also supported by GeniusII (CVON2017-2020). The Laboratory for Vascular Translational Science (L.V.T.S.) team is supported by Fondation Maladies Rares, Programme Hospitalier de Recherche Clinique (PHRC) (AOM06024), and the national project CHOPIN (CHolesterol Personalized Innovation), granted by the Agence Nationale de la Recherche (ANR-16-RHUS-0007). Y. Abou Khalil is supported by a grant from Minist?re de l'Education Nationale et de la Technologie (France). J.T. Haas was supported by an EMBO Long Term Fellowship (ALTF277-2014). B. Staels is a recipient of an ERC Advanced Grant (no. 694717). Both are also supported by PreciNASH (ANR 16-RHUS-0006). Research at the Antwerp University Hospital was supported by the European Union: FP6 (HEPADIP Contract LSHMCT- 2005-018734). S. Francque has a senior clinical research fellowship from the Fund for Scientific Research (FWO) Flanders (1802154 N). S.E. Humphries received grants RG3008 and PG008/08 from the British Heart Foundation, and the support of the UCLH NIHR BRC. S.E. Humphries directs the UK Children's FH Register which has been supported by a grant from Pfizer (24052829) given by the International Atherosclerosis Society., ANR-16-RHUS-0006,PreciNASH,PreciNASH(2016), ANR-16-RHUS-0007,CHOPIN,CHOPIN(2016), Center for Liver, Digestive and Metabolic Diseases (CLDM), Restoring Organ Function by Means of Regenerative Medicine (REGENERATE), HAL UVSQ, Équipe, Récepteurs Nucléaires, Maladies Métaboliques et Cardiovasculaires (RNMCD - U1011), Service d'Endocrinologie, Métabolisme et Prévention des Maladies Cardio-vasculaires [CHU Pitié-Salpêtrière], and Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)
Subjects: Spliceosome, Physiology, RNA Splicing, Population, Hypercholesterolemia, Familial hypercholesterolemia, 030204 cardiovascular system & hematology, Biology, 03 medical and health sciences, 0302 clinical medicine, [SDV.MHEP.CSC]Life Sciences [q-bio]/Human health and pathology/Cardiology and cardiovascular system, Gene expression, medicine, Humans, education, Gene, 030304 developmental biology, 0303 health sciences, Gene knockdown, education.field_of_study, Intron, Nuclear Proteins, Hep G2 Cells, medicine.disease, Molecular biology, Endocytosis, [SDV.MHEP.CSC] Life Sciences [q-bio]/Human health and pathology/Cardiology and cardiovascular system, Lipoproteins, LDL, Cholesterol, HEK293 Cells, Cardiovascular diseases, Liver, Receptors, LDL, Mutation, LDL receptor, Hepatocytes, Spliceosomes, lipids (amino acids, peptides, and proteins), Human medicine, Cardiology and Cardiovascular Medicine
Abstract: Background: The LDLR (low-density lipoprotein receptor) in the liver is the major determinant of LDL-cholesterol levels in human plasma. The discovery of genes that regulate the activity of LDLR helps to identify pathomechanisms of hypercholesterolemia and novel therapeutic targets against atherosclerotic cardiovascular disease. Methods: We performed a genome-wide RNA interference screen for genes limiting the uptake of fluorescent LDL into Huh-7 hepatocarcinoma cells. Top hit genes were validated by in vitro experiments as well as analyses of data sets on gene expression and variants in human populations. Results: The knockdown of 54 genes significantly inhibited LDL uptake. Fifteen of them encode for components or interactors of the U2-spliceosome. Knocking down any one of 11 out of 15 genes resulted in the selective retention of intron 3 of LDLR . The translated LDLR fragment lacks 88% of the full length LDLR and is detectable neither in nontransfected cells nor in human plasma. The hepatic expression of the intron 3 retention transcript is increased in nonalcoholic fatty liver disease as well as after bariatric surgery. Its expression in blood cells correlates with LDL-cholesterol and age. Single nucleotide polymorphisms and 3 rare variants of one spliceosome gene, RBM25 , are associated with LDL-cholesterol in the population and familial hypercholesterolemia, respectively. Compared with overexpression of wild-type RBM25 , overexpression of the 3 rare RBM25 mutants in Huh-7 cells led to lower LDL uptake. Conclusions: We identified a novel mechanism of posttranscriptional regulation of LDLR activity in humans and associations of genetic variants of RBM25 with LDL-cholesterol levels.
Published: 2022

12. Detecting variation in starch granule size and morphology by high-throughput microscopy and flow cytometry

Author: Mercedes Thieme, Anton Hochmuth, Theresa Elisabeth Ilse, Jose A. Cuesta-Seijo, Szymon Stoma, Roger Meier, Simon Flyvbjerg Nørrelykke, Pai Rosager Pedas, Ilka Braumann, and Samuel C. Zeeman
Subjects: Polymers and Plastics, Organic Chemistry, Materials Chemistry, High-throughput genetic screen, Arabidopsis thaliana, Hordeum vulgare, Induced genetic variation, Automated image analysis
Abstract: Starch forms semi-crystalline, water-insoluble granules, the size and morphology of which vary according to biological origin. These traits, together with polymer composition and structure, determine the physicochemical properties of starch. However, screening methods to identify differences in starch granule size and shape are lacking. Here, we present two approaches for high-throughput starch granule extraction and size determination using flow cytometry and automated, high-throughput light microscopy. We evaluated the practicality of both methods using starch from different species and tissues and demonstrated their effectiveness by screening for induced variation in starch extracted from over 10,000 barley lines, yielding four with heritable changes in the ratio of large A-granules to small B-granules. Analysis of Arabidopsis lines altered in starch biosynthesis further demonstrates the applicability of these approaches. Identifying variation in starch granule size and shape will enable identification of trait-controlling genes for developing crops with desired properties, and could help optimise starch processing. ISSN:0144-8617 ISSN:1879-1344
Published: 2023

13. AutoTube: a novel software for the automated morphometric analysis of vascular networks in tissues

Author: Erica Russo, Maria Jadhav, Peter Runge, Szymon Stoma, Javier A. Montoya-Zegarra, Cornelia Halin, Simon F. Norrelykke, Michael Detmar, and Ann-Helen Willrodt
Subjects: 0301 basic medicine, Cancer Research, Lymphatic vessels, Blood vessels, Quantification, Morphometric analysis, Whole-mounts, Tube formation, Physiology, Angiogenesis, Clinical Biochemistry, Neovascularization, Physiologic, Cell Count, Cell Communication, Biology, 03 medical and health sciences, Mice, 0302 clinical medicine, Software, Image Processing, Computer-Assisted, Animals, Humans, Lymphangiogenesis, Cells, Cultured, Cell Size, Whole mount, Mice, Knockout, Original Paper, business.industry, In vitro toxicology, Endothelial Cells, Mice, Inbred C57BL, 030104 developmental biology, Lymphatic system, Vascular morphology, 030220 oncology & carcinogenesis, Microvessels, business, Biomedical engineering
Abstract: Due to their involvement in many physiologic and pathologic processes, there is a great interest in identifying new molecular pathways that mediate the formation and function of blood and lymphatic vessels. Vascular research increasingly involves the image-based analysis and quantification of vessel networks in tissue whole-mounts or of tube-like structures formed by cultured endothelial cells in vitro. While both types of experiments deliver important mechanistic insights into (lymph)angiogenic processes, the manual analysis and quantification of such experiments are typically labour-intensive and affected by inter-experimenter variability. To bypass these problems, we developed AutoTube, a new software that quantifies parameters like the area covered by vessels, vessel width, skeleton length and branching or crossing points of vascular networks in tissues and in in vitro assays. AutoTube is freely downloadable, comprises an intuitive graphical user interface and helps to perform otherwise highly time-consuming image analyses in a rapid, automated and reproducible manner. By analysing lymphatic and blood vascular networks in whole-mounts prepared from different tissues or from gene-targeted mice with known vascular abnormalities, we demonstrate the ability of AutoTube to determine vascular parameters in close agreement to the manual analyses and to identify statistically significant differences in vascular morphology in tissues and in vascular networks formed in in vitro assays., Angiogenesis, 22 (2)
Published: 2019
Full Text: View/download PDF

14. In Vivo Fluorescence Imaging of the Activity of CEA TCB, a Novel T-Cell Bispecific Antibody, Reveals Highly Specific Tumor Targeting and Fast Induction of T-Cell–Mediated Tumor Killing

Author: Steffi Lehmann, Teilo H Schaller, Joerg Zielonka, Johannes Sam, Szymon Stoma, Christian Klein, Markus Rudin, Christian Gerdes, Sara Colombetti, Anne Freimoser-Grundschober, Marina Bacac, Hans-Peter Grimm, Linda Fahrni, Tanja Fauti, Ramanil Perera, Pablo Umana, University of Zurich, and Bacac, Marina
Subjects: Cytotoxicity, Immunologic, 0301 basic medicine, Cancer Research, Biodistribution, Time Factors, Cell Survival, T-Lymphocytes, T cell, 10050 Institute of Pharmacology and Toxicology, 610 Medicine & health, Cell Communication, Peripheral blood mononuclear cell, 170 Ethics, Mice, 03 medical and health sciences, Antineoplastic Agents, Immunological, 0302 clinical medicine, Carcinoembryonic antigen, Antibody Specificity, In vivo, Cell Line, Tumor, Neoplasms, Antibodies, Bispecific, medicine, Animals, Humans, 10237 Institute of Biomedical Engineering, 1306 Cancer Research, Tissue Distribution, Cytotoxicity, Microscopy, Confocal, biology, Molecular biology, Carcinoembryonic Antigen, Molecular Imaging, 3. Good health, Disease Models, Animal, 030104 developmental biology, medicine.anatomical_structure, Oncology, 030220 oncology & carcinogenesis, biology.protein, 2730 Oncology, Female, Antibody, Biomarkers, Intravital microscopy, T-Lymphocytes, Cytotoxic
Abstract: Purpose: CEA TCB (RG7802, RO6958688) is a novel T-cell bispecific antibody, engaging CD3ϵ upon binding to carcinoembryonic antigen (CEA) on tumor cells. Containing an engineered Fc region, conferring an extended blood half-life while preventing side effects due to activation of innate effector cells, CEA TCB potently induces tumor lysis in mouse tumors. Here we aimed to characterize the pharmacokinetic profile, the biodistribution, and the mode of action of CEA TCB by combining in vitro and in vivo fluorescence imaging readouts. Experimental Design: CEA-expressing tumor cells (LS174T) and human peripheral blood mononuclear cells (PBMC) were cocultured in vitro or cografted into immunocompromised mice. Fluorescence reflectance imaging and intravital 2-photon (2P) microscopy were employed to analyze in vivo tumor targeting while in vitro confocal and intravital time-lapse imaging were used to assess the mode of action of CEA TCB. Results: Fluorescence reflectance imaging revealed increased ratios of extravascular to vascular fluorescence signals in tumors after treatment with CEA TCB compared with control antibody, suggesting specific targeting, which was confirmed by intravital microscopy. Confocal and intravital 2P microscopy showed CEA TCB to accelerate T-cell–dependent tumor cell lysis by inducing a local increase of effector to tumor cell ratios and stable crosslinking of multiple T cells to individual tumor cells. Conclusions: Using optical imaging, we demonstrate specific tumor targeting and characterize the mode of CEA TCB–mediated target cell lysis in a mouse tumor model, which supports further clinical evaluation of CEA TCB. Clin Cancer Res; 22(17); 4417–27. ©2016 AACR. See related commentary by Teijeira et al., p. 4277
Published: 2016

15. Quantitative spatial analysis of haematopoiesis-regulating stromal cells in the bone marrow microenvironment by 3D microscopy

Author: Markus G. Manz, Simon F. Norrelykke, Gábor Székely, Patrick M. Helbling, César Nombela-Arrieta, Anton S. Becker, Alvaro Gomariz, Ute Suessbier, Takashi Nagasawa, Andreas Boss, Gregory Paul, Orcun Goksel, Stephan Isringhausen, Szymon Stoma, University of Zurich, and Nombela-Arrieta, César
Subjects: 0301 basic medicine, Aging, Statistical methods, General Physics and Astronomy, Cell Count, Mice, Reticular cell, Bone Marrow, Cell Movement, Femur, Stem Cell Niche, lcsh:Science, Microscopy, Multidisciplinary, 10042 Clinic for Diagnostic and Interventional Radiology, 3100 General Physics and Astronomy, Cell biology, Extracellular Matrix, Haematopoiesis, medicine.anatomical_structure, Cellular Microenvironment, Stem cell, Confocal microscopy, Image processing, Stem-cell niche, Cell type, Stromal cell, Science, 610 Medicine & health, 1600 General Chemistry, Genetics and Molecular Biology, Bone Marrow Cells, Biology, General Biochemistry, Genetics and Molecular Biology, Article, 03 medical and health sciences, Imaging, Three-Dimensional, 1300 General Biochemistry, Genetics and Molecular Biology, medicine, Animals, Mesenchymal stem cell, Endothelial Cells, Mesenchymal Stem Cells, General Chemistry, Hematopoietic Stem Cells, Hematopoiesis, Mice, Inbred C57BL, 030104 developmental biology, Reticular connective tissue, 10032 Clinic for Oncology and Hematology, General Biochemistry, lcsh:Q, Bone marrow
Abstract: Sinusoidal endothelial cells and mesenchymal CXCL12-abundant reticular cells are principal bone marrow stromal components, which critically modulate haematopoiesis at various levels, including haematopoietic stem cell maintenance. These stromal subsets are thought to be scarce and function via highly specific interactions in anatomically confined niches. Yet, knowledge on their abundance, global distribution and spatial associations remains limited. Using three-dimensional quantitative microscopy we show that sinusoidal endothelial and mesenchymal reticular subsets are remarkably more abundant than estimated by conventional flow cytometry. Moreover, both cell types assemble in topologically complex networks, associate to extracellular matrix and pervade marrow tissues. Through spatial statistical methods we challenge previous models and demonstrate that even in the absence of major specific interaction forces, virtually all tissue-resident cells are invariably in physical contact with, or close proximity to, mesenchymal reticular and sinusoidal endothelial cells. We further show that basic structural features of these stromal components are preserved during ageing., Nature Communications, 9 (1), ISSN:2041-1723
Published: 2017

16. VEGF-A regulates cellular localization of SR-BI as well as transendothelial transport of HDL but not LDL

Author: Paolo Zanoni, Simon F. Norrelykke, Arnold von Eckardstein, Szymon Stoma, Srividya Velagapudi, Mustafa Yalcinkaya, Lucia Rohrer, Antonio Piemontese, Damir Perisa, Roger Meier, Michael Stebler, Andrzej J. Rzepiela, University of Zurich, and Rohrer, Lucia
Subjects: 0301 basic medicine, Vascular wall, Vascular Endothelial Growth Factor A, VEGF receptors, 610 Medicine & health, 030204 cardiovascular system & hematology, Biology, Transfection, p38 Mitogen-Activated Protein Kinases, 2705 Cardiology and Cardiovascular Medicine, 03 medical and health sciences, 0302 clinical medicine, 540 Chemistry, Humans, Protein Kinase Inhibitors, Cellular localization, Cells, Cultured, Phosphoinositide-3 Kinase Inhibitors, 10038 Institute of Clinical Chemistry, Endothelial Cells, Scavenger Receptors, Class B, Vascular Endothelial Growth Factor Receptor-2, Cell biology, High-Throughput Screening Assays, Lipoproteins, LDL, Protein Transport, 030104 developmental biology, Biochemistry, biology.protein, RNA Interference, lipids (amino acids, peptides, and proteins), Phosphatidylinositol 3-Kinase, Cardiology and Cardiovascular Medicine, Lipoproteins, HDL, Proto-Oncogene Proteins c-akt, Signal Transduction
Abstract: Objective— Low- and high-density lipoproteins (LDL and HDL) must pass the endothelial layer to exert pro- and antiatherogenic activities, respectively, within the vascular wall. However, the rate-limiting factors that mediate transendothelial transport of lipoproteins are yet little known. Therefore, we performed a high-throughput screen with kinase drug inhibitors to identify modulators of transendothelial LDL and HDL transport. Approach and Results— Microscopy-based high-content screening was performed by incubating human aortic endothelial cells with 141 kinase-inhibiting drugs and fluorescent-labeled LDL or HDL. Inhibitors of vascular endothelial growth factor (VEGF) receptors (VEGFR) significantly decreased the uptake of HDL but not LDL. Silencing of VEGF receptor 2 significantly decreased cellular binding, association, and transendothelial transport of 125 I-HDL but not 125 I-LDL. RNA interference with VEGF receptor 1 or VEGF receptor 3 had no effect. Binding, uptake, and transport of HDL but not LDL were strongly reduced in the absence of VEGF-A from the cell culture medium and were restored by the addition of VEGF-A. The restoring effect of VEGF-A on endothelial binding, uptake, and transport of HDL was abrogated by pharmacological inhibition of phosphatidyl-inositol 3 kinase/protein kinase B or p38 mitogen-activated protein kinase, as well as silencing of scavenger receptor BI. Moreover, the presence of VEGF-A was found to be a prerequisite for the localization of scavenger receptor BI in the plasma membrane of endothelial cells. Conclusions— The identification of VEGF as a regulatory factor of transendothelial transport of HDL but not LDL supports the concept that the endothelium is a specific and, hence, druggable barrier for the entry of lipoproteins into the vascular wall.
Published: 2017

17. Hypoxia Induces a HIF-1-Dependent Transition from Collective-to-Amoeboid Dissemination in Epithelial Cancer Cells

Author: Steffi Lehmann, Olga Ilina, Peter Friedl, Liying Jiang, Roberta Bianchi, Julia Odenthal, Reidar Grénman, Markus Rudin, Sjoerd van Helvert, Kristian Ikenberg, Szymon Stoma, Veronika te Boekhorst, Jael Xandry, University of Zurich, and Lehmann, Steffi
Subjects: 0301 basic medicine, Epithelial-Mesenchymal Transition, Cancer development and immune defence Radboud Institute for Molecular Life Sciences [Radboudumc 2], Cell, 10050 Institute of Pharmacology and Toxicology, Breast Neoplasms, 610 Medicine & health, 1100 General Agricultural and Biological Sciences, Biology, Mechanotransduction, Cellular, General Biochemistry, Genetics and Molecular Biology, 03 medical and health sciences, 0302 clinical medicine, Downregulation and upregulation, Cell Movement, 1300 General Biochemistry, Genetics and Molecular Biology, Cell Line, Tumor, 10049 Institute of Pathology and Molecular Pathology, medicine, Cell Adhesion, Humans, Epithelial–mesenchymal transition, Mechanotransduction, Neoplasm Metastasis, Sensory disorders Radboud Institute for Molecular Life Sciences [Radboudumc 12], Tumor microenvironment, Amoeboid movement, Mesenchymal stem cell, Twist-Related Protein 1, Nuclear Proteins, ta3122, ta3125, Cell biology, 030104 developmental biology, medicine.anatomical_structure, Head and Neck Neoplasms, 030220 oncology & carcinogenesis, Immunology, Cancer cell, Tumor Hypoxia, Female, Hypoxia-Inducible Factor 1, General Agricultural and Biological Sciences
Abstract: Contains fulltext : 170562.pdf (Publisher’s version ) (Closed access) Cancer metastases arise from a multi-step process that requires metastasizing tumor cells to adapt to signaling input from varying tissue environments [1]. As an early metastatic event, cancer cell dissemination occurs through different migration programs, including multicellular, collective, and single-cell mesenchymal or amoeboid migration [2-4]. Migration modes can interconvert based on changes in cell adhesion, cytoskeletal mechanotransduction [5], and/or proteolysis [6], most likely under the control of transcriptional programs such as the epithelial-to-mesenchymal transition (EMT) [7, 8]. However, how plasticity of tumor cell migration and EMT is spatiotemporally controlled and connected upon challenge by the tumor microenvironment remains unclear. Using 3D cultures of collectively invading breast and head and neck cancer spheroids, here we identify hypoxia, a hallmark of solid tumors [9], as an inducer of the collective-to-amoeboid transition (CAT), promoting the dissemination of amoeboid-moving single cells from collective invasion strands. Hypoxia-induced amoeboid detachment was driven by hypoxia-inducible factor 1 (HIF-1), followed the downregulation of E-cadherin, and produced heterogeneous cell subsets whose phenotype and migration were dependent ( approximately 30%) or independent ( approximately 70%) of Twist-mediated EMT. EMT-like and EMT-independent amoeboid cell subsets showed stable amoeboid movement over hours as well as leukocyte-like traits, including rounded morphology, matrix metalloproteinase (MMP)-independent migration, and nuclear deformation. Cancer cells undergoing pharmacological stabilization of HIFs retained their constitutive ability for early metastatic seeding in an experimental model of lung metastasis, indicating that hypoxia-induced CAT enhances cell release rather than early organ colonization. Induced by metabolic challenge, amoeboid movement may thus constitute a common endpoint of both EMT-dependent and EMT-independent cancer dissemination programs. 9 p.
Published: 2017

18. Modeling Dynamics of Cell-to-Cell Variability in TRAIL-Induced Apoptosis Explains Fractional Killing and Predicts Reversible Resistance

Author: Dirk Drasdo, Szymon Stoma, François Bertaux, Gregory Batt, Modelling and Analysis for Medical and Biological Applications (MAMBA), Laboratoire Jacques-Louis Lions (LJLL), Université Pierre et Marie Curie - Paris 6 (UPMC)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS)-Inria Paris-Rocquencourt, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Computational systems biology and optimization (Lifeware), Inria Paris-Rocquencourt, Université Pierre et Marie Curie - Paris 6 (UPMC)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), ANR-10-COSI-0007,Syne2Arti,Des réseaux de régulation génique aux tissus artificiels(2010), and ANR-10-BINF-0006,Iceberg,Des modèles de population aux populations de modèles: observation, modélisation et contrôle de l'expression génique au niveau de la cellule unique(2010)
Subjects: Cell, Gene Expression, Apoptosis, Biochemistry, TNF-Related Apoptosis-Inducing Ligand, 0302 clinical medicine, Protein biosynthesis, Biochemical Simulations, MCL1, lcsh:QH301-705.5, cell decision processes, Genetics, 0303 health sciences, Ecology, Cell Death, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], medicine.anatomical_structure, Computational Theory and Mathematics, Cell Processes, Modeling and Simulation, Physical Sciences, from single cell models to cell population dynamics, Extrinsic apoptosis, Signal transduction, Biological system, Research Article, Signal Transduction, TNF-Related Apoptosis-Inducing Ligand (TRAIL), Bioinformatics, Biology, Models, Biological, 03 medical and health sciences, Cellular and Molecular Neuroscience, protein fluctuations and transient cellular memory, medicine, Humans, Molecular Biology, Transcription factor, Ecology, Evolution, Behavior and Systematics, 01 Mathematical Sciences, 030304 developmental biology, 08 Information And Computing Sciences, Stochastic Processes, Kinetic model, Protein turnover, Biology and Life Sciences, Computational Biology, Cell Biology, 06 Biological Sciences, Probability Theory, stochastic protein turnover, mechanisms of TRAIL resistance, lcsh:Biology (General), [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], 030217 neurology & neurosurgery, Mathematics, HeLa Cells
Abstract: Isogenic cells sensing identical external signals can take markedly different decisions. Such decisions often correlate with pre-existing cell-to-cell differences in protein levels. When not neglected in signal transduction models, these differences are accounted for in a static manner, by assuming randomly distributed initial protein levels. However, this approach ignores the a priori non-trivial interplay between signal transduction and the source of this cell-to-cell variability: temporal fluctuations of protein levels in individual cells, driven by noisy synthesis and degradation. Thus, modeling protein fluctuations, rather than their consequences on the initial population heterogeneity, would set the quantitative analysis of signal transduction on firmer grounds. Adopting this dynamical view on cell-to-cell differences amounts to recast extrinsic variability into intrinsic noise. Here, we propose a generic approach to merge, in a systematic and principled manner, signal transduction models with stochastic protein turnover models. When applied to an established kinetic model of TRAIL-induced apoptosis, our approach markedly increased model prediction capabilities. One obtains a mechanistic explanation of yet-unexplained observations on fractional killing and non-trivial robust predictions of the temporal evolution of cell resistance to TRAIL in HeLa cells. Our results provide an alternative explanation to survival via induction of survival pathways since no TRAIL-induced regulations are needed and suggest that short-lived anti-apoptotic protein Mcl1 exhibit large and rare fluctuations. More generally, our results highlight the importance of accounting for stochastic protein turnover to quantitatively understand signal transduction over extended durations, and imply that fluctuations of short-lived proteins deserve particular attention., Author Summary TRAIL induces apoptosis selectively in cancer cells and is currently tested in clinics. Having a mechanistic understanding of TRAIL resistance could help to limit its apparition. Several observations suggested that protein level fluctuations play an important role in TRAIL resistance and its acquisition. However, quantitative, systems-level approaches to investigate their role in cellular decision-making processes are lacking. We propose a generic and principled approach to extend signal transduction models with protein fluctuation models for all proteins in the pathway. The key aspect is to use standard protein fluctuation models for long-lived proteins. We show that its application to TRAIL-induced apoptosis provide a quantitative, mechanistic explanation to previously published but yet unexplained critical observations.
Published: 2014

19. Bridging the gaps in systems biology

Author: Stefan Hohmann, Natasa Przulj, Edda Klipp, Marija Cvijovic, Andrea Pagnani, Joerg Stelling, Mats Jirstrand, Hans-Michael Kaltenbach, Jens Nielsen, Joachim Almquist, Jonas Hagmar, Judith A. H. Wodke, Andreas Raue, Frank Tobin, Sven Nelander, Marcus Krantz, Pedro Mendes, Riccardo Zecchina, Szymon Stoma, Chalmers University of Technology [Göteborg], Fraunhofer-Chalmers Center [Gothenburg] (FCC), Chalmers University of Technology [Göteborg]-Fraunhofer (Fraunhofer-Gesellschaft), University of Gothenburg (GU), Department of Biosystems Science and Engineering [ETH Zürich] (D-BSSE), Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich), Theoretical Biophysics [Berlin], Humboldt University Of Berlin, Manchester Institute of Biotechnology, University of Manchester [Manchester], Uppsala University, Systems Biology, Department of Applied Science and Technology [Politecnico di Torino] (DISAT), Politecnico di Torino = Polytechnic of Turin (Polito), Imperial College London, University of Freiburg [Freiburg], Computational systems biology and optimization (Lifeware), Inria Paris-Rocquencourt, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Tobin Consulting, European Project: 223137,EC:FP7:HEALTH,FP7-HEALTH-2007-B,FUTURESYSBIO(2008), Humboldt-Universität zu Berlin, and Publica
Subjects: Systems biology, Modeling, Sensitivity analysis, Model merging, Model standards, Biomedical Research, Computational biology, Biology, Models, Biological, Bridging (programming), 03 medical and health sciences, 0302 clinical medicine, Genetics, Humans, Complex systems biology, Molecular Biology, 030304 developmental biology, 0303 health sciences, Mathematical model, Modelling biological systems, General Medicine, Reference Standards, Data science, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, 030217 neurology & neurosurgery, Biological network
Abstract: International audience; Systems biology aims at creating mathematical models, i.e., computational reconstructions of biological systems and processes that will result in a new level of understanding-the elucidation of the basic and presumably conserved "design" and "engineering" principles of biomolecular systems. Thus, systems biology will move biology from a phenomenological to a predictive science. Mathematical modeling of biological networks and processes has already greatly improved our understanding of many cellular processes. However, given the massive amount of qualitative and quantitative data currently produced and number of burning questions in health care and biotechnology needed to be solved is still in its early phases. The field requires novel approaches for abstraction, for modeling bioprocesses that follow different biochemical and biophysical rules, and for combining different modules into larger models that still allow realistic simulation with the computational power available today. We have identified and discussed currently most prominent problems in systems biology: (1) how to bridge different scales of modeling abstraction, (2) how to bridge the gap between topological and mechanistic modeling, and (3) how to bridge the wet and dry laboratory gap. The future success of systems biology largely depends on bridging the recognized gaps.
Published: 2014

20. STL-based analysis of TRAIL-induced apoptosis challenges the notion of type I/type II cell line classification

Author: Gregory Batt, Oded Maler, François Bertaux, Alexandre Donzé, Szymon Stoma, Constraint programming (CONTRAINTES), Inria Paris-Rocquencourt, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), VERIMAG (VERIMAG - IMAG), Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Grenoble (INPG)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Université Joseph Fourier - Grenoble 1 (UJF), Nonlinear Analysis for Biology and Geophysical flows (BANG), Laboratoire Jacques-Louis Lions (LJLL), Université Pierre et Marie Curie - Paris 6 (UPMC)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS)-Inria Paris-Rocquencourt, and Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Apoptosis, TNF-Related Apoptosis-Inducing Ligand, Biochemical Simulations, lcsh:QH301-705.5, Caspase, 0303 health sciences, Ecology, biology, Effector, Systems Biology, 030302 biochemistry & molecular biology, Cell biology, Semantics, Order (biology), Computational Theory and Mathematics, Proto-Oncogene Proteins c-bcl-2, Modeling and Simulation, Caspases, Mitochondrial Membranes, Signal transduction, Signal Transduction, Research Article, Programmed cell death, Bioinformatics, Logic, Models, Biological, 03 medical and health sciences, Cellular and Molecular Neuroscience, Cell Line, Tumor, Terminology as Topic, Genetics, Humans, Computer Simulation, Molecular Biology, Biology, Ecology, Evolution, Behavior and Systematics, 01 Mathematical Sciences, 030304 developmental biology, 08 Information And Computing Sciences, Computational Biology, Membrane Proteins, 06 Biological Sciences, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Signaling Networks, Membrane protein, lcsh:Biology (General), Cell culture, biology.protein, Neuroscience
Abstract: Extrinsic apoptosis is a programmed cell death triggered by external ligands, such as the TNF-related apoptosis inducing ligand (TRAIL). Depending on the cell line, the specific molecular mechanisms leading to cell death may significantly differ. Precise characterization of these differences is crucial for understanding and exploiting extrinsic apoptosis. Cells show distinct behaviors on several aspects of apoptosis, including (i) the relative order of caspases activation, (ii) the necessity of mitochondria outer membrane permeabilization (MOMP) for effector caspase activation, and (iii) the survival of cell lines overexpressing Bcl2. These differences are attributed to the activation of one of two pathways, leading to classification of cell lines into two groups: type I and type II. In this work we challenge this type I/type II cell line classification. We encode the three aforementioned distinguishing behaviors in a formal language, called signal temporal logic (STL), and use it to extensively test the validity of a previously-proposed model of TRAIL-induced apoptosis with respect to experimental observations made on different cell lines. After having solved a few inconsistencies using STL-guided parameter search, we show that these three criteria do not define consistent cell line classifications in type I or type II, and suggest mutants that are predicted to exhibit ambivalent behaviors. In particular, this finding sheds light on the role of a feedback loop between caspases, and reconciliates two apparently-conflicting views regarding the importance of either upstream or downstream processes for cell-type determination. More generally, our work suggests that these three distinguishing behaviors should be merely considered as type I/II features rather than cell-type defining criteria. On the methodological side, this work illustrates the biological relevance of STL-diagrams, STL population data, and STL-guided parameter search implemented in the tool Breach. Such tools are well-adapted to the ever-increasing availability of heterogeneous knowledge on complex signal transduction pathways., PLoS Computational Biology, 9 (5), ISSN:1553-734X, ISSN:1553-7358
Published: 2013

21. Flux based transport enhancement as a plausible unifying mechanism for Auxin transport in meristem development

Author: Jérôme Chopard, Marianne Schaedel, Jan Traas, Christophe Godin, Mikaël Lucas, Szymon Stoma, Reproduction et développement des plantes (RDP), École normale supérieure de Lyon (ENS de Lyon)-Institut National de la Recherche Agronomique (INRA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS), Modeling plant morphogenesis at different scales, from genes to phenotype (VIRTUAL PLANTS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de la Recherche Agronomique (INRA)-Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Botanique et Modélisation de l'Architecture des Plantes et des Végétations (UMR AMAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud]), Développement et amélioration des plantes (UMR DAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Université Montpellier 2 - Sciences et Techniques (UM2)-Centre National de la Recherche Scientifique (CNRS), École normale supérieure - Lyon (ENS Lyon)-Institut National de la Recherche Agronomique (INRA)-Université Claude Bernard Lyon 1 (UCBL), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Centre National de la Recherche Scientifique (CNRS)-Université Montpellier 2 - Sciences et Techniques (UM2)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de la Recherche Agronomique (INRA)-École normale supérieure - Lyon (ENS Lyon), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut de Recherche pour le Développement (IRD [France-Sud]), Traas, Jan, and Godin, Christophe
Subjects: 0106 biological sciences, F62 - Physiologie végétale - Croissance et développement, 01 natural sciences, Facilitated Diffusion, Developmental Biology/Pattern Formation, lcsh:QH301-705.5, ComputingMilieux_MISCELLANEOUS, chemistry.chemical_classification, 0303 health sciences, Vegetal Biology, Ecology, 000 - Autres thèmes, Cell Polarity, Phyllotaxis, Protein Transport, Computational Theory and Mathematics, Modeling and Simulation, Plant hormone, Research Article, Signal Transduction, Meristem, Biology, Models, Biological, 03 medical and health sciences, Cellular and Molecular Neuroscience, Plant Biology/Plant Growth and Development, Auxin, Botany, Genetics, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, transport d'auxine, Hormone transport, Molecular Biology, Process (anatomy), Ecology, Evolution, Behavior and Systematics, Plant Physiological Phenomena, 030304 developmental biology, Indoleacetic Acids, auxine, Mechanism (biology), Computational Biology, Membrane Transport Proteins, méristème, biology.organism_classification, chemistry, lcsh:Biology (General), Developmental Biology/Plant Growth and Development, Computer Science, transport, Biophysics, Biologie végétale, 010606 plant biology & botany, Developmental Biology, hormone végétale
Abstract: Plants continuously generate new organs through the activity of populations of stem cells called meristems. The shoot apical meristem initiates leaves, flowers, and lateral meristems in highly ordered, spiralled, or whorled patterns via a process called phyllotaxis. It is commonly accepted that the active transport of the plant hormone auxin plays a major role in this process. Current hypotheses propose that cellular hormone transporters of the PIN family would create local auxin maxima at precise positions, which in turn would lead to organ initiation. To explain how auxin transporters could create hormone fluxes to distinct regions within the plant, different concepts have been proposed. A major hypothesis, canalization, proposes that the auxin transporters act by amplifying and stabilizing existing fluxes, which could be initiated, for example, by local diffusion. This convincingly explains the organised auxin fluxes during vein formation, but for the shoot apical meristem a second hypothesis was proposed, where the hormone would be systematically transported towards the areas with the highest concentrations. This implies the coexistence of two radically different mechanisms for PIN allocation in the membrane, one based on flux sensing and the other on local concentration sensing. Because these patterning processes require the interaction of hundreds of cells, it is impossible to estimate on a purely intuitive basis if a particular scenario is plausible or not. Therefore, computational modelling provides a powerful means to test this type of complex hypothesis. Here, using a dedicated computer simulation tool, we show that a flux-based polarization hypothesis is able to explain auxin transport at the shoot meristem as well, thus providing a unifying concept for the control of auxin distribution in the plant. Further experiments are now required to distinguish between flux-based polarization and other hypotheses., Author Summary Plants continuously generate new organs through the activity of populations of stem cells called meristems. The shoot apical meristem (SAM) initiates leaves, flowers, and lateral organs in highly ordered, spiraled, or whorled arrangements via a process called phyllotaxis. Auxin, a plant hormone, plays an essential role in this process. It is actively transported from cell to cell by specific membrane-associated transporters. In the SAM, this coordinated transport creates organized auxin fluxes resulting in hormone accumulation at precise positions, where organ formation is triggered. One key question in this process is to understand how auxin transport is coordinated. To address this issue, we have investigated a specific hypothesis, the canalization hypothesis, whereby every cell senses and attempts to stabilize existing hormone fluxes. Because such a patterning process would require the interaction of hundreds of cells, it is impossible to estimate on a purely intuitive basis whether it would be able to generate the observed organ positions. We, therefore, developed a computational approach to test this hypothesis and showed that a flux-based mechanism is indeed able to generate phyllotactic patterns and is consistent with biological data describing meristem development.
Published: 2008

22. OpenAlea: An open-source platform for the integration of heterogeneous FSPM components

Author: Samuel Dufour-Kowalski, Christophe Pradal, Nicolas Dones, Pierre Barbier de Reuille, Frédéric Boudon, Jérôme Chopard, David da Silva, Jean-Baptiste Durand, Pascal Ferraro, Christian Fournier, Yann Guédon, Aïda Ouangraoua, Colin Smith, Szymon Stoma, Frédéric Théveny, Hervé Sinoquet, Christophe Godin, Développement et amélioration des plantes (UMR DAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Université Montpellier 2 - Sciences et Techniques (UM2)-Centre National de la Recherche Scientifique (CNRS), Modeling plant morphogenesis at different scales, from genes to phenotype (VIRTUAL PLANTS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de la Recherche Agronomique (INRA)-Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Laboratoire de Physique et Physiologie Intégratives de l'Arbre Fruitier et Forestier (PIAF), Institut National de la Recherche Agronomique (INRA)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP), Modelling and Inference of Complex and Structured Stochastic Systems (MISTIS), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS), Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Department of Plant Sciences [Univ California Davis] (Plant - UC Davis), University of California [Davis] (UC Davis), University of California (UC)-University of California (UC), Architecture et Fonctionnement des Espèces Fruitières [AGAP] (AFEF), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Reproduction et développement des plantes (RDP), École normale supérieure de Lyon (ENS de Lyon)-Institut National de la Recherche Agronomique (INRA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS), Botanique et Modélisation de l'Architecture des Plantes et des Végétations (UMR AMAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud]), Centre National de la Recherche Scientifique (CNRS)-Université Montpellier 2 - Sciences et Techniques (UM2)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Laboratoire Jean Kuntzmann (LJK), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Centre National de la Recherche Scientifique (CNRS)-Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Department of Plant Sciences [Davis, CA], University of California-University of California, Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de la Recherche Agronomique (INRA)-École normale supérieure - Lyon (ENS Lyon), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut de Recherche pour le Développement (IRD [France-Sud]), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Inria Sophia Antipolis - Méditerranée (CRISAM), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut National Polytechnique de Grenoble (INPG), Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université Sciences et Technologies - Bordeaux 1-Université Bordeaux Segalen - Bordeaux 2, Architecture et Fonctionnement des Espèces Fruitières, Institut National de la Recherche Agronomique (INRA), and École normale supérieure - Lyon (ENS Lyon)-Institut National de la Recherche Agronomique (INRA)-Université Claude Bernard Lyon 1 (UCBL)
Subjects: open source, plant modeling, U10 - Informatique, mathématiques et statistiques, F01 - Culture des plantes, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, component framework
Abstract: International audience; The open source OpenAlea project's goal is to share and reuse heterogeneous models from the FSPM community. In this poster, we present our development strategy to create an open source research platform as well as some of the main components of OpenAlea.
Published: 2007

23. Spatio-Temporal Simulation Environment: A Microscopy Image Based Modelization Framework

Author: Edda Klipp and Szymon Stoma
Subjects: Spatial contextual awareness, Computer science, business.industry, Confocal, Microscopy, Image acquisition, Computer vision, Artificial intelligence, business, Instrumentation, Image based
Abstract: Recently, the advancements in single cell microscopy such as fluorescent proteins (FP), used as reporters of biomolecular interactions [1], together with new image acquisition techniques (e.g. confocal [2], two-photon [3]) cause an important increase in the role of the imaging in molecular biology. These techniques open up many new areas of research the popularization of a spatial context to name explicitly one of the most important among them.
Published: 2010

24. STSE: Spatio-Temporal Simulation Environment Dedicated to Biology

Author: Szymon Stoma, Susanne Gerber, Martina Fröhlich, and Edda Klipp
Subjects: Theoretical computer science, Saccharomyces cerevisiae Proteins, Computer science, Cell, Saccharomyces cerevisiae, lcsh:Computer applications to medicine. Medical informatics, Models, Biological, Biochemistry, Set (abstract data type), Software, Structural Biology, medicine, Image Processing, Computer-Assisted, Spatial analysis, lcsh:QH301-705.5, Molecular Biology, computer.programming_language, Fluorescent Dyes, Biological data, Molecular cell biology, Microscopy, business.industry, Applied Mathematics, Python (programming language), Computer Science Applications, medicine.anatomical_structure, Workflow, lcsh:Biology (General), Cytoplasm, lcsh:R858-859.7, Mitogen-Activated Protein Kinases, Software engineering, business, computer, Intracellular
Abstract: Background Recently, the availability of high-resolution microscopy together with the advancements in the development of biomarkers as reporters of biomolecular interactions increased the importance of imaging methods in molecular cell biology. These techniques enable the investigation of cellular characteristics like volume, size and geometry as well as volume and geometry of intracellular compartments, and the amount of existing proteins in a spatially resolved manner. Such detailed investigations opened up many new areas of research in the study of spatial, complex and dynamic cellular systems. One of the crucial challenges for the study of such systems is the design of a well stuctured and optimized workflow to provide a systematic and efficient hypothesis verification. Computer Science can efficiently address this task by providing software that facilitates handling, analysis, and evaluation of biological data to the benefit of experimenters and modelers. Results The Spatio-Temporal Simulation Environment (STSE) is a set of open-source tools provided to conduct spatio-temporal simulations in discrete structures based on microscopy images. The framework contains modules to digitize, represent, analyze, and mathematically model spatial distributions of biochemical species. Graphical user interface (GUI) tools provided with the software enable meshing of the simulation space based on the Voronoi concept. In addition, it supports to automatically acquire spatial information to the mesh from the images based on pixel luminosity (e.g. corresponding to molecular levels from microscopy images). STSE is freely available either as a stand-alone version or included in the linux live distribution Systems Biology Operational Software (SB.OS) and can be downloaded from http://www.stse-software.org/. The Python source code as well as a comprehensive user manual and video tutorials are also offered to the research community. We discuss main concepts of the STSE design and workflow. We demonstrate it's usefulness using the example of a signaling cascade leading to formation of a morphological gradient of Fus3 within the cytoplasm of the mating yeast cell Saccharomyces cerevisiae. Conclusions STSE is an efficient and powerful novel platform, designed for computational handling and evaluation of microscopic images. It allows for an uninterrupted workflow including digitization, representation, analysis, and mathematical modeling. By providing the means to relate the simulation to the image data it allows for systematic, image driven model validation or rejection. STSE can be scripted and extended using the Python language. STSE should be considered rather as an API together with workflow guidelines and a collection of GUI tools than a stand alone application. The priority of the project is to provide an easy and intuitive way of extending and customizing software using the Python language.
Full Text: View/download PDF

Catalog

Books, media, physical & digital resources

See catalog results

Searchworks

Select search scope, currently: Articles

Catalog

books, media & more in Jio Institute collections

Articles

journal articles & other e-resources

Refine your results

24 results on '"Szymon Stoma"'

1. Quantitative spatial analysis of haematopoiesis-regulating stromal cells in the bone marrow microenvironment by 3D microscopy

2. Modeling dynamics of cell-to-cell variability in TRAIL-induced apoptosis explains fractional killing and predicts reversible resistance.

3. STL-based analysis of TRAIL-induced apoptosis challenges the notion of type I/type II cell line classification.

4. Flux-based transport enhancement as a plausible unifying mechanism for auxin transport in meristem development.

5. Supplementary Information from In Vivo Fluorescence Imaging of the Activity of CEA TCB, a Novel T-Cell Bispecific Antibody, Reveals Highly Specific Tumor Targeting and Fast Induction of T-Cell–Mediated Tumor Killing

6. Data from In Vivo Fluorescence Imaging of the Activity of CEA TCB, a Novel T-Cell Bispecific Antibody, Reveals Highly Specific Tumor Targeting and Fast Induction of T-Cell–Mediated Tumor Killing

7. Video 1 from In Vivo Fluorescence Imaging of the Activity of CEA TCB, a Novel T-Cell Bispecific Antibody, Reveals Highly Specific Tumor Targeting and Fast Induction of T-Cell–Mediated Tumor Killing

8. Supplementary Figures from In Vivo Fluorescence Imaging of the Activity of CEA TCB, a Novel T-Cell Bispecific Antibody, Reveals Highly Specific Tumor Targeting and Fast Induction of T-Cell–Mediated Tumor Killing

9. Video 5 from In Vivo Fluorescence Imaging of the Activity of CEA TCB, a Novel T-Cell Bispecific Antibody, Reveals Highly Specific Tumor Targeting and Fast Induction of T-Cell–Mediated Tumor Killing

10. Video 4 from In Vivo Fluorescence Imaging of the Activity of CEA TCB, a Novel T-Cell Bispecific Antibody, Reveals Highly Specific Tumor Targeting and Fast Induction of T-Cell–Mediated Tumor Killing

11. Posttranscriptional Regulation of the Human LDL Receptor by the U2-Spliceosome

12. Detecting variation in starch granule size and morphology by high-throughput microscopy and flow cytometry

13. AutoTube: a novel software for the automated morphometric analysis of vascular networks in tissues

14. In Vivo Fluorescence Imaging of the Activity of CEA TCB, a Novel T-Cell Bispecific Antibody, Reveals Highly Specific Tumor Targeting and Fast Induction of T-Cell–Mediated Tumor Killing

15. Quantitative spatial analysis of haematopoiesis-regulating stromal cells in the bone marrow microenvironment by 3D microscopy

16. VEGF-A regulates cellular localization of SR-BI as well as transendothelial transport of HDL but not LDL

17. Hypoxia Induces a HIF-1-Dependent Transition from Collective-to-Amoeboid Dissemination in Epithelial Cancer Cells

18. Modeling Dynamics of Cell-to-Cell Variability in TRAIL-Induced Apoptosis Explains Fractional Killing and Predicts Reversible Resistance

19. Bridging the gaps in systems biology

20. STL-based analysis of TRAIL-induced apoptosis challenges the notion of type I/type II cell line classification

21. Flux based transport enhancement as a plausible unifying mechanism for Auxin transport in meristem development

22. OpenAlea: An open-source platform for the integration of heterogeneous FSPM components

23. Spatio-Temporal Simulation Environment: A Microscopy Image Based Modelization Framework

24. STSE: Spatio-Temporal Simulation Environment Dedicated to Biology

Catalog

Searchworks

Select search scope, currently: Articles Catalog books, media & more in Jio Institute collections Articles journal articles & other e-resources

Search

Search Constraints

Refine your results

Search Limiters

Topic

Publication Year Range

Language

Publication Type

Journal

Database

Publisher

24 results on '"Szymon Stoma"'

Search Results

Catalog

Select search scope, currently: Articles

Catalog

books, media & more in Jio Institute collections

Articles

journal articles & other e-resources