DOCSLIB.ORG

Abstracts of Talks at Mini-Symposia

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text

Load more

Abstracts of talks presented at mini-symposia at the 8-th European Conference on Mathematical and Theoretical Biology, and Annual Meeting of the Society for Mathematical Biology, Kraków, June 28 - July 2, 2011 Contents Mini-symposium 1. Vector-borne diseases 19 Mario Recker 19 Erida Gjini 19 Daniel Coffield Jr. 20 Yves Dumont 21 Ben Adams 22 Mini-symposium 2. Modelling dengue fever epidemiology 23 Maira Aguiar 23 Eduardo Massad 24 Helena Sofia Rodrigues 25 Jose Lourenço 26 Nico Stollenwerk 26 Mini-symposium 3. Recent developments in the study of Lotka- Volterra and Kolmogorov systems 29 Janusz Mierczynski 29 Zhanyuan Hou 29 Yasuhiro Takeuchi 30 Joanna Balbus 30 Stephen Baigent 31 Mini-symposium 4. Connecting microscale and macroscale models of cellular migration 33 Ruth Baker 33 Matthew Simpson 34 John Fozard 34 Evgeniy Khain 35 Alistair Middleton 36 Mini-symposium 5. The emergence of resistance in cancer using mathematical modelling 37 Alexander Anderson 37 Edward Flach 37 Jasmine Foo 38 Heiko Enderling 39 David Basanta 39 Mini-symposium 6. Modeling physiological systems: model validation and experimental design issues 41 Jerry Batzel 41 3 European Conference on Mathematical and Theoretical Biology 2011 Mette Olufsen 42 Mostafa Bachar 43 Franz Kappel 43 Johnny Ottesen 44 Julian King 45 Mini-symposium 7. Ecology and evolution of infectious diseases 47 Troy Day 47 Akira Sasaki 47 Andy White 48 Samuel Alizon 49 Eva Kisdi 50 Mini-symposium 8. Game theoretical modelling and optimization in evolution and ecology I 51 Magnus Lindh 51 Maciej Jan Ejsmond 52 Frédéric Grognard 52 Krzysztof Argasinski 53 Mini-symposium 9. Game theoretical modelling and optimization in evolution and ecology II 55 Christoforos Hadjichrysanthou 55 Irina Kareva 56 Chaitanya Gokhale 56 Vlastimil Krivan 57 Mark Broom 58 Mini-symposium 10. Multiscale modeling of biological systems: from physical tools to applications in cancer modeling I 59 Dirk Drasdo 59 Vitaly Volpert 60 Yangjin Kim 60 Paul Macklin 61 Luigi Preziosi 62 Mini-symposium 11. Multiscale modeling of biological systems: from physical tools to applications in cancer modeling II 65 John Lowengrub 65 Cristian Vasile Achim 65 Isabell Graf 66 Simon Praetorius 67 Arnaud Chauviere 67 Mini-symposium 12. Plants, growth and transport processes I 69 Mariya Ptashnyk 69 Wilfried Konrad 69 Anita Roth-Nebelsick 71 4 European Conference on Mathematical and Theoretical Biology 2011 Roland Pieruschka 72 Konstantinos Zygalakis 72 Mini-symposium 13. Plants, growth and transport processes II 75 Vitaly Volpert 75 Leah Band 75 Rosemary Dyson 76 Robert Nolet 77 Andrés Chavarría-Krauser 77 Mini-symposium 14. Mathematical Modeling of Mosquito-Borne Diseases 79 Alun Lloyd 79 Carrie Manore 79 Angelina Mageni Lutambi 80 Susana Barbosa 81 Nakul Chitnis 81 Mini-symposium 15. Modeling Dynamics of Complex Biological Systems 83 German Enciso 83 Deena Schmidt 83 Anna Cai 84 Richard Gejji 85 Paul Hurtado 85 Mini-symposium 16. Multiscale modelling of reaction kinetics in biology 87 Simon Cotter 87 Per Lotstedt 87 Konstantinos Zygalakis 88 Ovidiu Radulescu 89 Mini-symposium 17. Modeling viral hepatitis dynamics in-vivo and in-vitro in the era of direct anti-viral agents I 91 Avidan U. Neumann 91 Lior Strauss 92 Jeremie Guedj 93 Eva Herrmann 93 Robert B. Nachbar 94 Mini-symposium 18. Modeling viral hepatitis dynamics in-vivo and in-vitro in the era of direct anti-viral agents II 97 Harel Dahari 97 Jun Nakabayashi 97 Jonathan Forde 98 Narendra Dixit 99 Piero Colombatto 99 5 European Conference on Mathematical and Theoretical Biology 2011 Mini-symposium 19. Statistical methods in computational neuroscience II 103 Sonja Gruen 103 Ryota Kobayashi 104 Adeline Samson 105 Martin Paul Nawrot 106 Klaus Holst 107 Mini-symposium 20. Multiscale mathematics of liver: bridging molecular systems biology to virtual physiological human scale 109 Peter Hunter 109 Hermann-Georg Holzhuetter 109 Stefan Hoehme 110 Tim Ricken 111 Mini-symposium 21. Bridging the Divide: Cancer Models in Clinical Practice 113 Avner Friedman 113 Michael Meyer-Hermann 113 Marisa Eisenberg 114 Harsh Jain 115 Holger Perfahl 115 Mini-symposium 22. Bridging Time Scales in Biological Sciences 117 Konstantin Fackeldey 117 Volkmar Liebscher 118 Iris Antes 118 Susanna Röblitz 118 Mini-symposium 23. Delay Differential Equations and Applications I 121 Michael C. Mackey 121 Alberto d’Onofrio 121 Yukihiko Nakata 122 Jacek Miekisz 122 Philipp Getto 123 Mini-symposium 24. Delay Differential Equations and Applications II 125 Hassan Hbid 125 Samuel Bernard 125 Marek Bodnar 127 Monika Piotrowska 128 Antoni Leon Dawidowicz 128 Mini-symposium 25. Reports from US - African BioMathematics Initiative: Conservation Biology 131 Holly Gaff 131 Robyn Nadolny 132 6 European Conference on Mathematical and Theoretical Biology 2011 Gina Himes Boor 133 Ruscena Wiederholt 134 Stefano Ermon 134 Holly Gaff, Sadie Ryan 135 Mini-symposium 26. Mathematical models of gene regulation 137 Michael C. Mackey 137 Dagmar Iber 137 Thomas Höfer 138 Tomas Gedeon 138 Felix Naef 139 Mini-symposium 27. Heart rate dynamics: models and measures of complexity (part I) 141 Jose Amigó 141 Agnieszka Kaczkowska 141 Beata Graff 142 Jan Gierałtowski 143 Danuta Makowiec 144 Mini-symposium 28. Heart rate dynamics: models and measures of complexity (part II) 147 Piotr Podziemski 147 Monika Petelczyc 148 Teodor Buchner 149 Jaroslaw Piskorski 150 Jerzy Ellert 151 Krystyna Ambroch 152 Mini-symposium 29. Applications of nonnegative Radon measure spaces with metric structure to population dynamic models 153 Jose A. Carrillo 153 Piotr Gwiazda 153 Gael Raoul 154 Agnieszka Ulikowska 154 Grzegorz Jamróz 155 Mini-symposium 30. Modeling and analysis of tumor invasion I 157 Vittorio Cristini 157 Haralampos Hatzikirou 157 Caterina Guiot 158 Georgios Lolas 159 Mini-symposium 31. Modeling and analysis of tumor invasion II 161 Sergei Fedotov 161 Kevin Painter 161 Miguel A. Herrero 162 Chiara Giverso 162 Andreas Deutsch 163 7 European Conference on Mathematical and Theoretical Biology 2011 Mini-symposium 32. Epidemic models: Networks and stochasticity I165 Thomas House 165 David Sirl 165 Kieran Sharkey 166 Ken Eames 166 Peter Simon 167 Mini-symposium 33. Epidemic models: Networks and stochasticity II 169 Tom Britton 169 Alan McKane 169 Michael Taylor 170 Adam Kleczkowski 170 Christel Kamp 171 Mini-symposium 34. From one to many: Cell-based modeling of collective, emergent behaviors in biology -I 173 Dirk Drasdo 173 C. Anthony Hunt 174 Roeland Merks 175 Katarzyna A. Rejniak 176 James Glazier 176 Mini-symposium 35. From one to many: Cell-based modeling of collective, emergent behaviors in biology -II 179 Hans G. Othmer 179 Heiko Enderling 179 Holger Perfahl 180 Charles Reichhardt 181 Yi Jiang 181 Mini-symposium 36. Epidemics of Neglected Tropical Diseases 183 Lourdes Esteva 183 Claudia Ferreira 184 Roberto Kraenkel 185 Mini-symposium 37. Models in Spatial Ecology 187 Roberto Kraenkel 187 Renato Mendes Coutinho 187 Franciane Azevedo 188 Erin Dauson 189 Mini-symposium 38. Cell migration during development: modelling and experiment 191 Paul Kulesa 191 Louise Dyson 192 Matthew Simpson 193 Michelle Wynn 193 Kevin Painter 194 Mini-symposium 39. Biofluids, Solute Transport, and Hemodynamics195 8 European Conference on Mathematical and Theoretical Biology 2011 Harold Layton 195 Roger Evans 196 Anita Layton 197 Niels-Henrik Holstein-Rathlou 197 S. Randall Thomas 198 Mini-symposium 40. Analysis of mathematical models for cancer growth and treatment, Part I 201 Avner Friedman 201 Miguel A. Herrero 201 Anna Marciniak-Czochra 202 Alberto d’Onofrio 203 Mary Ann Horn 203 Mini-symposium 41. Analysis of mathematical models for cancer growth and treatment, Part II 205 Vincenzo Capasso 205 Alberto Gandolfi 206 K. Renee Fister 207 Urszula Ledzewicz 207 Jan Poleszczuk 208 Mini-symposium 42. Analysis of mathematical models for cancer growth and treatment, Part III 211 Arnaud Chauviere 211 Chloe Gerin 212 Svetlana Bunimovich 213 Sébastien Benzekry 214 Khalid Kassara 215 Mini-symposium 43. Analysis of mathematical models for cancer growth and treatment, Part IV 217 Antonio Fasano 217 Leonid Hanin 218 Miroslaw Lachowicz 219 Michal Krzeminski 219 Christian Groh 220 Mini-symposium 44. Analysis of mathematical models for cancer growth and treatment, Part V 223 Jean Clairambault 223 Heinz Schaettler 224 Evans Afenya 225 Andrzej Nowakowski 225 Akisato Kubo 226 Mini-symposium 45. Turing !! Turing?? on morphogenesis via experimental and theoretical approaches 229 S. Seirin Lee 229 Tetsuya Nakamura 230 9 European Conference on Mathematical and Theoretical Biology 2011 Denis Headon 231 Eamonn Gaffney 232 Shigeru Kondo 232 Mini-symposium 46. Statistical Analysis of Biological Signals I 233 Jaroslaw Harezlak 233 Christiana Drake 233 Markus Knappitsch 234 T. Kozubowski 235 Boguslaw Obara 236 Mini-symposium 47. Statistical Analysis of Biological Signals II 239 Aleksander Weron 239 Elżbieta Gajecka-Mirek 239 Jacek Leśkow 240 Mariola Molenda 241 A. Panorska 241 Mini-symposium 48. Fluid-structure interaction problems in biomechanics 243 Eunok Jung 243 Christina Hamlet 243 Karin Leiderman 244 Sarah Olson 245 Katarzyna A. Rejniak 245 Mini-symposium 49. Modeling of immune responses and calcium signaling I 247 Markus Covert 247 Marta Iwanaszko 247 Pawel Paszek 248 Roberto Bertolusso 249 Mini-symposium 50. Modeling of immune responses and calcium signaling II 251 James Faeder 251 Pawel Kocieniewski 252 Tomasz Lipniacki 253 Joanna Jaruszewicz 254 Paweł Żuk 255 Mini-symposium 51. Modeling of immune responses and calcium signaling III 257 Geneviève Dupont 257 Alexander Skupin 257 Je-Chiang Tsai 258 Beata Hat 258 Piotr Szopa 259 Mini-symposium 52. Modeling of immune responses and calcium signaling IV 261 10 European Conference on Mathematical and Theoretical Biology 2011 Jacek Miekisz 261 Paulina Szymanska 261 Jakub Pekalski 262 Michał Komorowski 263 Martin Falcke 264 Mini-symposium 53. Modeling of immune responses and calcium signaling V 265 Ruediger Thul 265 Kevin Thurley 265 Michal Dyzma 266 Zbigniew Peradzyski 267 Bogdan Kaźmierczak 268 Mini-symposium 54. B and T cell immune responses 271 Yoram Louzoun 271 Emmanuelle Terry 272 Marek Kochanczyk 273 Andrey Shuvaev 274 Mini-symposium 55.
Recommended publications
  • The Impact of Tumor Stromal Architecture on Therapy Response and Clinical Progression

    The Impact of Tumor Stromal Architecture on Therapy Response and Clinical Progression

    bioRxiv preprint doi: https://doi.org/10.1101/451047; this version posted October 24, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license. The impact of tumor stromal architecture on therapy response and clinical progression Philipp M. Altrock*,1, Nara Yoon*,2, Joshua A. Bull*,3, Hao Wu4, Javier Ruiz-Ramírez 5, Daria Miroshnychenko1, Gregory J. Kimmel1, Eunjung Kim1, Robert J. Vander Velde1, Katarzyna Rejniak1, Brandon J. Manley1, Fabian Spill*,6, Andriy Marusyk*,1 Abstract— Advances in molecular oncology research complicated by the fact that stroma is spatially complex and culminated in the development of targeted therapies that act on heterogeneous and dynamically changing during disease defined molecular targets either on tumor cells directly (such as progression and therapy response. This lack of attention to inhibitors of oncogenic kinases), or indirectly by targeting the tumor stroma is also fully applicable to clinical diagnostics and tumor microenvironment (such as anti-angiogenesis drugs). decision-making in clear cell renal cell carcinoma (ccRCC), an These therapies can induce strong clinical responses, when aggressive epithelial malignancy. Despite availability of properly matched to patients. Unfortunately, most targeted targeted therapy directed against neoplastic cells directly therapies ultimately fail as tumors evolve resistance. Tumors (mTOR inhibitors) or indirectly (anti-angiogenic agents), late consist not only of neoplastic cells, but also of stroma, whereby stage ccRCC remains incurable7,8. The different stages of this “stroma” is the umbrella term for non-tumor cells and disease show distinct stromal architectures (Figure 1).
  • Program Update: Year Three

    Program Update: Year Three

    PHYSICAL SCIENCES-ONCOLOGY CENTER PROGRAM Program Update: Year Three Fall 2012 Table of Contents 1. Executive Summary .................................................................................................................................................................1 2. Physical Sciences-Oncology Program Organization ...............................................................................................................5 2.1. Introduction ..................................................................................................................................................................7 2.2. Office of Physical Sciences-Oncology Mission ............................................................................................................7 2.3. Program History ...........................................................................................................................................................8 2.3.1 Overview of Spring 2008 Think Tank Meetings ................................................................................................8 2.3.2 Program Development and Funding History ...................................................................................................10 2.4. Strategic Approach and Objectives ...........................................................................................................................11 2.4.1 A Focus on Addressing “Big Questions” in Oncology ....................................................................................11
  • Microenvironment Influences Cancer Cell Mechanics from Tumor Growth

    Microenvironment Influences Cancer Cell Mechanics from Tumor Growth

    Microenvironment Influences Cancer Cell Mechanics from Tumor Growth 5 to Metastasis Deepraj Ghosh and Michelle R. Dawson Abstract cell motility can easily be combined with anal- The microenvironment in a solid tumor in- ysis of critical cell fate processes, including cludes a multitude of cell types, matrix pro- adhesion, proliferation, and drug resistance, teins, and growth factors that profoundly in- to determine how changes in mechanics con- fluence cancer cell mechanics by providing tribute to cancer progression. This biophysical both physical and chemical stimulation. This approach can be used to systematically inves- tumor microenvironment, which is both dy- tigate the parameters in the tumor that control namic and heterogeneous in nature, plays a cancer cell interactions with the stroma and to critical role in cancer progression from the identify specific conditions that induce tumor- growth of the primary tumor to the develop- promoting behavior, along with strategies for ment of metastatic and drug-resistant tumors. inhibiting these conditions to treat cancer. In- This chapter provides an overview of the bio- creased understanding of the underlying bio- physical tools used to study cancer cell me- physical mechanisms that drive cancer pro- chanics and mechanical changes in the tumor gression may provide insight into novel thera- microenvironment at different stages of cancer peutic approaches in the fight against cancer. progression, including growth of the primary tumor, local invasion, and metastasis. Quan- titative single cell biophysical analysis of in- Keywords tracellular mechanics, cell traction forces, and Cell mechanics · Deformation · Microrheology · Traction force · Epithelial to D. Ghosh mesenchymal transition (EMT) · Motility · Department of Molecular Pharmacology, Physiology, and Adhesion · Metastasis Biotechnology, Brown University, Providence, RI, USA M.
  • Nanostructures De Titanate De Baryum: Modélisation,Simulations

    Nanostructures De Titanate De Baryum: Modélisation,Simulations

    Nanostructures de titanate de Baryum : modélisation,simulations numériques et étude expérimentale. Gentien Thorner To cite this version: Gentien Thorner. Nanostructures de titanate de Baryum : modélisation,simulations numériques et étude expérimentale.. Autre. Université Paris Saclay (COmUE), 2016. Français. NNT : 2016SACLC093. tel-01427275 HAL Id: tel-01427275 https://tel.archives-ouvertes.fr/tel-01427275 Submitted on 5 Jan 2017 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. NNT : 2016SACLC093 THÈSE DE DOCTORAT DE L’UNIVERSITÉ PARIS-SACLAY PRÉPARÉE À “CENTRALESUPÉLEC” ECOLE DOCTORALE N° (573) Interfaces : approches interdisciplinaires, fondements, applications et innovation Physique Par Monsieur Gentien Thorner BARIUM TITANATE NANOSTRUCTURES: modeling, numerical simulations and experiments Thèse présentée et soutenue à Châtenay-Malabry, le 28 novembre 2016: Composition du Jury : Philippe Papet Professeur, Université de Montpellier Président Laurent Bellaiche Professeur, Université d'Arkansas Rapporteur Gregory Geneste Ingénieur de Recherche, CEA Rapporteur Christine Bogicevic Ingénieur de Recherche, CentraleSupélec Examinatrice Jean-Michel Kiat Directeur de Recherche, CentraleSupélec Directeur de thèse 3 Remerciements À un moment relativement crucial d’une thèse, quelques pensées iraient tout d’abord vers son directeur Jean-Michel Kiat pour un soutien indéfectible pendant une période s’étalant d’un premier contact lors d’un stage de recherche en deux mille dix jusqu’à la soutenance en deux mille seize.
  • Exploratory Modelling and the Case of Turing Patterns

    Exploratory Modelling and the Case of Turing Patterns

    to appear in: Christian, A., Hommen, D., Retzlaff, N., Schurz, G. (Eds.): Philosophy of Science. Between the Natural Sciences, the Social Sciences, and the Humanities, New York: Springer 2018. Models in Search of Targets: Exploratory Modelling and the Case of Turing Patterns Axel Gelfert Department of Philosophy, Literature, History of Science & Technology Technical University of Berlin Straße des 17. Juni 135, 10623 Berlin email: [email protected] Abstract Traditional frameworks for evaluating scientific models have tended to downplay their exploratory function; instead they emphasize how models are inherently intended for specific phenomena and are to be judged by their ability to predict, reproduce, or explain empirical observations. By contrast, this paper argues that exploration should stand alongside explanation, prediction, and representation as a core function of scientific models. Thus, models often serve as starting points for future inquiry, as proofs of principle, as sources of potential explanations, and as a tool for reassessing the suitability of the target system (and sometimes of whole research agendas). This is illustrated by a case study of the varied career of reaction- diffusion models in the study of biological pattern formation, which was initiated by Alan Turing in a classic 1952 paper. Initially regarded as mathematically elegant, but biologically irrelevant, demonstrations of how, in principle, spontaneous pattern formation could occur in an organism, such Turing models have only recently rebounded, thanks to advances in experimental techniques and computational methods. The long-delayed vindication of Turing’s initial model, it is argued, is best explained by recognizing it as an exploratory tool (rather than as a purported representation of an actual target system).
  • 3D Models of Angiogenesis

    3D Models of Angiogenesis

    ID: XX-XXXX; -19-0026 1 1 E Zucchelli et al. 3D angiogenesis models 1:1 H135–H143 MINI REVIEW New artery of knowledge: 3D models of angiogenesis Eleonora Zucchelli1, Qasim A Majid1 and Gabor Foldes1,2 1National Heart and Lung Institute, Imperial College London, London, UK 2Heart and Vascular Center, Semmelweis University, Budapest, Hungary Correspondence should be addressed to G Foldes: [email protected] Abstract Angiogenesis and vasculogenesis are complex processes by which new blood vessels are Key Words formed and expanded. They play a pivotal role not only in physiological development f angiogenesis and growth and tissue and organ repair, but also in a range of pathological conditions, f endothelial cells from tumour formation to chronic inflammation and atherosclerosis. Understanding the f 3D assays multistep cell-differentiation programmes and identifying the key molecular players of physiological angiogenesis/vasculogenesis are critical to tackle pathological mechanisms. While many questions are yet to be answered, increasingly sophisticated in vitro, in vivo and ex vivo models of angiogenesis/vasculogenesis, together with cutting-edge imaging techniques, allowed for recent major advances in the field. This review aims to summarise the three-dimensional models available to study vascular network formation and to discuss advantages and limitations of the current systems. Introduction The major role of the vascular system is to supply sufficient vessels, another type of vasculature exists, which form the levels of oxygen and nutrients to the bodily organs. lymphatic network. Lymphatic vessels are unidirectional, Naturally, any disruption to this system manifests itself blind-ended capillaries, which arise from the venous as a host of diseases including, but not limited to, stroke, vasculature, and they are also formed by endothelial peripheral artery disease and other ischaemic cardiovascular cells.
  • Vegetative Turing Pattern Formation: a Historical Perspective

    Vegetative Turing Pattern Formation: a Historical Perspective

    Vegetative Turing Pattern Formation: A Historical Perspective Bonni J. Kealy* and David J. Wollkind Washington State University Department of Mathematics Pullman, WA 99164-3113 Joint Mathematics Meetings AMS-ALS Special Session on the Life and Legacy of Alan Turing January 2012 Bonni J. Kealy* and David J. Wollkind Washington State University Vegetative Turing Pattern Formation: A Historical Perspective Alan Turing Born Alan Mathison Turing 23 June 1912 Maida Vale, London, England Died 7 June 1954 (aged 41) Wilmslow, Chesire, England Figure: Turing at the time of his election to Fellowship of the Royal Society Bonni J. Kealy* and David J. Wollkind Washington State University Vegetative Turing Pattern Formation: A Historical Perspective Alan Turing: World Class Distance Runner Alan Turing achieved world-class Marathon standards. His best time of 2 hours, 46 minutes, 3 seconds, was only 11 minutes slower than the winner in the 1948 Olympic Games. In a 1948 cross-country race he finished ahead of Tom Richards who was to win the silver medal in the Olympics. Figure: From The Times, 25 August 1947 Figure: Running in 1946 Bonni J. Kealy* and David J. Wollkind Washington State University Vegetative Turing Pattern Formation: A Historical Perspective Paper: The Chemical Basis of Morphogenesis Figure: An example of a `dappled' Figure: Philosophical Transactions of pattern as resulting from a type (a) the Royal Society of London. Series B, morphogen system. A marker of unit Biological Sciences, Vol. 237, No. 641. length is shown. (Aug. 14, 1952), pp. 37-72. Bonni J. Kealy* and David J. Wollkind Washington State University Vegetative Turing Pattern Formation: A Historical Perspective Examples Bonni J.
  • Alan Turing 1 Alan Turing

    Alan Turing 1 Alan Turing

    Alan Turing 1 Alan Turing Alan Turing Turing at the time of his election to Fellowship of the Royal Society. Born Alan Mathison Turing 23 June 1912 Maida Vale, London, England, United Kingdom Died 7 June 1954 (aged 41) Wilmslow, Cheshire, England, United Kingdom Residence United Kingdom Nationality British Fields Mathematics, Cryptanalysis, Computer science Institutions University of Cambridge Government Code and Cypher School National Physical Laboratory University of Manchester Alma mater King's College, Cambridge Princeton University Doctoral advisor Alonzo Church Doctoral students Robin Gandy Known for Halting problem Turing machine Cryptanalysis of the Enigma Automatic Computing Engine Turing Award Turing test Turing patterns Notable awards Officer of the Order of the British Empire Fellow of the Royal Society Alan Mathison Turing, OBE, FRS ( /ˈtjʊərɪŋ/ TEWR-ing; 23 June 1912 – 7 June 1954), was a British mathematician, logician, cryptanalyst, and computer scientist. He was highly influential in the development of computer science, giving a formalisation of the concepts of "algorithm" and "computation" with the Turing machine, which can be considered a model of a general purpose computer.[1][2][3] Turing is widely considered to be the father of computer science and artificial intelligence.[4] During World War II, Turing worked for the Government Code and Cypher School (GC&CS) at Bletchley Park, Britain's codebreaking centre. For a time he was head of Hut 8, the section responsible for German naval cryptanalysis. He devised a number of techniques for breaking German ciphers, including the method of the bombe, an electromechanical machine that could find settings for the Enigma machine.
  • Correspondences Between Parameters in a Reaction-Diffusion Model and Connexin Functions During Zebra�Sh Stripe Formation

    Correspondences Between Parameters in a Reaction-Diffusion Model and Connexin Functions During ZebraSh Stripe Formation

    Correspondences between parameters in a reaction-diffusion model and connexin functions during zebrash stripe formation Akiko Nakamasu ( [email protected] ) Kumamoto Univ. Article Keywords: Pattern formation, Turing pattern, mathematical model, Reaction-diffusion system Posted Date: July 14th, 2021 DOI: https://doi.org/10.21203/rs.3.rs-658862/v2 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License 1 Title; Correspondences between parameters in a reaction-diffusion 2 model and connexin functions during zebrafish stripe formation. 3 Running Head; Link between experimental and theoretical Turing patterns 4 Akiko Nakamasu* 5 International Research Organization for Advanced Science and Technologies, Kumamoto 6 University, 2-39-1 Kurokami, Chuo-Ku, Kumamoto, 860-8555 Japan 7 * Corresponding author 8 Email: [email protected] 9 Keywords 10 Pattern formation, Turing pattern, Mathematical model, Reaction-diffusion system. 11 1 1 Statements 2 I had read and understood your journal’s policies, and believe that neither the 3 manuscript nor the study violates any of these, as follow. Data can be provided on request. 4 This research was supported by Grant-in-Aid for Scientific Research on Innovative Areas 5 (Japan Society for the Promotion of Science), Periodicity, and its modulation in plants. 6 No.20H05421. There are no conflicts of interest to declare. This study is not related to the 7 ethics approval and patient consent statement. Then the manuscript including pictures from 8 Usui et al. (2019) Development 146, dev181065 in Fig.4 and Fig. 6. The use of the pictures 9 was permitted by the author M.
  • According to Turing, a Kinetic System with Two Or More Morphogenes And

    According to Turing, a Kinetic System with Two Or More Morphogenes And

    Turing Instabilities and Patterns Near a Hopf Bifurcation Rui Dilão Grupo de Dinâmica Não-Linear, Instituto Superior Técnico, Av. Rovisco Pais, 1049-001 Lisboa, Portugal. and Institut des Hautes Études Scientifiques, 35, route de Chartres, 91440, Bures-sur-Yvette, France. [email protected] Phone: +(351) 218417617; Fax: +(351) 218419123 Abstract We derive a necessary and sufficient condition for Turing instabilities to occur in two- component systems of reaction-diffusion equations with Neumann boundary conditions. We apply this condition to reaction-diffusion systems built from vector fields with one fixed point and a supercritical Hopf bifurcation. For the Brusselator and the Ginzburg- Landau reaction-diffusion equations, we obtain the bifurcation diagrams associated with the transition between time periodic solutions and asymptotically stable solutions (Turing patterns). In two-component systems of reaction-diffusion equations, we show that the existence of Turing instabilities is neither necessary nor sufficient for the existence of Turing pattern type solutions. Turing patterns can exist on both sides of the Hopf bifurcation associated to the local vector field, and, depending on the initial conditions, time periodic and stable solutions can coexist. Keywords: Turing instability, Turing patterns, Hopf bifurcation, Brusselator, Ginzburg- Landau. 1 1-Introduction A large number of real systems are modeled by non-linear reaction-diffusion equations. These systems show dynamic features resembling wave type phenomena, propagating solitary pulses and packets of pulses, stable patterns and turbulent or chaotic space and time behavior. For example, in the study of the dispersal of a population with a logistic type local law of growth, Kolmogorov, Petrovskii and Piscunov, [1], introduced a nonlinear reaction- diffusion equation with a solitary front type solution, propagating with finite velocity.
  • A Bibliography of Publications of Alan Mathison Turing

    A Bibliography of Publications of Alan Mathison Turing

    A Bibliography of Publications of Alan Mathison Turing Nelson H. F. Beebe University of Utah Department of Mathematics, 110 LCB 155 S 1400 E RM 233 Salt Lake City, UT 84112-0090 USA Tel: +1 801 581 5254 FAX: +1 801 581 4148 E-mail: [email protected], [email protected], [email protected] (Internet) WWW URL: http://www.math.utah.edu/~beebe/ 24 August 2021 Version 1.227 Abstract This bibliography records publications of Alan Mathison Turing (1912– 1954). Title word cross-reference 0(z) [Fef95]. $1 [Fis15, CAC14b]. 1 [PSS11, WWG12]. $139.99 [Ano20]. $16.95 [Sal12]. $16.96 [Kru05]. $17.95 [Hai16]. $19.99 [Jon17]. 2 [Fai10b]. $21.95 [Sal12]. $22.50 [LH83]. $24.00/$34 [Kru05]. $24.95 [Sal12, Ano04, Kru05]. $25.95 [KP02]. $26.95 [Kru05]. $29.95 [Ano20, CK12b]. 3 [Ano11c]. $54.00 [Kru05]. $69.95 [Kru05]. $75.00 [Jon17, Kru05]. $9.95 [CK02]. H [Wri16]. λ [Tur37a]. λ − K [Tur37c]. M [Wri16]. p [Tur37c]. × [Jon17]. -computably [Fai10b]. -conversion [Tur37c]. -D [WWG12]. -definability [Tur37a]. -function [Tur37c]. 1 2 . [Nic17]. Zycie˙ [Hod02b]. 0-19-825079-7 [Hod06a]. 0-19-825080-0 [Hod06a]. 0-19-853741-7 [Rus89]. 1 [Ano12g]. 1-84046-250-7 [CK02]. 100 [Ano20, FB17, Gin19]. 10011-4211 [Kru05]. 10th [Ano51]. 11th [Ano51]. 12th [Ano51]. 1942 [Tur42b]. 1945 [TDCKW84]. 1947 [CV13b, Tur47, Tur95a]. 1949 [Ano49]. 1950s [Ell19]. 1951 [Ano51]. 1988 [Man90]. 1995 [Fef99]. 2 [DH10]. 2.0 [Wat12o]. 20 [CV13b]. 2001 [Don01a]. 2002 [Wel02]. 2003 [Kov03]. 2004 [Pip04]. 2005 [Bro05]. 2006 [Mai06, Mai07]. 2008 [Wil10]. 2011 [Str11]. 2012 [Gol12]. 20th [Kru05]. 25th [TDCKW84].
  • Beyond Turing: Far-From-Equilibrium Patterns and Mechano-Chemical Feedback

    Beyond Turing: Far-From-Equilibrium Patterns and Mechano-Chemical Feedback

    bioRxiv preprint doi: https://doi.org/10.1101/2021.03.10.434636; this version posted March 11, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Beyond Turing: Far-from-equilibrium patterns and mechano-chemical feedback Frits Veerman,* Moritz Mercker†and Anna Marciniak-Czochra‡ Abstract Turing patterns are commonly understood as specific instabilities of a spatially homogeneous steady state, resulting from activator-inhibitor interaction destabilised by diffusion. We argue that this view is restrictive and its agreement with biological observations is problematic. We present two alternative to the ‘classical’ Turing analysis of patterns. First, we employ the abstract framework of evolution equations to enable the study of far-from-equilibrium patterns. Second, we introduce a mechano-chemical model, with the surface on which the pattern forms being dynamic and playing an active role in the pattern formation, effectively replacing the inhibitor. We highlight the advantages of these two alternatives vis- à-vis the ‘classical’ Turing analysis, and give an overview of recent results and future challenges for both approaches. Keywords: pattern formation, evolution equations, reaction-diffusion, mechano-chemical models, far-from-equilibrium patterns 1 Introduction The prevailing ‘Turing theory’ of pattern formation in biology, and treatment of associated ‘Turing pat- terns’, can be traced to the work of Hans Meinhardt, germinating from his seminal paper with Alfred Gierer [1]. While Turing in his pioneering paper [2] introduced the framework of reaction-diffusion equations, and the different scenarios in which pattern-like solutions can occur, Gierer and Meinhardt took a distinctly more ‘modelling’ point of view.