000032177 001__ 32177
000032177 005__ 20210121082904.0
000032177 0247_ $$2doi$$a10.1371/journal.pcbi.1004129
000032177 0248_ $$2sideral$$a90508
000032177 037__ $$aART-2015-90508
000032177 041__ $$aeng
000032177 100__ $$aTorres-Sánchez, A.
000032177 245__ $$aAn integrative approach for modeling and simulation of heterocyst pattern formation in cyanobacteria filaments
000032177 260__ $$c2015
000032177 5060_ $$aAccess copy available to the general public$$fUnrestricted
000032177 5203_ $$aHeterocyst differentiation in cyanobacteria filaments is one of the simplest examples of cellular differentiation and pattern formation in multicellular organisms. Despite of the many experimental studies addressing the evolution and sustainment of heterocyst patterns and the knowledge of the genetic circuit underlying the behavior of single cyanobacterium under nitrogen deprivation, there is still a theoretical gap connecting these two macroscopic and microscopic processes. As an attempt to shed light on this issue, here we explore heterocyst differentiation under the paradigm of systems biology. This framework allows us to formulate the essential dynamical ingredients of the genetic circuit of a single cyanobacterium into a set of differential equations describing the time evolution of the concentrations of the relevant molecular products. As a result, we are able to study the behavior of a single cyanobacterium under different external conditions, emulating nitrogen deprivation, and simulate the dynamics of cyanobacteria filaments by coupling their respective genetic circuits via molecular diffusion. These two ingredients allow us to understand the principles by which heterocyst patterns can be generated and sustained. In particular, our results point out that, by including both diffusion and noisy external conditions in the computational model, it is possible to reproduce the main features of the formation and sustainment of heterocyst patterns in cyanobacteria filaments as observed experimentally. Finally, we discuss the validity and possible improvements of the model.
000032177 536__ $$9info:eu-repo/grantAgreement/ES/MINECO/FIS2011-25167$$9info:eu-repo/grantAgreement/ES/MINECO/FIS2012-38266-C02-01
000032177 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000032177 590__ $$a4.587$$b2015
000032177 591__ $$aMATHEMATICAL & COMPUTATIONAL BIOLOGY$$b5 / 56 = 0.089$$c2015$$dQ1$$eT1
000032177 591__ $$aBIOCHEMICAL RESEARCH METHODS$$b9 / 77 = 0.117$$c2015$$dQ1$$eT1
000032177 592__ $$a3.476$$b2015
000032177 593__ $$aCellular and Molecular Neuroscience$$c2015$$dQ1
000032177 593__ $$aComputational Theory and Mathematics$$c2015$$dQ1
000032177 593__ $$aEcology$$c2015$$dQ1
000032177 593__ $$aMolecular Biology$$c2015$$dQ1
000032177 593__ $$aGenetics$$c2015$$dQ1
000032177 593__ $$aModeling and Simulation$$c2015$$dQ1
000032177 593__ $$aEcology, Evolution, Behavior and Systematics$$c2015$$dQ1
000032177 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000032177 700__ $$0(orcid)0000-0002-3484-6413$$aGómez-Gardeñes, J.$$uUniversidad de Zaragoza
000032177 700__ $$0(orcid)0000-0002-9551-624X$$aFalo, F.$$uUniversidad de Zaragoza
000032177 7102_ $$12003$$2395$$aUniversidad de Zaragoza$$bDpto. Física Materia Condensa.$$cÁrea Física Materia Condensada
000032177 773__ $$g11, 3 (2015), [18 pp.]$$pPLoS Comput. Biol.$$tPLoS computational biology$$x1553-734X
000032177 8564_ $$s1131824$$uhttps://zaguan.unizar.es/record/32177/files/texto_completo.pdf$$yVersión publicada
000032177 8564_ $$s103764$$uhttps://zaguan.unizar.es/record/32177/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000032177 909CO $$ooai:zaguan.unizar.es:32177$$particulos$$pdriver
000032177 951__ $$a2021-01-21-08:17:25
000032177 980__ $$aARTICLE