FORMA 14 : Complex Systems Workshop: Sevilla 22-25 Octubre 2014
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I. The Workshop
WHAT IS FORMA? FORMA is an initiative to promote agent-based thinking among researchers from every single discipline such as art, biology, maths, sociology, economics and urbanism. Agent-based systems are used to study complex realities, which can hardly be shaped by traditional methods. The aim of this special event is therefore, to provide participants with a solid agent-based modelling expertise that will enhance their understanding of complex processes within their field of research. The workshop is organized around a limited number of case studies, selected among proposals submitted by researchers from all over Spain. Each case study has a clear target to be addressed during the three full-days of the event, and the authors of the proposal are assigned an agent-based modelling expert plus a team of students, typically from Computer Science and Mathematics, that are in the process of developing modelling skills. *** Forma provides an interdisciplinary space where students can work together to solve real problems, while researchers from any field meet the opportunity to work hand in hand with highly skilled programmers towards their research targets. This event unfolds as a great networking tool, where minds with different assets meet to pursue common interests. After this first edition, many of the teams have continued developing their models, or established other kinds of collaboration, even sending abstracts together for international conferences etc. Apart from the "hands-on" work, the workshop includes top level speakers from the European Network for Social Intelligence, as well as daily tutorials on "soft-learning- curve" languages for agent-based modelling, such as NetLogo, and also Processing. Researchers with no previous knowledge of these tools, will develop enough understanding to prototype new case studies in the future through agent-based modelling, even if only conceptually.
OPENCALLANDSELECTIONPROCESS Inthissecondedition, atotalof tenproposals were received,from whicha totalof seven were selected, leaving out only three proposals that were too broad for the scopeoftheworkshopornotaimedatagentbasedsystems. Thecommunicationstrategyoftheopencallforproposals,beingthesecondedition of FORMA, was mainly channelled via mailing lists of the major universities in Andalucía and other important universities in Spain. Four of the selected proposals had some connection to the University of Seville, followed by University Pablo de Olavide(1),UniversityofGranada(1)andCentralUniversityofEcuador(1). Other digital channels like twitter were used intensively to spread the call for proposalsandparticipants. SELECTIONCRITERIAFORCASESTUDIES FORMA is targeted towards studying complex processes by means of agentbased modelling. The criteria followed during the selection of case studies in this edition havebeen: Contemporarysubjects:Thecasestudyshall addressatopicalong thelinesof the new information paradigm, digital societies, technological advances and other subjectsinwhichnetworkbehaviourplaysadecisiverole. Complex processes: Models should aim to clarify a welldefined phenomenon, whichduetoitsinherentcomplexityrequiresadvancedcomputationaltechniquesand aninterdisciplinaryapproach. Spatial interaction: The workshop targets the study of complex processes in whichspatiality,beitliteralorabstract,hasastronginfluenceoverthebehaviourof thesystematplay. Visual understanding: Proposals shall request for results that can be directly communicatedthroughagraphicvisualizationofthemodelandbeeasilyunderstood byallaudiences. Clear identification of the problem: Because of the limited time to develop the modelsduringtheworkshop,itisfundamentalthatakeyquestionisclearlyidentified intheauthor'sproposal.
CASESTUDIESSELECTED N1.LeisureVillage Correspondanceauthor:AlbertodeAustriaMillán N2.HostingcapacityofelPalmardelaFrontera Correspondanceauthor:AntonioPiñeroValverde N3.Evolutionofhumancooperationandviolence:theoriginof metapopulationsandsocioeconomicnetworkswithinprehistory Correspondanceauthor:DanielGarcíaRivero N4.PedestrianizationofthehistoricalcentreofQuito.awaytotheconservation oftheculturalheritage Correspondanceauthor:ElizabethRegaladoBolaños N5.MetropolitanexpansionandmobilityinAndalucia Correspondanceauthor:GwendolinedeOliveiraNeves N6.Electoratebehaviorduring2004Spainelections Correspondanceauthor:JoséLuisSáezLozano N7.Couplingbetweenmessengerrnasynthesisanddegradationduring genomeexpression Correspondanceauthor:SebastiánChávezdeDiego *N=NetLogo,P=Processing
TEAMS & ORGANIZATION Once the case studies were selected, one month prior to the workshop, a brief documentation explaining each proposal was uploaded to the website. Then, all participants were invited to fill in a form via email indicating three of the selected case studies ordered by preference, and also, a short note on their background. With that information the organization distributed the participants in teams of 4 to 8 members per team, and assigned a modelling assistant to each one of them as well. Every participant was informed of the team they had been assigned to beforehand. The workshop opened on Wednesday 22th Oct, 5.00 pm with a short introduction. Then followed a lecture by Joaquín Borrego Díaz introducing agent based modeling. And finally, a round of ten-minute presentations of all the proposals, carried out by their respective authors. Next day kicked off at 9.00 am with a lecture by Fernando Sancho Caparrini, suggesting the ODD(SEA) protocol as a means to create a common understanding between researchers and technical modelling experts. Afterwards, teams started working on their proposals until evening, first addressing the main features of the model on a conceptual level, and then outlining the structure of the code itself (teams were asked to write an ODD(SEA) protocol and to design work-flow diagrams of their models before actually coding them). Modeling assistants had the task to lead and work hand in hand with all team members. Directors performed individual desk-critics with all teams throughout the day. During the first half of the day, there were two parallel programming sessions of two hours each, to introduce Processing and NetLogo to those participants who had no previous experience with either of the two languages. The third day, Friday 24th, opened with another (morning) lecture by Fernando Sancho Caparrini, looking into real applications of agent based modeling. The rest of the day unfolded quite like Thursday, including the two-hour programming sessions, working in teams (now focusing on code development) and desk-critics. Finally, on Saturday 25th, there was a last lecture by Gonzalo A. Aranda Corral around the potential of JADE. Afterwards, all teams presented the results of their models throughout the morning, and by the end of the last presentation there was a discussion session and debate which marked the end of FORMA14.
CREDITS Organized by: Culturadigital http://culturadigital.cc Chair of Computer Science and Artificial Intelligence, Universidad de Sevilla http://www.cs.us.es Collaborators: Chair of Information Technology, Universidad de Huelva http://www.uhu.es/dti/ Support: eCompleXcity Project TIC-6064 http://ecomplexcity.org Fidetia
Directors: Joaquín Borrego Díaz. Chair of Computer Science and Artificial Intelligence, Universidad de Sevilla Fernando Sancho Caparrini. Chair of Computer Science and Artificial Intelligence, Universidad de Sevilla Gonzalo A. Aranda Corral. Chair of Information Technology, Universidad de Huelva Coordination: Jaime de Miguel Rodríguez Ismael Domínguez Sánchez de la Blanca Modeling assistants: Juan Galán Páez David Solís Martín Yago Fernández Rodríguez Francisco Romero Campero Jesús Horno Cobo Fernando de la Matta Martín Angel Linares García Teodoro González Manchón José Manuel Camacho Sosa María Eugenia Villafañe Jaime de Miguel Rodríguez Graphic design: Javier Aldarias Chinchilla Special thanks: To all the persons not listed above, involved in the coordination of the event, and to all participants, especially students, whose enthusiasm and commitment gave life to the second edition of this workshop.
systemlevel(asmacrodynamicstheories)? Thesefeaturesdirectlyinfluencedbythetheoriesfitintothemacroconfiguration. Will the model provide the evidence on these fundamental principles, such as its scope,itsusefulnessinrealscenarios,validationorindicationsformodification? The translation to the potential scenarios becomes real . Similarly we can work on generic aspects with a set of random patches, we could set a particular context importingapatchesmeshpresetaccordingtoacitystudyoradevelopmentplan. Doyouuseconsolidatednoveltheoriesormodel? Theprojectwandersbetweendifferentconcepts.Ontheonehandtheclassicstudy of natural forms and developments of the city, social parameterization and its symbioticrelationshipwitheachofthezoning.Furthermore,therelevanceofincluding thesedynamicswithinthebroadspectrumofthenewscienceofcities,wherefrom thecalculationthroughthenewtools,wecangetextendeddata. Wecouldthereforequoteabasicbibliography: ChristopherAlexander:Languagepatterns. MichaelBatty:Thenewscienceofcities. DavidGrahameShame:UrbanDesignsince1945. KevinLynch:Thegoodshapeofthecity. JaneJacobs:DeathandLifeofGreatAmericanCities. 4.2.Emergency: Which results in the model are expected to vary in complex ways and perhaps unpredictable inside a change of the particular characteristics of individuals or environment? Especially the distribution in the patches, as an end itself. Another full and unpredictableresultwouldbethetotallifeofthewholepopulation,oritsfluctuationin time. Whatmodelresultsalreadyimposedbytherulesandthereforelessdependentonthe behaviorofindividuals? Acrucialpartoftheoriginoftheseresultsistherulethatimposethereplacementof buildings"abandoned"bythecommonusearoundeachpatch. 4.3.Adaptation: Whichadaptivetraitshavetheindividuals? Eachturtlehasadaptivecapacities.Thisoccursfromtwoviews: a. A collective vision: At the end of each stage if the turtle has been happy generatesanewfamilywithkinship. b. An individualistic view: At the end of each phase its happiness is rechecked abouttheirchangedenvironment. Noneofthesechangesoccursintheagentitself. Whichrulesdotheyhavetomakedecisionsorchangetheirbehaviorinresponseto changesinthemselvesorintheirenvironment? In their relationship with the environment, we will handle a variable threshold for happinessthatwilldeterminethedynamicbehavioroftheturtlewiththepatch. In connection with its offspring, the options are equitables to generate the same or directlyadjacenttypes.(Eg.Ifyourtypeis1,youcanonlygenerate1or2) Dothesefeaturestrytoincreasesomekindofindicatorofindividualsuccessrelated toitsobjectives?Orjusttheindividualsreproducecertainbehaviorsthatareimplicitly
assumed to be conducive to success or adaptation (eg, "scroll right 70% of the time")? We could consider an indicator of individual success if this process of natural adaptation achieves to extend the life of the whole set. An equitable distribution of areasortypesoffamilyvaluescouldalsobeusefulinthistrial.True,thisistheresult ofrepetitiveactionsthatsupplythedatafromtheircombinationovertime. 4.4.Objectives: What are the objectives individuals through adaptive processes that govern their behavior? Behappy,buildahome(building)andgenerateoffspring. Howcanwemeasuretheseobjectives,aswellastheircompliance? Wecanknowthestatisticsofhappinessandthenumberofnewindividuals/houses foreachphase. Which criteria use the individual agents to evaluate alternatives when they have to makedecisions? Itsdynamicintheworldwillcomedependingonthedistributionofownaffinitiesofits kind. 4.5.Learning: Dotheychangeadaptivetraitsovertimeasaresultoftheexperience? Notbytheagents. Aretheyawareofchanges,includingplannedoraresimplyresponsestoachanging environment? Theyaresimpleanswers.Theonlyplanningagentcorrespondstotheobserver. 4.6.Prediction: Thereisnotpredictivecapabilitieswithintheactorsinthemodel. 4.7.Perception: Whatstatevariables,internalortheenvironment,itisassumedthatagentsperceive? The turtles perceive the type of plot (patch) in an editable radio. Building plots perceivethenumberofagentsinitslocation. Whatmeasurementmodelsareusedbytheagentsforthisperception? Turtleshaveasystemofweightsaccordingtoaffinities.Thebuildingcellsperceive thenumberofagents. Whatisthescopeofthesignalsthatanagentcanperceive,localorglobal? Theradiusoftheturtlesisinalocalscopeanditcanbeedited.Forthescopeofthe buildingitwillobviouslylimitbyitsborders. 4.8.Interaction: Whattypesofinteractionsareassumedtoberelevantbetweentheagents? The optimal combination of patches for each agent turtle, causes it the stop at the properlocation.Converselyiftheturtledoesnotfindtheoptimalplaceinthepatchit will continue moving. The stay at the end of a phase above any cell causes its change into edification. If It keeps moving without solution for a complete phase it causesthedisappearanceoftheturtle.Theabsenceofnewusersinanalreadybuilt cell,causesittheincorporationtotheremainingpatcheswitharandomusebasedin themostcommonuses.
Istheredirectinteractions,asiftheencountersbetweenagentsinfluenceoverthem? The one that came from the process of building construction and their subsequent destruction. Isthereindirectinteractions,asifcompetingforanintermediateresource? Yes,thecombinationbetweenthepatchesandtheturtles'needsindirectlyinfluences theoptionsofhappinessofthem. 4.9.Randomness: Whichprocessesaremodeledassumingtheyarefullyorpartiallyrandom? On a percentage basis, the placement of the initial state of the zones is done randomly. (Exclude in this set the cells "houses") This is done through an editable numberofareagenerators. Placingtheturtleagents(people)onthemapintheinitialstateisrandom. Thepositionofthedescendants,althoughtitisconditionedwithinapatcharoundthe parent,itisdonerandomly. Themovementoftheturtlesandtheirdegreeofrotationisalsorandom. Thenumberofoffspring,althoughlimitedbeetween03,israndomtoo. Thetypeofoffspringalsohasadegreeofrandomness,alwaysifitfitsany01degree ofseparationfromthefather. Does the randomness use to generate variability in processes for which it is not consideredimportanttomodeltheircauses? We can consider that the placement of both turtles and patches, do not have a significantcausality. Randomness is used to generate events or behaviors that occur at a specific frequencyknown? The degree of randomness from the number of descendant, offspring type and positionofthesehave,withinlimits,aknownfrequencythatwetrytouse. 4.11.Observation: Whatdataaregeneratedandcollectedfromthesimulationforanalysis? Numberofhappyagents. Numberofunhappyagents. Peoplewholeavethemodel. Peoplewhodie. Housesbuilt. NumberofPeople. Numberoffamilytype. %Byfamilytype. Howsuchdataarecollected,andatwhattimeortimes? Attheendofeachtickthedataiscollected.Althoughvaluesthatdonotchangeuntil theendofeachphase(eg.numberofhouses)donotshowanychangeintheregular ticks. Areallthedatageneratedused,orjustasample,inimitationofwhatusuallyhappens inanempiricalstudy? Everythingisused. 5.INITIALIZATION
Whatistheinitialstateofthemodel,thatis,attimet=0ofthesimulationrun? Theinitialphaseisthetick=0,withthedistributionofpatchesandturtles. How many entities are in the virtual society initially, and what values, accurate or randomdistribution,arethestatevariablesoftheentities? Two, patchesandturtles. Thepatches aredistributed within aframeworkof 81x81. The percentage allocations of the 4 zones, and the number of these generators are setineditablesinputscrolls.Theplaceofthegeneratorsandthegrowthoftheturtles atthisstageisdonewitharandomdistribution.Theinitialorientationoftheseturtles isalsorandomaboutits360degreesofrotation. Isitalwaysthesameormayvarybetweendifferentrunsofthesimulation? Wecanvarycompletelywhilerespectingthelimits. Doestheinitializationcorrespondstoastateoftherealworld,thatis,isempirically calibrated(datadriven),orthevaluesarearbitrary? Theyaretotallyarbitraries,althoughitsboundarieshavesomeinspirationinthereal world.Alsotheyareconsideredaseditableenoughtoallowadaptationtorealvalues. Inthecaseoftheinitialvaluesoftheaffinitiesofeachtypeoffamily,thissimulatea simplificationofasocialstudyhelpbypsichologistenvolvedintheexperiment. 6.DATAENTRY Does the model use data from external resources (data files, or other models) to representprocessesthatvaryintimeduringthesimulation? No.AlthoughItadmitsthisentry. 7.SUBMODELS Howaretheybeendesignedorselectedsuchmodels? Aspartofjointvariableswithinthepossibilitiesofthetoolcreated. Fromwhichothersystemsarethismodels"extracted"or"inspired"forcurrentuse? Fromdifferentpossibleurbanexperiences.Wehavemovedtheseinitialvaluesfrom therealvaluestothepossibilitiesinthetool. 8.SIMULATIONANDEXPERIMENTAL Whichexperimentscanprovethevalidityofthemodel? Inourcasewehavedesignedfourexperiments,withinsomeintentionalpremises: Case1 StandardHome Predictionofasmoothcurvepopulation Case2 IntitialCropaffluence Fastdevelopmentprocess Predictionofabigcity,controledbytheurbanfamiliesandsoonfall Case3 Nearsightednessfamilies Predictionofsmalltowns(butreallydense) Withouthacorrectreplacementofuses,thefinalmapwillnothaverelevantdata Case4 BigScaleProject,inspiratedbytheauthor Greatinitialcompetition ConfusePrediction
Acoherentreplacementofuseswillgenerateanhelpfulfinalmap Whichparametersfromthemodelshouldbemodified(andforwhatrangeofvalues) tocheckmodelbehavior? Case1 Population:400 Threshold:15 Visionpeople:25 Radioext:1.4 Case2 Population:150 Threshold:20 Visionpeople:25 Culture:50% Services:13% Production:13% Tertiary:13% Radioext:1.4 Case3 Population:350 Threshold:40 Visionpeople:20 Culture:25% Services:25% Production:25% Tertiary:25% Radioext:1.8 Case4 Population:7000 Threshold:35 Visionpeople:25 Culture:25% Services:25% Production:25% Tertiary:25% Radioext:1.4 What representations can make from the variables of the study (measurable outcomes)tofacilitateunderstandingofthemodel? Atarepresentationlevel,weworkwith3differentgraphics: Happiness:Itshowsthelinesofhappyandunhappyagentsovertime Building:Itshowsthetotalnumberofbuildingsbyturnandoverthetime. Population Development: It shows the total number of agents for each type of familyovertime. Didyoustoreinformationthroughouttheexecutionofthemodel?(Specifywhich) Yes.Alargeamountofdataissaved: Numberofhappyagents Numberofunhappyagents Peoplewholeavethemodel Peoplewhodie
Housesbuilt TotalPeople Numberbyfamilytype %byfamilytype 9.MODELANALYSISANDCONCLUSIONS How the model behaves in front of the initial assumptions (known behavior under realisticconditions)? Foreachofthe4experimentsindicated,byverydifferentways: Case1:Thisfirstonehasbehavedinanintuitiveway.Asmoothgrowthcurve,a stable zenith for 10% of total time and predictable decline even in its process of exchangeofrolesbetweenfamilies. Case2:Inthiscase,theoutcomewasmuchmoreextremeinmanyrespects.The large initial growth, with an implicit and disproportionate distribution among families, causedanincreaseinbuiltand,asexpected,alargeandrapiddeclineofthewhole livablesurface.Yetoverallsurvivalwasgreaterthanincase1. Case3:Inthisexperimentwehadgotasimilarcaselikethefirstoneintermsof the numbers of the set data, although the visual representation became really different. The turtles were concentrated in large population nodes. This was maybe the cause of a fall curve of the total population really less smooth than in previous examples. Case4:Itsbehaviorappearedinanunexpectedway.Afterstronginitialcompetition onthefirstticks,severalselfsufficientcommunitiesofthe3"lower"familyclasses were generated in different places in the patch. With a very small number of population,about10individualspercommunity,theyreachasituationofstabilityof nearly10,000ticksuntilitsextinction. Howdoesthemodelbehaveunderunknownconditions? Wehavealreadyseenthisinthefirstcase,witharandominitialdataalmostentirely. Whatpossibleimprovementsallowedthemodeltoviewtheresults? Manyofthem.Ifweonlythinkintheirincorporationintotheinfrastructureofwork,we couldciteafew: Theincorporationofatransportpatternthathelpustocheckitsinfluenceoverother areas, not so much as condition for the movement of the turtles, because the conceptsandthepursuitofhappinessticks/yeardonotneedthem. The incorporation of empty areas, ensuring that the perimeters of the urban complexesbegintoappear. Asampleofdifferentvaluesaccordingtothedataofoccupancypercell,whichin thiscasewehavesimplifiedinabinarylevel. Whatinterestingvariationscouldbemade? An interesting variation would deepen a more complete social study. This could be composed in two ways. Detailing more degrees of relationship in families that we havealreadycreated,orintroducingfamiliesnotsomuchinasocialclassdistinction butbyothercoefficientsdependingofdifferentwaysoflife(Age,groupwork...) Flowchart:
Code:N1_code.nlogo ViewitonGithub:https://github.com/culturadigital/forma14/blob/master/N1.nlogo Modelsnapshots: Figure1.Systeminitializationwithadistributionvalueof20%
Figure2.Systemduringdevelopmentandagglomerationwithadistributionvalueof20% Figure3.Comparativedataanalysisofthedifferentscenarios Resultsandconclusions: Thereareseveralconclusions.Thefirstone,andthemostobvious,appearsfromthe possibilities to translate this tool, using the same arguments but inside a new combinatorialinfrastructure,andourcapabilitytocarryittoreallyenthusiasticplaces. Therefore we conclude that the link between theoretical considerations about urbanism and the results of a complex computation is more alive than ever, embeddedinthedebateonthepositionofthemachineanditsresults.Thedesigners findhereapathforafurtherexplorationthatforceustodevelopourdesigntools. Insidetheworkdevelopeditself,wecanalsodrawaconclusionabouttheextremely importance to understand the complexity of the different affinities of the inhabitant, including thislikeanargument forthe hope withinurban decisionsthat can respect somemultidisciplinarycommitteesincludingsociologicalinthethinktankgroupsof the city. For this, the inhabitants analyzed also should be individuals and different, combinedandcompetitivebecauseistheonlywaytoreachaparameterdegreeabout theoptimumplaces. Finally we found the great value about include the time in the virtual models. For a simulation about an urban complex model, we conclude essential to incorporate a study intime,whateveritsdevelopment. Thecity is notunderstoodbutasasetof terms,allofthembasedonthetransient.Evenwiththesimplificationofourmodel,it hasbeentremendouslyhelpfulthatwecouldcontrolthisfactor.Wewereabletogeta certain urban analogies thattranscendthemodelspacethanksthetime factorfrom
thedifferentagentsandpatches. References: ChristopherAlexander:APatternLanguage. MichaelBatty:Thenewscienceofcities. DavidGrahameShame:UrbanDesignsince1945. KevinLynch:ATheoryofGoodCityForm. JaneJacobs:TheDeathandLifeofGreatAmericanCities.
N2. HOSTING CAPACITY OF EL PALMAR DE LA FRONTERA Authors: Antonio Piñero Valverde - Universidad de Sevilla [email protected] Irene Luque Martín Collaborators: Gonzalo Antonio Aranda Corral (lead) Daniel Corzo García Guillermo de la Cruz Dorado Jorge Calvo Martín David Sánchez Martínez Paloma Rubio Rodríguez Enrique Soler Santos Myriam González Limón Brief: The purpose of the model is through the concept of carrying capacity of El Palmar in Vejer de la Frontera, found in the urban planning process: which of the proposals capacity host user creates a better balance between the three principals impacts by an intervention of urban planning: social, economic and environmental impact. The objective of this model is the approach of urban scale simulation. The process model is divided into two scenarios: baseline to the input data, and execution of 80 ticks which equals the study performed 20 years after ordination. The first is when we select the increase (PI, SI or TI), select whether or not seasonal pattern, and spatial distribution of agents occurs. Once distributed agents begin ticks every four ticks the counter is reset. This means that the occupancy level discussed in previous sections, corresponding to four ticks, so the functions of ticks occur 80 times, but every four ticks are generated different situations because of the 4 quarters of a year. The status of each agent begins execution and occurs in the appended order functions. Once done the whole process, in each graph of the environmental, social and landscape impacts and a joint graph where we see the consequences of proposed urban model. Key question: What is the optimal hosting capacity of El Palmar? ODD Protocol: 1. Overview Purpose: The purpose of the model is, attending to the idea of maximum hosting capacity in Palmar area of Vejer de la Frontera, to shed light on a key question within the planning process: what is the hosting capacity that generates a better balance between the three main layers of impact that unfold from a planned urban intervention; social, economic and environmental?
Landscape Impact - 3. Public spaces and vegetation (trees)
Social Impact
Economic Impact Code: N2_code.nlogo View it on Github: https://github.com/culturadigital/forma14/blob/master/N2.nlogo Model snapshots: Figure 2. This first version was an attempt to deploy all the agents in the image. Two types of land were created: land hosting any possible use (residential, tourism, services) and open green areas. For the spatial arrangement we used land use percentages and impact increments.
Figure 3. The second version introduced the maximum values for global impact. This way, the model will reduce the number of users every time the global impact threshold is surpassed, which creates a flow of the number of users troughout time.nThe problem with this version is that the model always tops the global impact threshold and therefore the number of users is constantly decreasing. This leads us to identify potential mistakes in the algorithms calculating the individual impact levels. Figure 4. The third version incorporates the variability of the individual impacts in order to adjust them throughout the process. For example, the energy impact may vary according to the initial percentage of solar panels and that poses a strong influence over the evolution of its final values. The same applies to the rest of individual impacts. With this model we finalized the modeling process, from which a general idea of its coding could be obtained as well as the absence of certain aspects that would be necessary in order for it to work as an agent system. Conclusions: Forma14 has been a continuation of Forma13, but in the first edition we focused on understanding how to apply agent systems to our research problem, then in this edition, we started off from a conceptual model where a formal approach to the idea of hosting capacity was established. During the process, we realized that although most of the ODD protocol was apt for modelling, we were lacking concepts in the line of adaptation and learning. In our view, the base of the model is correct, but the work of the conceptualization remains constant through time. The model, ambitious in the amount of variables and relationships, needs conceptual simplification and further complexity in the way
behaviours evolve through time. References: Batty, M. "Urban Modeling", International Encyclopedia of Human Geography, vol. 12, pp. 51-58. Martínez, F.L. & Morales, Y.O. "Agent-based simulation approach to urban dynamics modeling", DYNA (Colombia), vol. 79, no. 173 PART II, pp. 34. G. Lobo. "La Ciudad no es un juguete: cómo SimCity juega con el urbanismo". Zhang, H., Wang, X., Yong, Y. & Ho, H.H. "Eco-health evaluation for the Shanghai metropolitan area during the recent industrial transformation (1990-2003)", Journal of environmental management, vol. 88, no. 4, pp. 1047. Annex:
N3. EVOLUTION OF HUMAN COOPERATION AND VIOLENCE: THE ORIGIN OF METAPOPULATIONS AND SOCIO-ECONOMIC NETWORKS WITHIN PREHISTORY Author: Daniel García Rivero - Universidad de Sevilla [email protected] Collaborators: Juan Galán Páez (lead) - Universidad de Sevilla Rafael Gómez Galbarro Francisco Perfectti Pedro Ros Reina Aline Lara Galicia Antonio Paredes Moreno Brief: According to currently available archeological data, the origin of human populations (of hundreds or thousands of individuals) takes plane in the recent Prehistory, more specifically, in the Bronze Age period. These groups of individuals are called chiefdoms in social anthropology: chiefdoms societies are known to own a number of features as certain internal variation, division of labor, foodstuff storage, craftsmen and other segments of society exempt of physical effort (like agriculture or livestock), social hierarchy, etc. In particular, in the Iberian Peninsula is at the beginning of Copper Age (around year 3000 BC) when starts the emergence of big populations as well as extensive and sound cooperation networks interlinking those. At this point, archeological sites show: large fortified settlements (with walls, towers with loopholes and other poliorcetics related elements); developed weaponry (knifes, arrowheads, spears, etc.); material culture with biased (non-uniform) spatial distributions at both, intra and inter-societal levels, where some objects denote certain social level and therefore can only be found in certain segments of society; asymmetric and hierarchical settlements organization around possibly assembled territories (by means of religion and politics); first collective burials (megalith) and princely graves; etc. Key question: Under which conditions and historical factors it is possible the emergence of complex and large agriculture and livestock-based societies where collectivity prevails over individuality (around IV-III millenniums BC)? ODD Protocol: 1. Hypothesis: Figure 1. Geographical area of research
Cooperation between individuals or groups tends to be higher in contexts where resource competition is high, implying a higher violence rate. That is to say, situations where group collective interests are threatened. Higher the competition, higher the cooperation, leading to the emergence of larger communities, and larger the communities, greater their capacities to compete for resources (more powerful armies, stronger economies, etc.) Figure 2. Preliminary ideas before model development The main theoretical ideas the model must implement are the following: a) Extensive farming development (including irrigation, animal drawn tools, etc.). Archeological sites show an increase of agricultural tools like teeth-made sickles and thresh; hand grinders, etc. b) Demographic growth due to: the higher availability of resources from agriculture and livestock; production of dairy products, textile industry, etc. Archeological sites show an increase in the number and size of populations. c) Strong resource competition due to: heavily populated areas; huge areas devoted to agriculture and livestock (for large populations), need of resources stockade and products interchange between distant populations. d) Increase of violence and development of poliorcetics. e) Development of inter-societal bonds at different levels (individuals, segments of society, populations, etc.). These relationships emerge due to a number of hypothesized historical issues and situations, as well as the necessity of creating bigger entities with a higher combat strength. Archeological sites show settlement patterns in big areas and networks of goods interchange along these areas. f) Increase of internal variation in terms of labor division, leading to more competitive societies. Some segments of society have tasks that are not directly related to foodstuff production. Settlements show different social levels spatially distributed (in some areas valuable objects have been found). Large and heterogeneous ones rapidly replaced small and homogeneous settlements. g) Beginning of new cultural systems (symbolic systems, religions focused on human deified leaders, etc.) allowing these new societies increasing even more their population and size. Deified leaders increase society integration and cohesion since decrease ideological differences between individuals. Features to be considered and measured during model execution (to be matched after simulations with real data collected from Archeological sites): 1. Demography, for which variables are: Number of settlements. Size of settlements. Dispersion/pattern of settlements. 2. Warfare technology, for which variables are:
Presence/absence of defensive ditches and walls surrounding settlements. Presence/absence of weapons. 3. Hierarchy and social status, for which variables are: Presence/absence of differentiated spaces within settlements. Presence/absence of social prestige objects. Dispersion/pattern of social prestige objects. These variables are informative of the degree of internal variation and level of social hierarchy of the populations. Parameters and behavioral rules to be considered for individuals and groups within the model (model is situated in an environment where resources are limited): Individual profit: individuals aim to maximize their profit. Cooperation limits: An individual will cooperate with others as long as it is not detrimental for itself. Relatives’ selection: An individual tends to cooperate with individuals closer (from a genetic and/or cultural point of view) to itself. Cooperation rules (cf. Nowak 2006): Relatives’ selection, direct/indirect/network reciprocity and group selection. Competition/rivalry between individuals and populations (cf. Hutchinson 1981: 275- 295): 1. If the influence an individual from a settlement A has over another individual from a settlement B is higher than the influence it has over an individual from its own settlement (A), and the influence the individual from settlement B has over an individual from settlement A is lower than the influence it has over an individual from its own settlement, then settlement A will take the place of settlement B. 2. If both individuals have more influence over other settlement than over their own, then the winning settlement will depend on settlements’ population. 3. If both individuals has more influence over their own settlements than over others settlements, then settlements’ coexistence is possible. Other factors: 1. Social hierarchy level (existence of a leaders). 2. Religious hierarchy level (existence of divine leaders). 2. Description of the developed model: Entities of the model (agents/individuals, units of space, environment, etc.): Each agent represents human settlement. Model extension (in terms of time and space): The world consists in a 250 X 150 grid corresponding to a 250 (East-West) X 150 (North-South) Km area, and is executed for 2,000 ticks, which is equivalent to a 2,000 years period. Attributes and/or state variables: Agents (settlements) own the following attributes: Diversity level: Internal variation level of society. Values: 1. Livestock 2. Agriculture 3. Livestock and agriculture 4. Livestock, agriculture and craftwork 5. Livestock, agriculture, craftwork and leadership Technology level: 1. Plow 2. Poliorcetics 3. Metallurgy The values for these two attributes are:
a) Diversity: 0 / 1 --> 2 --> 3 --> 4 b) Technology: 1 --> 2 --> 3 Figure 3 & 4. Socio-economic evolution and technologies Behavioral rules - Agents (settlements) follow the following basic rules: Agents (settlements) aim to maximize their profit (population increase and replication). Figure 5. Split and cooperation/attack symbols Settlements’ population limit depends on their Diversity and Technology levels. Initially each settlement has a population limit of 100. The limit will increase till 150 when society achieves the plow (Technology level 1, which also requires Diversity level 2). In this case the population growth rate will increase to 20% (from an initial value of 10%). Societies achieving leadership (Diversity level 4) will see their population limit increased till 500. Agents (settlements) interact with each other within a range of 25 patches (25 Km) starting with the closest ones. The result of each interaction could be cooperation or fight: F(A,A) = Settlement(A) * (1 + 0.165 * M(A)) where M(X) = plow(X) + metallurgy(X) + livestock(X) and agriculture(X) + craftwork(X) + leadership(X) – poliorcetics(X) (all of these take 0 or 1 if settlement A already achieved it or not).
Figure 2. Facades affected by traffic in the model The different processes are executed on the follow order: 1. Generation of the buildings and streets with a random direction. 2. The generated medium is validated. If it is not valid the previous step is executed. 3. The user can define the pedestrian streets. 4. The vehicles are generated with a size and direction. 5. During the execution of the model the total polution and the building's affectation are measured. 6. The polution is showed in a graph. All entities are executed in the same time to allow see the interatation each other and determinate how affect each of them in the historical centre. 4. Design concepts Basic principales: Is considerated that the traffic that go through to the Quito centre causes a long-term structural damage over the buildings and and monuments located near of those streets. Is considered the hypothesis that a partial reduction of the traffic could minimize the impact of this phenomenon. Emergence: The decision of take a new way to allow to a vehicle to arrive to the target, the change of the traffic flow because of to close a street and the posibility to create new streets to connect the south with the north of the Quito centre in the rush hours are modeled like emerging results. On the other hand, the directions of the streets and the patrimonial locations not depend of the individuals behaviour. Adaptation: The vehicules must to find a new way to arrive to theirs destinations keeping in mind the direction streets and the existing connectivity between differents streets. The main objetive of the vehicles is arrive to the destination in the lower possible time, so the movement of the vehicles is modeled following a direction N-S or S-N with a low percent of times taken a direction W-O or O-W. That behaviour sure that a vehicle will arrive to the south or to the north crossing the centre.
Flow chart: Code: N4_code.nlogo View it on Github: https://github.com/culturadigital/forma14/blob/master/N4.nlogo Model snapshots: Figure 3. System status at simulation startup
Figure 4. System status at runtime References: Gordón, M. (2012) "Movilidad Sustentable en Quito: una visión desde los más vulnerables", (Flacso-AVYA YALA). Carrión, F. "Peatonización del Centro Histórico", FLACSO, Facultad Latinoamericana de Ciencias Sociales. Sede Ecuador. Gutiérrez, J. "Transporte, Movilidad y Turismo en los Centros Históricos", Ería 47 (1998), pags 241-248. http://www.andes.info.ec/es/noticias/meta-ecuador-es-hacer-turismo-primera- fuente-ingresos-no-petroleros-dice-ministra.html http://repositorio.ute.edu.ec/bitstream/123456789/8379/38/18024_43.pdf
N5. METROPOLITAN EXPANSION AND MOBILITY IN ANDALUCIA Authors: Gwendoline de Oliveira Neves - Universidad Pablo de Olavide [email protected] Claudia Hurtado Rodríguez - Universidad Pablo de Olavide Jesús Santiago Ramos - Universidad Pablo de Olavide Collaborators: Yago Fernández Rodríguez (co-lead) Teodoro González Manchón (co-lead) José Núñez Porras Francisco José García Gómez Carlos Falgueras García Brief: Since the second half of the twentieth century a deep change is being consummated in the urban planning´s history due mainly to private transport development. This change can be seen especially in the design of urban profile that associated to a slight increase in urban population, a large increase in the urbanized area. This mutation is characterized by an accentuation of land consumption and a deep reorganization of spaces towards greater territorial specialization setting new mono-functional centrality linked to leisure, commercial and tertiary spaces and supported in the development of networks which facilitate the flows. From this perspective, the issue of the metropolitan dimension of urban development from the formal understanding of spatial organization poses a fundamental question: metropolitan areas by definition have no end because of the developmental model on which they are based? In a first stage, the question of the metropolitan´s definition in a territorial dimension as a particular way to organize the land use involves in one hand, consideration of the density and organization of urbanized space, and on the other hand the integration of road network as a cohesive element being considering as optimal a model that tends towards the sustainability of the whole. Based upon a reflection on the need for a greater consideration of territorial variability of the phenomena in constructing models, it is intended to use the proposed methods and analytic tools within the framework of this experience to tackle this reflection from a diachronic and quantitative approach of an Andalusian metropolitan area illustrative of the degree of conversion of Spanish urban model from the 1990s and the first decade of the XXI century, proposing an approach to the dynamics spaces classified as mono-centric metropolitan (Sevilla), polycentric (Cadiz-Jerez de la Frontera) or linear (Malaga-Costa del Sol). In this way it is intended make available to managers, technicians and metropolitan citizens a global scale tool, allowing not only to place thematically each metropolitan area in their context but also to provide a support instrument to the decision making at the time of carrying out historical follow and a simulation scenario for the development of interventions. Key question: Is there a limit to the urban expansion of metropolitan areas? ODD Protocol: 1. Overview Purpose: The purpose of the model is to simulate metropolitan urban growth in order to gain a better understanding of Andalucia’s metropolitan morphogenetic processes. The model will be used to evaluate current theories of urban growth at a metropolitan scale. Entities, state variables, and scales - Agents in the model are: (0) Natural and potentially developable rural land. (3) Urban dense and continuous land (2) Urban sprawl and continuous land (1) Urban sprawl and scattered land
(0) Natural and potentially developable rural land. (3) Urban dense and continuous land: These are spaces with a dense structure, mainly built areas with a strong component of residential land use but including others like commercial, industrial, leisure, retail, services, etc., which are not significant enough in their size or amount to be considered individually as a separate category. Private gardened areas and other open spaces are sparse. There are no morphological features taken into account (historic quarters, extensions etc.) (2) Urban sprawl and continuous land: Unbuilt spaces are gardened areas or open spaces without agricultural activity. It is common to find swimming pools and regularplanned trails for the purpose of connecting the plots, which are often hosting a single residential building. They are usually at a certain distance from urban centres and connected to these through a single road. (1) Urban sprawl and scattered land: In this case, open spaces are mostly either of agricultural use of any kind or ranching, cattle raising etc. Buildings may include farming/ranching infrastructure but there is still a substantial proportion of residential housing. They are typically located next to second tier urban settlements or occupying river meadows. There might be some wild greens scattered among the urbanized areas. These attributes are measured in hectares, and the dimensions of the model are 1000 columns and 948 rows of 100x100 cells, corresponding to the metropolitan area of Seville from 1956 to 1984. 1 patch in the model corresponds to 1 Ha, and 1 tick to 1 year. Process overview and scheduling: (0) Natural and potentially developable rural land: May change to any other state: 1, 2, 3 (3) Urban dense and continuous land - Does not change 2. Urban sprawl and continuous land - May change to 1 or 0 1. Urban sprawl and scattered land - May change to 2 or 0 The order in which each process is executed goes as follows: 1. Calculate the maximum number of patches which can potentially grow during 1 year, per use (z) 2. Identify the adjacent cells for each layer 3. Set attributes to adjacent patches for each layer according to constraints (contact with water bodies, slopes etc.) and conditions (land use and roads) 4. Pick those patches showing higher values for each layer Each process is divided in three layers according to its urban density (3, 2, 1) and they are executed from higher to lower density (3, 2, 1) The sets of rules governing the behaviour of each bacterium are: Uptake of nutrient particles in order to increase the bacterial biomass, with excretion of the end product to the spatial cell. Reproduction by bipartition with the generation of two "new" bacterial cells when the mass is greater than a threshold. Viability that is assumed till the mass is under certain threshold, translating the cell to a latency state. (i) Active motion only when the bacterium has not achieved nutrient (randomly to a neighbour patch) The sets of rules regarding spatial cells include: The entrance of fresh medium. The culture stirring or agitation to avoid adhesion of bacteria in the input flow
mechanism or in order to achieve homogenization. Output of medium (with nutrient and product) and bacteria. The global scheduling of the simulation model is made up of various elements: (1) initialization of the system with the input data chosen by the user, where initial configuration of the bacterial population and the spatial environment are set up, as well as the parameters for the operating protocol; (2) the main loop (time step), in which all the rules for each bacterium and the medium are implemented and repeated, and the external actions on the system are applied, until reaching the end of the simulation; (3) the output of results at the end of each time step. 2. Design concepts Basic principles, strategies and hypothesis: The underlying concepts and theories of the model are urban growth and the morphogenesis of metropolitan processes in relation to the development of urban mobility. In particular, the hypothesis being explored in this study is that the transportation network is an element that articulates urban growth. The modelling strategy has been to create a transportation network from which, based on a set of constraints and conditions, the process of growth is organized. In absence of constraints and under favourable conditions, then the denser the network gets, the denser compact the urban fabric. In addition, there are micro-hypotheses which define the behaviour of the constraints and conditions, and also sub-models such as the one involved in the deployment of shopping malls. The model will offer a contrast with the real scenario as it works within a time frame from which data is readily available (1956-1984). The comparison and results will be used to evaluate well known theories of urban growth in the context of Andalucia, bringing in some innovative elements as well. Emergence: The behaviour of the agents will determine the expansion of the urban fabrics of types (1) and (2). However, there is no emergent behaviour expected as it is controlled beforehand. Gradients and masks are less dependent on the interaction between agents. Adaptation - Land use layers can expand and merge with other layers. Then, agents make decisions according to their interaction and context as follows: (0) Natural and potentially developable rural land - May change to any other state: 1, 2, 3 (3) Urban dense and continuous land - Does not change. Stops when in contact with the mask and may trigger a change in agents type (2) into agents type (1) (2) Urban sprawl and continuous land - May change to agent type (1) – densification, and may trigger a change in agents type (0) or (3) into agents type (2) – growth. (1) Urban sprawl and scattered land - May change to agent type (2) – densification, or spill over agents type (0) – growth. Agents do not keep an individual record of success to evaluate their performance, but instead it is assumed that growing until they reach the yearly average (%) of their type is a successful behaviour. Therefore, the objective of the agents is to grow and expand, which can be measured by calculating the area gained for every tick-year and comparing it with the real one in its spatial and statistical organization. Interaction: The most relevant interactions between agents are the processes of sprawling and compacting of the urban fabric. There is also a direct interaction between different land uses as well as between land types and the external conditions such as topography, water bodies, transportation network and the mask (non-
developable land). Patches at border conditions can be very attractive drawing competition from agents of types (1), (2) and (3) Collectives: Agents form groups that are affected by and may affect them. These groups are an emergent property of the agent’s behaviour. However, group’s characteristics and their attributes are designed by the modeller beforehand. Observation: The main data observed are growth in terms of area per each land type and the visualization of the process. Data is collected at the end of the process and all the data is used as opposed to what usually happens in empirical experiments. 3. Details Initialization: The initial state of the model is the real situation (cartography and values) as in 1956. There are three initial types of agents that remain until the end of the runtime. Input data: Real world maps. Submodels: It is already a submodel of a more complex model. Simulation and experimentation: The model shall be considered valid as far as it comes close to reality in its quantitative and spatial dimensions. After tweaking the coefficients affecting the different parameters at play, it was possible to achieve a more or less realistic result which was short of accuracy in a 30% due to some problems with the input data.
Flow chart: Diagram 01 IP for use i (*)= (GP+(PC*GC)+(PU*GU))/5 GP = Slope gradient PC = Wheighting of distances to road network (adjustable with sliders) GC = Gradient of distances to road network PU = Weighting of distances to urban centres (adjustable with sliders) GU = Gradient of distances to urban centres Zi = Number of patches that could potentially change to state “ni” at each iteration, which will be then recalculated after each tick. In any case, those with a higher IP value will change state. When all these patches will have changed state (for n3, n2 and n1), if the number of ticks equals 28 the simulation will end, otherwise the process starts over again. Code: N5_code.nlogo View it on Github: https://github.com/culturadigital/forma14/blob/master/N5.nlogo
Model snapshots: Figure 1. This image shows the initial state of the model before loading geographic information layers from the complete set of maps. At this moment we set the value for the maximum speed, we choose continuous mode and hit start. All layers under the project's directory will be loaded. Figure 2. In this image we observe the complete model in its initial state, including all the gegraphic layers: water, contours, roads, urban fabric (dense-continuous land, dense-scattered land, sprawl-scattered), distance between roads, and any other layer that may be needed in the model. At this point we need to change the display seting to "ticks" in order to visualize the resuls properly, and ponderation factors need to be set according to those parameters affecting urban expansion that we want to observe more closely during the simulation. Finally we can proceed to run the simulation to see the evolution.
Figure 3. In this image we observe the simulation finalized after 28 ticks (28 years). We may now compare the results with a real map to check how accurate is our model. In this case there is an error of 6,41% It also shows how population density has increased within each of the designated land types (densecontinuous, dense-scattered and sprawl-scattered). During the execution of the process we can track the variations of these values in a graph. At this point we may run another simulation for the following 28 years or start over again with a different setting of the ponderation factors. Conclusions: Our participation in the FORMA'14 workshop has proven to be a very interesting experience, especially as a first approach to the possibilities of modeling in the field of the analysis of metropolitan spatial dynamics. In relation to the developed model, the first tests show the potential of this tool, although a certain distance in relation to the expected results are observed both in the quantification of urban growth and with regard to its spatial configuration. Some aspects that should be addressed in the future are: (a) the improvement of the input image layers, in order to correct the shortcomings detected in relation to the road network and the urban attraction layers; (b) the weighting of the parameters, in order to adjust more accurately the effect of the different factors considered on the results; and (c) the resolution of some technical problems in relation to the input information. From the perspective of the conceptual design of the model and its future development, it seems clear the convenience of incorporating urban and regional planning as a key factor in urban growth processes. In conclusion, the experience has been satisfactory and is a promising starting point that invites to further explore the potential of this tool. References: Sudhira, H. S., Ramachandra, T. V., Jagadish, K. S., (2004) : "Urban sprawl: metrics, dynamics and modelling using GIS". International Journal of Applied Earth Observation and Geoinformation, 5(1), pp. 29-39
Proportion of transcribing RNA plymerases that are active (non backtracked) mRNA molecules present in the cell Half-life of the mRNA These continuous variables can be experimentally measured in a rather accurate manner. We have chosen GAL1, one of the best characterized genes of the yeast Saccharomyces cerevisiae, which is our favourite model organism. The discrete variables of the system are the following: Presence of the mRNA degradation factors (Yes/No) Presence of the RNA polymerase II reactivation factors (Yes/No) Capability of the RNA polymerase II to become backtracked (Yes/No) Xrn1, Ccr4 and TFIIS are non-essential proteins in yeast. The mutant strains lacking these factors are viable in standard growth conditions. When compared with the wild type, mRNA concentrations are surprisingly similar in these strains. The modelling of the system should allow explaining the robustness of the mRNA synthesis/degradation system. This work is part of the project entitled "Crosstalk between transcription and mRNA degradation: influence of chromatin and RNA polymerase II backtracking", financed by The Spanish Ministry of Economy and Competitiveness (MINECO) (grant BFU2013-48643-C3-1-P). Although carried out in a yeast experimental system, the high degree of evolutionary conservation of gene expression mechanisms makes this work of general interest for Biology and Biomedicine. Key question: Is there a cross-regulation between mRNA synthesis and degradation during genome expression? ODD Protocol: 1. Overview Goal: Modelling gene transcription and its coupling to mRNA degradation, to increase our knowledge on the molecular behaviour of this system and to test several alternative hypothesis based on previous experimental work. Entities, process overview and scheduling: There are four kind of agents in the model: Gene, Polymerase, mRNA, TFIIS, Ccr4, Xrn1 Gene promoter: It does not move Entry site of the polymerase into the gene The promoter may have different levels of activity. This is modulated by Xrn1 It blocks its activity when a polymerase is transcribing the gene Gene body: It does not move It is downstream of the promoter It is the informative part of the gen. It is copied into mRNA by the polymerase Gene terminator: It does not move It is downstream of the gene body Exit site of the polymerase. In this the polyA tail is added to the mRNA and then the mRNA is released from the gene Polymerase: Nanomachine that reads the information encoded in the gene body producing the mRNA molecule It moves randomly in the cell Enters the gene through the promoter It may backtrack and arrest Backtracked polymerases can reactivate by its interaction with TFIIS It terminates transcription and mRNA synthesis at the terminator
mRNA: a. information (5') It is the body of the mRNA and contains the information encoded by the body of the gene It is polar: its two ends are different (3' end and 5' end) It is produced by the polymerase by reading the gene body It also contains a polyA tail in the 3' end It moves randomly in the cell together with the polyA tail Its polyA tail is degraded by Ccr4 Its information (5' end part) is degraded by Xrn1, provided that the polyA tail is fully degraded It is characterized by its half-life (how fast is it degraded) b. polyA (3') It is the tail composed by a high numbers of A nucleotides that is added to the mRNA 3' end immediately before termination It is fused to the informative part of the mRNA in its 3' end It moves randomly in the cell together with the informative part It is degraded by Ccr4 Its degradation is a requirement for the degradation of the information part of the mRNA During transcription termination, it may be imprinted for degradation by the transfer of Ccr4 from the polymerase to the polyA TFIIS It reactivates backtracked polymerases It moves randomly in the cell Ccr4 It moves randomly in the cell It degrades the polyA After degrading the polyA it stays bound to the mRNA until this is completely degraded by Xrn1 When mRNA degradation is complete, Ccr4 acquires de capacity of interacting with the transcribing polymerase, keeping its degradation capacity After interacting with the polymerase, it follows the polymerase until the terminator. Then it is being released and loses its transcriptional capacity. In the terminator it switches back to the degration mode, losing its transcriptional capacity, and binds the polyA of the mRNA that is just transcribed Xrn1 It moves randomly in the cell It degrades the information part of the mRNA (5' end) after the complete degradation of the polyA by Ccr4 After degrading the mRNA it switches to a transcriptional mode: it can stimulate the activity of the promoter, keeping its degradation capacity. After stimulating the promoter, it loses its transcriptional capacity The variables are absolute numbers, only the promoter activity and frequency of polymerase backtracking ranks between 0 and 1. The spatial environment of model is the cell (3D) but It is not necessary a very high spatial resolution and we simplify to a 2D space. Your time interval is from t=0 mRNA concentration reaches a dynamic balance (steady state). 2. Design concepts The model is based on our knowledge on the mRNA transcription and degradation phenomena. Two elements are especially relevant: The mRNA degradation machinery (Ccr4, Xrn1) can moves into the nucleus and influence gene transcription after mRNA
degradation. And the Polymerase backtracking is important for the coupling between gene transcription and mRNA degradation. Many specific molecular details of the degradation/transcription coupling are unknown. The purpose of the study is a first comparison of the global consequences of different possible options for some of these details. Emergence: The steady state (dynamic balance) of mRNA concentration is an expected emerging result of the model. Flow chart: Diagram 1. Agent: Gene promoter Diagram 2. Agent: Gene terminator
Diagram 3. Agent: Polymerase Diagram 4. Agent: mRNA
Diagram 5. Agent: TFIIS Code: N7_code.nlogo View it on Github: https://github.com/culturadigital/forma14/blob/master/N7.nlogo Model snapshots: Figure 1. Symbols: X, gene; black circle, RNA polymerase; green rectangle, mRNA; blue rectangle, mRNA being degraded by Ccr4; yellow rectangle, polyA-less mRNA waiting for Xrn1; orange dot, TFIIS; black pentagone; Ccr4; purple triangle, Xrn1.
Figure 2. Graphic detail of the steady state (dynamic balance) of mRNA concentration. Conclusions: By using the NetLogo software, we have developed a computational multiagent-based model of the mRNA synthesis/ degradation system. The results obtained support the expected behaviour for this system in the cellular context. These results, combined with experimental measurement of different variables of the system in mutant strains lacking the key genes of the process, should allow us better defining the synthesis / degradation coupling of mRNA and, in particular, confirming the importance of the backtracking phenomenon in this circular process. References: Haimovich, G; Medina, D; Causs, S; Garber, M; Millán-Zambrano, G; Barkai, O; Chávez, S; Pérez-Ortín, JE; Darzacq, X; Choder, M. (2013) Gene expresión is circular: factors for mRNA degradation also foster mRNA synthesis. Cell. 153: 1000 - 1011. Gómez-Herreros, F; de Miguel-Jiménez, L; Millán-Zambrano, G; Peñate, X; Delgado-Ramos, L; Muñoz-Centeno, M.C; Chávez, S. (2012) One step back before moving forward: regulation of transcription elongation by arrest ans backtracking. FEBS Letters 586: 2820-2825.