Made available in DSpace on 2016-03-15T19:38:06Z (GMT). No. of bitstreams: 1 Fabio Tokio Miki.pdf: 3084755 bytes, checksum: a9dcafd5096585d0dd40b1b9985073fe (MD5) Previous issue date: 2006-10-09 / Fundo Mackenzie de Pesquisa / Cellular automata are dynamical and computational systems, totally discrete in time, space and their state variables. It is known that, for elementary cellular automata, the set of all possible configurations that can appear at any finite number of time steps in their temporal evolution constitutes a regular language. As a consequence, such a set of strings can be represented by a minimal deterministic finite automaton, and the quantity of states and transitions among them may be considered a measure of the (regular language) complexity of the rule at issue; performing such a process may be computationally intensive, but it is well solved in the literature. However, when the target is the limit finite automaton, that is, the one after an infinite number of time steps, the machine may not exist for some rules, and the currently existing method fails to automatically generate it for some rules for which it is known otherwise that a solution does exist. This work aims at helping the solution of the latter problem, although the actual derivation of the algorithm to automatically generate the limit finite automaton has not yet been possible. However, it goes further the currently existing method, by means of a new algorithm for automatically yielding the growth expressions of the finite automaton representative of each time step, including some cases not reported so far, therefore shedding light over the issue, and opening perspectives for a subsequent automatic derivation of the limit finite automaton. / Autômatos celulares são sistemas dinâmicos e computacionais totalmente discretos no tempo, no espaço e em suas variáveis de estado. Sabe-se que, para um autômato celular elementar, o conjunto de todas as configurações possíveis de se obter decorrida uma quantidade finita de passos de tempo de sua evolução temporal constitui uma linguagem regular. Com isso, esse conjunto de cadeias pode ser representado por um autômato finito determinístico mínimo, e a quantidade de estados e transições entre eles pode ser considerada uma medida da complexidade (em linguagem regular) da regra elementar em questão; tal processo, apesar de eventualmente custoso computacionalmente, está bem resolvido na literatura. No entanto, quando se deseja obter a representação do autômato finito limite, isto é, para uma quantidade infinita de passos de tempo, essa máquina pode não existir para algumas regras, e, mesmo em alguns casos em que se sabe que ela existe, não há ainda um método que a gere automaticamente. O presente trabalho caminha na direção de ajudar a solucionar este último problema, apesar de que ainda não foi possível derivar o algoritmo de comportamento limite. No entanto, avança-se aqui com relação ao método atualmente existente, no sentido de, através de um novo algoritmo, derivar automaticamente expressões de crescimento do autômato finito representativo de cada passo de tempo, inclusive em casos ainda não reportados, o que lança luz sobre a questão original, abrindo perspectivas para que a obtenção automática do autômato finito limite possa ser obtida posteriormente.

Orientador: Rafael Dias Loyola / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Biologia / Made available in DSpace on 2018-08-23T15:32:42Z (GMT). No. of bitstreams: 1 Sant'Anna_ClaraLuzBraga_M.pdf: 15999631 bytes, checksum: d43098e998277d4decc83772d68234c2 (MD5) Previous issue date: 2013 / Resumo: O processo de invasão biológica por espécies exóticas pode levar a substituição de espécies nativas. Este processo é crítico quando ocorre no interior de Unidades de Conservação (UC). Para entender a dinâmica de invasão e expansão das espécies invasoras é necessário levar em conta as características da espécie invasora associadas às características abióticas e bióticas dos ecossistemas invadidos, o fator temporal e mecanismos específicos a cada sistema. Estudamos através de análises exploratórias e modelos de simulação em Autômato Celular, o processo de expansão da Brachiaria spp. no Parque Nacional das Emas (Parna Emas), uma das mais importantes Unidades de Conservação do Cerrado. Utilizamos dados da distribuição espaço-temporal da gramínea dos anos de 2002 e 2012, em 80 Km das estradas internas do parque (25% das estradas) para associar a expansão às variáveis ambientais: distância relativa à margem das estradas internas do parque, categoria de zoneamento, tipo de fitofisionomia de Cerrado e declividade. Os fatores ambientais mais relevantes no processo de expansão da Brachiaria spp. foram: as estradas internas do Parna Emas, cuja presença e alta freqüência de uso influenciam positivamente a expansão da gramínea, e em áreas com intenso tráfego de veículos a abundância da Brachiaria spp. foi dez vezes maior que em áreas onde o acesso é restrito; a disponibilidade de luz e espaço foram fatores limitantes à expansão, áreas de fitofisionomias abertas de Cerrado, como Campo Limpo, mostraram ser mais susceptíveis a invasão, com expansão até vinte vezes maior que áreas florestais; e a declividade, mesmo sutil, pareceu direcionar a expansão da gramínea para regiões de menor altitude. Assim indicamos algumas medidas de manejo: restringir ao máximo o uso das estradas internas do Parna Emas, aplicar medidas fitossanitárias nos veículos, botas e vestimentas de funcionários e visitantes, a fim de inviabilizar as sementes dispersadas por estes, priorizar o manejo em áreas de Campo Limpo, em especial uma região no centro do Parna Emas que pode estar funcionando como fonte interna de sementes, e recomendamos atenção aos locais em declividade, de modo a prever a direção da expansão e assim tomar as medidas preventivas cabíveis. Esperamos assim que nosso estudo contribua para o desenvolvimento de melhores políticas e ações de monitoramento, manejo e controle de Brachiaria spp. em Unidades de Conservação, em geral, e no Parque Nacional das Emas, em particular / Abstract: The process of biological invasion by exotic species may lead to replacement of native species. This process is critical when it occurs within Protected Areas (PA). To understand the dynamics of invasion and spread of invasive species is necessary to take into account the characteristics of invasive species associated with abiotic and biotic characteristics of invaded ecosystems, the temporal issue and specific mechanisms to each system. We study by exploratory analyzes and Cellular Automaton simulation models, the expansion process of Brachiaria spp. in Emas National Park (Parna Emas), one of the most important Protected Areas of the Cerrado. We used data from spatial-temporal distribution of this species with a time lag of 10 years among them (2002 and 2012), over 80 km of internal roads of the Parna Emas (25% of the roads) to link the expansion of this invasive grass to environmental variables such as: relative distance along the sideroads inside the park, category of zonation, type of Cerrado vegetation and slope. The results indicate that the most important environmental factors in the expansion process of Brachiaria spp. where: the internal roads of the Parna Emas, whose presence and high frequency of use positively affect the growth of the grass, and in areas with intense vehicle traffic the abundance of Brachiaria spp. was ten times higher than in areas where access is restricted; light availability was the limiting factor to expansion, so that areas of open Cerrado physiognomies as Campo Limpo, proved to be more susceptible to invasion, with expansion up to twenty times greater than forested areas; and the slope, even subtle (about 1 degree), seemed drive the expansion of grass to the lower areas. From these results we point out some management measures such as: restricting the most the use of internal roads of Parna Emas and apply phytosanitary measures in vehicles, boots and clothing of staff and visitors in order to make impracticable the seeds dispersed by them; prioritize management in areas of Campo Limpo, one in particular placed in the center of Parna Emas that may be functioning as internal source of seeds; and we recommend paying attention to the places in declivity, in order to predict the direction of expansion and thus take the necessary preventive measures. We thus hope that our study will contribute to the development of better policies and actions of monitoring, management and control of Brachiaria spp. in Protected Areas, in general, and in Emas National Park in particular / Mestrado / Ecologia / Mestra em Ecologia

Orientadores: Laecio Carvalho de Barros, Rodney Carlos Bassanezi / Tese (doutorado) - Universidade Estadual de Campinas, Instituto de Matematica, Estatistica e Computação Cientifica / Made available in DSpace on 2018-08-04T02:48:57Z (GMT). No. of bitstreams: 1 Peixoto_MagdadaSilva_D.pdf: 4244795 bytes, checksum: 6ed64db20c222bb5bb2e020084610233 (MD5) Previous issue date: 2005 / Resumo: A Morte Súbita dos Citros é uma doença que afeta e mata laranjeiras doces e as tangerineiras Cmvo e Ponkan enxertadas sobre limoeiros Cmvo e Volkameriano, que representam cerca de 85% dos pomares cítricos de São Paulo e sul do Triângulo Mineiro. Acredita-se que tal doença seja causada por vírus transmitidos por pulgões (vetor) de citros. Para estudar a disseminação da doença propomos um modelo de simulação a partir de autômatos celulares, o qual leva em conta o efeito do vento nas distâncias alcançadas pelo pulgão. Para estabelecer a relação entre espaço percorrido pelo vetor e a intensidade do vento utilizamos um sistema baseado em regras fuzzy. Para estudar a densidade populacional dos pulgões, levamos em conta que eles têm a joaninha como principal predador natural. Estabelecemos uma base de regras fuzzy para modelar a interação pulgões e joaninhas. As trajetórias obtidas no plano de fase são comparadas com as de um modelo determinístico presa-predador do tipo Holling- Tanner e também a um modelo determinístico presa-predador de Lotlm- Volterra. A partir dos resultados obtidos, iniciamos um estudo para investigar alguma política de controle da Morte Súbita dos Citros / Abstract: Citrus Sudden Death is a relatively new disease that has affected and killed sweet oranges grafted on the Rangpur lime. This is a worrying characteristic because the Rangpur lime amounts to 85% of the citrus orchards in São Paulo and the south of "Triângulo Mineiro". It is believed that the disease is caused by a virus transmitted by citrus aphids (vectors). We have adopted the Cellular Automata model to study the advance of the disease, which considers the effect of the wind in the distances reached by the aphids. We have adopted the rule-based fuzzy system to establish the relationship between the space covered by the vector and the intensity of the wind. To study the population density of aphids, we have considered that the ladybug is its main enemy. The trajectories of the phase-plane are compared to those of deterministic predatorprey models of the Holling- Tanner and Lotka- Volterra types. From the obtained results, we have begun a study to investigate some control policies for the Citrus Sudden Death / Doutorado / Matematica Aplicada / Doutor em Matemática Aplicada

This work describes a method of the secondary structure prediction of proteins based on cellular automaton (CA) model - CASSP. Optimal model and CA transition rule parameters are acquired by evolutionary algorithm. Prediction model uses only statistical characteristics of amino acids, so its prediction is fast. Achieved results was compared with results of other tools for this purpose. Prediction cooperation with a existing tool PSIPRED was also tested. It didn't succeed to beat this existing tool, but partial improvement was achieved in prediction of only alpha-helix secondary structure motif, what can be helful if we need the best prediction of alpha-helices. It was developed also a web interface of designed system.

Molecular-based quantum cellular automata (m-QCA), as an extension of quantum-dot QCAs, offer a novel alternative in which binary information can be encoded in the molecular charge configuration of a cell and propagated via nearest-neighbor Coulombic cell-cell interactions. Appropriate functionality of m-QCAs involves a complex relationship between quantum mechanical effects, such as electron transfer processes within the molecular building blocks, and electrostatic interactions between cells. In the first part of this document, the influence of structural distortions in single m-QCA is addressed within a minimal model using an diabatic-to-adiabatic transformation. Thus, it is shown that even small changes of the classical square geometry between driver and target cells, such as those induced by distance variations or shape distortions, can make cells respond to interactions in a far less symmetric fashion, modifying and potentially impairing the expected computational behavior of the m-QCA. The model has been further extended to consider time-dependent external electric fields in which a special emphasis is given to the profiles in which this external parameter can interact with the associated molecular complex. The results of the model have been validated by a direct comparison with first-principle calculations allowing to conclude the plausibility to induce the intra-molecular charge transfer process in a controllable manner via the interaction with the external electric field. The influence played by the electric field profile in the response of the molecular complex is also investigated. The results suggests a major role played by this variable in terms of the time length in which the intra-molecular charge transfer can be observed. In the second part, first-principle theoretical calculations of the self-assembly properties and electronic structure of Ferrocene-functionalized complexes have been carried out. Hence, five different molecular complexes which offer a potential playground to realistic implement the m-QCA paradigm have been investigated. The main emphasis is given to study the interaction between localized charge-carrier molecular states and the delocalized surface states. The results of these calculations demonstrate the possibility to obtain real systems in which intra-molecular charge localization can be combined with self-assembly scaffolding and absorbed on either Highly oriented pyrolytic graphite (HOPG) or metallic-surfaces. Finally, the validation of these findings is carried out via comparison with accesible experimental results and opening the gate to plausible strategies where the paradigm can be implemented.

Das Auftreten von Starkregen führt zu steigendem Wasserstand und Wasservolumen in den Fließgewässern und zu Überschwemmungen der Flussauen. Tritt Hochwasser im menschlichen Siedlungsraum auf, entstehen Schäden an privatem und öffentlichem Eigentum sowie im gewerblichen Bereich. Deshalb ist es notwendig Dynamiken der Flächennutzungsänderungen zu untersuchen, da die Flächennutzung die Art der anfälligen Elemente im Falle eines Hochwassers und die Schadenshöhe bestimmt. Als Simulationswerkzeug wird der rasterbasierte zelluläre Automat LuSIM (vgl. STRÖBL et al. 2003) genutzt, der als Erweiterung für ArcView 3.x zur Verfügung steht. Das Untersuchungsgebiet befindet sich in der Elbaue, zum Teil im Stadtgebiet Dresden und zum Teil im Stadtgebiet von Radebeul im Landkreis Meißen. In der vorliegenden Arbeit wird die zukünftige urbane Flächennutzungsentwicklung im Untersuchungsgebiet nach drei Szenarios simuliert. Die Neuentstehung von Wohnbauland ist dabei die treibende Kraft. Die Arbeit leistet damit einen Beitrag zur ex ante-Analyse und Bewertung der Hochwasserrisiken. Der zelluläre Automat verortet neue potentielle Wohnstandorte basierend auf Eignungsregeln und dem Bedarf an neuem Wohnbauland. Die Regeln zur Eignung jeder Rasterzelle als Wohnstandort leite ich aus der Analyse historischer Nutzungsänderungen ab. Den Bedarf an zukünftigem Wohnbauland ermittle ich aus dem vom Bundesamt für Bauwesen und Raumordnung (2006) in seiner Raumordnungsprognose 2020/2050 prognostizierten Neubau von Wohnungen.

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Cancer, a leading cause of death globally, is characterized by the uncontrolled growth of abnormal cells evolving due to natural selection. A cancerous tumor is a complex ecosystem of heterogeneous cell populations that, over time, acquire new traits like therapy resistance. Despite progress in experimental methods, measuring genetic and phenotypic processes on time scales relevant to tumor evolution is still challenging. As a result, the mechanisms that lead to tumor heterogeneity, evolution, progression, and response to treatment remain largely unclear. Mathematical models can help address this challenge, allowing us to test hypotheses, predict cellular behavior, and optimize cancer treatment. In this thesis, I investigate the role of genetic and phenotypic heterogeneity in tumor evolution using mathematical models and analysis. Discrete stochastic models are well-suited to study tumor evolution due to the involvement of rare stochastic events and small populations. Here, I introduce evolutionary lattice-gas cellular automata (evo-LGCA), a generalization of classical lattice-gas cellular automata (LGCA). LGCA are discrete mathematical models describing the interactions of moving agents, such as cancer cells, on a regular lattice, with discretized velocities, and in discrete time steps. Agents are indistinguishable and obey an exclusion principle that prevents them from being simultaneously in the same state, causing unwanted behavior. In contrast, in evo-LGCA, agents are distinguishable, have unique properties, and can be in the same state, minimizing model artifacts. This makes evo-LGCA particularly suitable for studying the complexity of tumors. Using this framework, I investigate the interplay of evolutionary dynamics and population growth. In particular, I am interested in the role of the distribution of fitness effects (DFE). The DFE determines the strength and frequency of the effect of mutations. I present an evo-LGCA model for tumor evolution, in which cells can divide, die, move, and mutate given an arbitrary but fixed DFE. From the dynamics of the evo-LGCA model, I derive an integro-partial differential equation, predicting the distribution in fitness space over time. This equation is equivalent to the replicator-mutator equation, establishing a connection to population genetics and evolutionary game theory. Additionally, I derive a generalized version of Fisher’s fundamental theorem of natural selection, a classic theorem stating that a population’s change in mean fitness is proportional to the population’s variance in fitness. However, it neglects the effect of mutations and the dynamics of higher moments, such as the variance. My generalization is a hierarchy of equations for the time evolution of all moments of the fitness distribution, depending on the DFE. Through simulations of the evo-LGCA model, I show that continuum approximations are suitable in regimes of frequent mutations with weak effects on fitness and large, well-mixed populations. I further establish that the fastest-growing cells spearhead spreading populations, accelerating the expansion speed. Next, I examine the evolutionary dynamics within small, clinically undetectable tumors. Cancer cells quickly accumulate weakly disadvantageous passenger mutations, whereas beneficial driver mutations are rare but have a significant effect. Previous studies have shown that this leads to competition between passenger and driver mutations, affecting population fitness. Populations below a critical population size accumulate deleterious mutations too quickly, leading to extinction. I highlight how small cancer cell populations can bypass potential extinction through swift invasion of their microenvironment. This invasion can be seen as an adaptation to counteract the accumulation of disadvantageous mutations. Lastly, I examine the complex relationship between evolution and phenotypic plasticity, focusing on the phenotypic change between proliferative and migratory phenotypes relevant to tumors like glioblastoma, a deadly brain tumor. Contrary to previous studies, I propose that evolution acts on the cellular decision-making process in response to the environment rather than on phenotypic traits like cell motility. I study this hypothesis with an evo-LGCA model that tracks individual cells’ phenotypic and genetic states. I assume cells change between migratory and proliferative states controlled by inherited and mutation-driven genotypes and the cells’ microenvironment in the form of cell density. Cells at the tumor edge evolve to favor migration over proliferation and vice versa in the tumor bulk. Notably, this phenotypic heterogeneity can be realized by two distinct regulations of the phenotypic switch. I predict the outcome of the evolutionary process with a mathematical analysis, revealing a dependence on microenvironmental parameters. The emerging synthetic tumors display varying levels of heterogeneity, which I show are predictors of the cancer’s recurrence time after treatment. Interestingly, higher phenotypic heterogeneity predicts poor treatment outcomes, unlike genetic heterogeneity. In conclusion, this thesis offers a mathematical framework for studying heterogeneous populations. Applying it to tumor evolution, I gained new insights into the relationship between discrete and continuous evolution models and the interplay of population growth and evolutionary dynamics. I also proposed a novel perspective on phenotypic plasticity accounting for cell decision-making, demonstrating the predictive value of phenotypic heterogeneity.:1. Introduction [13] 1.1 Background on Cancer [13] 1.1.1 Definition [13] 1.1.2 Hallmarks of Cancer [13] 1.1.3 Cancer as a Genetic Disease [14] 1.1.4 Tumor Evolution [15] 1.1.5 Tumor Heterogeneity [17] 1.2 Mathematical Models of Tumor Evolution and Heterogeneity [19] 1.2.1 Overview [19] 1.2.2 Deterministic Approaches [20] 1.2.3 Agent-Based Approaches [24] 1.2.4 Hybrid Models [26] 1.2.5 Evolutionary Game Theory [27] 1.3 Research Questions and Dissertation Outline [27] 2. Evolutionary Lattice-Gas Cellular Automata [31] 2.1 Cellular Automaton Basics [31] 2.2 Lattice-Gas Cellular Automata [33] 2.2.1 Origins [33] 2.2.2 Definition [34] 2.2.3 Extensions [39] 2.3 Evolutionary Lattice-Gas Cellular Automata [43] 2.3.1 Concept [43] 2.3.2 State Space [44] 2.3.3 Dynamics [45] 2.4 Discussion [49] 3. Bridging Micro- and Macroscale of Evolutionary Dynamics [51] 3.1 Connecting Discrete and Continuous Models of Evolution [51] 3.2 Model Definition [53] 3.3 Mathematical Analysis [55] 3.3.1 Mean-Field Approximation of Evolutionary Dynamics [55] 3.3.2 A Generalized Fundamental Theorem of Natural Selection [57] 3.3.3 Derivation of Local Replicator-Mutator Equation [61] 3.3.4 Finite-Size Correction [62] 3.3.5 Spatial Growth Dynamics [63] 3.4 Comparison with Agent-Based Simulations [64] 3.4.1 Well-Mixed Populations [64] 3.4.2 Expanding Populations [68] 3.5 Discussion [69] 4. The Interplay of Invasion and Mutational Meltdown [73] 4.1 Muller’s Ratchet in Tumors [73] 4.2 Influence of Invasion on Evolutionary Dynamics [74] 4.3 Model Parameterization [74] 4.4 Tug-of-War between Driver and Passenger Mutations [76] 4.5 Invasion as a Strategy against Mutational Meltdown [79] 4.6 Discussion [80] 5. Evolution under the Go-or-Grow Dichotomy [85] 5.1 Phenotypic Plasticity [85] 5.2 The Role of Cell Decision-Making in Evolutionary Dynamics [86] 5.3 Model Definition [87] 5.4 Emergence of Phenotypic and Genetic Heterogeneity [90] 5.4.1 Migratory Phenotype Favored by Minimal Apoptosis Rates [91] 5.4.2 Emerging Spatial Heterogeneity for Low Switching Threshold [91] 5.4.3 Repulsive Strategy Favored by High Switching Threshold [92] 5.4.4 Prediction of Optimal Go-or-Grow Strategy [92] 5.5 Heterogeneity as a Predictor of Treatment Outcomes [95] 5.6 Discussion [98] 6. Discussion & Outlook [103] A. Mathematical Derivations [107] B. Supplementary Simulations [113] C. Software [119] Bibliography [121]

Artificial intelligence has traditionally been approached through centralized architectures and optimization of specific metrics on large datasets. However, the frontiers of fields spanning cognitive science, biology, physics, and computer science suggest that intelligence is better understood as a multi-scale, decentralized, emergent phenomenon. As such, scaling up approaches that mirror the natural world may be one of the next big advances in AI. This thesis presents Coralai, a framework for efficiently simulating the emergence of diverse artificial life ecosystems integrated with modular physics. The key innovations of Coralai include: 1) Hosting diverse Neural Cellular Automata organisms in the same simulation that can interact and evolve; 2) Allowing user-defined physics and weather that organisms adapt to and can utilize to enact environmental changes; 3) Hardware-acceleration using Taichi, PyTorch, and HyperNEAT, enabling interactive evolution of ecosystems with 500k evolved parameters on a grid of 1m+ 16-channel physics-governed cells, all in real-time on a laptop. Initial experiments with Coralai demonstrate the emergence of diverse ecosystems of organisms that employ a variety of strategies to compete for resources in dynamic environments. Key observations include competing mobile and sessile organisms, organisms that exploit environmental niches like dense energy sources, and cyclic dynamics of greedy dominance out-competed by resilience.