Orientador: Camila Pires Cremasco Gabriel / Resumo: Para uma satisfatória produtividade, a cultura do rabanete (Raphanus sativus L.) exige principalmente boa qualidade do solo e grande disponibilidade de água. A irrigação é uma técnica artificial utilizada para disponibilizar água as plantas. Seu uso deve ser criterioso e para que se obtenha menores custos de produção, deve-se evitar o uso desnecessário de água, e consequentemente, energia elétrica. Formas de utilização da água são cada vez mais estudadas, entre elas, a água de irrigação tratada magneticamente, que tem mostrado aprimoramentos produtivos em diversas culturas. O objetivo deste trabalho foi o desenvolvimento de um conjunto de sistemas computacionais baseados em regras fuzzy para previsão de componentes de produção de culturas irrigadas. Para tanto, foram utilizados dados de um experimento conduzido com água de irrigação convencional ou tratada magneticamente, sendo avaliado variáveis biométricas, tais como: peso verde do bulbo, número de folhas, comprimento da raiz, diâmetro do bulbo, comprimento do bulbo, peso verde da raiz, peso verde da folha, peso seco da raiz, peso seco da folha e peso seco do bulbo. Como resultado, foi apresentado um conjunto de softwares com uma interface de simples uso e fácil compreensão, que poderá auxiliar os produtores na estimativa dos resultados das variáveis biométricas do rabaneteiro e de outras culturas irrigadas. / Abstract: For satisfactory productivity, the cultivation of radish (Raphanus sativus L.) mainly requires good soil quality and great availability of water. Irrigation is an artificial technique used to make water available to plants. Its use must be judicious and to obtain lower production costs, unnecessary use of water and, consequently, electric energy should be avoided. Ways of using water are increasingly studied, among them, magnetically treated irrigation water, which has shown productive improvements in several cultures. The objective of this work was the development of a set of computational systems based on fuzzy rules for forecasting production components of irrigated crops. For that, data from an experiment conducted with conventional irrigation water or magnetically treated water were used, and biometric variables were evaluated, such as green bulb weight, number of leaves, root length, bulb diameter, bulb length, green weight root weight, green leaf weight, dry root weight, dry leaf weight, and dry bulb weight. As a result, a set of software was presented with a simple to use and easy to understand interface, which can assist producers in estimating the results of the biometric variables of the radish feet and other irrigated crops. / Doutor

In a multi-cellular organism, cells constantly receive signals on their internal condition and surrounding environment. In response to various signals, cells proliferate, move around or even undergo suicide. The signal-response is controlled by complex molecular machinery, understanding of which is an important goal of basic molecular biological research. Such understanding is also valuable for clinical application, since lethal diseases like cancer show maladaptive responses to growth-regulating signals. Because the multiple feedbacks in the molecular regulatory machinery obscure cause-effect relations, it is hard to understand these control systems by intuition alone. Here we translate the molecular interactions into differential equations and recapture the cellular physiological properties with the help of numerical simulations and non-linear dynamical tools. The models address the physiological features of programmed cell death, the cell fate decision by p53 and the dynamics of the NF-?B control system. These models identify key molecular interactions responsible for the observed physiological properties, and they generate experimentally testable predictions to validate the assumptions made in the models. / Ph. D.

Complex networks are composed of a large number of interacting nodes. Examples of complex networks include the topology of the Internet, connections between websites or web pages in the World Wide Web (WWW), and connections between participants in social networks.Due to their ubiquity, modeling complex networks is importantfor answering many research questions that cannot be answered without a mathematical model. For example, mathematical models of complex networks can be used to find the most vulnerable nodes to protect during a virus attack in theInternet, to predict connections between websites in the WWW, or to find members of different communities insocial networks. Researchers have analyzed complex networksand concluded that they are distinguished from other networks by four specific statistical properties. These four statistical properties are commonly known in this field as: (i) thesmall world effect,(ii) high average clustering coefficient, (iii) scale-free power law degree distribution, and (iv) emergence of community structure. These four statistical properties are further described later in this dissertation. Mostmodels used to generate complex networks attempt to produce networks with these statistical properties. Additionally, most of these network models generate homogeneous complex networks where all the networknodes are considered to have the same properties. Homogenous complex networks neglect the heterogeneous nature ofthe nodes in many complexnetworks. Moreover, somemodels proposed for generating heterogeneous complexnetworks are not general as they make specific assumptions about the properties of the network.Including heterogeneity in the connection algorithm of a modelwould makeitmore suitable for generating the subset of complex networks that exhibit selective linking.Additionally, all modelsproposed, to date, for generating heterogeneous complex networks do not preserve all four of the statistical properties of complexnetworks stated above. Thus, formulation of a model for the generation of general heterogeneous complex networkswith characteristics that resemble as much as possible the statistical properties common to the real-world networks that have received attention from the research community is still an open research question. In this work, we propose two new types of models to generate heterogeneous complex networks. First, we introduce the Integrated Attribute Similarity Model (IASM). IASM uses preferential attachment(PA) to connect nodes based on a similarity measure for node attributes combined with a node's structural popularity measure. IASM integrates the attribute similarity measure and a structural popularity measure in the computation of the connection function used to determine connectionsbetween each arriving (newly created) node and the existing(previously created or old) network nodes. IASM is also the first model known to assign an attribute vector having more than one element to each node, thus allowing different attributes per node in the generated complex network. Networks generated using IASM have a power law degree distribution and preserve the small world phenomenon. IASM models are enhanced to increase their clustering coefficient using a triad formation step (TFS). In a TFS, a node connects to the neighbor of the node to which it was previously connected through preferential attachment, thus forming a triad. The TFS increases the number of triads that are formed in the generated network which increases the network's average clustering coefficient. We also introduce a second novel model,the Settling Node Adaptive Model (SNAM). SNAM reflects the heterogeneous nature of connectionstandard requirements for nodes. The connectionstandard requirements for a noderefers to the values of attribute similarity and/or structural popularityof old node ythat node new xwould find acceptable in order to connect to node y.SNAM is novel in that such a node connection criterion is not included in any previous model for the generation of complex networks. SNAM is shown to be successful in preserving the power law degree distribution, the small world phenomenon, and the high clustering coefficient of complex networks. Next,we implement a modification to the IASM and SNAM models that results in the emergence of community structure.Nodes are classified into classes according to their attribute values. The connection algorithm is modified to include the class similarity values between network nodes. This community structure model preservesthe PL degree distribution, small world property, and does not affect average clustering coefficient values expected from both IASM and SNAM. Additionally, the model exhibits the presence of community structure having most of the connections made between nodes belonging to the same class with only a small percent of the connections made between nodes of different classes. We perform a mathematical analysis of IASM and SNAM to study the degree distribution for networks generated by both models. This mathematical analysis shows that networks generated by both models have a power law degree distribution. Finally, we completed a case study to illustrate the potential value of our research on the modeling of heterogeneous complex networks. This case study was performed on a Facebook dataset. The case study shows that SNAM, with some modifications to the connection algorithm, is capable of generating a network with almost the same characteristics as found for the original dataset. The case study providesinsight on how the flexibility of SNAM's connection algorithm can be an advantagethat makes SNAM capable of generating networks with different statistical properties. Ideas for future research areas includestudyingthe effect of using eigenvector centrality, instead of degree centrality, on the emergence of community structure in IASM; usingthe nodeindex as an indication for its order of arrival to the network and distributing added connections fairly among networknodes along the life of the generated network; experimenting with the nature of attributesto generatea more comprehensive model; and usingtime sensitive attributes in the models, where the attribute can change its value with time, / Ph. D.

The hybrid activated sludge-biofilm system called Integrated Fixed Film Activated Sludge (IFAS) has recently become popular for enhanced nitrification and denitrification in aerobic zones because it is an alternative to increasing the volume of treatment plant units to accomplish year round nitrification and nitrogen removal. Biomass is retained on the fixed-film media and remains in the aerobic reactor, thus increasing the effective mean cell resident time (MCRT) of the biomass and providing the temperature sensitive, slow growing nitrifiers a means of staying in the system when they otherwise would washout. While the utilization of media in aerobic zones to enhance nitrification and denitrification has been the subject of several studies and full-scale experiments, the effects and performances of fixed film media integrated into the anoxic zones of biological nutrient removal (BNR) systems have not adequately been evaluated as well as the impacts of integrated media upon enhanced biological phosphorus removal (EBPR). Also, user-friendly software designed specifically to simulate the complex mixture of biological processes that occur in IFAS systems are not available. The purpose of this research was to more fully investigate the effects of integrated fixed film media on EBPR, to evaluate the impacts of media integrated into the anoxic zone on system performance, and to develop a software program that could be used to simulate the effects of integrating the various types of media into suspended growth biological nutrient removal (BNR) systems. The UCT type configuration was chosen for the BNR system, and Accuweb rope-like media was selected for integration into the anoxic zones of two IFAS systems. The media also was integrated into the aerobic reactors of one of the systems for comparison and for further investigation of the performance of the Accuweb media on enhanced nitrification and denitrification in the aerobic zones. The experiments were conducted at 10 day total MCRT during the initial phase, and then at 6 days MCRT for the experimental temperature of 10 oC. A13 hour hydraulic retention time (HRT) was used throughout the study. A high and a low COD/TP ratio were used during the investigation to further study the effects of integrated media on EBPR. The PC Windows based IFAS program began with the concepts of IAWQ model No. 2 and a zero-dimensional biofilm model was developed and added to predict the IFAS processes. Experimental data from the initial study and existing data from similar studies performed at high temperatures (>10oC) indicated that there were no significant differences in BNR performances between IFAS systems with media integrated into the anoxic and aerobic or only aerobic zones and a suspended growth control system maintained at the same relative high MCRT and temperature values. Even though greater biological nitrogen removal could not be achieved for the experimental conditions used, the experimental results indicated that the IFAS systems with fixed film media installed in the anoxic zone have a greater potential for denitrification than conventional BNR systems. As much as 30 percent of the total denitrification was observed to occur in the aerobic zones of the system installed the media only anoxic zones and 37% in the system with integrated media in both anoxic and aerobic zones where as no denitrification was observed in the aerobic zones of the control system when the systems were operated at 6 days MCRT and COD/TP of 52. It is statistically confirmed EBPR can be maintained in IFAS systems as well as Control systems, but the IFAS processes tend to have more phosphorus release in the anoxic zones with integrated fixed film installed. Further, the combination of split flow to the anoxic zone and fixed film media in the anoxic zone resulted in the decreased EBPR performances in the IFAS system relative to the control system. / Ph. D.

In eukaryotes, distinct nutrient signals are integrated in order to produce robust cellular responses to fluctuations in the environment. This process of signal integration is attributed to the crosstalk between nutrient specific signaling pathways, as well as the large degree of overlap between their regulatory targets. In the budding yeast Saccharomyces cerevisiae, these distinct pathways have been well characterized. However, the significant overlap between these pathways confounds the interpretation of the overall regulatory logic in terms of nutrient-dependent cell state determination. Here, we propose a literature-curated molecular mechanism of the integrated nutrient signaling pathway in budding yeast, focussing on carbon and nitrogen signaling. We build a computational model of this pathway to reconcile the available experimental data with our proposed molecular mechanism. We evaluate the robustness of the model fit to data with respect to the variations in the values of kinetic parameters used to calibrate the model. Finally, we use the model to make novel, experimentally testable predictions of transcription factor activities in mutant strains undergoing complex nutrient shifts. We also propose a novel framework, called BoolODE for utilizing published Boolean models to generate synthetic datasets used to benchmark the performance of algorithms performing gene regulatory network inference from single cell RNA sequencing data. / Doctor of Philosophy / An important problem in biology is how organisms sense and adapt to ever changing environments. A good example of an environmental cue that affects animal behavior is the availability of food; scarcity of food forces animals to search for food-rich habitats, or go into hibernation. At the level of single cells, a range of behaviors are observed depending on the amount of food, or nutrients present in the environment. Moreover, different types of nutrients are important for different biological functions in single cells, and each different nutrient type will have to be available in the right quantities to support cellular growth. At the subcellular level, intricate molecular machineries exist which sense the amounts of each nutrient type, and interpret this information in order to make a decision on how best to respond. This interpretation and integration of nutrient information is a complex, poorly understood process even in a simple unicellular organism like the budding yeast. In order to understand this process, termed nutrient signaling, we propose a mathematical model of how yeasts respond to nutrient availability in the environment. Our model advances the state of knowledge by presenting the first comprehensive mathematical model of the nutrient signaling machinery, accounting for a variety of experimental observations from the last three decades of yeast nutrient signaling. We use our model to make predictions on how yeasts might behave when supplied with different combinations of nutrients, which can be verified by experiments. Finally, the cellular machinery that helps yeasts respond to nutrient availability in the environment is very similar to the machinery in cancer cells that causes them to grow rapidly. Our proposed model can serve as a stepping stone towards the construction of a model of cancer's responses to its nutritional environment.

Protein regulatory networks are the hallmark of many important biological functionalities. Two of these functionalities are mammalian cell cycle progression and near-perfect adaptive responses. Modeling and simulating these functionalities are crucial stages to understanding and predicting them as systems-level properties of cells. In the context of the mammalian cell cycle, the timing of DNA synthesis, mitosis and cell division is regulated by a complex network of biochemical reactions that control the activities of a family of cyclin-dependent kinases. The temporal dynamics of this reaction network is typically modeled by nonlinear differential equations describing the rates of the component reactions. This approach provides exquisite details about molecular regulatory processes but is hampered by the need to estimate realistic values for the many kinetic constants that determine the reaction rates. To avoid this problem, modelers often resort to "qualitative" modeling strategies, such as Boolean switching networks, but these models describe only the coarsest features of cell cycle regulation. In this work, we describe a hybrid approach that combines features of continuous and discrete networks. The model is evaluated in terms of flow cytometry measurements of cyclin proteins in asynchronous populations of human cell lines. Using our hybrid approach, modelers can quickly create quantitatively accurate, computational models of protein regulatory networks found in various contexts within cells. Large-scale protein regulatory networks, such as the one that controls the progression of the mammalian cell cycle, also contain small-scale motifs or modules that carry out specific dynamical functions. Systematic characterization of smaller, interacting, network motifs whose individual behavior is well known under certain conditions is therefore of great interest to systems biologists. We model and simulate various 3-node network motifs to find near-perfect adaptation behavior. This behavior entails that a system responds to a change in its environmental cues, or signals, by coming back nearly to its pre-signal state even in the continued presence of the signal. We let various topologies evolve in their parameter space such that they eventually stumble upon a region where they score well under a pre-defined scoring metric. We find many such parameter sample sets across various classes of topologies. / Ph. D.

We present a mathematical model that describes how cyanobacterial communities use natural light as a source of energy and water as a source of electrons to perform photosynthesis and therefore, grow and co-survive together with other bacterial species. We apply our model to a phototrophic population of bacteria, namely, cyanobacteria. Our model involves the use of light as a source of energy and inorganic carbon as a source of nutrients. First, we study a single species model involving only cyanobacteria, then we include heterotrophs in the two species model. The model consists of ordinary differential equations describing bacteria and chemicals evolution in time. Stability analysis results show that adding heterotrophs to a population of cyanobacteria increases the level of inorganic carbon in the medium, which in turns allows cyanobacteria to perform more photosynthesis. This increase of cyanobacterial biomass agrees with experimental data obtained by collaborators at the Center for Biofilm Engineering at Montana State University. / Mathematics

In this work, two differential equation models for smallpox are numerically solved to find the optimal intervention policy. In each model we look for the range of values of the parameters that give rise to the worst case scenarios. Since the scale of an epidemic is determined by the number of people infected, and eventually dead, as a result of infection, we attempt to quantify the scale of the epidemic and recommend the optimum intervention policy. In the first case study, we mimic a densely populated city with comparatively big tourist population, and heavily used mass transportation system. A mathematical model for the transmission of smallpox is formulated, and numerically solved. In the second case study, we incorporate five different stages of infection: (1) susceptible (2) infected but asymptomatic, non infectious, and vaccine-sensitive; (3) infected but asymptomatic, noninfectious, and vaccine-in-sensitive; (4) infected but asymptomatic, and infectious; and (5) symptomatic and isolated. Exponential probability distribution is used for modeling this case. We compare outcomes of mass vaccination and trace vaccination on the final size of the epidemic.

<p>    </p> <p>Migration is a ubiquitous phenomenon in biology and is relevant to all scales ranging from bacteria to human beings. It is relevant to fundamental biological processes like bacterial chemotaxis, development, disease progression, etc. So, understanding migration is pivotal to addressing fundamental questions in biology. We address three broad questions relevant to cell migration using models from physics: (i) What are the critical features of cancer cell migration? (ii) Is it possible to explain complex cell migration data using minimal bio- chemical networks? And (iii) how does cell-to-cell communication affect its migration at the population level? To address these questions we performed (i) mathematical analysis using the Cellular Potts model, simulations using the Biased Persistent random walk model, and steady-state analysis of cell response to graded signals to explain cancer cell migration in response to single and multiple chemical and mechanical signals, (ii) rigorous network anal- ysis of ∼ 500,000 minimal networks having features of fundamental biochemical processes like regulation, conversion or molecular binding to understand the origin of antagonism in multiple cue cancer cell migration experiments and (iii) the steady-state analysis of Keller- Segel equations mimicking collective cell migration to understand the role of cell to cell communication on chemotaxis of a bacterial population. From our analysis, we found that (i) persistence and bias in cancer cell migration are decoupled from each other owing to a lack of memory about past movements and for any general cell migration they are inherently constrained to take only a fixed set of values. (ii) Bias in cancer cell migration in response to a combination of chemoattractant gradients can be less than the response to individual gradients (antagonism in bias) while the speed remains unaltered. This antagonism in bias and lack thereof in speed can be explained by several minimal networks having molecular regulation, conversion, or binding as its central feature and all these distinct mechanisms show convergence and saturation of an internal molecule common to both the chemoattrac- tants. (iii) By analyzing the role of cell-cell communication in bacterial chemotaxis using the Keller-Segel model we find that communication enhances chemotaxis only when it is adaptive to its external surroundings and cell-to-cell variability helps in increasing the chemotactic drift in the bacterial population. </p>

In the present study, the problem of optimization of thermal cracker (pyrolysis) operation is discussed. The main objective in thermal cracker optimization is the estimation of the optimal flow rates of different feeds (such as, Gas-oil, Propane, Ethane and Debutanized natural gasoline) to the cracking furnace under the restriction on ethylene and propylene production. Thousands of combinations of feeds are possible. Hence the optimization needs an efficient strategy in searching for the global minimum. The optimization problem consists of maximizing the economic profit subject to a number of equality and inequality constraints. Modelling, simulation and optimal operation via optimization of the thermal cracking reactor has been carried out by gPROMS (general PROcess Modelling System) software. The optimization problem is posed as a Non-Linear Programming problem and using a Successive Quadratic Programming (SQP) method for solving constrained nonlinear optimization problem with high accuracy within gPROMS software. New results have been obtained for the control variables and optimal cost of the cracker in comparison with previous studies.