2D Swarm Meerkats Behavior Modelling

Animal behavior is the connection or link between the molecular and physiological aspects of biology and the ecological. Behavior is the bridge between organisms and environment also between the nervous system and the ecosystem. Besides that, behavior is generally the animal's "first line of defense" in response to environmental change. Therefore, careful observation of the behavior can provide us a great information. Behavior is one of the most important features of animal life. As a human, behavior plays a critical role in our lives. This is because behavior is the part of an organism that interacts with its environment. Many problems occur in human society are often related to the interaction between environment or genetics with behavior. The fields of socioecology and animal behavior deal with the issue of environment behavioral interactions at an accurate level and a proximate level. Therefore, social scientists are turning to animal behavior as a framework to interpret human society and to find out possible sources of societal problems. In this study, the foraging behavior of Meerkat will be studied. In this thesis, the foraging behavior of Meerkat will be studied and the parameters for simulation of Meerkats foraging behavior are designed. The designed parameters including the number of agents, number of group, range of perception and number of food. However, there are not much works done on Meerkats therefore, survey form is used in designing these 14 sets of parameters. Only the choices that have higher percentage is focused in designing the 14 sets of parameters for simulation. The performance of each 14 sets of simulation are compared based on the result obtained from the simulations such as the highest mean quality the simulation can achieve and the number of ticks required to reach the highest mean quality. The higher the mean quality the better the performance. The smaller the number of ticks required to reach the highest mean quality the better the performance

Robust And Scalable Learning Of Complex Dataset Topologies Via Elpigraph

Large datasets represented by multidimensional data point clouds often possess non-trivial distributions with branching trajectories and excluded regions, with the recent single-cell transcriptomic studies of developing embryo being notable examples. Reducing the complexity and producing compact and interpretable representations of such data remains a challenging task. Most of the existing computational methods are based on exploring the local data point neighbourhood relations, a step that can perform poorly in the case of multidimensional and noisy data. Here we present ElPiGraph, a scalable and robust method for approximation of datasets with complex structures which does not require computing the complete data distance matrix or the data point neighbourhood graph. This method is able to withstand high levels of noise and is capable of approximating complex topologies via principal graph ensembles that can be combined into a consensus principal graph. ElPiGraph deals efficiently with large and complex datasets in various fields from biology, where it can be used to infer gene dynamics from single-cell RNA-Seq, to astronomy, where it can be used to explore complex structures in the distribution of galaxies.Comment: 32 pages, 14 figure

Coordination Mechanisms of Mammalian Embryo Implantation

A direct interaction between the extraembryonic and the uterine tissues during embryo implantation generates a unique biomechanical context for the blastocyst. However, our mechanistic understanding of the regulation of blastocyst morphogenesis during implantation is limited by the inaccessibility in vivo and remaining challenges to model feto-maternal interaction ex vivo. To overcome these limitations, I applied microfabrication and biomaterial engineering to model biomechanical cues of the murine intrauterine environment ex vivo with high precision and tunability. I identify that embryo-uterine adhesion and tissue geometry are critical for successful peri-implantation development. In a specific parameter range, closely resembling in utero conditions, the 3D geometrically patterned hydrogel supports mouse blastocysts through implantation and enables robust peri-implantation morphogenesis; promotes the development of the Reichert’s membrane and all extraembryonic tissues, including giant trophoblast, which directly interacts with the uterus. To monitor in toto peri-implantation embryo dynamics, the culture method was integrated with inverted view InVi-SPIM and multiview MuVi-SPIM light-sheet microscopes. I show that integrin-mediated adhesion by the mural trophectoderm provides the mechanism of trophectoderm tension release, driving the morphogenesis of the extraembryonic ectoderm and egg cylinder patterning. Moreover, the embryo-uterine adhesion enables collective trophoblast migration, dependent on Rac1. Finally, I demonstrate that the uterine tissue geometry spatially coordinates collective trophoblast migration to delineate space for egg cylinder growth. Together, this study reveals essential mechanisms of dynamic embryo-uterus interactions during peri-implantation development

Advances in modelling of epithelial to mesenchymal transition

Epithelial to Mesenchymal Transition (EMT) is a cellular transformation process that is employed repeatedly and ubiquitously during vertebrate morphogenesis to build complex tissues and organs. Cellular transformations that occur during cancer cell invasion are phenotypically similar to developmental EMT, and involve the same molecular signalling pathways. EMT processes are diverse, but are characterised by: a loss of cell-cell adhesion; a gain in cell-matrix adhesion; an increase in cell motility; the secretion of proteases that degrade basement membrane proteins; an increased resistance to apoptosis; a loss of polarisation; increased production of extracellular matrix components; a change from a rounded to a fibroblastic morphology; and an invasive phenotype. This thesis focuses explicitly on endocardial EMT, which is the EMT that occurs during vertebrate embryonic heart development. The embryonic heart initially forms as a tube, with myocardium externally, endocardium internally, with these tissue layers separated by a thick extracellular matrix termed the cardiac jelly. Some of the endocardial cells in specific regions of the embryonic heart tube undergo EMT and invade the cardiac jelly. This causes cellularised swellings inside the embryonic heart tube termed the endocardial cushions. The emergence of the four chambered double pump heart of mammals involves a complex remodelling that the endocardial cushions play an active role in. Even while heart remodelling is taking place, the heart tube is operating as a single-circulation pump, and the endocardial cushions are performing a valve-like function that is critical to the survival of the embryo (Nomura-Kitabayashi et al. 2009). As the endocardial cushions grow and remodel, they become the valve leaflets of the foetal heart. The endocardial cushions also contribute tissue to the septa (walls) of the heart. Their correct formation is thus essential to the development of a fully functional, fully divided, double-pump system. It has been shown that genetic mutations that cause impaired endocardial EMT lead to the development of a range of congenital heart defects (Fischer et al. 2007). An extensive review is conducted of existing experimental investigations into endocardial EMT. The information extracted from this review is used to develop a multiscale conceptual model of endocardial EMT, including the major protein signalling pathways involved, and the cellular phenotypes that they induce or inhibit. After considering the requirements for computational simulations of EMT, and reviewing the various techniques and simulation packages available for multi-cell modelling, cellular Potts modelling is selected as having the most appropriate combination of features. The open source simulation platform Compucell3D is selected for model development, due to the flexibility, range of features provided and an existing implementation of multiscale models; that include subcellular models of reaction pathways. Based on the conceptual model of endocardial EMT, abstract computational simulations of key aspects are developed, in order to investigate qualitative behaviour under different simulated conditions. The abstract simulations include a 2D multiscale model of Notch signalling lateral induction, which is the mechanism by which the embryonic heart tube is patterned into cushion and non-cushion forming regions. Additionally, a 3D simulation is used to investigate the possible role of contact-inhibited mitosis, upregulated by the VEGF protein, in maintaining an epithelial phenotype. One particular in vitro investigation of endocardial EMT (Luna-Zurita et al. 2010) is used to develop quantitative simulations. The quantitative data used for fitting the simulations consist of cell shape metrics that are derived from simple processing of the imaging results. Single cell simulations are used to investigate the relationship between cell motility and cell shape in the cellular Potts model. The findings are then implemented in multi-cell models, in order to investigate the relationship between cell-cell adhesion, cell-matrix adhesion, cell motility and cell shape during EMT

A computational modelling of cellular and supra-cellular networks to unravel the control of EMT

"Over the last decade, Epithelial-to-Mesenchymal Transition (EMT) has gained the attention of cancer researchers due to its potential to promote cancer migration and metastasis. However, the complexity of EMT intertwined regulation and the involvement of multiple signals in the tumour microenvironment have been limiting the understanding of how this process can be controlled. Cell-cell adhesion and focal adhesion dynamics are two critical properties that change during EMT, which provide a simple way to characterize distinct modes of cancer migration. Therefore, the main focus of this thesis is to provide a framework to predict critical microenvironment and de-regulations in cancer that drive interconversion between adhesion phenotypes, accounting for main microenvironment signals and signalling pathways in EMT. Here, we address this issue through a systems approach using the logical modelling framework to generate new testable predictions for the field.(...)"Instituto Gulbenkian de Ciência (FCG-IGC

Automated measurement of cell motility and proliferation

BACKGROUND: Time-lapse microscopic imaging provides a powerful approach for following changes in cell phenotype over time. Visible responses of whole cells can yield insight into functional changes that underlie physiological processes in health and disease. For example, features of cell motility accompany molecular changes that are central to the immune response, to carcinogenesis and metastasis, to wound healing and tissue regeneration, and to the myriad developmental processes that generate an organism. Previously reported image processing methods for motility analysis required custom viewing devices and manual interactions that may introduce bias, that slow throughput, and that constrain the scope of experiments in terms of the number of treatment variables, time period of observation, replication and statistical options. Here we describe a fully automated system in which images are acquired 24/7 from 384 well plates and are automatically processed to yield high-content motility and morphological data. RESULTS: We have applied this technology to study the effects of different extracellular matrix compounds on human osteoblast-like cell lines to explore functional changes that may underlie processes involved in bone formation and maintenance. We show dose-response and kinetic data for induction of increased motility by laminin and collagen type I without significant effects on growth rate. Differential motility response was evident within 4 hours of plating cells; long-term responses differed depending upon cell type and surface coating. Average velocities were increased approximately 0.1 um/min by ten-fold increases in laminin coating concentration in some cases. Comparison with manual tracking demonstrated the accuracy of the automated method and highlighted the comparative imprecision of human tracking for analysis of cell motility data. Quality statistics are reported that associate with stage noise, interference by non-cell objects, and uncertainty in the outlining and positioning of cells by automated image analysis. Exponential growth, as monitored by total cell area, did not linearly correlate with absolute cell number, but proved valuable for selection of reliable tracking data and for disclosing between-experiment variations in cell growth. CONCLUSION: These results demonstrate the applicability of a system that uses fully automated image acquisition and analysis to study cell motility and growth. Cellular motility response is determined in an unbiased and comparatively high throughput manner. Abundant ancillary data provide opportunities for uniform filtering according to criteria that select for biological relevance and for providing insight into features of system performance. Data quality measures have been developed that can serve as a basis for the design and quality control of experiments that are facilitated by automation and the 384 well plate format. This system is applicable to large-scale studies such as drug screening and research into effects of complex combinations of factors and matrices on cell phenotype

Satellite and UAV Platforms, Remote Sensing for Geographic Information Systems

The present book contains ten articles illustrating the different possible uses of UAVs and satellite remotely sensed data integration in Geographical Information Systems to model and predict changes in both the natural and the human environment. It illustrates the powerful instruments given by modern geo-statistical methods, modeling, and visualization techniques. These methods are applied to Arctic, tropical and mid-latitude environments, agriculture, forest, wetlands, and aquatic environments, as well as further engineering-related problems. The present Special Issue gives a balanced view of the present state of the field of geoinformatics

Mathematical Fuzzy Logic in the Emerging Fields of Engineering, Finance, and Computer Sciences

Mathematical fuzzy logic (MFL) specifically targets many-valued logic and has significantly contributed to the logical foundations of fuzzy set theory (FST). It explores the computational and philosophical rationale behind the uncertainty due to imprecision in the backdrop of traditional mathematical logic. Since uncertainty is present in almost every real-world application, it is essential to develop novel approaches and tools for efficient processing. This book is the collection of the publications in the Special Issue “Mathematical Fuzzy Logic in the Emerging Fields of Engineering, Finance, and Computer Sciences”, which aims to cover theoretical and practical aspects of MFL and FST. Specifically, this book addresses several problems, such as:- Industrial optimization problems- Multi-criteria decision-making- Financial forecasting problems- Image processing- Educational data mining- Explainable artificial intelligence, etc

Tumor Cell Migration and Interaction with ECM and Stroma in 3D Tissue Models

Tumors are often described as ”wounds that do not heal”. Tumor progression and woundhealing both feature sustained proliferative signaling, evasion from immune destruction, cell death resistance, inflammation, angiogenesis, extracellular matrix(ECM) remodeling, and activated cell migration. Unlike normal wound healing which often ends up with restored tissue homeostasis, pathological ECM remodeling is frequently implicated in fibrotic diseases and solid tumors. In addition, the dysregulated ECM signatures are directly associated with poor prognosis and also immunotherapy failure in certain types of cancers. In this dissertation, we used 3D in vitro cocultures to understand how tumor cells co-op with stromal/immune components, how ECM remodeling is hijacked, and how ECM architecture impacts tumor progression. We first investigated the impact of fibroblasts. Fibroblasts are the most abundant cell types in the tumor stroma. The density of cancer-associated fibroblasts(CAFs) has been shown directly correlated with poor prognosis in some types of solid tumors. To uncover potential mechanisms behind the quantitative relationship between CAFs and tumor dissemination, we developed our coculture model by varying the density ratios between normal human lung fibroblasts and breast cancer cells(MDA-MB-231s). We found that fibroblasts increase tumor cell motility and facilitate the transition from confined to diffusive tumor cell motions, indicative of an uncaging effect. Furthermore, the ECM is globally and locally remodeled substantially with the presence of fibroblasts. Moreover, these fibroblast-mediated phenomena are in part dependent on matrix metalloproteinases. We then investigated the impact of macrophages. In this study, we developed a 3D collagen co-culture system to mimic the melanoma microenvironment and investigate how interactions between melanoma cancer cells, fibroblasts, and macrophages shape the early stages of macrophage immune activity. In this in vitro model, we captured the macrophage immunosuppressive transition. Macrophages in the model displayed increased motility and acquired a phenotype that was similar to tumor-associated macrophages(TAMs) from melanoma tumors. This model may provide a platform for further studies on TAMs targeted immune therapy in melanoma. In the end, we investigated the impact of ECM architecture in tumor progression. Reconstruction of a biomimetic scaffold is critical in 3D in vitro models. Here we introduce a new type of thick collagen bundles that highly mimic in vivo ECM structure. We fabricated this type of thickened collagen bundles by introducing mechanical agitation during the transient gelation process. Thickened collagen patches are interconnected with a loose collagen network, highly resembling collagen architecture in human skin scars. This type of thickened collagen bundles promotes tumor cell dissemination. The effect is significantly augmented in the presence of fibroblasts. The application of this type of collagen triggers different morphology and migration behaviors of tumor cells and highlights the importance of mesoscale architectures. Overall, this dissertation investigated the roles of stromal and non-stromal components in tumor progression through 3D in vitro models. The coculture models established in this dissertation may be further extended to test novel therapeutics targeted at CAFs, TAMs or ECM architecture

Quantitative Characterization of cancer microenvironment

Ph.DDOCTOR OF PHILOSOPH