Multi-level models and infrastructures for simulating biological system development

The hierarchical organisation of biological systems plays a crucial role in the pattern formation of gene expression resulting from the morphogenetic processes, where autonomous internal dynamics of cells, as well as cell-to-cell interactions through membranes, are responsible for the emergent peculiar structures of the individual phenotype. Being able to reproduce the systems dynamics at different levels of such a hierarchy might be very useful for studying such a complex phenomenon of self-organisation. The idea is to model the phenomenon in terms of a large and dynamic network of compartments, where the interplay between inter-compartment and intra-compartment events determines the emergent behaviour resulting in the formation of spatial patterns. According to these premises the thesis proposes a review of the different approaches already developed in modelling developmental biology problems, as well as the main models and infrastructures available in literature for modelling biological systems, analysing their capabilities in tackling multi-compartment / multi-level models. The thesis then introduces a practical framework, MS-BioNET, for modelling and simulating these scenarios exploiting the potential of multi-level dynamics. This is based on (i) a computational model featuring networks of compartments and an enhanced model of chemical reaction addressing molecule transfer, (ii) a logic-oriented language to flexibly specify complex simulation scenarios, and (iii) a simulation engine based on the many-species/many-channels optimised version of Gillespie’s direct method. The thesis finally proposes the adoption of the agent-based model as an approach capable of capture multi-level dynamics. To overcome the problem of parameter tuning in the model, the simulators are supplied with a module for parameter optimisation. The task is defined as an optimisation problem over the parameter space in which the objective function to be minimised is the distance between the output of the simulator and a target one. The problem is tackled with a metaheuristic algorithm. As an example of application of the MS-BioNET framework and of the agent-based model, a model of the first stages of Drosophila Melanogaster development is realised. The model goal is to generate the early spatial pattern of gap gene expression. The correctness of the models is shown comparing the simulation results with real data of gene expression with spatial and temporal resolution, acquired in free on-line sources.

PyGirl Generating Whole-System VMs from high-level models using PyPy

Virtual machines emulating hardware devices are generally implemented in low-level languages and using a low-level style for performance reasons. This trend results in largely difficult to understand, difficult to extend and unmaintainable systems. As new general techniques for virtual machines arise, it gets harder to incorporate or test these techniques because of early design and optimization decisions. In this paper we show how such decisions can be postponed to later phases by separating virtual machine implementation issues from the high-level machine-specific model. We construct compact models of whole-system VMs in a high-level language, which exclude all low-level implementation details. We use the pluggable translation toolchain PyPy to translate those models to executables. During the translation process, the toolchain reintroduces the VM implementation and optimization details for specific target platforms. As a case study we implement an executable model of a hardware gaming device. We show that our approach to VM building increases understandability, maintainability and extendability while preserving performance.

System Level Simulation and Radio Resource Management for Distributed Antenna Systems with Cognitive Radio and Multi-Cell Cooperation

4th International Conference on Future Generation Communication Technologies (FGCT 2015), Luton, United Kingdom.

INTERCELLULAR AND SYSTEM-LEVEL ASPECTS OF THE METABOLIC INTERACTIONS BETWEEN NEURONS AND GLIA

Quand on parle de l'acide lactique (aussi connu sous le nom de lactate) une des premières choses qui vient à l'esprit, c'est son implication en cas d'intense activité musculaire. Sa production pendant une activité physique prolongée est associée avec la sensation de fatigue. Il n'est donc pas étonnant que cette molécule ait été longtemps considérée comme un résidu du métabolisme, possiblement toxique et donc à éliminer. En fait, il a été découvert que le lactate joue un rôle prépondérant dans le métabolisme grâce à son fort potentiel énergétique. Le cerveau, en particulier les neurones qui le composent, est un organe très gourmand en énergie. Récemment, il a été démontré que les astrocytes, cellules du cerveau faisant partie de la famille des cellules gliales, utilisent le glucose pour produire du lactate comme source d'énergie et le distribue aux neurones de manière adaptée à leur activité. Cette découverte a renouvelé l'intérêt scientifique pour le lactate. Aujourd'hui, plusieurs études ont démontré l'implication du lactate dans d'autres fonctions de la physiologie cérébrale. Dans le cadre de notre étude, nous nous sommes intéressés au rapport entre neurones et astrocytes avec une attention particulière pour le rôle du lactate. Nous avons découvert que le lactate possède la capacité de modifier la communication entre les neurones. Nous avons aussi décrypté le mécanisme grâce auquel le lactate agit, qui est basé sur un récepteur présent à la surface des neurones. Cette étude montre une fonction jusque-là insoupçonnée du lactate qui a un fort impact sur la compréhension de la relation entre neurones et astrocytes. - Relatively to its volume, the brain uses a large amount of glucose as energy source. Furthermore, a tight link exists between the level of synaptic activity and the consumption of energy equivalents. Astrocytes have been shown to play a central role in the regulation of this so-called neurometabolic coupling. They are thought to deliver the metabolic substrate lactate to neurons in register to glutamatergic activity. The astrocytic uptake of glutamate, released in the synaptic cleft, is the trigger signal that activates an intracellular cascade of events that leads to the production and release of lactate from astrocytes. The main goal of this thesis work was to obtain detailed information on the metabolic and functional interplay between neurons and astrocytes, in particular on the influence of lactate besides its metabolic effects. To gain access to both spatial and temporal aspects of these dynamic interactions, we used optical microscopy associated with specific fluorescent indicators, as well as electrophysiology. In the first part of this thesis, we show that lactate decreases spontaneous neuronal, activity in a concentration-dependent manner and independently of its metabolism. We further identified a receptor-mediated pathway underlying this modulatory action of lactate. This finding constituted a novel mechanism for the modulation of neuronal transmission by lactate. In the second part, we have undergone a characterization of a new pharmacological tool, a high affinity glutamate transporter inhibitor. The finality of this study was to investigate the detailed pharmacological properties of the compound to optimize its use as a suppressor of glutamate signal from neuron to astrocytes. In conclusion, both studies have implications not only for the understanding of the metabolic cooperation between neurons and astrocytes, but also in the context of the glial modulation of neuronal activity. - Par rapport à son volume, le cerveau utilise une quantité massive de glucose comme source d'énergie. De plus, la consommation d'équivalents énergétiques est étroitement liée au niveau d'activité synaptique. Il a été montré que dans ce couplage neurométabolique, un rôle central est joué par les astrocytes. Ces cellules fournissent le lactate, un substrat métabolique, aux neurones de manière adaptée à leur activité glutamatergique. Plus précisément, le glutamate libéré dans la fente synaptique par les neurones, est récupéré par les astrocytes et déclenche ainsi une cascade d'événements intracellulaires qui conduit à la production et libération de lactate. Les travaux de cette thèse ont visé à étudier la relation métabolique et fonctionnelle entre neurones et astrocytes, avec une attention particulière pour des rôles que pourrait avoir le lactate au-delà de sa fonction métabolique. Pour étudier les aspects spatio-temporels de ces interactions dynamiques, nous avons utilisé à la fois la microscopie optique associée à des indicateurs fluorescents spécifiques, ainsi que l'électrophysiologie. Dans la première partie de cette thèse, nous montrons que le lactate diminue l'activité neuronale spontanée de façon concentration-dépendante et indépendamment de son métabolisme. Nous avons identifié l'implication d'un récepteur neuronal au lactate qui sous-tend ce mécanisme de régulation. La découverte de cette signalisation via le lactate constitue un mode d'interaction supplémentaire et nouveau entre neurones et astrocytes. Dans la deuxième partie, nous avons caractérisé un outil pharmacologique, un inhibiteur des transporteurs du glutamate à haute affinité. Le but de cette étude était d'obtenir un agent pharmacologique capable d'interrompre spécifiquement le signal médié par le glutamate entre neurones et astrocytes pouvant permettre de mieux comprendre leur relation. En conclusion, ces études ont une implication non seulement pour la compréhension de la coopération entre neurones et astrocytes mais aussi dans le contexte de la modulation de l'activité neuronale par les cellules gliales.

System-level Support for Mobile Ad hoc Communications: an Algorithmic & Practical Approach

A mobile ad hoc network (MANET) is a decentralized and infrastructure-less network. This thesis aims to provide support at the system-level for developers of applications or protocols in such networks. To do this, we propose contributions in both the algorithmic realm and in the practical realm. In the algorithmic realm, we contribute to the field by proposing different context-aware broadcast and multicast algorithms in MANETs, namely six-shot broadcast, six-shot multicast, PLAN-B and ageneric algorithmic approach to optimize the power consumption of existing algorithms. For each algorithm we propose, we compare it to existing algorithms that are either probabilistic or context-aware, and then we evaluate their performance based on simulations. We demonstrate that in some cases, context-aware information, such as location or signal-strength, can improve the effciency. In the practical realm, we propose a testbed framework, namely ManetLab, to implement and to deploy MANET-specific protocols, and to evaluate their performance. This testbed framework aims to increase the accuracy of performance evaluation compared to simulations, while keeping the ease of use offered by the simulators to reproduce a performance evaluation. By evaluating the performance of different probabilistic algorithms with ManetLab, we observe that both simulations and testbeds should be used in a complementary way. In addition to the above original contributions, we also provide two surveys about system-level support for ad hoc communications in order to establish a state of the art. The first is about existing broadcast algorithms and the second is about existing middleware solutions and the way they deal with privacy and especially with location privacy. - Un réseau mobile ad hoc (MANET) est un réseau avec une architecture décentralisée et sans infrastructure. Cette thèse vise à fournir un support adéquat, au niveau système, aux développeurs d'applications ou de protocoles dans de tels réseaux. Dans ce but, nous proposons des contributions à la fois dans le domaine de l'algorithmique et dans celui de la pratique. Nous contribuons au domaine algorithmique en proposant différents algorithmes de diffusion dans les MANETs, algorithmes qui sont sensibles au contexte, à savoir six-shot broadcast,six-shot multicast, PLAN-B ainsi qu'une approche générique permettant d'optimiser la consommation d'énergie de ces algorithmes. Pour chaque algorithme que nous proposons, nous le comparons à des algorithmes existants qui sont soit probabilistes, soit sensibles au contexte, puis nous évaluons leurs performances sur la base de simulations. Nous montrons que, dans certains cas, des informations liées au contexte, telles que la localisation ou l'intensité du signal, peuvent améliorer l'efficience de ces algorithmes. Sur le plan pratique, nous proposons une plateforme logicielle pour la création de bancs d'essai, intitulé ManetLab, permettant d'implémenter, et de déployer des protocoles spécifiques aux MANETs, de sorte à évaluer leur performance. Cet outil logiciel vise à accroître la précision desévaluations de performance comparativement à celles fournies par des simulations, tout en conservant la facilité d'utilisation offerte par les simulateurs pour reproduire uneévaluation de performance. En évaluant les performances de différents algorithmes probabilistes avec ManetLab, nous observons que simulateurs et bancs d'essai doivent être utilisés de manière complémentaire. En plus de ces contributions principales, nous fournissons également deux états de l'art au sujet du support nécessaire pour les communications ad hoc. Le premier porte sur les algorithmes de diffusion existants et le second sur les solutions de type middleware existantes et la façon dont elles traitent de la confidentialité, en particulier celle de la localisation.

A system-level, molecular evolutionary analysis of mammalian phototransduction

Visual perception is initiated in the photoreceptor cells of the retina via the phototransduction system.This system has shown marked evolution during mammalian divergence in such complex attributes as activation time and recovery time. We have performed a molecular evolutionary analysis of proteins involved in mammalianphototransduction in order to unravel how the action of natural selection has been distributed throughout thesystem to evolve such traits. We found selective pressures to be non-randomly distributed according to both a simple protein classification scheme and a protein-interaction network representation of the signaling pathway. Proteins which are topologically central in the signaling pathway, such as the G proteins, as well as retinoid cycle chaperones and proteins involved in photoreceptor cell-type determination, were found to be more constrained in their evolution. Proteins peripheral to the pathway, such as ion channels and exchangers, as well as the retinoid cycle enzymes, have experienced a relaxation of selective pressures. Furthermore, signals of positive selection were detected in two genes: the short-wave (blue) opsin (OPN1SW) in hominids and the rod-specific Na+/Ca2+,K+ ion exchanger (SLC24A1) in rodents. The functions of the proteins involved in phototransduction and the topology of the interactions between them have imposed non-random constraints on their evolution. Thus, in shaping or conserving system-level phototransduction traits, natural selection has targeted the underlying proteins in a concerted manner.

A System-Level Simulation Model for a Protocol Processor

As the development of integrated circuit technology continues to follow Moore’s law the complexity of circuits increases exponentially. Traditional hardware description languages such as VHDL and Verilog are no longer powerful enough to cope with this level of complexity and do not provide facilities for hardware/software codesign. Languages such as SystemC are intended to solve these problems by combining the powerful expression of high level programming languages and hardware oriented facilities of hardware description languages. To fully replace older languages in the desing flow of digital systems SystemC should also be synthesizable. The devices required by modern high speed networks often share the same tight constraints for e.g. size, power consumption and price with embedded systems but have also very demanding real time and quality of service requirements that are difficult to satisfy with general purpose processors. Dedicated hardware blocks of an application specific instruction set processor are one way to combine fast processing speed, energy efficiency, flexibility and relatively low time-to-market. Common features can be identified in the network processing domain making it possible to develop specialized but configurable processor architectures. One such architecture is the TACO which is based on transport triggered architecture. The architecture offers a high degree of parallelism and modularity and greatly simplified instruction decoding. For this M.Sc.(Tech) thesis, a simulation environment for the TACO architecture was developed with SystemC 2.2 using an old version written with SystemC 1.0 as a starting point. The environment enables rapid design space exploration by providing facilities for hw/sw codesign and simulation and an extendable library of automatically configured reusable hardware blocks. Other topics that are covered are the differences between SystemC 1.0 and 2.2 from the viewpoint of hardware modeling, and compilation of a SystemC model into synthesizable VHDL with Celoxica Agility SystemC Compiler. A simulation model for a processor for TCP/IP packet validation was designed and tested as a test case for the environment.

System-level characterization of Th2 cell development and immune cell responses to ZnO and TiO2 nanoparticles

Asthma and allergy are common diseases and their prevalence is increasing. One of the hypotheses that explains this trend is exposure to inhalable chemicals such as traffi c-related air pollution. Epidemiological research supports this theory, as a correlation between environmental chemicals and allergic respiratory diseases has been found. In addition to ambient airborne particles, one may be exposed to engineered nanosized materials that are actively produced due to their favorable physico-chemical properties compared to their bulk size counterparts. On the cellular level, improper activity of T helper (Th) cells has been connected to allergic reactions. Th cells can differentiate into functionally different effector subsets, which are identifi ed according to their characteristic cytokine profi les resulting in specifi c ability to communicate with other cells. Th2 cells activate humoral immunity and stimulate eradication of extracellular pathogens. However, persistent predominance of Th2 cells is involved in a development of number of allergic diseases. The cytokine environment at the time of antigen recognition is the major factor determining the polarization of a naïve Th cell. Th2 cell differentiation is initiated by IL4, which signals via transcription factor STAT6. Although the importance of this pathway has been evaluated in the mouse studies, the signaling components involved have been largely unknown. The aim of this thesis was to identify molecules, which are under the control of IL4 and STAT6 in Th cells. This was done by using system-level analysis of STAT6 target genes at genome, mRNA and protein level resulting in identifi cation of various genes previously not connected to Th2 cell phenotype acquisition. In the study, STAT6-mediated primary and secondary target genes were dissection from each other and a detailed transcriptional kinetics of Th2 cell polarization of naïve human CD4+ T cells was collected. Integration of these data revealed the hierarchy of molecular events that mediates the differentiation towards Th2 cell phenotype. In addition, the results highlighted the importance of exploiting proteomics tools to complement the studies on STAT6 target genes identifi ed through transcriptional profi ling. In the last subproject, the effects of the exposure with ZnO and TiO2 nanoparticles was analyzed in Jurkat T cell line and in primary human monocyte-derived macrophages and dendritic cells to evaluate their toxicity and potential to cause infl ammation. Identifi cation of ZnO-derived gene expression showed that the same nanoparticles may elicit markedly distinctive responses in different cell types, thus underscoring the need for unbiased profi ling of target genes and pathways affected. The results gave additional proof that the cellular response to nanosized ZnO is due to leached Zn2+ ions. The approach used in ZnO and TiO2 nanoparticle study demonstrated the value of assessing nanoparticle responses through a toxicogenomics approach. The increased knowledge of Th2 cell signaling will hopefully reveal new therapeutic nodes and eventually improve our possibilities to prevent and tackle allergic infl ammatory diseases.

A System for Models of First Order Theories

If you want to know whether a property is true or not in a specific algebraic structure,you need to test that property on the given structure. This can be done by hand, which can be cumbersome and erroneous. In addition, the time consumed in testing depends on the size of the structure where the property is applied. We present an implementation of a system for finding counterexamples and testing properties of models of first-order theories. This system is supposed to provide a convenient and paperless environment for researchers and students investigating or studying such models and algebraic structures in particular. To implement a first-order theory in the system, a suitable first-order language.( and some axioms are required. The components of a language are given by a collection of variables, a set of predicate symbols, and a set of operation symbols. Variables and operation symbols are used to build terms. Terms, predicate symbols, and the usual logical connectives are used to build formulas. A first-order theory now consists of a language together with a set of closed formulas, i.e. formulas without free occurrences of variables. The set of formulas is also called the axioms of the theory. The system uses several different formats to allow the user to specify languages, to define axioms and theories and to create models. Besides the obvious operations and tests on these structures, we have introduced the notion of a functor between classes of models in order to generate more co~plex models from given ones automatically. As an example, we will use the system to create several lattices structures starting from a model of the theory of pre-orders.

The 'direct costs' of livestock disease: The development of a system of models for the analysis of 30 endemic livestock diseases in Great Britain

The 'direct costs' attributable to 30 different endemic diseases of farm animals in Great Britain are estimated using a standardised method to construct a simple model for each disease that includes consideration of disease prevention and treatment costs. The models so far developed provide a basis for further analyses including cost-benefit analyses for the economic assessment of disease control options. The approach used reflects the inherent livestock disease information constraints, which limit the application of other economic analytical methods. It is a practical and transparent approach that is relatively easily communicated to veterinary scientists and policy makers. The next step is to develop the approach by incorporating wider economic considerations into the analyses in a way that will demonstrate to policy makers and others the importance of an economic perspective to livestock disease issues.

System level design space exploration for MPSoC : methods, algorithms and new infrastructure

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From Radio Channel Modeling to a System Level Perspective in Body-Centric Communications

Body-centric communications are emerging as a new paradigm in the panorama of personal communications. Being concerned with human behaviour, they are suitable for a wide variety of applications. The advances in the miniaturization of portable devices to be placed on or around the body, foster the diffusion of these systems, where the human body is the key element defining communication characteristics. This thesis investigates the human impact on body-centric communications under its distinctive aspects. First of all, the unique propagation environment defined by the body is described through a scenario-based channel modeling approach, according to the communication scenario considered, i.e., on- or on- to off-body. The novelty introduced pertains to the description of radio channel features accounting for multiple sources of variability at the same time. Secondly, the importance of a proper channel characterisation is shown integrating the on-body channel model in a system level simulator, allowing a more realistic comparison of different Physical and Medium Access Control layer solutions. Finally, the structure of a comprehensive simulation framework for system performance evaluation is proposed. It aims at merging in one tool, mobility and social features typical of the human being, together with the propagation aspects, in a scenario where multiple users interact sharing space and resources.

Advantages and Applications of Transforming Empirical Model Input Space with Dimensional Models

Model-based calibration of steady-state engine operation is commonly performed with highly parameterized empirical models that are accurate but not very robust, particularly when predicting highly nonlinear responses such as diesel smoke emissions. To address this problem, and to boost the accuracy of more robust non-parametric methods to the same level, GT-Power was used to transform the empirical model input space into multiple input spaces that simplified the input-output relationship and improved the accuracy and robustness of smoke predictions made by three commonly used empirical modeling methods: Multivariate Regression, Neural Networks and the k-Nearest Neighbor method. The availability of multiple input spaces allowed the development of two committee techniques: a 'Simple Committee' technique that used averaged predictions from a set of 10 pre-selected input spaces chosen by the training data and the "Minimum Variance Committee" technique where the input spaces for each prediction were chosen on the basis of disagreement between the three modeling methods. This latter technique equalized the performance of the three modeling methods. The successively increasing improvements resulting from the use of a single best transformed input space (Best Combination Technique), Simple Committee Technique and Minimum Variance Committee Technique were verified with hypothesis testing. The transformed input spaces were also shown to improve outlier detection and to improve k-Nearest Neighbor performance when predicting dynamic emissions with steady-state training data. An unexpected finding was that the benefits of input space transformation were unaffected by changes in the hardware or the calibration of the underlying GT-Power model.