979 resultados para BIOLOGICAL MODELS


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Abstract Sitting between your past and your future doesn't mean you are in the present. Dakota Skye Complex systems science is an interdisciplinary field grouping under the same umbrella dynamical phenomena from social, natural or mathematical sciences. The emergence of a higher order organization or behavior, transcending that expected of the linear addition of the parts, is a key factor shared by all these systems. Most complex systems can be modeled as networks that represent the interactions amongst the system's components. In addition to the actual nature of the part's interactions, the intrinsic topological structure of underlying network is believed to play a crucial role in the remarkable emergent behaviors exhibited by the systems. Moreover, the topology is also a key a factor to explain the extraordinary flexibility and resilience to perturbations when applied to transmission and diffusion phenomena. In this work, we study the effect of different network structures on the performance and on the fault tolerance of systems in two different contexts. In the first part, we study cellular automata, which are a simple paradigm for distributed computation. Cellular automata are made of basic Boolean computational units, the cells; relying on simple rules and information from- the surrounding cells to perform a global task. The limited visibility of the cells can be modeled as a network, where interactions amongst cells are governed by an underlying structure, usually a regular one. In order to increase the performance of cellular automata, we chose to change its topology. We applied computational principles inspired by Darwinian evolution, called evolutionary algorithms, to alter the system's topological structure starting from either a regular or a random one. The outcome is remarkable, as the resulting topologies find themselves sharing properties of both regular and random network, and display similitudes Watts-Strogtz's small-world network found in social systems. Moreover, the performance and tolerance to probabilistic faults of our small-world like cellular automata surpasses that of regular ones. In the second part, we use the context of biological genetic regulatory networks and, in particular, Kauffman's random Boolean networks model. In some ways, this model is close to cellular automata, although is not expected to perform any task. Instead, it simulates the time-evolution of genetic regulation within living organisms under strict conditions. The original model, though very attractive by it's simplicity, suffered from important shortcomings unveiled by the recent advances in genetics and biology. We propose to use these new discoveries to improve the original model. Firstly, we have used artificial topologies believed to be closer to that of gene regulatory networks. We have also studied actual biological organisms, and used parts of their genetic regulatory networks in our models. Secondly, we have addressed the improbable full synchronicity of the event taking place on. Boolean networks and proposed a more biologically plausible cascading scheme. Finally, we tackled the actual Boolean functions of the model, i.e. the specifics of how genes activate according to the activity of upstream genes, and presented a new update function that takes into account the actual promoting and repressing effects of one gene on another. Our improved models demonstrate the expected, biologically sound, behavior of previous GRN model, yet with superior resistance to perturbations. We believe they are one step closer to the biological reality.

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The pharmacology of synthetic organoselenium compounds indicates that they can be used as antioxidants, enzyme inhibitors, neuroprotectors, anti-tumor and anti-infectious agents, and immunomodulators. In this review, we focus on the effects of diphenyl diselenide (DPDS) in various biological model organisms. DPDS possesses antioxidant activity, confirmed in several in vitro and in vivo systems, and thus has a protective effect against hepatic, renal and gastric injuries, in addition to its neuroprotective activity. The activity of the compound on the central nervous system has been studied since DPDS has lipophilic characteristics, increasing adenylyl cyclase activity and inhibiting glutamate and MK-801 binding to rat synaptic membranes. Systemic administration facilitates the formation of long-term object recognition memory in mice and has a protective effect against brain ischemia and on reserpine-induced orofacial dyskinesia in rats. On the other hand, DPDS may be toxic, mainly because of its interaction with thiol groups. In the yeast Saccharomyces cerevisiae, the molecule acts as a pro-oxidant by depleting free glutathione. Administration to mice during cadmium intoxication has the opposite effect, reducing oxidative stress in various tissues. DPDS is a potent inhibitor of d-aminolevulinate dehydratase and chronic exposure to high doses of this compound has central effects on mouse brain, as well as liver and renal toxicity. Genotoxicity of this compound has been assessed in bacteria, haploid and diploid yeast and in a tumor cell line.

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This paper presents implementation of a low-power tracking CMOS image sensor based on biological models of attention. The presented imager allows tracking of up to N salient targets in the field of view. Employing "smart" image sensor architecture, where all image processing is implemented on the sensor focal plane, the proposed imager allows reduction of the amount of data transmitted from the sensor array to external processing units and thus provides real time operation. The imager operation and architecture are based on the models taken from biological systems, where data sensed by many millions of receptors should be transmitted and processed in real time. The imager architecture is optimized to achieve low-power dissipation both in acquisition and tracking modes of operation. The tracking concept is presented, the system architecture is shown and the circuits description is discussed.

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Every high resolution imaging system suffers from the bottleneck problem. This problem relates to the huge amount of data transmission from the sensor array to a digital signal processing (DSP) and to bottleneck in performance, caused by the requirement to process a large amount of information in parallel. The same problem exists in biological vision systems, where the information, sensed by many millions of receptors should be transmitted and processed in real time. Models, describing the bottleneck problem solutions in biological systems fall in the field of visual attention. This paper presents the bottleneck problem existing in imagers used for real time salient target tracking and proposes a simple solution by employing models of attention, found in biological systems. The bottleneck problem in imaging systems is presented, the existing models of visual attention are discussed and the architecture of the proposed imager is shown.

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A number of glycoconjugates, including glycolipids and glycoproteins, participate in the process of host-cell invasion by Trypanosoma cruzi and one of the most important carbohydrates involved on this interaction is sialic acid. It is known that parasite trans-sialidase participates with sialic acid in a coordinated fashion in the initial stages of invasion. Given the importance of these sialogycoconjugates, this review sets out various possible biological models for the interaction between the parasite and mammalian cells that possess a sialylated receptor/ligand system.

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In the present study, in vitro techniques were used to investigate a range of biological activities of known natural quassinoids isobrucein B (1) and neosergeolide (2), known semi-synthetic derivative 1,12-diacetylisobrucein B (3), and a new semi-synthetic derivative, 12-acetylneosergeolide (4). These compounds were evaluated for general toxicity toward the brine shrimp species Artemia franciscana, cytotoxicity toward human tumour cells, larvicidal activity toward the dengue fever mosquito vector Aedes aegypti, haemolytic activity in mouse erythrocytes and antimalarial activity against the human malaria parasite Plasmodium falciparum. Compounds 1 and 2 exhibited the greatest cytotoxicity against all the tumor cells tested (IC50 = 5-27 µg/L) and against multidrug-resistant P. falciparum K1 strain (IC50 = 1.0-4.0 g/L) and 3 was only cytotoxic toward the leukaemia HL-60 strain (IC50 = 11.8 µg/L). Quassinoids 1 and 2 (LC50 = 3.2-4.4 mg/L) displayed greater lethality than derivative 4 (LC50 = 75.0 mg/L) toward A. aegypti larvae, while derivative 3 was inactive. These results suggest a novel application for these natural quassinoids as larvicides. The toxicity toward A. franciscana could be correlated with the activity in several biological models, a finding that is in agreement with the literature. Importantly, none of the studied compounds exhibited in vitro haemolytic activity, suggesting specificity of the observed cytotoxic effects. This study reveals the biological potential of quassinoids 1 and 2 and to a lesser extent their semi-synthetic derivatives for their in vitro antimalarial and cytotoxic activities.

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Understanding how biological visual systems perform object recognition is one of the ultimate goals in computational neuroscience. Among the biological models of recognition the main distinctions are between feedforward and feedback and between object-centered and view-centered. From a computational viewpoint the different recognition tasks - for instance categorization and identification - are very similar, representing different trade-offs between specificity and invariance. Thus the different tasks do not strictly require different classes of models. The focus of the review is on feedforward, view-based models that are supported by psychophysical and physiological data.

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En aquest estudi, la toxicitat de diversos metalls pesants i l'arsènic va ser analitzada utilitzant diferents models biològics. En la primera part d'aquest treball, el bioassaig de toxicitat Microtox, el qual està basat en la variació de l'emissió lumínica del bacteri luminiscent Vibrio fischeri, va ser utilitzat per establir les corbes dosi-resposta de diferents elements tòxics com el Zn(II), Pb(II), Cu(II), Hg(II), Ag(I), Co(II), Cd(II), Cr(VI), As(V) i As(III) en solucions aquoses. Els experiments es varen portar a terme a pH 6.0 i 7.0 per tal de mostrar que el pH pot influir en la toxicitat final mesurada d'alguns metalls degut als canvis relacionats amb la seva especiació química. Es varen trobar diferents tipus de corbes dosi-resposta depenent del metall analitzat i el pH del medi. En el cas de l'arsènic, l'efecte del pH en la toxicitat de l'arsenat i l'arsenit es va investigar utilitzant l'assaig Microtox en un rang de pHs comprès entre pH 5.0 i 9.0. Els valors d'EC50 determinats per l'As(V) disminueixen, reflectint un augment de la toxicitat, a mesura que el pH de la solució augmenta mentre que, en el cas de l'As(III), els valors d'EC50 quasi bé no varien entre pH 6.0 i 8.0 i només disminueixen a pH 9.0. HAsO42- i H2AsO3- es varen definir com les espècies més tòxiques. Així mateix, una anàlisi estadística va revelar un efecte antagònic entre les espècies químiques d'arsenat que es troben conjuntament a pH 6.0 i 7.0. D'altra banda, els resultats de dos mètodes estadístics per predir la toxicitat i les possibles interaccions entre el Co(II), Cd(II), Cu(II), Zn(II) i Pb(II) en mescles binàries equitòxiques es varen comparar amb la toxicitat observada sobre el bacteri Vibrio fischeri. L'efecte combinat d'aquests metalls va resultar ser antagònic per les mescles de Co(II)-Cd(II), Cd(II)-Zn(II), Cd(II)-Pb(II) i Cu(II)-Pb(II), sinèrgic per Co(II)-Cu(II) i Zn(II)-Pb(II) i additiu en els altres casos, revelant un patró complex de possibles interaccions. L'efecte sinèrgic de la combinació Co(II)-Cu(II) i la forta disminució de la toxicitat del Pb(II) quan es troba en presència de Cd(II) hauria de merèixer més atenció quan s'estableixen les normatives de seguretat ambiental. La sensibilitat de l'assaig Microtox també va ser determinada. Els valors d'EC20, els quals representen la toxicitat llindar mesurable, varen ser determinats per cada element individualment i es va veure que augmenten de la següent manera: Pb(II) < Ag(I) < Hg(II)  Cu(II) < Zn(II) < As(V) < Cd(II)  Co(II) < As(III) < Cr(VI). Aquests valors es varen comparar amb les concentracions permeses en aigues residuals industrials establertes per la normativa oficial de Catalunya (Espanya). L'assaig Microtox va resultar ser suficientment sensible per detectar els elements assajats respecte a les normes oficials referents al control de la contaminació, excepte en el cas del cadmi, mercuri, arsenat, arsenit i cromat. En la segona part d'aquest treball, com a resultats complementaris dels resultats previs obtinguts utilitzant l'assaig de toxicitat aguda Microtox, els efectes crònics del Cd(II), Cr(VI) i As(V) es varen analitzar sobre la taxa de creixement i la viabilitat en el mateix model biològic. Sorprenentment, aquests productes químics nocius varen resultar ser poc tòxics per aquest bacteri quan es mesura el seu efecte després de temps d'exposició llargs. Tot i això, en el cas del Cr(VI), l'assaig d'inhibició de la viabilitat va resultar ser més sensible que l'assaig de toxicitat aguda Microtox. Així mateix, també va ser possible observar un clar fenomen d'hormesis, especialment en el cas del Cd(II), quan s'utilitza l'assaig d'inhibició de la viabilitat. A més a més, diversos experiments es varen portar a terme per intentar explicar la manca de toxicitat de Cr(VI) mostrada pel bacteri Vibrio fischeri. La resistència mostrada per aquest bacteri podria ser atribuïda a la capacitat d'aquest bacteri de convertir el Cr(VI) a la forma menys tòxica de Cr(III). Es va trobar que aquesta capacitat de reducció depèn de la composició del medi de cultiu, de la concentració inicial de Cr(VI), del temps d'incubació i de la presència d'una font de carboni. En la tercera part d'aquest treball, la línia cel·lular humana HT29 i cultius primaris de cèl·lules sanguínies de Sparus sarba es varen utilitzar in vitro per detectar la toxicitat llindar de metalls mesurant la sobreexpressió de proteines d'estrès. Extractes de fangs precedents de diverses plantes de tractament d'aigues residuals i diferents metalls, individualment o en combinació, es varen analitzar sobre cultius cel·lulars humans per avaluar el seu efecte sobre la taxa de creixement i la capacitat d'induir la síntesi de les proteïnes Hsp72 relacionades amb l'estrès cel·lular. No es varen trobar efectes adversos significatius quan els components s'analitzen individualment. Nogensmenys, quan es troben conjuntament, es produeix un afecte advers sobre tan la taxa de creixement com en l'expressió de proteins d'estrès. D'altra banda, cèl·lules sanguínies procedents de Sparus sarba es varen exposar in vitro a diferents concentracions de cadmi, plom i crom. La proteïna d'estrès HSP70 es va sobreexpressar significativament després de l'exposició a concentracions tan febles com 0.1 M. Sota les nostres condicions de treball, no es va evidenciar una sobreexpressió de metal·lotioneïnes. Nogensmenys, les cèl·lules sanguínies de peix varen resultar ser un model biològic interessant per a ser utilitzat en anàlisis de toxicitat. Ambdós models biològics varen resultar ser molt adequats per a detectar acuradament la toxicitat produïda per metalls. En general, l'avaluació de la toxicitat basada en l'anàlisi de la sobreexpressió de proteïnes d'estrès és més sensible que l'avaluació de la toxicitat realitzada a nivell d'organisme. A partir dels resultats obtinguts, podem concloure que una bateria de bioassaigs és realment necessària per avaluar acuradament la toxicitat de metalls ja que existeixen grans variacions entre els valors de toxicitat obtinguts emprant diferents organismes i molts factors ambientals poden influir i modificar els resultats obtinguts.

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We review and structure some of the mathematical and statistical models that have been developed over the past half century to grapple with theoretical and experimental questions about the stochastic development of aging over the life course. We suggest that the mathematical models are in large part addressing the problem of partitioning the randomness in aging: How does aging vary between individuals, and within an individual over the lifecourse? How much of the variation is inherently related to some qualities of the individual, and how much is entirely random? How much of the randomness is cumulative, and how much is merely short-term flutter? We propose that recent lines of statistical inquiry in survival analysis could usefully grapple with these questions, all the more so if they were more explicitly linked to the relevant mathematical and biological models of aging. To this end, we describe points of contact among the various lines of mathematical and statistical research. We suggest some directions for future work, including the exploration of information-theoretic measures for evaluating components of stochastic models as the basis for analyzing experiments and anchoring theoretical discussions of aging.

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In the laboratory of Dr. Dieter Jaeger at Emory University, we use computer simulations to study how the biophysical properties of neurons—including their three-dimensional structure, passive membrane resistance and capacitance, and active membrane conductances generated by ion channels—affect the way that the neurons transfer synaptic inputs into the action potential streams that represent their output. Because our ultimate goal is to understand how neurons process and relay information in a living animal, we try to make our computer simulations as realistic as possible. As such, the computer models reflect the detailed morphology and all of the ion channels known to exist in the particular neuron types being simulated, and the model neurons are tested with synaptic input patterns that are intended to approximate the inputs that real neurons receive in vivo. The purpose of this workshop tutorial was to explain what we mean by ‘in vivo-like’ synaptic input patterns, and how we introduce these input patterns into our computer simulations using the freely available GENESIS software package (http://www.genesis-sim.org/GENESIS). The presentation was divided into four sections: first, an explanation of what we are talking about when we refer to in vivo-like synaptic input patterns

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P-GENESIS is an extension to the GENESIS neural simulator that allows users to take advantage of parallel machines to speed up the simulation of their network models or concurrently simulate multiple models. P-GENESIS adds several commands to the GENESIS script language that let a script running on one processor execute remote procedure calls on other processors, and that let a script synchronize its execution with the scripts running on other processors. We present here some brief comments on the mechanisms underlying parallel script execution. We also offer advice on parallelizing parameter searches, partitioning network models, and selecting suitable parallel hardware on which to run P-GENESIS.

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This paper investigates the use of virtual reality (VR) technologies to facilitate the analysis of plant biological data in distinctive steps in the application pipeline. Reconstructed three-dimensional biological models (primary polygonal models) transferred to a virtual environment support scientists' collaborative exploration of biological datasets so that they obtain accurate analysis results and uncover information hidden in the data. Examples of the use of virtual reality in practice are provided and a complementary user study was performed.

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It is system dynamics that determines the function of cells, tissues and organisms. To develop mathematical models and estimate their parameters are an essential issue for studying dynamic behaviors of biological systems which include metabolic networks, genetic regulatory networks and signal transduction pathways, under perturbation of external stimuli. In general, biological dynamic systems are partially observed. Therefore, a natural way to model dynamic biological systems is to employ nonlinear state-space equations. Although statistical methods for parameter estimation of linear models in biological dynamic systems have been developed intensively in the recent years, the estimation of both states and parameters of nonlinear dynamic systems remains a challenging task. In this report, we apply extended Kalman Filter (EKF) to the estimation of both states and parameters of nonlinear state-space models. To evaluate the performance of the EKF for parameter estimation, we apply the EKF to a simulation dataset and two real datasets: JAK-STAT signal transduction pathway and Ras/Raf/MEK/ERK signaling transduction pathways datasets. The preliminary results show that EKF can accurately estimate the parameters and predict states in nonlinear state-space equations for modeling dynamic biochemical networks.