49,084 research outputs found
Systems biology in animal sciences
Systems biology is a rapidly expanding field of research and is applied in a number of biological disciplines. In animal sciences, omics approaches are increasingly used, yielding vast amounts of data, but systems biology approaches to extract understanding from these data of biological processes and animal traits are not yet frequently used. This paper aims to explain what systems biology is and which areas of animal sciences could benefit from systems biology approaches. Systems biology aims to understand whole biological systems working as a unit, rather than investigating their individual components. Therefore, systems biology can be considered a holistic approach, as opposed to reductionism. The recently developed āomicsā technologies enable biological sciences to characterize the molecular components of life with ever increasing speed, yielding vast amounts of data. However, biological functions do not follow from the simple addition of the properties of system components, but rather arise from the dynamic interactions of these components. Systems biology combines statistics, bioinformatics and mathematical modeling to integrate and analyze large amounts of data in order to extract a better understanding of the biology from these huge data sets and to predict the behavior of biological systems. A āsystemā approach and mathematical modeling in biological sciences are not new in itself, as they were used in biochemistry, physiology and genetics long before the name systems biology was coined. However, the present combination of mass biological data and of computational and modeling tools is unprecedented and truly represents a major paradigm shift in biology. Significant advances have been made using systems biology approaches, especially in the field of bacterial and eukaryotic cells and in human medicine. Similarly, progress is being made with āsystem approachesā in animal sciences, providing exciting opportunities to predict and modulate animal traits
Modeling the Economic Interaction of Agents with Diverse Abilities to Recognize Equilibrium Patterns
The Rules of Human T Cell Fate in vivo.
The processes governing lymphocyte fate (division, differentiation, and death), are typically assumed to be independent of cell age. This assumption has been challenged by a series of elegant studies which clearly show that, for murine cells in vitro, lymphocyte fate is age-dependent and that younger cells (i.e., cells which have recently divided) are less likely to divide or die. Here we investigate whether the same rules determine human T cell fate in vivo. We combined data from in vivo stable isotope labeling in healthy humans with stochastic, agent-based mathematical modeling. We show firstly that the choice of model paradigm has a large impact on parameter estimates obtained using stable isotope labeling i.e., different models fitted to the same data can yield very different estimates of T cell lifespan. Secondly, we found no evidence in humans in vivo to support the model in which younger T cells are less likely to divide or die. This age-dependent model never provided the best description of isotope labeling; this was true for naĆÆve and memory, CD4+ and CD8+ T cells. Furthermore, this age-dependent model also failed to predict an independent data set in which the link between division and death was explored using Annexin V and deuterated glucose. In contrast, the age-independent model provided the best description of both naĆÆve and memory T cell dynamics and was also able to predict the independent dataset
What Economists can learn from physics and finance
Some economists (Mirowski, 2002) have asserted that the neoclassical economic model was motivated by Newtonian mechanics. This viewpoint encourages confusion. Theoretical mechanics is firmly grounded in reproducible empirical observations and experiments, and provides a very accurate description of macroscopic motions to within high decimal precision. In stark contrast, neo-classical economics, or ārational expectationsā (ratex), is a merely postulated model that cannot be used to describe any real market or economy, even to zeroth order in perturbation theory. In mechanics we study both chaotic and complex dynamics whereas ratex restricts itself to equilibrium. Wigner (1967) has isolated the reasons for what he called āthe unreasonable effectiveness of mathematics in physicsā. In this article we isolate the reason for what Velupillai (2005), who was motivated by Wigner (1960), has called the ineffectiveness of mathematics in economics. I propose a remedy, namely, that economic theory should strive for the same degree of empirical success in modeling markets and economies as is exhibited by finance theory.Nonequilibrium; empirically based modelling; stochastic processes; complexity
Towards dynamical network biomarkers in neuromodulation of episodic migraine
Computational methods have complemented experimental and clinical
neursciences and led to improvements in our understanding of the nervous
systems in health and disease. In parallel, neuromodulation in form of electric
and magnetic stimulation is gaining increasing acceptance in chronic and
intractable diseases. In this paper, we firstly explore the relevant state of
the art in fusion of both developments towards translational computational
neuroscience. Then, we propose a strategy to employ the new theoretical concept
of dynamical network biomarkers (DNB) in episodic manifestations of chronic
disorders. In particular, as a first example, we introduce the use of
computational models in migraine and illustrate on the basis of this example
the potential of DNB as early-warning signals for neuromodulation in episodic
migraine.Comment: 13 pages, 5 figure
Modelling the Economic Interaction of Agents with Diverse Abilities to Recognise Equilibrium Patterns
We model differences among agents in their ability to recognise temporal patterns of prices. Using the concept of DeBruijin sequences in two dynamic models of markets, we demonstrate the existence of equilibria in which prices fluctuate in a pattern that is independent of the fundamentals and that can be recognised only by the more competent agents.DeBruijin, price fluctuations, sunspots, bounded rationality, bounded recall.
Control of complex networks requires both structure and dynamics
The study of network structure has uncovered signatures of the organization
of complex systems. However, there is also a need to understand how to control
them; for example, identifying strategies to revert a diseased cell to a
healthy state, or a mature cell to a pluripotent state. Two recent
methodologies suggest that the controllability of complex systems can be
predicted solely from the graph of interactions between variables, without
considering their dynamics: structural controllability and minimum dominating
sets. We demonstrate that such structure-only methods fail to characterize
controllability when dynamics are introduced. We study Boolean network
ensembles of network motifs as well as three models of biochemical regulation:
the segment polarity network in Drosophila melanogaster, the cell cycle of
budding yeast Saccharomyces cerevisiae, and the floral organ arrangement in
Arabidopsis thaliana. We demonstrate that structure-only methods both
undershoot and overshoot the number and which sets of critical variables best
control the dynamics of these models, highlighting the importance of the actual
system dynamics in determining control. Our analysis further shows that the
logic of automata transition functions, namely how canalizing they are, plays
an important role in the extent to which structure predicts dynamics.Comment: 15 pages, 6 figure
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