Due to the limitations of available gene expression data, (i.e. noise and size of time series), modelling gene regulatory networks is still restricted, especially in terms of their quantitative analysis. To date, the ...
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ISBN:
(纸本)9780889868625
Due to the limitations of available gene expression data, (i.e. noise and size of time series), modelling gene regulatory networks is still restricted, especially in terms of their quantitative analysis. To date, the only criterion used for model evaluation is the residual error between observed and simulated data. This does not assign good fitness to models that can simulate the general oscillation, but are shifted with respect to observed data. Given that oscillatory behaviour of such complexsystems is mostly driven by the topology of regulatory networks, these models may contain important information on network structure, which can shed light on evolutionary parameter optimisation. In consequence, a second model evaluation criterion is introduced here, namely the Pearson correlation coefficient between simulated and observed time series, which enables good fit to be assessed for candidate solutions able to approximate the general behaviour seen in the data. This is employed in a nested optimisation algorithm, which separately analyses the structure and parameters of the models. The method is evaluated using both synthetic and real microarray gene expression data, (Yeast cell cycle), and results show that models obtained in this way display more plausible connections, also contributing to simulation of quantitative behaviour.
Random matrix theory (RMT) filters have recently been shown to improve the optimisation of financial portfolios. This paper studies the effect of three RMT filters on realised portfolio risk, using bootstrap analysis ...
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BACKGROUND:Recent advances in Immunology highlighted the importance of local properties on the overall progression of HIV infection. In particular, the gastrointestinal tract is seen as a key area during early infecti...
BACKGROUND:Recent advances in Immunology highlighted the importance of local properties on the overall progression of HIV infection. In particular, the gastrointestinal tract is seen as a key area during early infection, and the massive cell depletion associated with it may influence subsequent disease progression. This motivated the development of a large-scale agent-based model.
RESULTS:Lymph nodes are explicitly implemented, and considerations on parallel computing permit large simulations and the inclusion of local features. The results obtained show that GI tract inclusion in the model leads to an accelerated disease progression, during both the early stages and the long-term evolution, compared to a theoretical, uniform model.
CONCLUSIONS:These results confirm the potential of treatment policies currently under investigation, which focus on this region. They also highlight the potential of this modelling framework, incorporating both agent-based and network-based components, in the context of complexsystems where scaling-up alone does not result in models providing additional insights.
Random matrix theory (RMT) filters have recently been shown to improve the optimisation of financial portfolios. This paper studies the effect of three RMT filters on realised portfolio risk, using bootstrap analysis ...
Random matrix theory (RMT) filters have recently been shown to improve the optimisation of financial portfolios. This paper studies the effect of three RMT filters on realised portfolio risk, using bootstrap analysis and out-of-sample testing. We considered the case of a foreign exchange and commodity portfolio, weighted towards foreign exchange, and consisting of 39 assets. This was intended to test the limits of RMT filtering, which is more obviously applicable to portfolios with larger numbers of assets. We considered both equally and exponentially weighted covariance matrices, and observed that, despite the small number of assets involved, RMT filters reduced risk in a way that was consistent with a much larger S&P 500 portfolio. The exponential weightings indicated showed good consistency with the value suggested by Riskmetrics, in contrast to previous results involving stocks. This decay factor, along with the low number of past moves preferred in the filtered, equally weighted case, displayed a trend towards models which were reactive to recent market changes. On testing portfolios with fewer assets, RMT filtering provided less or no overall risk reduction. In particular, no long term out-of-sample risk reduction was observed for a portfolio consisting of 15 major currencies and commodities.
Several algorithms and techniques widely used in Computer Science have been adapted from, or inspired by, known biological phenomena. This is a consequence of the multidisciplinary background of most early computer sc...
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ISBN:
(纸本)9780769539508
Several algorithms and techniques widely used in Computer Science have been adapted from, or inspired by, known biological phenomena. This is a consequence of the multidisciplinary background of most early computer scientists. The field has now matured, and permits development of tools and collaborative frameworks which play a vital role in advancing current biomedical research. In this paper, we briefly present examples of the former, and elaborate upon two of the latter, applied to immunological modelling and as a new paradigm in gene expression.
Understanding the dynamics of disease spread is of crucial importance, in contexts such as estimating load on medical services to risk assessment and intervention policies against large-scale epidemic outbreaks. Howev...
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Understanding the dynamics of disease spread is of crucial importance, in contexts such as estimating load on medical services to risk assessment and intervention policies against large-scale epidemic outbreaks. However, most of the information is available after the spread itself, and preemptive assessment is far from trivial. Here, we investigate the use of agent-based simulations to model such outbreaks in a stylised urban environment. For most diseases, infection of a new individual may occur from casual contact in crowds as well as from repeated interactions with social partners such as work colleagues or family members. Our model therefore accounts for these two *** in this paper is the initial framework for such a model, detailing implementation of geographical features and generation of social structures. Preliminary results are a promising step towards large-scale simulations and evaluation of potential intervention policies.
Leaves play a vital role in the development of a plant, as they are major resource collectors. Adequate representations of leaves are therefore required for the modelling of plants. Such representations may be importa...
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ISBN:
(纸本)0975840002
Leaves play a vital role in the development of a plant, as they are major resource collectors. Adequate representations of leaves are therefore required for the modelling of plants. Such representations may be important to generate a realistic visualisation, or they may be used to study biological processes such as photosynthesis and canopy light environment. Highly accurate leaf surface representations are rarely used by the plant modelling community. This paper aims to show how detailed, accurate representations of leaf surfaces can be created from data;representations that may then be used as parts of virtual plants for applications in fields as diverse as the arts, agriculture or computer games. The techniques used here are mathematical methods of surface fitting applied to data that has been sampled from real leaves with a laser scanner (Polhemus FastSCAN 3D). These methods are interpolating finite element techniques, one using linear triangular elements, the other piecewise cubic triangles. The size of a laser-scanned data set can be enormous and it may be important to represent the surface with significantly fewer points. An incremental algorithm is therefore used to identify significant points that result in a surface fit that approximates the entire data set to a pre-specified accuracy. The algorithms are applied to two examples, a Frangipani leaf and a Flame Tree leaf. Figure 1 visualises results for the Flame Tree leaf. The images represent (a) a photo of this particular leaf, (b) the complete set of more than 5000 digitised data points, (c) positions of data points after application of the incremental algorithm for the piecewise cubic approach with an accuracy of 1%, (d) the same rotated to show the shape of the surface represented by these points, (e) the resulting triangulation and (f) the surface fit. From these point locations, guidelines are deduced describing where data points should be positioned, for example for measurement by lower resolution de
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