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At least 1,567 records · Page 87Linked to original sources
PubMedDate not supplied

Coding of information in models of tuberous electroreceptors.

Weakly electric fish continuously emit a quasi-sinusoidal electric organ discharge (EOD) to probe their near environment (electrolocation). P-type tuberous receptors located on their skin respond to amplitude modulations of the EOD by varying their firing rate. These receptors, and the neuronal circuitry downstream from them, must encode and process low-frequency stimuli due to prey and obstacles and certain communication signals, as well as high-frequency communication signals emitted by other fish. We ultimately seek the biophysics that govern the encoding process, and in particular, the sensitivity to certain stimulus features. Since the pyramidal cells to which these receptors project can also be monitored, studies of weakly electric fish offer a great opportunity for deciphering the encoding/decoding problem. Here we briefly summarize our recent advances on this issue. We then present new results on the encoding properties and relative modeling advantages of two widely used classes of neuron models of electroreceptor activity: a leaky integrate-and-fire dynamical model, and a non-dynamical modulated stochastic point process model. The quality of encoding, based on the stimulus reconstruction method, is assessed as a function of firing rate and stimulus contrast, in the context of bandlimited Gaussian stimuli. Our main conclusion is that the quality of encoding increases strongly with firing rate, but also depends on the actual combination of biophysical parameters that determine this rate.

Animals↗
PubMed1999

A three-dimensional model for arterial tree representation, generated by constrained constructive optimization.

The computational method of constrained constructive optimization (CCO) has been generalized in two important respects: (1) arterial model trees are now grown within a convex, three-dimensional piece of tissue and (2) terminal flow variability has been incorporated into the model to account for the heterogeneity of blood flow observed in real vascular beds. Although no direct information from topographic anatomy enters the model, computer-generated CCO trees closely resemble corrosion casts of real arterial trees, both on a visual basis and with regard to morphometric parameters. Terminal flow variability was found to induce transitions in the connective structure early in the trees' development. The present generalization of CCO offers--for the first time--the possibility to generate optimized arterial model trees in three dimensions, representing a realistic geometrical substrate for hemodynamic simulation studies. With the implementation of terminal flow variability the model is ready to simulate processes such as the adaptation of arterial diameters to changes in blood flow rate or the formation of different patterns of angiogenesis induced by changing needs of blood supply.

Algorithms↗
PubMed2003

Optimal operation of batch processes via the tracking of active constraints.

This paper presents a new measurement-based optimization framework for batch processes whereby optimal operation can be achieved via the tracking of active constraints. It is shown that, under mild assumptions and to a first-order approximation, tracking the necessary conditions of optimality is equivalent to tracking active constraints (both during the batch and at the end of the batch). Thus the optimal input trajectories can be adjusted using measurements without the use of a model of the process. When only batch-end measurements are available, the proposed method leads itself to an efficient batch-to-batch optimization scheme. The approach is illustrated via the simulation of a semibatch reactor under uncertainty.

Chemical Industry↗
PubMed1985

The fixation of chromosomal rearrangements in a subdivided population with local extinction and colonization.

The fixation of major chromosomal rearrangements with a heterozygote disadvantage is modelled as a simple example of Wright's shifting balance process of evolution in a strongly subdivided population. Chromosomal mutations with an inferior heterozygote become fixed in a local population (or deme) by random genetic drift and spread by migration and colonization. Wright postulated a process of selective diffusion in which the numbers of emigrants and colonizers dispersed from a deme increase with the mean fitness of individuals in it. The present models show that interdeme selection during the spread of a mutation depends more on the capacity of the mutant to invade and become established in other demes than on selective diffusion, unless there is rapid local extinction and colonization. The intensity of interdeme selection is reduced by random local extinction and colonization, and when these processes are rapid (with no selective diffusion) the expected fixation rate of spontaneous mutations with a heterozygote disadvantage approaches that in a single isolated deme. Local extinction and colonization, and selection on the homozygotes, accelerate the spread of chromosomal mutations which are destined to be fixed.

Animals↗
PubMed1995

Predicting conditional probability distributions: a connectionist approach.

Most traditional prediction techniques deliver a single point, usually the mean of a probability distribution. For multimodal processes, instead of predicting the mean, it is important to predict the full distribution. This article presents a new connectionist method to predict the conditional probability distribution in response to an input. The main idea is to transform the problem from a regression problem to a classification problem. The conditional probability distribution network can perform both direct predictions and iterated predictions, the latter task being specific for time series problems. We compare this new method to fuzzy logic and discuss important differences, and also demonstrate the architecture on two time series. The first is the benchmark laser series used in the Santa Fe competition, a deterministic chaotic system. The second is a time series from a Markov process which exhibits structure on two time scales. The network produces multimodal predictions for this series. We compare the predictions of the network with a nearest-neighbor predictor and find that the conditional probability network is more than twice as likely a model.

Neural Networks, Computer↗
PubMed2006

Linking data to models: data regression.

Mathematical models are an essential tool in systems biology, linking the behaviour of a system to the interactions between its components. Parameters in empirical mathematical models must be determined using experimental data, a process called regression. Because experimental data are noisy and incomplete, diagnostics that test the structural identifiability and validity of models and the significance and determinability of their parameters are needed to ensure that the proposed models are supported by the available data.

Animals↗
PubMed2002

The role of chromosomal instability in tumor initiation.

Chromosomal instability (CIN) is a defining characteristic of most human cancers. Mutation of CIN genes increases the probability that whole chromosomes or large fractions of chromosomes are gained or lost during cell division. The consequence of CIN is an imbalance in the number of chromosomes per cell (aneuploidy) and an enhanced rate of loss of heterozygosity. A major question of cancer genetics is to what extent CIN, or any genetic instability, is an early event and consequently a driving force for tumor progression. In this article, we develop a mathematical framework for studying the effect of CIN on the somatic evolution of cancer. Specifically, we calculate the conditions for CIN to initiate the process of colorectal tumorigenesis before the inactivation of tumor suppressor genes.

Animals↗
PubMed2006

Using recurrence quantification analysis determinism for noise removal in cardiac optical mapping.

Selecting signal processing parameters in optical imaging by utilizing the change in Determinism, a measure introduced in Recurrence Quantification Analysis, provides a novel method using the change in residual noise Determinism for improving noise quantification and removal across signals exhibiting disparate underlying tissue pathologies. The method illustrates an improved process for selecting filtering parameters and how using measured signal-to-noise ratio alone can lead to improper parameter selection.

Algorithms↗
PubMedDate not supplied

Change of complexity patterns in human posture during aging.

Human posture is a prototypical example of a complex control system. The joint output of several physiological - most likely nonlinearly interacting - processes leads to correctional movements which enable us to stand upright. These correctional body movements reflect some features of the underlying control mechanisms. We analyze the movements of quietly standing persons by means of various types of fractal measures, which are designed to capture 'degrees of complexity'. We observe changes of these fractal measures as a function of age and show that aging goes hand in hand with a decrease of complexity in movement patterns towards more regular movements. We try to explain these results in a stochastic resonance framework. We conjecture that the reduction of posture complexity is linked to deteriorated balance performance and argue that clinical treatment of age-related balance problems should focus on regaining this complexity therapeutically. We line out two possible starting points for actual therapy.

Adult↗
PubMed2002

Planning the efficient allocation of research funds: an adapted application of a non-parametric Bayesian value of information analysis.

The issue of the efficient allocation of research funds has been addressed using various quantitative methods. Bayesian value of information (VoI) analysis provides an explicit and comprehensive analytic process for the comparison of alternative sources of research. This paper presents an adapted non-parametric application of a VoI analysis of prospective trials comparing alternative adjuvant therapies for postmenopausal women with node positive early breast cancer. The results show that such trials would produce substantial net benefits, though the extent of the net benefits is clearly influenced by the assumed length of usefulness of the research. The application of the VoI methodology shows that such analyses are practical and the recent increase in the use of stochastic decision models in the economic evaluation of health care technologies facilitates further applications of VoI analyses to inform the allocation of research funds.

Aged↗
PubMed2006

On Bayesian inference for the K function.

The K function is a summary of spatial dependence in spatial point processes. In practice one observes a realization of the spatial point process, called a spatial point pattern. Although the K function of a spatial point process is typically unknown, several estimators of the process K function have been put forth. These estimators, however, are based upon empirical averages; the complicated distributional properties of the estimators unfortunately complicates interval estimation. In this paper, we propose a Bayesian inferential framework, allowing inference for the K function of the spatial point process (including interval estimation). Of particular interest is the unique use of the posterior predictive distribution to (efficiently) enable such inferences. To demonstrate our technique, the well known Swedish pine sapling data (Strand, 1972) is analyzed, including a discussion on evaluating model fit.

Algorithms↗
PubMed2002

An effective frequency domain approach to tuning non-PID controllers for high performance.

In this paper, a new tuning method is proposed for the design of non-PID controllers for complex processes to achieve high performance. Compared with the existing PID tuning methods, the proposed non-PID controller design method can yield better performance for a wide range of complex processes. A suitable objective transfer function for the closed-loop system is chosen according to process characteristics. The corresponding ideal controller is derived. Model reduction is applied to fit the ideal controller into a much simpler and realizable form. Stability analysis is given and simulation examples are provided to demonstrate the effectiveness of the proposed method.

Computer Simulation↗
PubMed2002

Locality of global stochastic interaction in directed acyclic networks.

The hypothesis of invariant maximization of interaction (IMI) is formulated within the setting of random fields. According to this hypothesis, learning processes maximize the stochastic interaction of the neurons subject to constraints. We consider the extrinsic constraint in terms of a fixed input distribution on the periphery of the network. Our main intrinsic constraint is given by a directed acyclic network structure. First mathematical results about the strong relation of the local information flow and the global interaction are stated in order to investigate the possibility of controlling IMI optimization in a completely local way. Furthermore, we discuss some relations of this approach to the optimization according to Linsker's Infomax principle.

Animals↗
PubMed2006

Incorporating movement into models of grey seal population dynamics.

1. One of the most difficult problems in developing spatially explicit models of population dynamics is the validation and parameterization of the movement process. We show how movement models derived from capture-recapture analysis can be improved by incorporating them into a spatially explicit metapopulation model that is fitted to a time series of abundance data. 2. We applied multisite capture-recapture analysis techniques to photo-identification data collected from female grey seals at the four main breeding colonies in the North Sea between 1999 and 2001. The best-fitting movement models were then incorporated into state-space metapopulation models that explicitly accounted for demographic and observational stochasticity. 3. These metapopulation models were fitted to a 20-year time series of pup production data for each colony using a Bayesian approach. The best-fitting model, based on the Akaike Information Criterion (AIC), had only a single movement parameter, whose confidence interval was 82% less than that obtained from the capture-recapture study, but there was some support for a model that included an effect of distance between colonies. 4. The state-space modelling provided improved estimates of other demographic parameters. 5. The incorporation of movement, and the way in which it was modelled, affected both local and regional dynamics. These differences were most evident as colonies approached their carrying capacities, suggesting that our ability to discriminate between models should improve as the length of the grey seal time series increases.

Animal Migration↗
PubMed1989

Evolutionary learning and hierarchical Markov systems.

The observation is made that various forms of evolutionary learning systems and classical evolutionary processes can be formally described as hierarchical systems of Markov processes. This leads to a simplification of issues such as convergence criteria and limiting behavior of such systems. The hierarchical structures in question are derived from the notion of rules and meta-rules for moving on graphs studied previously.

Biological Evolution↗
PubMed2005

Extinction risk, coloured noise and the scaling of variance.

The impact of temporally correlated fluctuating environments (coloured noise) on the extinction risk of populations has become a main focus in theoretical population ecology. In this study we particularly focus on the extinction risk in strongly correlated environments. Here, we found that, in contrast to moderate auto-correlation, the extinction risk was highly dependent on the process of noise generation, in particular on the method of variance scaling. Such scaling is commonly applied to avoid variance-driven biases when comparing the extinction risk under white and coloured noise. We show that for strong auto-correlation often-used scaling techniques lead to a high variability in the variances of the resulting time series and thus to deviations in the subsequent extinction risk. Therefore, we present an alternative scaling method that always delivers the target variance, even in the case of strong auto-correlation. In contrast to earlier techniques, our very intuitive method is not bound to auto-regressive processes but can be applied to all types of coloured noises. We strongly recommend our method to generate time series when the target of interest is the effect of noise colour on extinction risk not obscured by any variance effects.

Animals↗
PubMed2007

Hypothesis testing via integrated computer modeling and digital fluorescence microscopy.

Computational modeling has the potential to add an entirely new approach to hypothesis testing in yeast cell biology. Here, we present a method for seamless integration of computational modeling with quantitative digital fluorescence microscopy. This integration is accomplished by developing computational models based on hypotheses for underlying cellular processes that may give rise to experimentally observed fluorescent protein localization patterns. Simulated fluorescence images are generated from the computational models of underlying cellular processes via a "model-convolution" process. These simulated images can then be directly compared to experimental fluorescence images in order to test the model. This method provides a framework for rigorous hypothesis testing in yeast cell biology via integrated mathematical modeling and digital fluorescence microscopy.

Computational Biology↗
PubMed2002

Separation of biosynthetic polyunsaturated fatty acid (PUFA) with supercritical fluid.

The separation of biosynthetic polyunsaturated fatty acid (PUFA) was undertaken with supercritical fluid. The polyunsaturated fatty acid was synthesised by WL-1021, which is a strain of marine bacterium isolated from marine fish. The polyunsaturated fatty acid can be very efficiently extracted from WL-1021 that has the characteristics of rapid growth in the artificial medium. The PUFA in WL-1021 has been successfully separated by supercritical fluid extraction (SFE). The SFE process parameters including pressure, temperature and culture temperature variations have been measured by High Performance Liquid Chromatography (HPLC), GC/FT-IR and GC/MS.

Animals↗