Coupling of cell fate selection model enhances DNA damage response and may underlie BE phenomenon.

Double-strand break-induced (DSB) cells send signal that induces DSBs in neighbour cells, resulting in the interaction among cells sharing the same medium. Since p53 network gives oscillatory response to DSBs, such interaction among cells could be modelled as an excitatory coupling of p53 network oscillators. This study proposes a plausible coupling model of three-mode two-dimensional oscillators, which models the p53-mediated cell fate selection in globally coupled DSB-induced cells. The coupled model consists of ATM and Wip1 proteins as variables. The coupling mechanism is realised through ATM variable via a mean-field modelling the bystander signal in the intercellular medium. Investigation of the model reveals that the coupling generates more sensitive DNA damage response by affecting cell fate selection. Additionally, the authors search for the cause-effect relationship between coupled p53 network oscillators and bystander effect (BE) endpoints. For this, they search for the possible values of uncertain parameters that may replicate BE experiments' results. At certain parametric regions, there is a correlation between the outcomes of cell fate and endpoints of BE, suggesting that the intercellular coupling of p53 network may manifest itself as the form of observed BEs.

Bifurcation analysis of bistable and oscillatory dynamics in biological networks using the root-locus method.

Most of the biological systems including gene regulatory networks can be described well by ordinary differential equation models with rational non-linearities. These models are derived either based on the reaction kinetics or by curve fitting to experimental data. This study demonstrates the applicability of the root-locus-based bifurcation analysis method for studying the complex dynamics of such models. The effectiveness of the bifurcation analysis in determining the exact parameter regions in each of which the system shows a certain dynamical behaviour, such as bistability, oscillation, and asymptotically equilibrium dynamics is shown by considering two mostly studied gene regulatory networks, namely Gardner's genetic toggle switch and p53 gene network possessing two-phase (mono-stable/oscillation) dynamics.

Revealing determinants of two-phase dynamics of P53 network under gamma irradiation based on a reduced 2D relaxation oscillator model.

This study proposes a two-dimensional (2D) oscillator model of p53 network, which is derived via reducing the multidimensional two-phase dynamics model into a model of ataxia telangiectasia mutated (ATM) and Wip1 variables, and studies the impact of p53-regulators on cell fate decision. First, the authors identify a 6D core oscillator module, then reduce this module into a 2D oscillator model while preserving the qualitative behaviours. The introduced 2D model is shown to be an excitable relaxation oscillator. This oscillator provides a mechanism that leads diverse modes underpinning cell fate, each corresponding to a cell state. To investigate the effects of p53 inhibitors and the intrinsic time delay of Wip1 on the characteristics of oscillations, they introduce also a delay differential equation version of the 2D oscillator. They observe that the suppression of p53 inhibitors decreases the amplitudes of p53 oscillation, though the suppression increases the sustained level of p53. They identify Wip1 and P53DINP1 as possible targets for cancer therapies considering their impact on the oscillator, supported by biological findings. They model some mutations as critical changes of the phase space characteristics. Possible cancer therapeutic strategies are then proposed for preventing these mutations' effects using the phase space approach.

Aggregation for Computing Multi-Modal Stationary Distributions in 1-D Gene Regulatory Networks.

This paper proposes aggregation-based, three-stage algorithms to overcome the numerical problems encountered in computing stationary distributions and mean first passage times for multi-modal birth-death processes of large state space sizes. The considered birth-death processes which are defined by Chemical Master Equations are used in modeling stochastic behavior of gene regulatory networks. Computing stationary probabilities for a multi-modal distribution from Chemical Master Equations is subject to have numerical problems due to the probability values running out of the representation range of the standard programming languages with the increasing size of the state space. The aggregation is shown to provide a solution to this problem by analyzing first reduced size subsystems in isolation and then considering the transitions between these subsystems. The proposed algorithms are applied to study the bimodal behavior of the lac operon of E. coli described with a one-dimensional birth-death model. Thus, the determination of the entire parameter range of bimodality for the stochastic model of lac operon is achieved.

Two-dimensional polynomial type canonical relaxation oscillator model for p53 dynamics.

p53 network, which is responsible for DNA damage response of cells, exhibits three distinct qualitative behaviours; low state, oscillation and high state, which are associated with normal cell cycle progression, cell cycle arrest and apoptosis, respectively. The experimental studies demonstrate that these dynamics of p53 are due to the ATM and Wip1 interaction. This paper proposes a simple two-dimensional canonical relaxation oscillator model based on the identified topological structure of ATM and Wip1 interaction underlying these qualitative behaviours of p53 network. The model includes only polynomial terms that have the interpretability of known ATM and Wip1 interaction. The introduced model is useful for understanding relaxation oscillations in gene regulatory networks. Through mathematical analysis, we investigate the roles of ATM and Wip1 in forming of these three essential behaviours, and show that ATM and Wip1 constitute the core mechanism of p53 dynamics. In agreement with biological findings, we show that Wip1 degradation term is a highly sensitive parameter, possibly related to mutations. By perturbing the corresponding parameters, our model characterizes some mutations such as ATM deficiency and Wip1 overexpression. Finally, we provide intervention strategies considering our observation that Wip1 seems to be an important target to conduct therapies for these mutations.

Online Learning ARMA Controllers With Guaranteed Closed-Loop Stability.

This paper presents a novel online block adaptive learning algorithm for autoregressive moving average (ARMA) controller design based on the real data measured from the plant. The method employs ARMA input-output models both for the plant and the resulting closed-loop system. In a sliding window, the plant model parameters are identified first offline using a supervised learning algorithm minimizing an ε -insensitive and regularized identification error, which is the window average of the distances between the measured plant output and the model output for the input provided by the controller. The optimal controller parameters are then determined again offline for another sliding window as the solution to a constrained optimization problem, where the cost is the ε -insensitive and regularized output tracking error and the constraints that are linear inequalities of the controller parameters are imposed for ensuring the closed-loop system to be Schur stable. Not only the identification phase but also the controller design phase uses the input-output samples measured from the plant during online learning. In the developed online controller design method, the controller parameters can always be kept in a parameter region providing Schur stability for the closed-loop system. The ε -insensitiveness provides robustness against disturbances, so does the regularization better generalization performance in the identification and the control. The method is tested on benchmark plants, including the inverted pendulum and dc motor models. The method is also tested on an emulated and also a real dc motor by online block adaptive learning ARMA controllers, in particular, Proportional-Integral-Derivative controllers.

Integrating segmentation methods from different tools into a visualization program using an object-based plug-in interface.

In medical visualization, segmentation is an important step prior to rendering. However, it is also a difficult procedure because of the restrictions imposed by variations in image characteristics, human anatomy, and pathology. Moreover, what is interesting from clinical point of view is usually not only an organ or a tissue itself, but also its properties together with adjacent organs or related vessel systems that are going in and coming out. For an informative rendering, these necessitate the usage of different segmentation methods in a single application, and combining/representing the results together in a proper way. This paper describes the implementation of an interface, which can be used to plug-in and then apply a segmentation method to a medical image series. The design is based on handling each segmentation procedure as an object where all parameters of each object can be specified individually. Thus, it is possible to use different plug-ins with different interfaces and parameters for the segmentation of different tissues in the same dataset while rendering all of the results together is still possible. The design allows access to insight registration and segmentation toolkit, Java, and MATLAB functionality together, eases sharing and comparing segmentation techniques, and serves as a visual debugger for algorithm developers.

Semiautomatic transfer function initialization for abdominal visualization using self-generating hierarchical radial basis function networks.

As being a tool that assigns optical parameters used in interactive visualization, Transfer Functions (TF) have important effects on the quality of volume rendered medical images. Unfortunately, finding accurate TFs is a tedious and time consuming task because of the trade off between using extensive search spaces and fulfilling the physician's expectations with interactive data exploration tools and interfaces. By addressing this problem, we introduce a semi-automatic method for initial generation of TFs. The proposed method uses a Self Generating Hierarchical Radial Basis Function Network to determine the lobes of a Volume Histogram Stack (VHS) which is introduced as a new domain by aligning the histograms of slices of a image series. The new self generating hierarchical design strategy allows the recognition of suppressed lobes corresponding to suppressed tissues and representation of the overlapping regions which are parts of the lobes but can not be represented by the Gaussian bases in VHS. Moreover, approximation with a minimum set of basis functions provides the possibility of selecting and adjusting suitable units to optimize the TF. Applications on different CT and MR data sets show enhanced rendering quality and reduced optimization time in abdominal studies.

Patient oriented and robust automatic liver segmentation for pre-evaluation of liver transplantation.

Identifying liver region from abdominal computed tomography-angiography (CTA) data sets is one of the essential steps in evaluation of transplantation donors prior to the hepatic surgery. However, due to gray level similarity of adjacent organs, injection of contrast media and partial volume effects; robust segmentation of the liver is a very difficult task. Moreover, high variations in liver margins, different image characteristics with different CT scanners and atypical liver shapes make the segmentation process even harder. In this paper, we propose a three stage (i.e. pre-processing, classification, post-processing); automatic liver segmentation algorithm that adapts its parameters according to each patient by learning the data set characteristics in parallel to segmentation process to address all the challenging aspects mentioned above. The efficiency in terms of the time requirement and the overall segmentation performance is achieved by introducing a novel modular classification system consisting of a K-Means based simple classification system and an MLP based complex one which are combined with a data-dependent and automated switching mechanism that decides to apply one of them. Proposed approach also makes the design of the overall classification system fully unsupervised that depends on the given CTA series only without requiring any given training set of CTA series. The segmentation results are evaluated by using area error rate and volume calculations and the success rate is calculated as 94.91% over a data set of diverse CTA series of 20 patients according to the evaluation of the expert radiologist. The results show that, the proposed algorithm gives better results especially for atypical liver shapes and low contrast studies where several algorithms fail.

A composite neural network model for perseveration and distractibility in the Wisconsin card sorting test.

A composite artificial neural network model is proposed to simulate the performance of the Wisconsin Card Sorting Test. The Wisconsin Card Sorting Test is a test of executive functions where prefrontal deficits are matched to some quantitative measures such as percentage of perseverative errors and number of failures to maintain set. In this work, the proposed model is used to simulate the performances of healthy subjects and patients with prefrontal involvement particularly on these measures. The model is designed in such a way that one of the subsystems, namely, the Hopfield network, serves as the working memory and the other, the Hamming block, as the hypothesis generator. The results show that the proposed relatively simple model is capable of simulating the wide range of the performances of both normal subjects and prefrontal patients on the Wisconsin Card Sorting Test. While lowering the Hamming distance in the Hamming block gave rise to progressively more perseverative responses, changing the threshold vector of the Hopfield network resulted in more set maintenance failures. The former manipulation disrupts the abstraction or mental flexibility and the latter sustained attention or perseverance both of which are the major functions of the prefrontal system.

An energy function-based design method for discrete Hopfield associative memory with attractive fixed points.

An energy function-based autoassociative memory design method to store a given set of unipolar binary memory vectors as attractive fixed points of an asynchronous discrete Hopfield network (DHN) is presented. The discrete quadratic energy function whose local minima correspond to the attractive fixed points of the network is constructed via solving a system of linear inequalities derived from the strict local minimality conditions. The weights and the thresholds are then calculated using this energy function. If the inequality system is infeasible, we conclude that no such asynchronous DHN exists, and extend the method to design a discrete piecewise quadratic energy function, which can be minimized by a generalized version of the conventional DHN, also proposed herein. In spite of its computational complexity, computer simulations indicate that the original method performs better than the conventional design methods in the sense that the memory can store, and provide the attractiveness for almost all memory sets whose cardinality is less than or equal to the dimension of its elements. The overall method, together with its extension, guarantees the storage of an arbitrary collection of memory vectors, which are mutually at least two Hamming distances away from each other, in the resulting network.

Automatic detection of epileptiform events in EEG by a three-stage procedure based on artificial neural networks.

This paper introduces a three-stage procedure based on artificial neural networks for the automatic detection of epileptiform events (EVs) in a multichannel electroencephalogram (EEG) signal. In the first stage, two discrete perceptrons fed by six features are used to classify EEG peaks into three subgroups: 1) definite epileptiform transients (ETs); 2) definite non-ETs; and 3) possible ETs and possible non-ETs. The pre-classification done in the first stage not only reduces the computation time but also increases the overall detection performance of the procedure. In the second stage, the peaks falling into the third group are aimed to be separated from each other by a nonlinear artificial neural network that would function as a postclassifier whose input is a vector of 41 consecutive sample values obtained from each peak. Different networks, i.e., a backpropagation multilayer perceptron and two radial basis function networks trained by a hybrid method and a support vector method, respectively, are constructed as the postclassifier and then compared in terms of their classification performances. In the third stage, multichannel information is integrated into the system for contributing to the process of identifying an EV by the electroencephalographers (EEGers). After the integration of multichannel information, the overall performance of the system is determined with respect to EVs. Visual evaluation, by two EEGers, of 19 channel EEG records of 10 epileptic patients showed that the best performance is obtained with a radial basis support vector machine providing an average sensitivity of 89.1%, an average selectivity of 85.9%, and a false detection rate (per hour) of 7.5.

Automatic spike detection in EEG by a two-stage procedure based on support vector machines.

In this study, we introduce a two-stage procedure based on support vector machines for the automatic detection of epileptic spikes in a multi-channel electroencephalographic signal. In the first stage, a modified non-linear digital filter is used as a pre-classifier to classify the peaks into two subgroups: (i) spikes and spike like non-spikes (ii) trivial non-spikes. The pre-classification done in the first stage not only reduces the computation time but also increases the overall detection performance of the procedure. In the second stage, the peaks falling into the first group are aimed to be separated from each other by a support vector machine that would function as a post-classifier. Visual evaluation, by two experts, of 19 channel EEG records of 7 epileptic patients showed that the best performance is obtained providing 90.3% sensitivity, 88.1% selectivity and 9.5% false detection rate.

A Boolean Hebb rule for binary associative memory design.

A binary associative memory design procedure that gives a Hopfield network with a symmetric binary weight matrix is introduced in this paper. The proposed method is based on introducing the memory vectors as maximal independent sets to an undirected graph, which is constructed by Boolean operations analogous to the conventional Hebb rule. The parameters of the resulting network is then determined via the adjacency matrix of this graph in order to find a maximal independent set whose characteristic vector is close to the given distorted vector. We show that the method provides attractiveness for each memory vector and avoids spurious memories whenever the set of given memory vectors satisfy certain compatibility conditions, which implicitly imply sparsity. The applicability of the design method is finally investigated by a quantitative analysis of the compatibility conditions.