Narrow Results

Genetic algorithms (GAs) have been well applied in solving scheduling problems and their performance advantages have also been recognized. However, practitioners are often troubled by parameters setting when they are tuning GAs. Population Size (PS) has been shown to greatly affect the efficiency of GAs. Although some population sizing models exist in the literature, reasonable population sizing for task scheduling is rarely observed. In this paper, based on the PS deciding model proposed by Harik, we present a model to represent the relation between the success ratio and the PS for the GA applied in time-critical task scheduling, in which the efficiency of GAs is more necessitated than in solving other kinds of problems. Our model only needs some parameters easy to know through proper simplifications and approximations. Hence, our model is applicable. Finally, our model is verified through experiments.

Some larvae of Drosophila infected by parasitic wasps are able to encapsulate the larvae of the parasitoid, and the emerging hosts present a visible melanized capsule in the abdomen. In this paper, a model for estimating the infection rate RI by the rate of hosts presenting a capsule HC is developed. For Drosophila simulans parasitized by Leptopilina boulardi, the model RI = HC/(k+(1-k)HC), with k=0.123, is validated from experimental data. The validation process is based upon a bootstrap strategy over 12870 possibilities of grouping 8 elementary experimental results among 16. Validation consists in fitting the theoretical curve from a data set and in controlling the overlap of the curve with the confidence rectangle established with the complementary data set. This validation process appears to be independent of the confidence level. This infection-encapsulation model is applied to field observations in Tunisia and predicts high levels of infection. This prediction is confirmed at Nasr'Allah by a direct measure of the infection rate. The biological hypotheses involved in this model are discussed. The model merely allows one to follow the evolution of infection in population cages and in the wild, by catching and counting adult hosts, without access to breeding sites. The model is generalisable to other species of hosts and parasitoids presenting the encapsulation reaction.

In this paper we present a new neuroeconomics model for decision-making applied to the Attention-Deficit/Hyperactivity Disorder (ADHD). The model is based on the hypothesis that decision-making is dependent on the evaluation of expected rewards and risks assessed simultaneously in two decision spaces: the personal (PDS) and the interpersonal emotional spaces (IDS). Motivation to act is triggered by necessities identified in PDS or IDS. The adequacy of an action in fulfilling a given necessity is assumed to be dependent on the expected reward and risk evaluated in the decision spaces. Conflict generated by expected reward and risk influences the easiness (cognitive effort) and the future perspective of the decision-making. Finally, the willingness (not) to act is proposed to be a function of the expected reward (or risk), adequacy, easiness and future perspective. The two most frequent clinical forms are ADHD hyperactive(AD/HDhyp) and ADHD inattentive(AD/HDdin). AD/HDhyp behavior is hypothesized to be a consequence of experiencing high rewarding expectancies for short periods of time, low risk evaluation, and short future perspective for decision-making. AD/HDin is hypothesized to be a consequence of experiencing high rewarding expectancies for long periods of time, low risk evaluation, and long future perspective for decision-making.

The most deadly Ebola outbreak in the history, which started in December $2013$, is currently under control. The high case fatality rate of the Ebola outbreak inspired local and international control strategies. In this paper, the dynamics of Ebola virus disease is modeled in the presence of three control strategies. The model describes the evolution of the disease in the population when educational campaigns, active case-finding and pharmaceutical interventions are implemented as control strategies against the disease. We prove the existence of an optimal control set and analyze the necessary and sufficient conditions, optimality and transversality conditions. We conclude through numerical simulations that containing an Ebola outbreak needs early and long-term implementation of joint control strategies.

The effects of a single oral dose of liothyronine (L-T3) on thyroid stimulating hormone (TSH) and other related thyroid system parameters are partly understood despite therapeutic use of this hormone over many decades. We characterize individualized responses of the hypothalamus-pituitary-thyroid (HPT) axis and its related temporal hormonal profile using an electrical network model. Based on thyroid hormone responses from blood samples using a single 50$\mu$g oral dose of liothyronine in healthy persons with a normal operating euthyroid feedback HPT system, we derived an equivalent electrical circuit model for the system’s responses. The mathematical model was tested with a circuit simulator and validated with individualized clinical data. This signal processing technique makes the evaluation of bioequivalence and bioavailability of various preparations of liothyronine at an individualized level a feasible endeavor for clinical application.

Insulin secretion in pancreatic $\beta$-cells exhibits three oscillatory modes with distinct period ranges, called fast, slow, and ultradian modes. To unveil the mechanism underlying such oscillatory behaviors and their roles in blood glucose regulation, we propose a combined model for the glucose–insulin regulation system, incorporating both the cell-level insulin secretion mechanism and inter-organ interactions in the blood glucose regulation. Special emphasis is placed on the identification of the mechanism of the slow oscillation and its role associated with the whole-body glucose regulation. Via extensive numerical simulations, we obtain macroscopic behaviors of the three types of insulin/glucose oscillations in the whole-body as well as microscopic behaviors of the membrane potential and the calcium concentration in the $\beta$-cell. Finally, optimal regulatory strategies for the blood glucose level are discussed on the basis of the quantitative information obtained from the mathematical modeling and numerical simulations.

The mathematical modeling of biological morphogenesis processes is considered. Emphasis is placed throughout on the lessons of experience in modeling three-dimensional forms that evolve in time. The qualitative requirements of a model, the general components of a dynamic system, and the products of a morphogenesis modeling program are discussed. Examples are drawn frequently from phyllotaxis.

Systems biology is creating a context for interpreting the vast amounts of genomic and proteomic data being produced by pharmaceutical companies in support of drug development. While major data collection efforts capitalize on technical advances in miniaturization and automation and represent an industrialization of existing laboratory research, the transition from mental models to predictive computer simulations is setting the pace for advances in this field. This article addresses current approaches to the mathematical modeling of biological systems and assesses the potential impact of predictive biosimulation on drug discovery and development.

Proteases play a fundamental role in the control of intra- and extra-cellular processes by binding and cleaving specific amino acid sequences. Identifying these targets is extremely challenging. Current computational attempts to predict cleavage sites are limited, representing these amino acid sequences as patterns or frequency matrices. Here we present PoPS, a publicly accessible bioinformatics tool () that provides a novel method for building computational models of protease specificity, which while still being based on these amino acid sequences, can be built from any experimental data or expert knowledge available to the user. PoPS specificity models can be used to predict and rank likely cleavages within a single substrate, and within entire proteomes. Other factors, such as the secondary or tertiary structure of the substrate, can be used to screen unlikely sites. Furthermore, the tool also provides facilities to infer, compare and test models, and to store them in a publicly accessible database.

The last 10 years have seen the rise of many technologies that produce an unprecedented amount of genome-scale data from many organisms. Although the research community has been successful in exploring these data, many challenges still persist. One of them is the effective integration of such data sets directly into approaches based on mathematical modeling of biological systems. Applications in cancer are a good example. The bridge between information and modeling in cancer can be achieved by two major types of complementary strategies. First, there is a bottom–up approach, in which data generates information about structure and relationship between components of a given system. In addition, there is a top–down approach, where cybernetic and systems–theoretical knowledge are used to create models that describe mechanisms and dynamics of the system. These approaches can also be linked to yield multi-scale models combining detailed mechanism and wide biological scope. Here we give an overall picture of this field and discuss possible strategies to approach the major challenges ahead.

We propose an original program "Evolutionary constructor" that is capable of computationally efficient modeling of both population-genetic and ecological problems, combining these directions in one model of required detail level. We also present results of comparative modeling of stability, adaptability and biodiversity dynamics in populations of unicellular haploid organisms which form symbiotic ecosystems. The advantages and disadvantages of two evolutionary strategies of biota formation — a few generalists' taxa-based biota formation and biodiversity-based biota formation — are discussed.

The methods for constructing "chaotic" nonlinear systems of differential equations modeling gene networks of arbitrary structure and dimensionality with various types of symmetry are considered. It has been shown that an increase in modality of the functions describing the control of gene expression efficiency allows for a decrease in the dimensionality of these systems with retention of their chaotic dynamics. Three-dimensional "chaotic" cyclic systems are considered. Symmetrical and asymmetrical attractors with "narrow" chaos having a Moebius-like structure have been detected in such systems. As has been demonstrated, a complete symmetry of the systems with respect to permutation of variables does not prevent the emergence of their chaotic dynamics.

In this paper, we perform an analysis of bacterial cell-cycle models implementing different strategies to coordinately regulate genome replication and cell growth dynamics. It has been shown that the problem of coupling these processes does not depend directly on the dynamics of cell volume expansion, but does depend on the type of cell growth law. Our analysis has distinguished two types of cell growth laws, "exponential" and "linear", each of which may include both exponential and linear patterns of cell growth. If a cell grows following a law of the "exponential" type, including the exponential V(t) = V₀exp(kt) and linear V(t) = V₀(1 + kt) dynamic patterns, then the cell encounters the problem of coupling growth rates and replication. It has been demonstrated that to solve the problem, it is sufficient for a cell to have a repressor mechanism to regulate DNA replication initiation. For a cell expanding its volume by a law of the "linear" type, including exponential V(t) = V₀ + V₁exp(kt) and linear V(t) = V₀ + kt dynamic patterns, the problem of coupling growth rates and replication does not exist. In other words, in the context of the coupling problem, a repressor mechanism to regulate DNA replication, and cell growth laws of the "linear" type displays the attributes of universality. The repressor-type mechanism allows a cell to follow any growth dynamic pattern, while the "linear" type growth law allows a cell to use any mechanism to regulate DNA replication.

Alternative splicing is a widespread phenomenon in higher eukaryotes, where it serves as a mechanism to increase the functional diversity of proteins. This phenomenon has been described for different classes of proteins, including transcription regulatory proteins. We demonstrated that in the simplest genetic system model the formation of the alternatively spliced isoforms with opposite functions (activators and repressors) could be a cause of transition to chaotic dynamics. Under the simplest genetic system we understand a system consisting of a single gene encoding the structure of a transcription regulatory protein whose expression is regulated by a feedback mechanism. As demonstrated by numerical analysis of the models, if the synthesized isoforms regulate the expression of their own gene acting through different sites and independently of each other, for the generation of chaotic dynamics it is sufficient that the regulatory proteins have a dimeric structure. If regulatory proteins act through one site, the chaotic dynamics is generated in the system only when the repressor protein is either a tetrameric or a higher-dimensional multimer. In this case the activator can be a dimer. It was also demonstrated that if the transcription factor isoforms exhibit either activating or inhibiting activity and are lower-dimensional multimers (< 4), independently of the regulation type the model demonstrates either cyclic or stationary trajectories.

Today there are examples that prove the existence of chaotic dynamics at all levels of organization of living systems, except intracellular, although such a possibility has been theoretically predicted. The lack of experimental evidence of chaos generation at the intracellular level in vivo may indicate that during evolution the cell got rid of chaos. This work allows the hypothesis that one of the possible mechanisms for avoiding chaos in gene networks can be a negative evolutionary selection, which prevents fixation or realization of regulatory circuits, creating too mild, from the biological point of view, conditions for the emergence of chaos. It has been shown that one of such circuits may be a combination of negative autoregulation of expression of transcription factors at the level of their synthesis and degradation. The presence of such a circuit results in formation of multiple branches of chaotic solutions as well as formation of hyperchaos with equal and sufficiently low values of the delayed argument that can be implemented not only in eukaryotic, but in prokaryotic cells.

In this work, we study period control of the mammalian cell cycle via coupling with the cellular clock. For this, we make use of the oscillators’ synchronization dynamics and investigate methods of slowing down the cell cycle with the use of clock inputs. Clock control of the cell cycle is well established via identified molecular mechanisms, such as the CLOCK:BMAL1-mediated induction of the wee1 gene, resulting in the WEE1 kinase that represses the active form of mitosis promoting factor (MPF), the essential cell cycle component. To investigate the coupling dynamics of these systems, we use previously developed models of the clock and cell cycle oscillators and center our studies on unidirectional clock $\to$ cell cycle coupling. Moreover, we propose an hypothesis of a Growth Factor (GF)-responsive clock, involving a pathway of the non-essential cell cycle complex cyclin D/CDK4. We observe a variety of rational ratios of clock to cell cycle period, such as: 1:1, 3:2, 4:3, and 5:4. Finally, our protocols of period control are successful in effectively slowing down the cell cycle by the use of clock modulating inputs, some of which correspond to existing drugs.

A critical review of the current state of the art of the computing practices adopted by the earthquake engineering community is presented. Advanced computational tools are necessary for estimating the demand on seismically excited structures. Such computational methodologies can provide valuable information on a number of engineering parameters which have been proven essential for earthquake the engineering practice. The discussion extends from the finite element modeling of earthquake-resistant structures and the analysis procedures currently used to future developments considering the calculation of uncertainty and methodologies which rely on sophisticated computational methods. The objective is to provide a common ground of collaboration between the earthquake engineering and computational mechanics communities in an effort to mitigate future earthquake losses.

This work is about a heat transfer phenomenon in relation to the periodically laminated composite. The specific type of thermal loading, analyzed in this paper, require formulation of Robin boundary conditions. To consider a layered structure of analyzed composite, the tolerance averaging technique is used. This method allows to take into account a thickness of the layers and obtain the equations with continuous coefficients. To solve these equations, the finite difference method is used, because an analytical solution is not available in this case (in contrast to the analogous issue in relation to a homogeneous layer).

This paper introduces a new technique for analyzing the behavior of global interconnects in FPGAs, for nanoscale technologies. Using this new enhanced modeling method, new enhanced accurate expressions for calculating the propagation delay of global interconnects in nano-FPGAs have been derived. In order to verify the proposed model, we have performed the delay simulations in 45 nm, 65 nm, 90 nm, and 130 nm technology nodes, with our modeling method and the conventional Pi-model technique. Then, the results obtained from these two methods have been compared with HSPICE simulation results. The obtained results show a better match in the propagation delay computations for global interconnects between our proposed model and HSPICE simulations, with respect to the conventional techniques such as Pi-model. According to the obtained results, the difference between our model and HSPICE simulations in the mentioned technology nodes is (0.29–22.92)%, whereas this difference is (11.13–38.29)% for another model.

Dual radio frequency (RF) powers are widely used with commercial plasma etchers for various nanoscale patterns. However, it is challenging to understand the relationship among the dual RF powers and the etching processes. In this work, the effect of the dual RF bias powers on SiO₂ sputter etching was investigated in inductively coupled plasma (ICP). The relationship was studied among 2MHz and 27.12MHz RF bias powers, a 13.56MHz ICP source power, the ion bombardment energy, the ion density and the etching rate. The results show that the ion density of Ar plasma can be controlled in the region of 10⁹–10${}^{11}$ ions/cm³, and DC self-bias can be controlled by controlling the ratio of dual RF bias powers while the ion density is maintained with the operation of source power. This work reveals that the dual RF bias powers expand the process window of the ion density and the ion bombardment energy independently in the ICP plasma source. The sputter etching rate is also modeled using the ion-enhanced etching model, and the model shows good agreement with the etching rate data.