Multi-level discrete models of genetic networks, or the more general piecewise affine differential models, provide qualitative information on the dynamics of the system, based on a small number of parameters (such as synthesis and degradation rates). Boolean models also provide qualitative information, but are based simply on the structure of interconnections. To explore the relationship between the two formalisms, a piecewise affine differential model and a Boolean model are compared, for the carbon starvation response network in E. coli (...) . The asymptotic dynamics of both models are shown to be quite similar. This study suggests new tools for analysis and reduction of biological networks. (shrink)

This paper presents the architecture and initial feasibility results of a proto-type communication network that utilizes genetic programming to evolve services and protocols as part of network operation. The network evolves responses to environmental conditions in a manner that could not be preprogrammed within legacy network nodes a priori. A priori in this case means before network operation has begun. Genetic material is exchanged, loaded, and run dynamically within an active network. The (...) transfer and execution of code in support of the evolution of network protocols and services would not be possible without the active network environment. Rapid generation of network service code occurs via a genetic programming paradigm. Complexity and Algorithmic Information Theory play a key role in understanding and guiding code evolution within the network. (shrink)

The question of whether “genetic information” is a merely causal factor in development or can be made sense of semantically, in a way analogous to a language or other type of representation, has generated a long debate in the philosophy of biology. It is intimately connected with another intense debate, concerning the limits of genetic determinism. In this paper I argue that widespread attempts to draw analogies between genetic information and information contained in books, blueprints or computer (...) programs, are fundamentally inadequate. In development, gene exons are the central part of an intricate and densely ramified semantic Genetic Informational Network. DNA in the entire genome is in a state of continuous positive and negative feedback with itself and with its ‘environment’, and is ‘read’ and acted upon by the cell in various alternative and complementary ways. The linear combinatorial coding relation between codons and amino acids is but one aspect of semantic genetic information, which is, when considered in its entirety, a far wider and richer concept. (shrink)

Behaviors are quantitative traits determined through actions of multiple genes and subject to genome–environment interactions. Early studies concentrated on analyzing the effects of single genes on behaviors, often generating views of simplified linear genetic pathways. The genome era has generated a profound paradigm shift enabling us to identify all the genes that contribute to expression of a behavioral phenotype, to investigate how they are organized as functional ensembles and to begin to identify polymorphisms that contribute to phenotypic variation and (...) are targets for natural selection. Recent studies show that the genetic architecture of behavior is determined by dynamic and plastic modular networks of pleiotropic genes and that the behavioral phenotype manifests itself as an emergent property of such networks. Such networks are exquisitely sensitive to genetic background and sex effects. This review describes how Drosophila can serve as a model for uncovering fundamental principles of the genetic architecture of behavior. BioEssays 26:1299–1306, 2004. © 2004 Wiley Periodicals, Inc. (shrink)

Richard Scheines and Joe Ramsey. Simulating Genetic Regulartory Networks.

Medical explanations have often been thought on the model of biological ones and are frequently defined as mechanistic explanations of a biological dysfunction. In this paper, I argue that topological explanations, which have been described in ecology or in cognitive sciences, can also be found in medicine and I discuss the relationships between mechanistic and topological explanations in medicine, through the example of network medicine and medical genetics. Network medicine is a recent discipline that relies on the analysis (...) of various disease networks in order to find organizing principles in disease explanation. My aim is to show how topological explanations in network medicine can help solving the conceptual issues that pure mechanistic explanations of the genetics of disease are currently facing, namely the crisis of the concept of genetic disease, the progressive geneticization of diseases and the dissolution of the distinction between monogenic and polygenic diseases. However, I will also argue that topological explanations should not be considered as independent and radically different from mechanistic explanations for at least two reasons. First, in network medicine, topological explanations depend on and use mechanistic information. Second, they leave out some missing gaps in disease explanation that require, in turn, the development of new mechanistic explanations. Finally, I will insist on the specific contribution of topological explanations in medicine: they push us to develop an explanation of disease in general, instead of focusing on single explanations of individual diseases. This last point may have major consequences for biomedical research. (shrink)

There is growing interest in the evolutionary dynamics of molecular genetic pathways and networks, and the extent to which the molecular evolution of a gene depends on its position within a pathway or network, as well as over‐all network topology. Investigations on the relationships between network organization, topological architecture and evolutionary dynamics provide intriguing hints as to how networks evolve. Recent studies also suggest that genetic pathway and network structures may influence the action of (...) evolutionary forces, and may play a role in maintaining phenotypic robustness in organisms. BioEssays 26:479–484, 2004. © 2004 Wiley Periodicals, Inc. (shrink)

Cramer et al's proposal to view mental disorders as the outcome of network dynamics among symptoms obviates the need to invoke latent traits to explain co-occurrence of symptoms and syndromes. This commentary considers the consequences of such a network view for genetic association studies.

This study is to explore the optimization of the adaptive genetic algorithm in the backpropagation neural network, so as to expand the application of the BPNN model in nonlinear issues. Traffic flow prediction is undertaken as a research case to analyse the performance of the optimized BPNN. Firstly, the advantages and disadvantages of the BPNN and genetic algorithm are analyzed based on their working principles, and the AGA is improved and optimized. Secondly, the optimized AGA is applied (...) to optimize the standard BPNN, and the optimized algorithm is named as OAGA-BPNN. Finally, three different cases are proposed based on the actual scenario of traffic flow prediction to analyse the optimized algorithm on the matrix laboratory platform by simulation. The results show that the average error distribution of the GA-BPNN algorithm is about 1% with small fluctuation range, better calculation accuracy, and generalization performance in contrast to the BPNN. The average output error of the AGA-BPNN fluctuates around 0 and remains in a relatively stable range as a whole in contrast to that of GA-BPNN; the maximum fitness level keeps increasing during the evolution process but approaches the average value in later process, so the population diversity is hard to be guaranteed. The output error of the OAGA-BPNN fluctuates little compared with that of AGA-BPNN, and its maximum fitness continues to increase in the evolution process with guaranteed population diversity. In short, the OAGA-BPNN algorithm can achieve the best performance in terms of calculation accuracy, generalization performance, and population evolution. (shrink)

A categorical, higher dimensional algebra and generalized topos framework for Łukasiewicz–Moisil Algebraic–Logic models of non-linear dynamics in complex functional genomes and cell interactomes is proposed. Łukasiewicz–Moisil Algebraic–Logic models of neural, genetic and neoplastic cell networks, as well as signaling pathways in cells are formulated in terms of non-linear dynamic systems with n-state components that allow for the generalization of previous logical models of both genetic activities and neural networks. An algebraic formulation of variable ‘next-state functions’ is extended to (...) a Łukasiewicz–Moisil Topos with an n-valued Łukasiewicz–Moisil Algebraic Logic subobject classifier description that represents non-random and non-linear network activities as well as their transformations in developmental processes and carcinogenesis. The unification of the theories of organismic sets, molecular sets and Robert Rosen’s (M,R)-systems is also considered here in terms of natural transformations of organismal structures which generate higher dimensional algebras based on consistent axioms, thus avoiding well known logical paradoxes occurring with sets. Quantum bionetworks, such as quantum neural nets and quantum genetic networks, are also discussed and their underlying, non-commutative quantum logics are considered in the context of an emerging Quantum Relational Biology. (shrink)

A categorical, higher dimensional algebra and generalized topos framework for Łukasiewicz–Moisil Algebraic–Logic models of non-linear dynamics in complex functional genomes and cell interactomes is proposed. Łukasiewicz–Moisil Algebraic–Logic models of neural, genetic and neoplastic cell networks, as well as signaling pathways in cells are formulated in terms of non-linear dynamic systems with n-state components that allow for the generalization of previous logical models of both genetic activities and neural networks. An algebraic formulation of variable ‘next-state functions’ is extended to (...) a Łukasiewicz–Moisil Topos with an n-valued Łukasiewicz–Moisil Algebraic Logic subobject classifier description that represents non-random and non-linear network activities as well as their transformations in developmental processes and carcinogenesis. The unification of the theories of organismic sets, molecular sets and Robert Rosen’s (M,R)-systems is also considered here in terms of natural transformations of organismal structures which generate higher dimensional algebras based on consistent axioms, thus avoiding well known logical paradoxes occurring with sets. Quantum bionetworks, such as quantum neural nets and quantum genetic networks, are also discussed and their underlying, non-commutative quantum logics are considered in the context of an emerging Quantum Relational Biology. (shrink)

The Norwegian Association for Heredity Research played a key role in the rise of genetics as a research field in Norway. The immediate background of its establishment in 1919 was the need for an organization that could clarify scientific issues regarding eugenics and coordinate Norwegian representation in the organized international eugenics movement. The Association never assumed this role. Instead, Norway was represented in the international eugenics movement by the so-called Norwegian Consultative Eugenics Commission, whose leader, Jon Alfred Mjøen, was dismissed (...) as a pseudo-scientist by Norwegian geneticists. The paper explores the Association’s role in defining and delimiting scientific expert knowledge in the field of genetics and eugenics in Norway. It demonstrates how struggles about academic authority on the national arena were intertwined with struggles about representation and impact in the international eugenics movement and how transnational scientific networks where mobilized to legitimize and delegitimize notions about Nordic race supremacy, racial mixing and the politics of eugenic sterilizations. (shrink)

Water quality prediction is the basis of water environmental planning, evaluation, and management. In this work, a novel intelligent prediction model based on the fuzzy wavelet neural network including the neural network, the fuzzy logic, the wavelet transform, and the genetic algorithm was proposed to simulate the nonlinearity of water quality parameters and water quality predictions. A self-adapted fuzzy c-means clustering was used to determine the number of fuzzy rules. A hybrid learning algorithm based on a (...) class='Hi'>genetic algorithm and gradient descent algorithm was employed to optimize the network parameters. Comparisons were made between the proposed FWNN model and the fuzzy neural network, the wavelet neural network, and the neural network. The results indicate that the FWNN made effective use of the self-adaptability of NN, the uncertainty capacity of FL, and the partial analysis ability of WT, so it could handle the fluctuation and the nonseasonal time series data of water quality, while exhibiting higher estimation accuracy and better robustness and achieving better performances for predicting water quality with high determination coefficients R2 over 0.90. The FWNN is feasible and reliable for simulating and predicting water quality in river. (shrink)

It will be settled out for the open problem of designing an r-order finite-time state observer for reaction-diffusion genetic regulatory networks with time-varying delays. By assuming the Dirichlet boundary conditions, aiming to estimate the mRNA and protein concentrations via available network measurements. Firstly, sufficient F-T stability conditions for the filtering error system have been investigated via constructing an appropriate Lyapunov–Krasovskii functional and using several integral inequalities and convex technique simultaneously. These conditions are delay-dependent and reaction-diffusion-dependent and can be (...) checked by MATLAB toolbox. Furthermore, a method is proposed to design an r-order F-T state observer, and the explicit expressions of observer gains are given. Finally, a numerical example is presented to illustrate the effectiveness of the proposed method. (shrink)

This paper deals with the problem of reconstruction of the intergenic interaction graph from the raw data of genetic co-expression coming with new technologies of bio-arrays (DMA-arrays, protein-arrays, etc.). These new imaging devices in general only give information about the asymptotical part (fixed configurations of co-expression or limit cycles of such configurations) of the dynamical evolution of the regulatory networks (genetic and/or proteic) underlying the functioning of living systems. Extracting the casual structure and interaction coefficients of a gene (...) interaction network from the observed configurations is a complex problem. But if all the fixed configurations are supposedly observed and if they are factorizable into two or more subsets of values, then the interaction graph possesses as many connected components as the number of factors and the solution is obtained in polynomial time. This new result allows us for example to partly solve the topology of the genetic regulatory network ruling the flowering in Arabidopsis thaliana. (shrink)

The problem of finite-time stability of switched genetic regulatory networks with time-varying delays via Wirtinger’s integral inequality is addressed in this study. A novel Lyapunov–Krasovskii functional is proposed to capture the dynamical characteristic of GRNs. Using Wirtinger’s integral inequality, reciprocally convex combination technique and the average dwell time method conditions in the form of linear matrix inequalities are established for finite-time stability of switched GRNs. The applicability of the developed finite-time stability conditions is validated by numerical results.

In this paper we investigate a particular two-stage supply chain network design problem with fixed costs. In order to solve this complex optimization problem, we propose an efficient hybrid algorithm, which was obtained by incorporating a linear programming optimization problem within the framework of a genetic algorithm. In addition, we integrated within our proposed algorithm a powerful local search procedure able to perform a fine tuning of the global search. We evaluate our proposed solution approach on a set (...) of large size instances. The achieved computational results prove the efficiency of our hybrid genetic algorithm in providing high-quality solutions within reasonable running-times and its superiority against other existing methods from the literature. (shrink)

We deal with the design problem of nonfragile state estimator for discrete-time genetic regulatory networks with time-varying delays and randomly occurring uncertainties. In particular, the norm-bounded uncertainties enter into the GRNs in random ways in order to reflect the characteristic of the modelling errors, and the so-called randomly occurring uncertainties are characterized by certain mutually independent random variables obeying the Bernoulli distribution. The focus of the paper is on developing a new nonfragile state estimation method to estimate the concentrations (...) of the mRNA and the protein for considered uncertain delayed GRNs, where the randomly occurring estimator gain perturbations are allowed. By constructing a Lyapunov-Krasovskii functional, a delay-dependent criterion is obtained in terms of linear matrix inequalities by properly using the discrete-time Wirtinger-based inequality and reciprocally convex combination approach as well as the free-weighting matrix method. It is shown that the proposed method ensures that the estimation error dynamics is globally asymptotically stable and the desired estimator parameter is designed via the solutions to certain LMIs. Finally, we provide two numerical examples to illustrate the feasibility and validity of the proposed estimation results. (shrink)

Porosity is considered as one of the most important indicators for the characterization of the comprehensive performance of thermal barrier coatings. In this study, the ultrasonic technique and the artificial neural network optimized with the genetic algorithm are combined to develop an intelligent method for automatic detection and accurate prediction of TBCs’s porosity. A series of physical models of plasma-sprayed ZrO2 coating are established with a thickness of 288 μm and porosity varying from 5.71% to 26.59%, and the (...) ultrasonic reflection coefficient amplitude spectrum is constructed based on the time-domain numerical simulation signal. The characteristic features f 1, f 2, A max, Δ A of the URCAS, which are highly dependent on porosity, are extracted as input data to train the GA_BPNN model for predicting the unknown porosity. The average error of the prediction results is 1.45%, which suggests that the proposed method can achieve accurate detection and quantitative characterization of the porosity of TBCs with complex pore morphology. (shrink)

The logic of genetic discovery has changed little over time, but the focus of biology is shifting from simple genotype–phenotype relationships to complex metabolic, physiological, developmental, and behavioral traits. In light of this, the traditional reductionist view of individual genes as privileged difference‐making causes of phenotypes is re‐examined. The scope and nature of genetic effects in complex regulatory systems, in which dynamics are driven by regulatory feedback and hierarchical interactions across levels of organization are considered. This review argues (...) that it is appropriate to treat genes as specific actual difference‐makers for the molecular regulation of gene expression. However, they are often neither stable, proportional, nor specific as causes of the overall dynamic behavior of regulatory networks. Dynamical models, properly formulated and validated, provide the tools to probe cause‐and‐effect relationships in complex biological systems, allowing to go beyond the limitations of genetic reductionism to gain an integrative understanding of the causal processes underlying complex phenotypes. (shrink)

The topology optimization of computer communication network is studied based on improved genetic algorithm, a network optimization design model based on the establishment of network reliability maximization under given cost constraints, and the corresponding improved GA is proposed. In this method, the corresponding computer communication network cost model and computer communication network reliability model are established through a specific project, and the genetic intelligence algorithm is used to solve the cost model and computer (...) communication network reliability model, respectively. It has been proved that GA can solve the complex problems of computer working environment better, which is 80% higher than the general algorithm, and can select the optimal scheme pertinently. (shrink)

The potential of the soil to hold plant nutrients is governed by the cation-exchange capacity of any soil. Estimating soil CEC aids in conventional soil management practices to replenish the soil solution that supports plant growth. In this study, a multiple model integration scheme supervised with a hybrid genetic algorithm-neural network was developed and employed to predict the accuracy of soil CEC in Tabriz plain, an arid region of Iran. The standalone models and extreme learning machine ) were (...) implemented for incorporation into the MM-GANN. In addition, it was tested to enhance the prediction accuracy of the standalone models. The soil parameters such as clay, silt, pH, carbonate calcium equivalent, and soil organic matter were used as model inputs to predict soil CEC. With the use of several evaluation criteria, the results showed that the MM-GANN model involving the predictions of ELM and ANN models calibrated by considering all the soil parameters as inputs provided superior soil CEC estimates with a Nash Sutcliffe Efficiency = 0.87, Root Mean Square Error = 2.885, Mean Absolute Error = 2.249, Mean Absolute Percentage Error = 12.072, and coefficient of determination = 0.884. The proposed MM-GANN model is a reliable intelligence-based approach for the assessment of soil quality parameters intended for sustainability and management prospects. (shrink)

Comprehensive analysis of proteins to evaluate their genetic diversity, study their differences, and respond to the tensions is the main subject of an interdisciplinary field of study called proteomics. The main objective of the proteomics is to detect and quantify proteins and study their post-translational modifications and interactions using protein chemistry, bioinformatics, and biology. Any disturbance in proteins interactive network can act as a source for biological disorders and various diseases such as Alzheimer and cancer. Most current computational (...) methods for discovering protein complexes are usually based on specific topological characteristics of protein-protein networks. To identify the protein complexes, in this paper, we, first, present a new encoding method to represent solutions; we then propose a new clustering algorithm based on the genetic algorithm, named PPI-GA, employing a new multiobjective quality function. The proposed algorithm is evaluated on two gold standard and real-world datasets. The result achieved demonstrates that the proposed algorithm can detect important protein complexes, and it provides more accurate results compared with state-of-the-art protein complex identification algorithms. (shrink)

After reviewing theoretical reasons for doubting that machine learning methods can accurately infer gene regulatory networks from microarray data, we test 10 algorithms on simulated data from the sea urchin network, and on microarray data for yeast compared with recent experimental determinations of the regulatory network in the same yeast species. Our results agree with the theoretical arguments: most algorithms are at chance for determining the existence of a regulatory connection between gene pairs, and the algorithms that perform (...) better than chance are nonetheless so errorprone as to be of little practical use in these applications. (shrink)

The biomedical literature makes extensive use of the concept of a genetic program. So far, however, the nature of genetic programs has received no satisfactory elucidation from the standpoint of computer science. This unsettling omission has led to doubts about the very existence of genetic programs, on the grounds that gene regulatory networks lack a predetermined schedule of execution, which may seem to contradict the very idea of a program. I show, however, that we can make perfect (...) sense of genetic programs, if only we abandon the preconception that all computers have a von Neumann architecture. Instead, genetic programs instantiate the computational architecture of Post–Newell Production Systems. That is, genetic programs are unordered sets of conditional instructions, instructions that fire independently when their conditions are matched. For illustration I present a paradigm Production System that regulates the functioning of the well-known lac operon of E. coli. On close reflection it turns out that not only genes, but also proteins encode instructions. I propose, therefore, to rename genetic programs to biomolecular programs. Biomolecular and/or genetic programs, and the cellular computers than run them, are to be understood not as von Neumann computers, but as Post–Newell production systems. (shrink)

Many complex behaviors are genetically hardwired. Based on previous findings on genetic control of mating and other behaviors in invertebrate and mammalian systems, I suggest that genetic control of complex behaviors is modular: first, dedicated genes specify different behavioral patterns; secondly, separable genetic networks govern distinct behavioral components. I speculate that modular genetic encoding of complex behaviors may in part reflect modularity in brain development and function.Editor's suggested further reading in BioEssays From songs to synapses: Molecular (...) mechanisms of birdsong memory Abstract. (shrink)

This paper examines social network size in contemporary Western society based on the exchange of Christmas cards. Maximum network size averaged 153.5 individuals, with a mean network size of 124.9 for those individuals explicitly contacted; these values are remarkably close to the group size of 150 predicted for humans on the basis of the size of their neocortex. Age, household type, and the relationship to the individual influence network structure, although the proportion of kin remained relatively (...) constant at around 21%. Frequency of contact between network members was primarily determined by two classes of variable: passive factors (distance, work colleague, overseas) and active factors (emotional closeness, genetic relatedness). Controlling for the influence of passive factors on contact rates allowed the hierarchical structure of human social groups to be delimited. These findings suggest that there may be cognitive constraints on network size. (shrink)

Networks for the exchange and/or sharing of genetic data are developing in many countries. We focus here on the situations in the US and France. We highlight some recent and remarkable differences between these two countries concerning the mode of access to, and the storage and use of genetic data, particularly as concerns two-sided markets and dynamic consent or dynamic electronic informed consent. This brief overview suggests that, even though the organization and function of these two-sided markets remain (...) open to criticism, dynamic e-IC should be more widely used, especially in France, if only to determine its real effectiveness. (shrink)

Recent studies have revealed an astonishing diversity of sex chromosomes in many vertebrate lineages, prompting questions about the mechanisms of sex chromosome turnover. While there is considerable population genetic theory about the evolutionary forces promoting sex chromosome replacement, this theory has not yet been integrated with our understanding of the molecular and developmental genetics of sex determination. Here, we review recent data to examine four questions about how the structure of gene networks influences the evolution of sex determination. We (...) argue that patterns of epistasis, arising from the structure of genetic networks, may play an important role in regulating the rates and patterns of sex chromosome replacement. (shrink)