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)

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)

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)

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)

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 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)

This aricle discusses the similarity between the propagation of pathogens (viruses and worms) on computer networks and the proliferation of pathogens in cellular organisms (organisms with genetic material contained within a membrane-encased nucleus). It introduces several biological mechanisms which are used in these organisms to protect against such pathogens and presents security models for networked computers inspired by several biological paradigms, including genomics (RNA interference), proteomics (pathway mapping), and physiology (immune system). In addition, the study of epidemiological models (...) for disease control can inspire methods for controlling the spread of pathogens across multiple nodes of a network. It also presents results based on the authors’ research in immune system modeling. (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 model predictive control method based on recursive backpropagation neural network and genetic algorithm is proposed for a class of nonlinear systems with time delays and uncertainties. In the offline modeling stage, a multistep-ahead predictor with GA-RBP neural network is designed, where GA-BP neural network is used as a one-step prediction model and GA is employed to train the initial weights and bias of the BP neural network. The incorporation of GA into RBP can reduce the possibility (...) of the BP neural network falling into a local optimum instead of reaching global optimization. In the online optimizing stage, a multistep-ahead GA-RBP neural network predictor and an improved gradient descent method are proposed to efficiently solve the online optimization problem of nonlinear MPC by minimizing a modified quadratic criterion. The designed MPC strategy can avoid information loss while linearizing the controlled system and computing the Hessian matrix and its inverse matrix. Experimental results show that the proposed approach can reduce the computational burden and improve the performance of MPC for the solution of nonlinear controlled systems. (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)

Abstract.Since the gene splicing debates of the 1980s, the public has been exposed to an ongoing sequence of genetic and reproductive technologies. Many issue areas have outcomes that lose track of people's inner values or engender opposing religious viewpoints defying final resolution. This essay relocates the discussion of what is an acceptable application from the individual to the societal level, examining technologies that stand to address large numbers of people and thus call for policy resolution, rather than individual fiat, (...) in their application. A major source of guidance is the “Genetic Frontiers” series of professional dialogues and conferences held by the National Conference for Community and Justice from 2002 to 2004. Genetic testing, human gene therapy, genetic engineering of plants and animals, and stem cell technology are examined. While differences in perspective on the beginning of life persist, a stepwise approach to the examination of genetic testing reveals areas of general agreement. Stewardship of life, human co‐creativity with the divine, and social justice help define the bounds of application of genetic engineering and therapy; compassionate care plays a major role in establishing stem cell policy. Active, sustained dialogue is a useful resource for enabling sharing of religious values and crystallization of policies. (shrink)

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)

Chronic myeloid leukemia (CML) is a cancer of the hematopoietic system initiated by a single genetic mutation which results in the oncogenic fusion protein Bcr-Abl. Untreated, patients pass through different phases of the disease beginning with the rather asymptomatic chronic phase and ultimately culminating into blast crisis, an acute leukemia resembling phase with a very high mortality. Although many processes underlying the chronic phase are well understood, the exact mechanisms of disease progression to blast crisis are not yet revealed. (...) In this paper we develop a mathematical model of CML based on causal Bayesian networks in order to study possible disease progression mechanisms. Our results indicate that an increase of Bcr-Abl levels alone is not sufficient to explain the phenotype of blast crisis and that secondary changes such as additional mutations are necessary to explain disease progression and the poor therapy response of patients in blast crisis. (shrink)

The estrangement between genetic scientists and theologians originating in the 1960s is reflected in novel combinations of human thought (subject) and genes (investigational object), paralleling each other through the universal process known in chaos theory as self-similarity. The clash and recombination of genes and knowledge captures what Philip Hefner refers to as irony, one of four voices he suggests transmit the knowledge and arguments of the religion-and-science debate. When viewed along a tangent connecting irony to leadership, journal dissemination, and (...) the activities of the "public intellectual" and the public at large, the sequence of voices is shown to resemble the passage of genetic information from DNA to mRNA, tRNA, and protein, and from cell nucleus to surrounding environment. In this light, Hefner's inquiry into the voices of Zygon is bound up with the very subject matter Zygon covers. (shrink)

This paper examines processes of translation through which molecular genetic technologies and practices are incorporated into environmental health research and regulation. Specifically, it considers how scientists, risk assessors, and regulators have used genetically modified mouse models to translate across scientific disciplines, articulate emergent molecular forms, standards, and practices with the extant? gold standard,? and establish roles for molecular knowledge in risk assessment and regulation. Noting variation both within and between regulatory agencies in responses to data from these models, the (...) article describes also the role of public-private networks and their effects on professional and institutional imperatives which shape regulators? responses to the potential applications of these models. The conclusions address the importance of translation for understanding the wider implications of the molecularization of environmental health science. (shrink)

In the multidisciplinary field of developmental cognitive neuroscience, statistical associations between levels of description play an increasingly important role. One example of such associations is the observation of correlations between relatively common gene variants and individual differences in behavior. It is perhaps surprising that such associations can be detected despite the remoteness of these levels of description, and the fact that behavior is the outcome of an extended developmental process involving interaction of the whole organism with a variable environment. Given (...) that they have been detected, how do such associations inform cognitive-level theories? To investigate this question, we employed a multiscale computational model of development, using a sample domain drawn from the field of language acquisition. The model comprised an artificial neural network model of past-tense acquisition trained using the backpropagation learning algorithm, extended to incorporate population modeling and genetic algorithms. It included five levels of description—four internal: genetic, network, neurocomputation, behavior; and one external: environment. Since the mechanistic assumptions of the model were known and its operation was relatively transparent, we could evaluate whether cross-level associations gave an accurate picture of causal processes. We established that associations could be detected between artificial genes and behavioral variation, even under polygenic assumptions of a many-to-one relationship between genes and neurocomputational parameters, and when an experience-dependent developmental process interceded between the action of genes and the emergence of behavior. We evaluated these associations with respect to their specificity, to their developmental stability, and to their replicability, as well as considering issues of missing heritability and gene–environment interactions. We argue that gene–behavior associations can inform cognitive theory with respect to effect size, specificity, and timing. The model demonstrates a means by which researchers can undertake multiscale modeling with respect to cognition and develop highly specific and complex hypotheses across multiple levels of description. (shrink)

Through their transcript products genes regulate the rates at which an immense variety of transcripts and subsequent proteins occur. Understanding the mechanisms that determine which genes are expressed, and when they are expressed, is one of the keys to genetic manipulation for many purposes, including the development of new treatments for disease. Viewing each gene in a genome as a distinct variable that is either on or off, or more realistically as a continuous variable, the values of some of (...) these variables influence the values of others through the regulatory proteins they express, including, of course, the possibility that the rate of expression of a gene at one time may, in various circumstances, influence the rate of expression of that same gene at a later time. If we imagine an arrow drawn from each gene expression variable at a given time to a gene variable whose expression it influences a short while after, the result is a network, technically a directed acyclic graph. For example, the DAG in Figure 1 is a representation of a system in which the expression level of gene G1 at time 1 ) causes the expression level of G2, which in turn causes the expression level of G3. The arrows in Figure 1 which do not have a variable at their tails are “error terms” which represent all of the causes of a variable other than the ones explicitly represented in the DAG. The DAG describes more than associations—it describes causal connections among gene expression rates. A shock to a cell—by mutation, heating, chemical treatment, etc. may alter the DAG describing the relations among gene expressions, for example by activating a gene that was otherwise not expressed, producing a cascade of new expression effects. Although “knockout” experiments can reveal some of the underlying causal network of gene expression levels, unless guided by information from other sources, such experiments are limited in how much of the network structure they can reveal, due to the sheer number of possible combinations of experimental manipulations of genes necessary to reveal the complete causal network. Recent developments have made it possible to compare quantitatively the expression of tens of thousands of genes in cells from different sources in a single experiment, and to trace gene expression over time in thousands of genes simultaneously. cDNA microarrays are already producing extensive data, much of it available on the web. Thus there are calls for analytic software that can be applied to microarray and other data to help infer regulatory networks. In this paper we will review current techniques that are available for searching for the causal relations between variables, describe algorithmic and data gathering obstacles to applying these techniques to gene expression levels, and describe the prospects for overcoming these obstacles. (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)

Coherence and correspondence are classical contenders as theories of truth. In this paper we examine them instead as interacting factors in the dynamics of belief across epistemic networks. We construct an agent-based model of network contact in which agents are characterized not in terms of single beliefs but in terms of internal belief suites. Individuals update elements of their belief suites on input from other agents in order both to maximize internal belief coherence and to incorporate ‘trickled in’ (...) elements of truth as correspondence. Results, though often intuitive, prove more complex than in simpler models (Hegselmann & Krause 2002, 2006; Grim, Singer, Reade & Fisher 2015). The optimistic finding is that pressures toward internal coherence can exploit and expand on small elements of truth as correspondence is introduced into epistemic networks. Less optimistic results show that pressures for coherence can also work directly against the incorporation of truth, particularly when coherence is established first and new data is introduced later. (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)

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)

Predicting the phenotype of an organism from its genotype is a central question in genetics. Most importantly, we would like to find out if the perturbation of a single gene may be the cause of a disease. However, our current ability to predict the phenotypic effects of perturbations of individual genes is limited. Network models of genes are one tool for tackling this problem. In a recent study, (Lee et al.) it has been shown that network models covering the majority (...) of genes of an organism can be used for accurately predicting phenotypic effects of gene perturbations in multicellular organisms. BioEssays 30:707–710, 2008. © 2008 Wiley Periodicals, Inc. (shrink)

At the interface of developmental biology and evolutionary biology, the very criteria of scientific knowledge are up for grabs. A central issue is the status of evolutionary genetics models, which some argue cannot coherently be used with complex gene regulatory network (GRN) models to explain the same evolutionary phenomena. Despite those claims, many researchers use evolutionary genetics models jointly with GRN models to study evolutionary phenomena. This dissertation compares two recent research projects in which researchers jointly use the two kinds (...) of models. I use methods from the empirical social sciences, such as digital corpus analysis, content analysis, and structured case analysis. I infer three primary conclusions about projects of the kind studied. First, they employ an implicit concept of gene that enables the joint use of both kinds of models. Second, they pursue more epistemic aims besides mechanistic explanation of phenomena. Third, they don’t work to create and export broad synthesized theories. Rather, they focus on phenomena too complex to be understood by a common general theory, they distinguish parts of the phenomena, and they apply models from different theories to the different parts. For such projects, seemingly incompatible models are synthesized largely through mediated representations of complex phenomena. (shrink)

Gene regulatory networks are intensively studied in biology. One of the main aims of these studies is to gain an understanding of how the structure of genetic networks relates to specific functions such as chemotaxis and the circadian clock. Scientists have examined this question by using model organisms such as Drosophila and mathematical models. In the last years, synthetic models—engineered genetic networks—have become more and more important in the exploration of gene regulation. What is (...) the potential of this new approach in the investigation of gene network structures? How do synthetic models relate to model organisms and mathematical models? (shrink)

As one of the key technologies for accelerating the construction of the ubiquitous Internet of Things, demand response not only guides users to participate in power market operations but also increases the randomness of grid operations and the difficulty of load forecasting. In order to solve the problem of rough feature engineering processing and low prediction accuracy, a short-term load forecasting model of LSTM neural network considering demand response is proposed. First of all, in view of the strong randomness (...) and complexity of input features, the weighted method is used to process multiple input features to strengthen the contribution of effective features and tap the potential value of features. Secondly, an improved genetic algorithm is used to obtain the best LSTM parameters; finally, the special gate structure of the LSTM model is used to selectively control the influence of input variables on the model parameters and perform load forecasting. The experimental results show that the research has high prediction accuracy and application value and provides a new way for the development of power load forecasting. (shrink)

M. xanthus has a complex multicellular lifestyle including swarming, predation and development. These behaviors depend on the ability of the cells to achieve directed motility across solid surfaces. M. xanthus cells have evolved two motility systems including Type‐IV pili that act as grappling hooks and a controversial engine involving mucus secretion and fixed focal adhesion sites. The necessity for cells to coordinate the motility systems and to respond rapidly to environmental cues is reflected by a complex genetic network involving (...) at least three complete sets of chemosensory systems and eukaryotic‐like signaling proteins. In this review, we discuss recent advances suggesting that motor synchronization results from spatial oscillations of motility proteins. We further propose that these dynamics are modulated by the action of multiple upstream complementary signaling systems. M. xanthus is thus an exciting emerging model system to study the intricate processes of directed cell migration. BioEssays 30:733–743, 2008. © 2008 Wiley Periodicals, Inc. (shrink)

Complex diseases involve both a genetic component and a response to environmental factors or lifestyle changes. Recently, genome-wide association studies (GWAS) have succeeded in identifying hundreds of polymorphisms that are statistically associated with complex diseases. However, the association is usually weak and none of the associated allelic forms is either necessary or sufficient for the disease occurrence. We argue that this promotes a network view, centred on functional redundancy. We adapted reliability theory to the concerned sub-network, modelled as a (...) parallel array of functional modules. In our model, as long as one module remains active, the function correlated with the respective disease is ensured and disease does not occur. Genetic factors reduce the initial number of available modules while environment, contingent surroundings, personal history, epigenetics, and some intrinsic stochasticity influence their persistence time. This model reproduces age-specific incidence curves and explains the influence of environmental changes. It offers a new paradigm, according to which disease occurs due to a lack of functional elements, depending on many idiosyncratic factors. Genetic risk assessed from GWAS is only a statistical notion with no direct interpretation at the individual level. However, genomic profiling could be useful at population level in devising models to guide decisions in health care policy. (shrink)

Many natural and biological phenomena can be depicted as networks. Theoretical and empirical analyses of networks have become prevalent. I discuss theoretical biases involved in the delineation of biological networks. The network perspective is shown to dissolve the distinction between regulatory architecture and regulatory state, consistent with the theoretical impossibility of distinguishing a priori between “program” and “data”. The evolutionary significance of the dynamics of trans-generational and inter-organism regulatory networks is explored and implications are presented for (...) understanding the evolution of the biological categories development-heredity; plasticity-evolvability; and epigenetic-genetic. (shrink)

BackgroundClinical ethical practice (CEP) is required for healthcare workers (HCWs) to improve health-care delivery. However, there are gaps between accepted ethical standards and CEP in Ethiopia. There have been limited studies conducted on CEP in the country. Therefore, this study aimed to determine the magnitude and associated factors of CEP among healthcare workers in healthcare facilities in Ethiopia.MethodFrom February to April 2021, a mixed-method study was conducted in 24 health facilities, combining quantitative and qualitative methods. Quantitative (survey questionnaire) and qualitative (...) (semi-structured interviews) data were collected. For quantitative and qualitative data analysis, Stata version 14 and Atlas.ti version 7 were utilized. Multiple logistic regression and thematic analysis for quantative and qualitative respectively used.ResultsFrom a total of 432 study participants, 407 HCWs were involved in the quantitative analysis, 36 participants were involved in five focus group discussions (FGDs), and eleven key informant interviews (KIIs) were involved in the qualitative analysis. The score of good CEP was 32.68%. Similarly, the scores of good knowledge and attitude were 33.50% and 25.31%, respectively. In the multiple logistic regression models, satisfaction with the current profession, availability of functional CECs, compassionate leaders, previously thought clinical ethics in pre-service education and good attitude were significant factors associated with CEP. Among these significant factors, knowledge, compassionate leaders, poor infrastructure, a conducive environment and positive attitudes were also determinants of CEP according to qualitative findings.ConclusionsThe CEP in health care services in Ethiopia is low. Satisfaction with the current profession, functional CECs, positive attitude, compassionate leaders and previously thought clinical ethics were significant factors associated with CEP. The Ministry of Health (MoH) should integrate interventions by considering CECs, compassionate leadership, and positive attitudes and enhance the knowledge of health professionals. Additionally, digitalization, intersectoral collaboration and institutionalization are important for promoting CEP. (shrink)

This study aims to develop a hybrid model to represent the human mind from a functionalist point of view that can be adapted to artificial intelligence. The model is not a realistic theory of the neural network of the brain but an instrumentalist AI model, which means that there can be some other representative models too. It had been thought that the provability of an axiomatic system requires the completeness of a formal system. However, Gödel proved that (...) no consistent formal system can prove its own consistency. There is a paradoxical limit to provability. Both consistency and completeness are impossible together. These formal limits form the basis of our model: the hybrid model of the mind consists of a formal axiomatic system and an evolutionary algorithm (EA) of intelligence, which also includes the genetic algorithm (GA) of consciousness. The GA of consciousness operates based on intentions, functioning in conjunction with the principles of EA. This collaboration allows it to transcend the paradoxical formal limits of intelligence. However, GA overcomes the problem of paradoxicality at the cost of producing illusions. Following this collaboration gives us a GA operating system. After all, if the rational optimization task of the GA in question produces illusions, then the rationality must lie elsewhere in the paradox. (shrink)

Behaving presents an overview of the recent history and methodology of behavioral genetics and psychiatric genetics, informed by a philosophical perspective. Kenneth F. Schaffner addresses a wide range of issues, including genetic reductionism and determinism, "free will," and quantitative and molecular genetics. The latter covers newer genome-wide association studies that have produced a paradigm shift in the subject, and generated the problem of "missing heritability." Schaffner also presents cases involving pro and con arguments for genetic testing for IQ (...) and for Attention Deficit Hyperactivity Disorder. Schaffner examines the nature-nurture controversy and Developmental Systems Theory using C. elegans or "worm" studies as a test case, concluding that genes are special and provide powerful tools, including "deep homology," for investigating behavior. He offers a novel account of biological knowledge emphasizing the importance of models, mechanisms, pathways, and networks, which clarifies how partial reductions provide explanations of traits and disorders. The book also includes examinations of personality genetics and of schizophrenia and its etiology, alongside interviews with prominent researchers in the area, and discusses debates about psychosis that led to changes in the DSM-5 in 2013.Schaffner concludes by discussing additional philosophical implications of the genetic analyses in the book, some major worries about "free will," and arguments pro and con about why genes and DNA are so special. Though genes are special, newer perspectives presented in this book will be needed for progress in behavioral genetics- perspectives that situate genes in complex multilevel prototypic pathways and networks. With a mix of optimism and pessimism about the state of the field and the subject, Schaffner's book will be of interest to scholars in the history and philosophy of science, medicine, and psychiatry. (shrink)

I criticize Herbert Simon 's argument for the claim that complex natural systems must constitute decomposable, mereological or functional hierarchies. The argument depends on certain assumptions about the requirements for the successful evolution of complex systems, most importantly, the existence of stable, intermediate stages in evolution. Simon offers an abstract model of any process that succeeds in meeting these requirements. This model necessarily involves construction through a decomposable hierarchy, and thus suggests that any complex, natural, i.e., evolved, system (...) is constituted by a decomposable hierarchy. I argue that Stuart Kauffman's recent models of genetic regulatory networks succeed in specifying processes that could meet Simon 's requirements for evolvability without requiring construction through a decomposable hierarchy. Since Kauffman's models are at least as plausible as Simon 's model, Simon 's argument that complex natural systems must constitute decomposable, mereological or functional hierarchies does not succeed. (shrink)

Biochemical networks are often called upon to illustrate emergent properties of living systems. In this contribution, I question such emergentist claims by means of theoretical work on genetic regulatory models and random Boolean networks. If the existence of a critical connectivity Kc of such networks has often been coined “emergent” or “irreducible”, I propose on the contrary that the existence of a critical connectivity Kc is indeed mathematically explainable in network theory. This conclusion also applies to (...) many other types of formal networks and weakens the emergentist claim attached to bio-molecular networks, and by extension to living systems. (shrink)

Genes are gained and lost over the course of evolution. A recent study found that over 1,800 new genes have appeared during primate evolution and that an unexpectedly high proportion of these genes are expressed in the human brain. But what are the molecular functions of newly evolved genes and what is their impact on an organism's fitness? The acquisition of new genes may provide a rich source of genetic diversity that fuels evolutionary innovation. Although gene manipulation experiments are (...) not feasible in humans, studies in model organisms, such as Drosophila melanogaster, have shown that new genes can quickly become integrated into genetic networks and become essential for survival or fertility. Future studies of new genes, especially chimeric genes, and their functions will help determine the role of genetic novelty in the adaptation and diversification of species. (shrink)

As a brand-new marketing method, network marketing has gradually become one of the main ways and means for enterprises to improve profitability and competitiveness with its unique advantages. Using these marketing data to build a model can dig out useful information that the business is concerned about, and the company can then formulate marketing strategies based on this information. Sales forecasting is to speculate on the future based on historical sales. It is a tool for companies to determine production (...) volume and ensure the balance of product supply and sales. It can help companies make correct business decisions to maximize profits. The neural network can approximate the nonlinear function with arbitrary precision, and the time series prediction model based on the neural network can well reflect the nonlinear development trend of information. Based on the analysis of the shortcomings of the traditional BP network, this paper uses a genetic algorithm with good global search capabilities to improve the neural network. The thought and theory of optimizing the initial weight and threshold of the neural network of the GA algorithm are discussed in detail. While expounding the forecasting method, it uses specific examples to analyze the performance and characteristics of the GA-BP network in the enterprise network marketing forecasting. The results show that the GA-BP neural network is higher than the traditional BP neural network in terms of prediction accuracy and adaptability. (shrink)

C.H. Waddington is today remembered chiefly as a Drosophila developmental geneticist who developed the concepts of “canalization” and “the epigenetic landscape.” In his lifetime, however, he was widely perceived primarily as a critic of Neo-Darwinian evolutionary theory. His criticisms of Neo-Darwinian evolutionary theory were focused on what he saw as unrealistic, “atomistic” models of both gene selection and trait evolution. In particular, he felt that the Neo-Darwinians badly neglected the phenomenon of extensive gene interactions and that the “randomness” of mutational (...) effects, posited in the theory, was a false postulate. This last criticism dealt with the phenomenon known today as “developmental constraints.” Although population genetics itself has evolved considerably from its form at mid-20th century, much of Waddington’s critique, it is argued here, retains cogency. Yet, he did not attempt to develop a full-fledged alternative theory himself. Perhaps most surprisingly, in retrospect, is that he did not try to marry his work on gene interactions in development with his evolutionary interests, to create a theory of “genetic networks” and their evolution. Whether evolutionary genetics today will incorporate “network thinking” as a central element or whether there will be a general retreat to the more atomistic approach offered by genomics, remains an open question. (shrink)

Over the last decades, network-based approaches have become highly popular in diverse fields of biology, including neuroscience, ecology, molecular biology and genetics. While these approaches continue to grow very rapidly, some of their conceptual and methodological aspects still require a programmatic foundation. This challenge particularly concerns the question of whether a generalized account of explanatory, organisational and descriptive levels of networks can be applied universally across biological sciences. To this end, this highly interdisciplinary theme issue focuses on the definition, (...) motivation and application of key concepts in biological network science, such as explanatory power of distinctively network explanations, network levels, and network hierarchies. (shrink)

This paper develops computational models to monitor patients with hip replacement surgery. The Kinect camera is used to capture the movements of patients who are performing rehabilitation exercises with both lower limbs, specifically, ‘side step’ and ‘knee lift’ with each leg. The information is measured at 25 body points with their respective coordinates. Features selection algorithms are applied to the 75 attributes of the initial and final position vector of each rehab exercise. Different classification techniques have been tested and Bayesian (...) networks, supervised classifier system and genetic algorithm with neural network have been selected and jointly applied to identify the correct and incorrect movements during the execution of the rehabilitation exercises. Besides, prediction models of the evolution of a patient are developed based on the average values of some motion related variables. These models can help to fasten the recovery of these patients. (shrink)

Over the last two decades, network-focused approaches have become highly popular in diverse fields of biology, including neuroscience, ecology, molecular biology and genetics. While the network approach continues to grow very rapidly, some of its conceptual and methodological aspects still require a programmatic foundation. This challenge particularly concerns the question of whether a generalized account of explanatory, organisational and descriptive levels of networks can be applied universally across biological sciences. Consequently, the central focus of this theme issue will be (...) on the definition, motivation and application of key concepts in biological network science, such as levels, hierarchies, and explanatory directionality. A unification will be achieved by formulating norms for delimiting the distinctively network-topological class of explanations that connect general as well as very specific biological research questions. The impact of this theme issue is broad and encompassing, as it is highly interdisciplinary and opens a uniquely normative perspective on the foundational aspects of network-based explanations and modelling. This theme issue is intended to become a landmark publication for practical research as well as funding policy decisions, by unifying network approaches in biological sciences in terms of fundamental concepts and informing the public understanding of the network science. (shrink)

Many apparently complex mechanisms in biology, especially in embryology and molecular biology, can be explained easily by reasoning at the level of the “efficient cause” of the observed phenomenology: the mechanism can then be explained by a simple geometrical argument or a variational principle, leading to the solution of an optimization problem, for example, via the co-existence of a minimization and a maximization problem . Passing from a microscopic level to the macroscopic level often involves an averaging effect that gives (...) birth to a global functional feature . We will illustrate these general principles by building in four different domains of application “a minima” models and showing the main properties of their solutions: extraction of a minimal RNA structure functioning as the first “peptidic machine,” a kind of ancestral ribosome; study of a genetic regulatory network of Drosophila centred on Engrailed gene and expressing successively two genes inside a limit cycle; study of a genetic network regulating neural activity and proliferation in mammals; and study of a simple geometric model of epiboly in zebrafish. (shrink)

This volume maps the areas of ethical concern in the debate regarding the governance of genetic information, and suggests alternative ethical frameworks and models of regulation in order to inform its restructuring. Genetic governance is at the heart of medical and scientific developments, and is connected to global exploitation, issues of commodification, commercialisation and ownership, the concepts of property and intellectual property and concerns about individual and communal identity. Thus the decisions that are made in the next few (...) years about appropriate models of genetic governance will have knock-on effects for other areas of governance. In short the final answer to 'Who Decides?' in the context of genetic governance will fundamentally shape the ethical constructs of individuals and their networks and relationships in the public sphere. (shrink)

Of all the different types of public buildings, hospitals are the biggest energy consumers. Cooling systems for air conditioning and healthcare uses are particularly energy intensive. Forecasting hospital thermal-cooling demand is a remarkable and innovative method capable of improving the overall energy efficiency of an entire cooling system. Predictive models allow users to forecast the activity of water-cooled generators and adapt power generation to the real demand expected for the day ahead, while avoiding inefficient subcooling. In addition, the maintenance costs (...) related to unnecessary starts and stops and power-generator breakdowns occurring over the long term can be reduced. This study is based on the operations of a real hospital facility and details the steps taken to develop an optimal and efficient model based on a genetic methodology that searches for low-complexity models through feature selection, parameter tuning and parsimonious model selection. The methodology, called GAparsimony, has been tested with neural networks, support vector machines and gradient boosting techniques. Finally, a weighted combination of the three best models was created. The new operational method employed herein can be replicated in similar buildings with similar water-cooled generators. (shrink)

In this paper we present a new framework of idealization in biology. We characterize idealizations as a network of counterfactual and hypothetical conditionals that can exhibit different "degrees of contingency". We use this idea to say that, in departing more or less from the actual world, idealizations can serve numerous epistemic, methodological or heuristic purposes within scientific research. We defend that, in part, this structure explains why idealizations, despite being deformations of reality, are so successful in scientific practice. For illustrative (...) purposes, we provide an example from population genetics, the Wright-Fisher Model. (shrink)

Although the interdisciplinary nature of contemporary biological sciences has been addressed by philosophers, historians, and sociologists of science, the different ways in which engineering concepts and methods have been applied in biology have been somewhat neglected. We examine - using the mechanistic philosophy of science as an analytic springboard - the transfer of network methods from engineering to biology through the cases of two biology laboratories operating at the California Institute of Technology. The two laboratories study gene regulatory networks, (...) but in remarkably different ways. The research strategy of the Davidson lab fits squarely into the traditional mechanist philosophy in its aim to decompose and reconstruct, in detail, gene regulatory networks of a chosen model organism. In contrast, the Elowitz lab constructs minimal models that do not attempt to represent any particular naturally evolved genetic circuits. Instead, it studies the principles of gene regulation through a template-based approach that is applicable to any kinds of networks, whether biological or not. We call for the mechanists to consider whether the latter approach can be accommodated by the mechanistic approach, and what kinds of modifications it would imply for the mechanistic paradigm of explanation, if it were to address modelling more generally. (shrink)

Many animals display a capacity to regenerate tissues or even a complete body. One of the main goals of regenerative biology is to identify the genes and genetic networks necessary for this process. Drosophila offers an ideal model system for such studies. The wide range of genetic and genomic approaches available for use in flies has helped in initiating the deciphering of the mechanisms underlying regeneration, and the results may be applicable to other organisms, including mammals. (...) Moreover, most models of regeneration require experimental manipulation, whereas in Drosophila discrete domains can be ablated by genetically induced methods. Here, we present a summary of current research into imaginal disc regeneration and discuss the power of this tissue as a tool for understanding the genetics of regeneration. (shrink)

Beyond belief change and meme adoption, both genetics and infection have been spoken of in terms of information transfer. What we examine here, concentrating on the specific case of transfer between sub-networks, are the differences in network dynamics in these cases: the different network dynamics of germs, genes, and memes. Germs and memes, it turns out, exhibit a very different dynamics across networks. For infection, measured in terms of time to total infection, it is network type rather than (...) degree of linkage between sub-networks that is of primary importance. For belief transfer, measured in terms of time to consensus, it is degree of linkage rather than network type that is crucial. Genes model each of these other dynamics in part, but match neither in full. For genetics, like belief transfer and unlike infection, network type makes little difference. Like infection and unlike belief, on the other hand, the dynamics of genetic information transfer within single and between linked networks are much the same. In ways both surprising and intriguing, transfer of genetic information seems to be robust across network differences crucial for the other two. (shrink)

Various algorithms have been proposed for learning (partial) genetic regulatory networks through systematic measurements of differential expression in wild type versus strains in which expression of specific genes has been suppressed or enhanced, as well as for determining the most informative next experiment in a sequence. While the behavior of these algorithms has been investigated for toy examples, the full computational complexity of the problem has not received sufficient attention. We show that finding the true regulatory network requires (...) (in the worst-case) exponentially many experiments (in the number of genes). Perhaps more importantly, we provide an algorithm for determining the set of regulatory networks consistent with the observed data. We then show that this algorithm is infeasible for realistic data (specifically, nine genes and ten experiments). This infeasibility is not due to an algorithmic flaw, but rather to the fact that there are far too many networks consistent with the data (10 18 in the provided example). We conclude that gene perturbation experiments are useful in confirming regulatory network models discovered by other techniques, but not a feasible search strategy. (shrink)