Using optical add–drop multiplexer/remover multiplexer (OADM), it is possible to add or remove wavelengths and change or route them through the various nodes and networks. At this moment, key problems in add–drop multiplexer (ADM) are the bandwidth, modulation format, and reuse wavelength. In this article, the Optisystem software simulation is used as a platform to design, test, and verify the method applied to the current work; the OADM is proposed based on the metro network to get distribution between nodes (...) over a transmission link; OADM analysis was presented with four channels (193.1, 193.2, 193.3, and 193.4 THz) at total bandwidth of 1.6 Tb/s, none-return-to-zero (NRZ), and return to zero coding types. Experiment one shows that the average output power is −17.997 dBm, the average drop power is −17.997 dBm, and the average add power is −18.338 dBm, the average gain is −0.0429 dB, the average noise figure is 0 dB, the average power input signal is 10.679 dBm, the average of power output signal is 10.633 dBm, and the average output optical signal-to-noise ratio (OSNR) is 0 dB, However, the second experiment shows that the average output power is −24.238 dBm, the average drop power is −24.288 dBm, and the average add power is −24.753 dBm, the average gain is −0.0417 dB, the average noise figure is 0 dB, average power input signal is 7.691 dBm, average of power output signal is 7.677 dBm, and the average output OSNR 0 dB. The system supports four input channels, four add channels, four output channels, and four drop channels. The results are acceptable after three spans of Solitons fiber with 600 km length, 200 km for each span. Nonetheless, it is believed that it is well justified to adopt these schemes in the current optical network with a low cost for overall expenditure. (shrink)

In recent years, more and more people are applying Convolutional Neural Networks to the study of sound signals. The main reason is the translational invariance of convolution in time and space. Thereby the diversity of the sound signal can be overcome. However, in terms of sound direction recognition, there are also problems such as a microphone matrix being too large, and feature selection. This paper proposes a sound direction recognition using a simulated human head with microphones at both ears. Theoretically, (...) the two microphones cannot distinguish the front and rear directions. However, we use the original data of the two channels as the input of the convolutional neural network, and the resolution effect can reach more than 0.9. For comparison, we also chose the delay feature (GCC) for sound direction recognition. Finally, we also conducted experiments that used probability distributions to identify more directions. (shrink)

Studying drug relationships can provide deeper information for the construction and maintenance of biomedical databases and provide more important references for disease treatment and drug development. The research model has expanded from the previous focus on a certain drug to the systematic analysis of the pharmaceutical network formed between drugs. Network model is suitable for the study of the nonlinear relationship of the pharmaceutical relationship by modeling the data learning. Association rule mining is used to find the potential (...) correlations between the various sets of massive data. Therefore, based on the network model, this research proposed an algorithm for drug interaction under improved association rules, which achieved accurate analysis and decision-making of drug relationship. Meanwhile, this research applied the established association rule algorithm to discuss the relationship between Chinese medicine and mental illness medicine and conducted the algorithm research and simulation analysis of the association relationship. The results showed the association rule algorithm based on the network model constructed was better than other association algorithms. It had reliability and superiority in decision-making in improving the drug-drug relationship. It also promoted the rational use of medicines and played a guiding role in pharmaceutical research. This provides scientific research personnel with research basis and research ideas for disease-related diagnosis. (shrink)

A technique for the bilateral activation of neural nets that leads to a functional asymmetry of two simulated ''cerebral hemispheres'' is described. The simulation is designed to perform object recognition, while exhibiting characteristics typical of human consciousness-specifically, the unitary nature of conscious attention, together with a dual awareness corresponding to the ''nucleus'' and ''fringe'' described by William James (1890). Sensory neural nets self-organize on the basis of five sensory features. The system is then taught arbitrary symbolic labels for a small (...) number of similar stimuli. Finally, the trained network is exposed to nonverbal stimuli for object recognition, leading to Gaussian activation of the ''sensory'' maps-with a peak at the location most closely related to the features of the external stimulus. ''Verbal'' maps are activated most strongly at the labeled location that lies closest to the peak on homologous sensory maps. On the verbal maps activation is characterized by both excitatory and inhibitory Gaussians (a Mexican hat), the parameters of which are determined by the relative locations of the verbal labels. Mutual homotopic inhibition across the ''corpus callosum'' then produces functional cerebral asymmetries, i.e., complementary activation of homologous ''association'' and ''frontal'' maps within a common focus of attention-a nucleus in the left hemisphere and a fringe in the right hemisphere. An object is recognized as corresponding to a known label when the total activation of both hemispheres (nucleus plus fringe) is strongest for that label. The functional dualities of the cerebral hemispheres are discussed in light of the nucleus/fringe asymmetry. (shrink)

Random simulation of complex dynamical systems is generally used in order to obtain information about their asymptotic behaviour (i.e., when time or size of the system tends towards infinity). A fortunate and welcome circumstance in most of the systems studied by physicists, biologists, and economists is the existence of an invariant measure in the state space allowing determination of the frequency with which observation of asymptotic states is possible. Regions found between contour lines of the surface density of this invariant (...) measure are called confiners. An example of such confiners is given for a formal neural network capable of learning. Finally, an application of this methodology is proposed in studying dependency of the network's invariant measure with regard to: 1) the mode of neurone updating (parallel or sequential), and 2) boundary conditions of the network (searching for phase transitions). (shrink)

Large scale experiments at CERN’s Large Hadron Collider rely heavily on computer simulations, a fact that has recently caught philosophers’ attention. CSs obviously require appropriate modeling, and it is a common assumption among philosophers that the relevant models can be ordered into hierarchical structures. Focusing on LHC’s ATLAS experiment, we will establish three central results here: with some distinct modifications, individual components of ATLAS’ overall simulation infrastructure can be ordered into hierarchical structures. Hence, to a good degree of approximation, hierarchical (...) accounts remain valid at least as descriptive accounts of initial modeling steps. In order to perform the epistemic function Winsberg Model-based reasoning in scientific discovery. Kluwer Academic/plenum Publishers, New York, pp 255–269, 1999) assigns to models in simulation—generate knowledge through a sequence of skillful but non-deductive transformations—ATLAS’ simulation models have to be considered part of a network rather than a hierarchy, in turn making the associated simulation modeling messy rather than motley. Deriving knowledge-claims from this ‘mess’ requires two sources of justification: holistic validation :253–262, 2010; in Carrier M, Nordmann A Science in the context of application. Springer, Berlin, pp 115–130, 2011), and model coherence. As it turns out, the degree of model coherence sets HEP apart from other messy, simulation-intensive disciplines such as climate science, and the reasons for this are to be sought in the historical, empirical and theoretical foundations of the respective discipline. (shrink)

Richard Scheines and Joe Ramsey. Simulating Genetic Regulartory Networks.

This paper proposes a new financial market model based on the analysis of the minority game model. The agent in this model forms a network through information sharing, and the agent uses the minority game model to realize the evolution of the system. To better describe the financial market, we also adopt a prior connection strategy for the model. The network formed by the agent has the characteristics of a scale-free network, and as the initial network (...) connection probability increases, the growth rate of the corresponding agent’s average connection degree increases and then decreases after reaching the peak. (shrink)

This article addresses the ability of Parallel Distributed Processing (PDP) networks to generate stagewise cognitive development in accordance with Piaget's theory of cognitive epigenesis. We carried out a replication study of the simulation experiments by McClelland (1989) and McClelland and Jenkins (1991) in which a PDP network learns to solve balance scale problems. In objective tests motivated from catastrophe theory, a mathematical theory of transitions in epigenetical systems, no evidence for stage transitions in network performance was found. It (...) is concluded that PDP networks lack the ability to recover the positive outcomes of analogous catastrophe analyses of real cognitive developmental data. In an attempt to further characterize the learning behaviour of PDP networks, we carried out a second simulation study using the discrimination‐shift paradigm. The results thus obtained indicate that PDP learning is compatible with the learning of stimulus‐response relationships, not with the acquisition of mediating rules such as conceived in (neo‐)Piagetian theory. In closing, we speculate about the feasibility of simulating stagewise development with alternative network architectures. (shrink)

This article addresses the ability of Parallel Distributed Processing (PDP) networks to generate stagewise cognitive development in accordance with Piaget's theory of cognitive epigenesis. We carried out a replication study of the simulation experiments by McClelland (1989) and McClelland and Jenkins (1991) in which a PDP network learns to solve balance scale problems. In objective tests motivated from catastrophe theory, a mathematical theory of transitions in epigenetical systems, no evidence for stage transitions in network performance was found. It (...) is concluded that PDP networks lack the ability to recover the positive outcomes of analogous catastrophe analyses of real cognitive developmental data. In an attempt to further characterize the learning behaviour of PDP networks, we carried out a second simulation study using the discrimination‐shift paradigm. The results thus obtained indicate that PDP learning is compatible with the learning of stimulus‐response relationships, not with the acquisition of mediating rules such as conceived in (neo‐)Piagetian theory. In closing, we speculate about the feasibility of simulating stagewise development with alternative network architectures. (shrink)

Discussion about project management, in both the academic literature and industry, is predominantly based on theories of control, many of which have been developed since the 1950s. However, issues arise when these ideas are applied unilaterally to all types of projects and in all contexts. In complex environments, management problems arise from assuming that results, predicted at the start of a project, can be sufficiently described and delivered as planned. Thus, once a project reaches a critical size, a calendar, and (...) a certain level of ambiguity and interconnection, the analysis centered on control does not function adequately. Projects that involve complex situations can be described as adaptive complex systems, consistent in multiple interdependent dynamic components, multiple feedback processes, nonlinear relations, and management of hard data and soft data. In this study, through a complex network, the dynamic structure of a project and its trajectories are simulated using inference processes. Finally, some numerical simulations are described, leading to a decision making tool that identifies critical processes, thereby obtaining better performance outcomes of projects. (shrink)

In this paper, the multifeature fusion music classification algorithm and its simulation results are studied by deep confidence networks, the multifeature fusion music database is established and preprocessed, and then features are extracted. The simulation is carried out using multifeature fusion music data. The multifeature fusion music preprocessing includes endpoint detection, framing, windowing, and pre-emphasis. In this paper, we extracted the rhythm features, sound quality features, and spectral features, including energy, cross-zero rate, fundamental frequency, harmonic noise ratio, and 12 statistical (...) features, including maximum value, mean value, and linear slope. A total of 384-dimensional statistical features was extracted and compared with the classification ability of different emotional features. The deficiencies of the traditional classification algorithm are first studied, and then by introducing confusion, constructing multilevel classifiers, and tuning each level of the classifier, better recognition rates than traditional primary classification are obtained. This paper introduces label information for supervised training to further improve the features of multifunctional fusion music. Experiments show that this information has excellent performance in multifunctional fusion music recognition. The experiments compare the multilevel classifier with primary classification, and the multilevel classification with the primary classification and the classification performance is improved, and the recognition rate of the multilevel classification algorithm is also improved over the multilevel classification algorithm, proving that the excellent performance with multiple levels of classification. (shrink)

This article addresses the ability of Parallel Distributed Processing (PDP) networks to generate stagewise cognitive development in accordance with Piaget's theory of cognitive epigenesis. We carried out a replication study of the simulation experiments by McClelland (1989) and McClelland and Jenkins (1991) in which a PDP network learns to solve balance scale problems. In objective tests motivated from catastrophe theory, a mathematical theory of transitions in epigenetical systems, no evidence for stage transitions in network performance was found. It (...) is concluded that PDP networks lack the ability to recover the positive outcomes of analogous catastrophe analyses of real cognitive developmental data. In an attempt to further characterize the learning behaviour of PDP networks, we carried out a second simulation study using the discrimination‐shift paradigm. The results thus obtained indicate that PDP learning is compatible with the learning of stimulus‐response relationships, not with the acquisition of mediating rules such as conceived in (neo‐)Piagetian theory. In closing, we speculate about the feasibility of simulating stagewise development with alternative network architectures. (shrink)

Traffic congestion is a common problem in many countries, especially in big cities. At present, China’s urban road traffic accidents occur frequently, the occurrence frequency is high, the accident causes traffic congestion, and accidents cause traffic congestion and vice versa. The occurrence of traffic accidents usually leads to the reduction of road traffic capacity and the formation of traffic bottlenecks, causing the traffic congestion. In this paper, the formation and propagation of traffic congestion are simulated by using the improved medium (...) traffic model, and the control strategy of congestion dissipation is studied. From the point of view of quantitative traffic congestion, the paper provides the fact that the simulation platform of urban traffic integration is constructed, and a feasible data analysis, learning, and parameter calibration method based on RBF neural network is proposed, which is used to determine the corresponding decision support system. The simulation results prove that the control strategy proposed in this paper is effective and feasible. According to the temporal and spatial evolution of the paper, we can see that the network has been improved on the whole. (shrink)

Data center technology changes the mode of computing. Traditional DCs consist of a single layer and only have Ethernet connections among switches. Those old-fashioned DCs cannot fulfill the high resource demand compared with today’s DCs. The architectural design of the DCs is getting substantial importance and acting as the backbone of the network because of its essential feature of supporting and maintaining the rapidly increasing Internet-based applications which include search engines and social networking applications. Every application has its parameters, (...) like latency and blocking in the DC network. Every data center network has its specialized architecture. It has a specific arrangement of layers and switches, which increase or decrease the DC network’s efficiency. We develop a simulation tool that comprises two different DC architectures: basic tree architecture and c-Through architecture. Using this simulation, we analyze the traffic behavior and the performance of the simulated DCN. Our main purpose is to focus on mean waiting time, load, and blocking with respect to the traffic within the DCN. (shrink)

Recent computer simulations of evolving neural networks have shown that population-level behavioral asymmetries can arise without social interactions. Although these models are quite limited at present, they support the hypothesis that social pressures can be sufficient but are not necessary for population lateralization to occur, and they provide a framework for further theoretical investigation of this issue.

HSimulator is a multithread simulator for mass-action biochemical reaction systems placed in a well-mixed environment. HSimulator provides optimized implementation of a set of widespread state-of-the-art stochastic, deterministic, and hybrid simulation strategies including the first publicly available implementation of the Hybrid Rejection-based Stochastic Simulation Algorithm. HRSSA, the fastest hybrid algorithm to date, allows for an efficient simulation of the models while ensuring the exact simulation of a subset of the reaction network modeling slow reactions. Benchmarks show that HSimulator is (...) often considerably faster than the other considered simulators. The software, running on Java v6.0 or higher, offers a simulation GUI for modeling and visually exploring biological processes and a Javadoc-documented Java library to support the development of custom applications. HSimulator is released under the COSBI Shared Source license agreement. (shrink)

Human resource management risks are due to the failure of employer organization to use relevant human resources reasonably and can result in tangible or intangible waste of human resources and even risks; therefore, constructing a practical early warning model of human resource management risk is extremely important for early risk prediction. The back propagation neural network is an information analysis and processing system formed by using the error back propagation algorithm to simulate the neural function and structure of the (...) human brain, which can handle complex and changeable things that do not have an obvious linear relationship between output results and input factors, so as to find the objective connection between the two. Based on the summary and analysis of previous research works, this article expounded the research status and significance of early warning for human resource management risks, elaborated the development background, current status, and future challenges of the BP neural network, introduced the method and principle of the BP neural network’s connection weight calculation and learning training, performed the risk inducement analysis, index system establishment, and network node selection of human resource management, constructed an early warning model of human resource management risk based on the BP neural network, conducted the risk warning model training and detection based on the BP neural network, and finally carried out a simulation and its result analysis. The study results show that the early warning model of human resource management risk based on the BP network is effective, and this trained and tested BP network risk warning model can be used to conduct early warning empirical research on human resource risks to prevent human resource risks, ensure enterprise’s benign operation, and at the same time play a role in supervision and promotion of market order rectification. (shrink)

Finding suitable analysis techniques for networks generated from social processes is a difficult task when the population changes over time. Traditional social network analysis measures may not work in such circumstances. It is argued that agent-based social networks should not be constrained by a priori assumptions about the evolved network and/or the analysis techniques. In most agent-based social simulation models, the number of agents remains fixed throughout the simulation; this paper considers the case when this does not hold. (...) Thus the aim of this paper is to demonstrate how the network signatures change when the agents’ population depends upon endogenous social processes. We argue for a much wider attention from the social simulation community in addressing this open research problem. (shrink)

Despite the recent progress in the description of the molecular mechanisms of proliferation and differentiation controls in vitro, the regulation of the homeostasis of normal stratified epithelia remains unclear in vivo. Computer simulation represents a powerful tool to investigate the complex field of cell proliferation regulation networks. It provides huge computation capabilities to test, in a dynamic in silico context, hypotheses about the many pathways and feedback loops involved in cell growth and proliferation controls.Our approach combines a model of cell (...) proliferation and a spatial representation of cells in 2D using the Voronoi graph. The cell proliferation model includes intracellular (cyclins, Cyclin Dependent Kinases - CDKs, Retinoblastoma protein - Rb, CDK inhibitors) and extracellular controls (growth and differentiation factors, integrins). The Voronoi graph associates a polygon with every cell and the set of these polygons defines the tissue architecture. Thus, the model provides a quantitative model of extracellular signals and cell motility as a function of the neighborhood during time dependent simulations. (shrink)

Construction schedules play an important role in construction project management. However, during construction activities, risks may arise due to unexpected schedule changes, resulting in the ineffective delivery of projects. This study aims to reveal the law of schedule change risk propagation and to analyze the effects on the risk propagation through numerical simulations. First, construction projects are represented by activity-on-node networks. A model of risk propagation is then built based on a susceptible-infected model considering the effects of the nodal characteristics (...) on the propagation process. Next, the model is tested on a real-world project to examine cascading failures with varying parameters. The experimental results demonstrate that the model is effective in identifying the activities most capable of affecting a project schedule and evaluating the impact of schedule change risk propagation. This study will provide a basis for enhancing the robustness of AON networks and controlling the propagation of schedule change risks. (shrink)

Interactions of entrepreneurs through social networks provide an available path for corporate risk contagions. However, the issue how entrepreneurs’ social networks influence on corporate risk contagion is still received limited attention from scholars. In this study, a framework is proposed to describe entrepreneurs’ interaction and corporate value creation. The main results of multiagent simulations indicate the following. First, either weak ties or strong connections of social networks can enhance density of corporate risk contagion. However, only strong connections can be moderated (...) by entrepreneurs’ risk preference. Second, weak ties improve risk exposure of individual corporations, while strong connections may probably decrease systematical risk of the market. Third, weak ties are important for mature corporations to achieve business success. However, for startups, strong connections are more valuable to maintain. The findings of this study not only provide theoretical support from some widely accepted economic phenomenon but also provide explanations for conflicting results from some previous literatures. (shrink)

Knowledge has become an important resource that can help enterprises gain a competitive advantage in the market. In this regard, knowledge sharing in the process of cooperative innovation provides an important way to acquire knowledge. With the development of innovation, the innovation network has become an important carrier of knowledge sharing, which can also have an influence on knowledge sharing. Based on knowledge management theory and complex network theory, this study constructed a multilayer network environment for knowledge (...) sharing. It then identified the network elements and knowledge-sharing paths that influence knowledge sharing from microperspectives and macroperspectives. On this basis, the effects of node cohesion and weak connection on knowledge sharing in small-world and scale-free topologies were analyzed by computer simulation. The results showed that, in an innovation network with a scale-free topology, cohesion and weak ties had a great influence on the average knowledge level and knowledge equilibrium. Meanwhile, in small-world topological innovation networks, a mixed path had the best promotion effect on network knowledge sharing. (shrink)

Emergency scheduling of public resources on the cloud computing platform network can effectively improve the network emergency rescue capability of the cloud computing platform. To schedule the network common resources, it is necessary to generate the initial population through the Hamming distance constraint and improve the objective function as the fitness function to complete the emergency scheduling of the network common resources. The traditional method, from the perspective of public resource fairness and priority mapping, uses incremental (...) optimization algorithm to realize emergency scheduling of public resources, neglecting the improvement process of the objective function, which leads to unsatisfactory scheduling effect. An emergency scheduling method of cloud computing platform network public resources based on genetic algorithm is proposed. With emergency public resource scheduling time cost and transportation cost minimizing target, initial population by Hamming distance constraints, emergency scheduling model, and the corresponding objective function improvement as the fitness function, the genetic algorithm to individual selection and crossover and mutation probability were optimized and complete the public emergency resources scheduling. Experimental results show that the proposed method can effectively improve the efficiency of emergency resource scheduling, and the reliability of emergency scheduling is better. (shrink)

Soon after the first COVID-19 positive case was detected in Wuhan, China, the virus spread around the globe, and in no time, it was declared as a global pandemic by the WHO. Testing, which is the first step in identifying and diagnosing COVID-19, became the first need of the masses. Therefore, testing kits for COVID-19 were manufactured for efficiently detecting COVID-19. However, due to limited resources in the densely populated countries, testing capacity even after a year is still a limiting (...) factor for COVID-19 diagnosis on a larger scale and contributes to a lag in disease tracking and containment. Due to this reason, we started this study to provide a better cost-effective solution for enhancing the testing capacity so that the maximum number of people could get tested for COVID-19. For this purpose, we utilized the approach of artificial neural networks (ANN) to acquire the relevant data on COVID-19 and its testing. The data were analyzed by using Machine Learning, and probabilistic algorithms were applied to obtain a statistically proven solution for COVID-19 testing. The results obtained through ANN indicated that sample pooling is not only an effective way but also regarded as a “Gold standard” for testing samples when the prevalence of the disease is low in the population and the chances of getting a positive result are less. We further demonstrated through algorithms that pooling samples from 16 individuals is better than pooling samples of 8 individuals when there is a high likelihood of getting negative test results. These findings provide ground to the fact that if sample pooling will be employed on a larger scale, testing capacity will be considerably increased within limited available resources without compromising the test specificity. It will provide healthcare units and enterprises with solutions through scientifically proven algorithms, thus, saving a considerable amount of time and finances. This will eventually help in containing the spread of the pandemic in densely populated areas including vulnerably confined groups, such as nursing homes, hospitals, cruise ships, and military ships. (shrink)

The rapid development of online social media has significantly promoted product diffusion in online social networks. However, prior studies focusing on irrational behavior, such as overconfidence, in PDOSN are scarce. To investigate the effect of overconfidence on PDOSN, this study combined overconfidence and an evolutionary game to conduct a multiagent simulation on PDOSN. This combined method provided an effective reference to examine product diffusion in the context of irrational behavior. After careful consideration, this study identified three overconfidence scenarios, benefit, cost, (...) and benefit and cost overconfidence, developed a multiagent simulation model for PDOSN using various overconfidence scenarios, and conducted a comparison with real-world cases to validate the model’s feasibility. The findings indicated that adoption benefits and betrayal penalties had a positive effect on the results in all models, while adoption costs had the opposite effect. When benefit and cost overconfidence occurred simultaneously, benefit overconfidence offset the negative effect of cost overconfidence. Moderate connectivity, a large number of core nodes, and high reconnection probability fully promoted product diffusion. Benefit overconfidence and cost overconfidence had a significant impact on the results in different networks. As such, this study combined psychological theory with simulation methods, providing insights for future research on product diffusion. (shrink)

This article proposes an Analytic Hierarchy Process Dempster-Shafer and similarity-based network selection algorithm for the scenario of dynamic changes in user requirements and network environment; combines machine learning with network selection and proposes a decision tree-based network selection algorithm; combines multiattribute decision-making and genetic algorithm to propose a weighted Gray Relation Analysis and genetic algorithm-based network access decision algorithm. Firstly, the training data is obtained from the collaborative algorithm, and it is used as the training (...) set, and the network attributes are used as the attribute set, and the continuous attributes are discretized by dichotomization, and the attribute that can make the greatest information gain is selected as the division feature, and a decision tree with strong generalization ability is finally obtained, which is used as the decision basis for network access selection. The simulation results show that the algorithm proposed in this thesis can effectively improve user service quality under three services, and the algorithm is simple and effective with low complexity. It first uses AHP-DS hierarchical analysis to establish a recursive hierarchy for the network selection problem and obtains the subjective weights of network attributes through the judgment matrix. Then, it uses a genetic algorithm to adjust the subjective weight, defines the fitness function in the genetic algorithm-based on gray correlation analysis, adjusts the weights of the selection operator, crossover operator, and variation operator in the genetic algorithm, and gets the network with the largest fitness as the target network, which can effectively improve the user service quality. (shrink)

With the author(s)’ decision to opt for Open Choice the copyright of the article changed on 26 May 2021 to ©The Author(s) 2021 and the article is forthwith distributed under a Creative Commons Attribution.

As a result of the time‐ and context‐dependency of gene expression, gene regulatory and signaling pathways undergo dynamic changes during development. Creating a model of the dynamics of molecular interaction networks offers enormous potential for understanding how a genome orchestrates the developmental processes of an organism. The dynamic nature of pathway topology calls for new modeling strategies that can capture transient molecular links at the runtime. The aim of this paper is to present a brief and informative, but not all‐inclusive, (...) viewpoint on the computational aspects of modeling and simulation of a non‐static molecular network. BioEssays 26:68–72, 2004. © 2003 Wiley Periodicals, Inc. (shrink)