A structured approach for selection among candidate metabolic network models and estimation of unknown stoichiometric coefficients

A metabolic network model is one of the cornerstones of the emerging Metabolic Engineering methodology. In this article, special attention is therefore, given to the phase of model building. A five-stage structured approach to metabolic network modeling is introduced. The basic steps are: (1) to collect a priori knowledge on the reaction network and to build candidate network models, (2) to perform an a priori check of the model, (3) to estimate the unknown parameters in the model, (4) to check the identified model for acceptability from a biological and thermodynamic point of view, and (5) to validate the model with new data. The approach is illustrated with a growth system involving baker's yeast growing on mixtures of substrates. Special attention is given to the central uncertainties in metabolic network modeling, i.e., estimation of energetic parameters in the network and the choice of the source of anabolic reducing equivalents NADPH.     

一类求解约束非线性规划问题的神经网络模型

提出一类求解闭凸集上非线性规划问题的神经网络模型。理论分析和计算机模拟表明在适当的假设下所提出的神经网络模型大范围指数级收敛于非线性规划问题的解集。本文神经网络所采用的方法属于广义的最速下降法,甚至当规划问题地正定二次时,本文的模型也比已有的神经网络模型简单。     

Mean-field Boolean network model of a signal transduction network

In this paper we provide a mean-field Boolean network model for a signal transduction network of a generic fibroblast cell. The network consists of several main signaling pathways, including the receptor tyrosine kinase, the G-protein coupled receptor, and the Integrin signaling pathway. The network consists of 130 nodes, each representing a signaling molecule (mainly proteins). Nodes are governed by Boolean dynamics including canalizing functions as well as totalistic Boolean functions that depend only on the overall fraction of active nodes. We categorize the Boolean functions into several different classes. Using a mean-field approach we generate a mathematical formula for the probability of a node becoming active at any time step. The model is shown to be a good match for the actual network. This is done by iterating both the actual network and the model and comparing the results numerically. Using the Boolean model it is shown that the system is stable under a variety of parameter combinations. It is also shown that this model is suitable for assessing the dynamics of the network under protein mutations. Analytical results support the numerical observations that in the long-run at most half of the nodes of the network are active.     

Modelling the evolution of genetic regulatory networks

An evolutionary model of genetic regulatory networks is developed, based on a model of network encoding and dynamics called the Artificial Genome (AG). This model derives a number of specific genes and their interactions from a string of (initially random) bases in an idealized manner analogous to that employed by natural DNA. The gene expression dynamics are determined by updating the gene network as if it were a simple Boolean network. The generic behaviour of the AG model is investigated in detail. In particular, we explore the characteristic network topologies generated by the model, their dynamical behaviours, and the typical variance of network connectivities and network structures. These properties are demonstrated to agree with a probabilistic analysis of the model, and the typical network structures generated by the model are shown to lie between those of random networks and scale-free networks in terms of their degree distribution. Evolutionary processes are simulated using a genetic algorithm, with selection acting on a range of properties from gene number and degree of connectivity through periodic behaviour to specific patterns of gene expression. The evolvability of increasingly complex patterns of gene expression is examined in detail. When a degree of redundancy is introduced, the average number of generations required to evolve given targets is reduced, but limits on evolution of complex gene expression patterns remain. In addition, cyclic gene expression patterns with periods that are multiples of shorter expression patterns are shown to be inherently easier to evolve than others. Constraints imposed by the template-matching nature of the AG model generate similar biases towards such expression patterns in networks in initial populations, in addition to the somewhat scale-free nature of these networks. The significance of these results on current understanding of biological evolution is discussed.     

Control of bioreactors using a neural network model

Control of a continuous bioreactor based on a artificial neural network (ANN) model is carried out theoretically. The ANN model is identified, from input-output data of a bioreactor, using a three-layer feedforward network trained by a back propagation algorithm. The performance of the controller designed on the ANN model is compared with that of a conventional PI controller.     

农田林网地区风速减弱规律的探讨

根据能量平衡原理,利用风洞实验资料和野外观测资料分析了林网地区动量通量(或动力速度)的变化规律,得到了林网地区与旷野的动力速度之比关系式:并据此分析了林网地区的风速减弱规律。     

Desynchronized vasomotion and desynchronized fiber activation pattern enhance oxygenation in a model of skeletal muscle

Although the full physiological significance of vasomotion is still debated, it is generally thought to have a role in optimizing tissue oxygenation parameters. We study the effect of vasomotion rhythm in skeletal muscle on oxygen transport using a computational model. The model is used: (i) to test a novel hypothesis that “vasomotors” form a chemical network in which the rhythm adapts to meet oxygen demand in skeletal muscle and (ii) to study the contribution of desynchronized/chaotic vasomotion in optimizing oxygen delivery to skeletal muscle. We formulate a 2D grid model of skeletal muscle consisting of an interleaved arrangement of vessels and muscle fibers fired by a motor neuronal network. The vasomotors too form a network interacting by chemical means. When positive (negative) synapses dominate, the neuronal network exhibits synchronized (desynchronized) activity. Similarly, when positive (negative) chemical interactions dominate, the vessels exhibit synchronized (desynchronized) activity. Optimal oxygenation is observed when both neuronal network and vasomotor network exhibit desynchronous activity. Muscle oxygenation is thought to result by interactions between the fiber/neuron network and the vessel network; optimal oxygenation depends on precise rhythm-related conditions on the two networks. The model provides interesting insights into the phenomenon of muscle fatigue.     

Hydrogen bonds and the catalytic mechanism of human carbonic anhydrase II

A new model for catalysis of human carbonic anhydrase II is suggested. The model is based on the X-ray structure of the hydrogen bond network in the catalytic site. The outer part of the network is proposed to adjust the p K(a) of the catalytic site to the experimentally observed value of about 7. The inner part of the network is proposed to become a low-barrier hydrogen bond network in the transition state. The energy released in forming the low-barrier hydrogen bond network is used to catalyse the interconversion of CO(2) and HCO(3)(-). The suggested molecular mechanism is consistent with the generally accepted kinetic scheme for human carbonic anhydrase II.     

Mathematical model of the filtration in the vascular network of the ophidian glomerulus.

The present work is a mathematical model of the fluid filtration in the glomerular network occurring in snakes. The model is based on the differential form of Starling's hypothesis and takes into account the angioarchitecture of the network and the behaviour on the microrheology of blood with nucleated red cells. The model predicts the hemodynamics and the transvascular fluxes in each vascular segment within the network. The model is applied to a vascular network of the glomerulus of the garter snake. A value of 0.593 microns/(s.mmHg) was determined for the hydraulic conductivity of the glomerular capillaries using the geometrical data of the network together with experimental data for the pressures and the blood flow rate reported in the literature. The analysis shows that the local filtration rates cover a wide range. In some of the vascular segments, the filtration leads to such a high increase in colloid-osmotic pressure that the level of the transvascular hydrostatic pressure difference is reached. Mathematical simulations of the variation of the glomerular blood flow rate due to vasoactivity of preglomerular arterioles show the effect on the filtration rate and the hemorheologic parameters.     

Polydispersion in the diameter of fibers in fibrin networks: Consequences on the measurement of mass–length ratio by permeability and turbidity

The diameters of the fibrin fibers in a clot are not uniform. Morphometric analysis of transmission electron micrographs show a bimodal distribution. The effect of polydispersity of fiber diameter on mass–length ratio calculated from the turbidity and permeability of a clot has been investigated with the aid of a two network model, the networks being called Major and Minor. The fibers in the Major network are many times thicker than those in the Minor network. In a model in which Major network fibers are 10 times thicker than fibers of the Minor network, the fibers in the Minor network make a negligible contribution to the turbidity of the clot. However, they may have a marked effect on its permeability. Experiments with clots made from human fibrinogen show that the Minor network is stabilized by α-polymer and γ-γ linkages and that without such linkages it is washed away during permeation. It remains relatively intact in crosslinked clots. In agreement with the theoretical model, when mass–length ratios calculated from the turbidity are compared with those calculated from the permeability, the latter were reduced in crosslinked clots with an intact Minor network.     

Modeling the Citation Network by Network Cosmology

Citation between papers can be treated as a causal relationship. In addition, some citation networks have a number of similarities to the causal networks in network cosmology, e.g., the similar in-and out-degree distributions. Hence, it is possible to model the citation network using network cosmology. The casual network models built on homogenous spacetimes have some restrictions when describing some phenomena in citation networks, e.g., the hot papers receive more citations than other simultaneously published papers. We propose an inhomogenous causal network model to model the citation network, the connection mechanism of which well expresses some features of citation. The node growth trend and degree distributions of the generated networks also fit those of some citation networks well.     

Analysis of Road Network Pattern Considering Population Distribution and Central Business District

This paper proposes a road network growing model with the consideration of population distribution and central business district (CBD) attraction. In the model, the relative neighborhood graph (RNG) is introduced as the connection mechanism to capture the characteristics of road network topology. The simulation experiment is set up to illustrate the effects of population distribution and CBD attraction on the characteristics of road network. Moreover, several topological attributes of road network is evaluated by using coverage, circuitness, treeness and total length in the experiment. Finally, the suggested model is verified in the simulation of China and Beijing Highway networks.     

On the evolution of scale-free topologies with a gene regulatory network model

A novel approach to generating scale-free network topologies is introduced, based on an existing artificial gene regulatory network model. From this model, different interaction networks can be extracted, based on an activation threshold. By using an evolutionary computation approach, the model is allowed to evolve, in order to reach specific network statistical measures. The results obtained show that, when the model uses a duplication and divergence initialisation, such as seen in nature, the resulting regulation networks not only are closer in topology to scale-free networks, but also require only a few evolutionary cycles to achieve a satisfactory error value.     

马尾松自疏规律的人工神经网络模型研究

森林自然稀疏规律的研究已经有了很大发展,并提出了许多经验的或理论的表达式。本研究介绍了人工神经网络方法,首次建立了马尾松人工林自然稀疏规律的三层前馈反向传播神经网络模型。仿真结果表明,人工神经网络模型能很好地符合实际的观测资料,具有良好的使用价值,从而丰富了该领域的研究方法。     

Boolean network model predicts cell cycle sequence of fission yeast

A Boolean network model of the cell-cycle regulatory network of fission yeast (Schizosaccharomyces Pombe) is constructed solely on the basis of the known biochemical interaction topology. Simulating the model in the computer faithfully reproduces the known activity sequence of regulatory proteins along the cell cycle of the living cell. Contrary to existing differential equation models, no parameters enter the model except the structure of the regulatory circuitry. The dynamical properties of the model indicate that the biological dynamical sequence is robustly implemented in the regulatory network, with the biological stationary state G1 corresponding to the dominant attractor in state space, and with the biological regulatory sequence being a strongly attractive trajectory. Comparing the fission yeast cell-cycle model to a similar model of the corresponding network in S. cerevisiae, a remarkable difference in circuitry, as well as dynamics is observed. While the latter operates in a strongly damped mode, driven by external excitation, the S. pombe network represents an auto-excited system with external damping.     

Enriching the tactical network design of express service carriers with fleet scheduling characteristics

Express service carriers provide time-guaranteed deliveries of parcels via a network consisting of nodes and hubs. In this, nodes take care of the collection and delivery of parcels, and hubs have the function to consolidate parcels in between the nodes. The tactical network design problem assigns nodes to hubs, determines arcs between hubs, and routes parcels through the network. Afterwards, fleet scheduling creates a schedule for vehicles operated in the network. The strong relation between flow routing and fleet scheduling makes it difficult to optimise the network cost. Due to this complexity, fleet scheduling and network design are usually decoupled. We propose a new tactical network design model that is able to include fleet scheduling characteristics (like vehicle capacities, vehicle balancing, and drivers’ legislations) in the network design. The model is tested on benchmark data based on instances from an express provider, resulting in significant cost reductions.     

A Complex Network Approach to Distributional Semantic Models

A number of studies on network analysis have focused on language networks based on free word association, which reflects human lexical knowledge, and have demonstrated the small-world and scale-free properties in the word association network. Nevertheless, there have been very few attempts at applying network analysis to distributional semantic models, despite the fact that these models have been studied extensively as computational or cognitive models of human lexical knowledge. In this paper, we analyze three network properties, namely, small-world, scale-free, and hierarchical properties, of semantic networks created by distributional semantic models. We demonstrate that the created networks generally exhibit the same properties as word association networks. In particular, we show that the distribution of the number of connections in these networks follows the truncated power law, which is also observed in an association network. This indicates that distributional semantic models can provide a plausible model of lexical knowledge. Additionally, the observed differences in the network properties of various implementations of distributional semantic models are consistently explained or predicted by considering the intrinsic semantic features of a word-context matrix and the functions of matrix weighting and smoothing. Furthermore, to simulate a semantic network with the observed network properties, we propose a new growing network model based on the model of Steyvers and Tenenbaum. The idea underlying the proposed model is that both preferential and random attachments are required to reflect different types of semantic relations in network growth process. We demonstrate that this model provides a better explanation of network behaviors generated by distributional semantic models.