Drivers of structural features in gene regulatory networks: From biophysical constraints to biological function

Living cells can maintain their internal states, react to changing environments, grow, differentiate, divide, etc. All these processes are tightly controlled by what can be called a regulatory program. The logic of the underlying control can sometimes be guessed at by examining the network of influences amongst genetic components. Some associated gene regulatory networks have been studied in prokaryotes and eukaryotes, unveiling various structural features ranging from broad distributions of out-degrees to recurrent “motifs”, that is small subgraphs having a specific pattern of interactions. To understand what factors may be driving such structuring, a number of groups have introduced frameworks to model the dynamics of gene regulatory networks. In that context, we review here such in silico approaches and show how selection for phenotypes, i.e., network function, can shape network structure.     

Coarse-grain reconstruction of genetic networks from expression levels

In the postgenome era many efforts have been dedicated to systematically elucidate the complex web of interacting genes and proteins. These efforts include experimental and computational methods. Microarray technology offers an opportunity for monitoring gene expression level at the genome scale. By recourse to information theory, this study proposes a mathematical approach to reconstruct gene regulatory networks at a coarse-grain level from high throughput gene expression data. The method provides the a posteriori probability that a given gene regulates positively, negatively or does not regulate each one of the network genes. This approach also allows the introduction of prior knowledge and the quantification of the information gain from experimental data used in the inference procedure. This information gain can be used to choose those genes that will be perturbed in subsequent experiments in order to refine our knowledge about the architecture of an underlying gene regulatory network. The performance of the proposed approach has been studied by in numero experiments. Our results suggest that the approach is suitable for focusing on size-limited problems, such as recovering a small subnetwork of interest by performing perturbation over selected genes.     

Accelerating networks: Effects of preferential connections

Networks are commonly observed structures in complex systems with interacting and interdependent parts that self-organize. For nonlinearly growing networks, when the total number of connections increases faster than the total number of nodes, the network is said to accelerate. We propose a systematic model for the dynamics of growing networks represented by distribution kinetics equations. We define the nodal-linkage distribution, construct a population dynamics equation based on the association-dissociation process, and perform the moment calculations to describe the dynamics of such networks. For nondirectional networks with finite numbers of nodes and connections, the moments are the total number of nodes, the total number of connections, and the degree (the average number of connections per node), represented by the average moment. Size independent rate coefficients yield an exponential network describing the network without preferential attachment, and size dependent rate coefficients produce a power law network with preferential attachment. The model quantitatively describes accelerating network growth data for a supercomputer (Earth Simulator), for regulatory gene networks, and for the Internet.     

Inference of Molecular Regulatory Systems Using Statistical Path-Consistency Algorithm

One of the key challenges in systems biology and molecular sciences is how to infer regulatory relationships between genes and proteins using high-throughout omics datasets. Although a wide range of methods have been designed to reverse engineer the regulatory networks, recent studies show that the inferred network may depend on the variable order in the dataset. In this work, we develop a new algorithm, called the statistical path-consistency algorithm (SPCA), to solve the problem of the dependence of variable order. This method generates a number of different variable orders using random samples, and then infers a network by using the path-consistent algorithm based on each variable order. We propose measures to determine the edge weights using the corresponding edge weights in the inferred networks, and choose the edges with the largest weights as the putative regulations between genes or proteins. The developed method is rigorously assessed by the six benchmark networks in DREAM challenges, the mitogen-activated protein (MAP) kinase pathway, and a cancer-specific gene regulatory network. The inferred networks are compared with those obtained by using two up-to-date inference methods. The accuracy of the inferred networks shows that the developed method is effective for discovering molecular regulatory systems.     

Microstructuring of fused silica by laser-induced backside wet etching using picosecond laser pulses

The laser-induced backside wet etching (LIBWE) is an advanced laser processing method used for structuring transparent materials. LIBWE with nanosecond laser pulses has been successfully demonstrated for various materials, e.g. oxides (fused silica, sapphire) or fluorides (CaF₂, MgF₂), and applied for the fabrication of microstructures. In the present study, LIBWE of fused silica with mode-locked picosecond (t_p = 10 ps) lasers at UV wavelengths (λ₁ = 355 nm and λ₂ = 266 nm) using a (pyrene) toluene solution was demonstrated for the first time. The influence of the experimental parameters, such as laser fluence, pulse number, and absorbing liquid, on the etch rate and the resulting surface morphology were investigated. The etch rate grew linearly with the laser fluence in the low and in the high fluence range with different slopes. Incubation at low pulse numbers as well as a nearly constant etch rate after a specific pulse number for example were observed. Additionally, the etch rate depended on the absorbing liquid used; whereas the higher absorption of the admixture of pyrene in the used toluene enhances the etch rate and decreases the threshold fluence. With a λ₁ = 266 nm laser set-up, an exceptionally smooth surface in the etch pits was achieved. For both wavelengths (λ₁ = 266 nm and λ₂ = 355 nm), LIPSS (laser-induced periodic surface structures) formation was observed, especially at laser fluences near the thresholds of 170 and 120 mJ/cm², respectively.     

Laser heating and ablation at high repetition rate in thermal confinement regime

Laser heating and ablation of materials with low absorption and thermal conductivity (paint and cement) were under experimental and theoretical investigations. The experiments were made with a high repetition rate Q-switched Nd:YAG laser (10 kHz, 90 ns pulse duration and λ = 532 nm). High repetition rate laser heating resulted in pulse per pulse heat accumulation. A theoretical model of laser heating was developed and demonstrated a good agreement between the experimental temperatures measured with the infrared pyrometer and the calculated ones. With the fixed wavelength and laser pulse duration, the ablation threshold fluence of paint was found to depend on the repetition rate and the number of applied pulses. With a high repetition rate, the threshold fluence decreased significantly when the number of applied pulses was increasing. The experimentally obtained thresholds were well described by the developed theoretical model. Some specific features of paint heating and ablation with high repetition rate lasers are discussed.     

Approximately analytical solutions of the Manning-Rosen potential with the spin-orbit coupling term and spin symmetry

In this Letter the approximately analytical bound state solutions of the Dirac equation with the Manning-Rosen potential for arbitrary spin-orbit coupling quantum number k are carried out by taking a properly approximate expansion for the spin-orbit coupling term. In the case of exact spin symmetry, the associated two-component spinor wave functions of the Dirac equation for arbitrary spin-orbit quantum number k are presented and the corresponding bound state energy equation is derived. We study briefly two special cases; the general s-wave problem and the equal scalar and vector Manning-Rosen potential.     

Self-sustained oscillations of complex genomic regulatory networks

Recently, self-sustained oscillations in complex networks consisting of non-oscillatory nodes have attracted great interest in diverse natural and social fields. Oscillatory genomic regulatory networks are one of the most typical examples of this kind. Given an oscillatory genomic network, it is important to reveal the central structure generating the oscillation. However, if the network consists of large numbers of genes and interactions, the oscillation generator is deeply hidden in the complicated interactions. We apply the dominant phase-advanced driving path method proposed in Qian et al. (2010) [1] to reduce complex genomic regulatory networks to one-dimensional and unidirectionally linked network graphs where negative regulatory loops are explored to play as the central generators of the oscillations, and oscillation propagation pathways in the complex networks are clearly shown by tree branches radiating from the loops. Based on the above understanding we can control oscillations of genomic networks with high efficiency.     

Generalized relative entropy in functional magnetic resonance imaging

The generalized Kullback-Leibler distance D_q (q is the Tsallis parameter) is shown to be an useful measure for analysis of functional magnetic resonance imaging (fMRI) data series. This generalized form of entropy is used to evaluate the “distance” between the probability functions p₁ and p₂ of the signal levels related to periods of stimulus and non-stimulus in event-related fMRI experiments. The probability densities of the mean distance (averaged over the N epochs of the entire experiment) are obtained through numerical simulations for different values of signal-to-noise ratio (SNR) and found to be fitted very well by Gamma distributions (χ²<0.0008) for small values of N (N<30). These distributions allow us to determine the sensitivity and specificity of the method by construction of the receiver operating characteristic (ROC) curves. The performance of the method is also investigated in terms of the parameters q and L (number of signal levels) and our results indicate that the optimum choice is q=0.8 and L=3. The entropic index q is found to exert control on both sensitivity and specificity of the method. As q (q>0) is raised, sensitivity increases but specificity decreases. Finally, the method is applied in the analysis of a real event-related fMRI motor stimulus experiment and the resulting maps show activation in primary and secondary motor brain areas.     

Duplication： a Mechanism Producing Disassortative Mixing Networks in Biology

Assortative/disassortative mixing is an important topological property of a network. A network is called assortative mixing if the nodes in the network tend to connect to their connectivity peers, or disassortative mixing if nodes with low degrees are more likely to connect with high-degree nodes. We have known that biological networks such as protein-protein interaction networks （PPI）, gene regulatory networks, and metabolic networks tend to be disassortative. On the other hand, in biological evolution, duplication and divergence are two fundamental processes. In order to make the relationship between the property of disassortative mixing and the two basic biological principles clear and to study the cause of the disassortative mixing property in biological networks, we present a random duplication model and an anti-preference duplication model. Our results show that disassortative mixing networks can be obtained by both kinds of models from uncorrelated initial networks. Moreover, with the growth of the network size, the disassortative mixing property becomes more obvious.     

Epidemic outbreaks in growing scale-free networks with local structure

The class of generative models has already attracted considerable interest from researchers in recent years and much expanded the original ideas described in BA model. Most of these models assume that only one node per time step joins the network. In this paper, we grow the network by adding n interconnected nodes as a local structure into the network at each time step with each new node emanating m new edges linking the node to the preexisting network by preferential attachment. This successfully generates key features observed in social networks. These include power-law degree distribution p_k∼k^−(3+μ), where μ=(n−1)/m is a tuning parameter defined as the modularity strength of the network, nontrivial clustering, assortative mixing, and modular structure. Moreover, all these features are dependent in a similar way on the parameter μ. We then study the susceptible-infected epidemics on this network with identical infectivity, and find that the initial epidemic behavior is governed by both of the infection scheme and the network structure, especially the modularity strength. The modularity of the network makes the spreading velocity much lower than that of the BA model. On the other hand, increasing the modularity strength will accelerate the propagation velocity.     

Variable Phase Method Used to Calculate Ultracold Scattering Properties of 7Li33Cs

Elastic scattering properties of singlet and triplet states of ^7 Li^133 Cs at ultralow temperatures are calculated using the constructed potential curves gleaned from high-precision spectroscopy measurement. We show how to reach the scattering length and the number of bound states via the vaxiable phase method. The scattering lengths of the singlet and triplet states of^7 Li^133 Cs are 50.5 a.u. and -135.7 a.u., respectively. We derive two corrections, arising from long range interactions, accurately to at least first order, which provide upper and lower computed bounds to the scattering length. Our results are consistent with the recent experimental data and the theoretical calculation.     

Tungsten microcone arrays grown using nanosecond pulsed-Nd:YAG laser in a low-pressure He-gas atmosphere

Tungsten microcone arrays with a high aspect ratio are formed by the cumulative nanosecond pulsed-Nd:YAG laser irradiation of single-crystal tungsten under low pressure in an inert atmosphere. The morphology of the microcones and their density were strongly affected by the number of laser pulses. The microcones grew to a length of 20 μm with a diameter of about 1.5 μm at the tip after irradiation with more than 1200 pulses under our experimental conditions. They may have potential applications for emission cathodes in a field-emission display (FED) and in microelectronic devices. Received: 8 January 2001 / Accepted: 13 June 2001 / Published online: 2 October 2001     

Node similarity within subgraphs of protein interaction networks

We propose a biologically motivated quantity, twinness, to evaluate local similarity between nodes in a network. The twinness of a pair of nodes is the number of connected, labeled subgraphs of size n in which the two nodes possess identical neighbours. The graph animal algorithm is used to estimate twinness for each pair of nodes (for subgraph sizes n=4 to n=12) in four different protein interaction networks (PINs). These include an Escherichia coli PIN and three Saccharomyces cerevisiae PINs — each obtained using state-of-the-art high-throughput methods. In almost all cases, the average twinness of node pairs is vastly higher than that expected from a null model obtained by switching links. For all n, we observe a difference in the ratio of type twins (which are unlinked pairs) to type twins (which are linked pairs) distinguishing the prokaryote E. coli from the eukaryote S. cerevisiae. Interaction similarity is expected due to gene duplication, and whole genome duplication paralogues in S. cerevisiae have been reported to co-cluster into the same complexes. Indeed, we find that these paralogous proteins are over-represented as twins compared to pairs chosen at random. These results indicate that twinness can detect ancestral relationships from currently available PIN data.     

Extracting hidden fluctuation patterns of Hang Seng stock index from network topologies

We present a model of complex network generated from Hang Seng index (HSI) of Hong Kong stock market, which encodes stock market relevant both interconnections and interactions between fluctuation patterns of HSI in the network topologies. In the network, the nodes (edges) represent all kinds of patterns of HSI fluctuation (their interconnections). Based on network topological statistic, we present efficient algorithms, measuring betweenness centrality (BC) and inverse participation ratio (IPR) of network adjacency matrix, for detecting topological important nodes. We have at least obtained three uniform nodes of topological importance, and find the three nodes, i.e. 18.7% nodes undertake 71.9% betweenness centrality and closely correlate other nodes. From these topological important nodes, we can extract hidden significant fluctuation patterns of HSI. We also find these patterns are independent the time intervals scales. The results contain important physical implication, i.e. the significant patterns play much more important roles in both information control and transport of stock market, and should be useful for us to more understand fluctuations regularity of stock market index. Moreover, we could conclude that Hong Kong stock market, rather than a random system, is statistically stable, by comparison to random networks.     

An entropic measure for the teaching-learning process

Few phenomena are as complex as the teaching-learning (TL) process. The instruction efficiency and information on the state of knowledge of the student group are some key variables in this process. Guided by Shannon’s definition of information we propose an entropy based performance index (S_p) for monitoring the teaching-learning process. Our index is based on item response curves (IRCs) which have been recently employed in physics education research. Our proposed index is an explicit function of the ability θ. A preliminary survey indicates that S_p is low (high) for high (low) ability student groups. We propose a simple model to explain this. We have also carried out a number of controlled studies to study the dependence of S_p on student ability, peer instruction and collaborative learning. Our studies indicate that S_p plays a role analogous to entropy in statistical mechanics, with student ability being akin to inverse temperature, peer instruction to an ordering (magnetic) field and collaborative learning to interacting components.     

Complex network structure of musical compositions: Algorithmic generation of appealing music

In this paper we construct networks for music and attempt to compose music artificially. Networks are constructed with nodes and edges corresponding to musical notes and their co-occurring connections. We analyze classical music from Bach, Mozart, Chopin, as well as other types of music such as Chinese pop music. We observe remarkably similar properties in all networks constructed from the selected compositions. We conjecture that preserving the universal network properties is a necessary step in artificial composition of music. Power-law exponents of node degree, node strength and/or edge weight distributions, mean degrees, clustering coefficients, mean geodesic distances, etc. are reported. With the network constructed, music can be composed artificially using a controlled random walk algorithm, which begins with a randomly chosen note and selects the subsequent notes according to a simple set of rules that compares the weights of the edges, weights of the nodes, and/or the degrees of nodes. By generating a large number of compositions, we find that this algorithm generates music which has the necessary qualities to be subjectively judged as appealing.