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.     

The chemistry of 6-hydroxyflavylium: zwitterionic base and p-quinoidal chalcones. A multiswitchable system operated by proton, electron and photon inputs.

The complex network of chemical reactions of the compound 6-hydroxyflavylium perchlorate was studied by different techniques including UV-vis absorption spectrophotometry, stopped-flow, electrochemistry, and photochemistry. The network is characterized by the formation of a very reactive zwitterionic base as well as by p-quinoidal chalcones. The trans-chalcone is metastable in very acidic solutions and exhibits reversible redox reactions, allowing to introduce for the first time in the flavylium network an electrochemical input.     

A Big World Inside Small-World Networks

Real networks, including biological networks, are known to have the small-world property, characterized by a small “diameter”, which is defined as the average minimal path length between all pairs of nodes in a network. Because random networks also have short diameters, one may predict that the diameter of a real network should be even shorter than its random expectation, because having shorter diameters potentially increases the network efficiency such as minimizing transition times between metabolic states in the context of metabolic networks. Contrary to this expectation, we here report that the observed diameter is greater than the random expectation in every real network examined, including biological, social, technological, and linguistic networks. Simulations show that a modest enlargement of the diameter beyond its expectation allows a substantial increase of the network modularity, which is present in all real networks examined. Hence, short diameters appear to be sacrificed for high modularities, suggesting a tradeoff between network efficiency and advantages offered by modularity (e.g., multi-functionality, robustness, and/or evolvability).     

Analysis of Drosophila motor neuron activity patterns with neural analogs

A network of reciprocally inhibitory motorneurons has been previously postulated to account for the firing patterns of motor units during dipteran flight. Possible activity patterns of such a network were analyzed by means of appropriately interconnected neuromimes. This theoretical analysis showed that the proposed network can account for many aspects of dipteran motor unit activity patterns including firing in specific sequences, stability, and resetting of the firing pattern by antidromic spikes. In addition, the analysis showed that one aspect of physiological activity, motor unit phase locking, can not be explained simply on the basis of network properties alone; evidently specific membrane properties of the dipteran motor units also play an essential role in establishing the activity pattern.     

Establishment of apoptotic regulatory network for genetic markers of colorectal cancer and optimal selection of traditional Chinese medicine target

The paper aimed to screen out genetic markers applicable to early diagnosis for colorectal cancer and establish apoptotic regulatory network model for colorectal cancer, and to analyze the current situation of traditional Chinese medicine (TCM) target, thereby providing theoretical evidence for early diagnosis and targeted therapy of colorectal cancer. Taking databases including CNKI, VIP, Wanfang data, Pub Med, and MEDLINE as main sources of literature retrieval, literatures associated with genetic markers that are applied to early diagnosis of colorectal cancer were searched and performed comprehensive and quantitative analysis by Meta analysis, hence screening genetic markers used in early diagnosis of colorectal cancer. KEGG analysis was employed to establish apoptotic regulatory network model based on screened genetic markers, and optimization was conducted on TCM targets. Through Meta analysis, seven genetic markers were screened out, including WWOX, K-ras, COX-2, P53, APC, DCC and PTEN, among which DCC has the highest diagnostic efficiency. Apoptotic regulatory network was built by KEGG analysis. Currently, it was reported that TCM has regulatory function on gene locus in apoptotic regulatory network. The apoptotic regulatory model of colorectal cancer established in this study provides theoretical evidence for early diagnosis and TCM targeted therapy of colorectal cancer in clinic.     

A framework of integrating gene relations from heterogeneous data sources: an experiment on Arabidopsis thaliana

One of the most important goals of biological investigation is to uncover gene functional relations. In this study we propose a framework for extraction and integration of gene functional relations from diverse biological data sources, including gene expression data, biological literature and genomic sequence information. We introduce a two-layered Bayesian network approach to integrate relations from multiple sources into a genome-wide functional network. An experimental study was conducted on a test-bed of Arabidopsis thaliana. Evaluation of the integrated network demonstrated that relation integration could improve the reliability of relations by combining evidence from different data sources. Domain expert judgments on the gene functional clusters in the network confirmed the validity of our approach for relation integration and network inference.     

Genetic networks in nonpermissive temperature-induced cell differentiation of Sertoli TTE3 cells harboring temperature-sensitive SV40 large T-antigen

To identify the molecular basis by which nonpermissive temperature (NPT) induces cell differentiation in Sertoli TTE3 cells harboring temperature-sensitive SV40 large T-antigen, we performed global scale microarray and computational gene network analyses. In TTE3 cells, inactivation of the large T-antigen by a NPT at 39 degrees C led to cell differentiation accompanying elevation of transferrin, a marker for differentiation of Sertoli cells, and CDKN1A, a cyclin-dependent kinase inhibitor. Of the 22,690 probe sets analyzed, NPT down-regulated 498 probe sets and up-regulated 432 probe sets by >2.0-fold. Hierarchical clustering analysis showed six gene clusters. In the down-regulated cluster I, a significant genetic network including fibronectin 1 was associated with cellular growth and proliferation. In up-regulated cluster IV, a significant genetic network including CDKN1A was associated with cellular differentiation. The present results provide additional novel insights into the molecular basis of cell differentiation induced by NPT in cells.     

Reaction time impairments in decision-making networks as a diagnostic marker for traumatic brain injuries and neurological diseases

The presence of diffuse Focal Axonal Swellings (FAS) is a hallmark cellular feature in many neurological diseases and traumatic brain injury. Among other things, the FAS have a significant impact on spike-train encodings that propagate through the affected neurons, leading to compromised signal processing on a neuronal network level. This work merges, for the first time, three fields of study: (i) signal processing in excitatory-inhibitory (EI) networks of neurons via population codes, (ii) decision-making theory driven by the production of evidence from stimulus, and (iii) compromised spike-train propagation through FAS. As such, we demonstrate a mathematical architecture capable of characterizing compromised decision-making driven by cellular mechanisms. The computational model also leads to several novel predictions and diagnostics for understanding injury level and cognitive deficits, including a key finding that decision-making reaction times, rather than accuracy, are indicative of network level damage. The results have a number of translational implications, including that the level of network damage can be characterized by the reaction times in simple cognitive and motor tests.     

FANMOD: a tool for fast network motif detection

SUMMARY: Motifs are small connected subnetworks that a network displays in significantly higher frequencies than would be expected for a random network. They have recently gathered much attention as a concept to uncover structural design principles of complex biological networks. FANMOD is a tool for fast network motif detection; it relies on recently developed algorithms to improve the efficiency of network motif detection by some orders of magnitude over existing tools. This facilitates the detection of larger motifs in bigger networks than previously possible. Additional benefits of FANMOD are the ability to analyze colored networks, a graphical user interface and the ability to export results to a variety of machine- and human-readable file formats including comma-separated values and HTML.     

Network Science Based Quantification of Resilience Demonstrated on the Indian Railways Network

The structure, interdependence, and fragility of systems ranging from power-grids and transportation to ecology, climate, biology and even human communities and the Internet have been examined through network science. While response to perturbations has been quantified, recovery strategies for perturbed networks have usually been either discussed conceptually or through anecdotal case studies. Here we develop a network science based quantitative framework for measuring, comparing and interpreting hazard responses as well as recovery strategies. The framework, motivated by the recently proposed temporal resilience paradigm, is demonstrated with the Indian Railways Network. Simulations inspired by the 2004 Indian Ocean Tsunami and the 2012 North Indian blackout as well as a cyber-physical attack scenario illustrate hazard responses and effectiveness of proposed recovery strategies. Multiple metrics are used to generate various recovery strategies, which are simply sequences in which system components should be recovered after a disruption. Quantitative evaluation of these strategies suggests that faster and more efficient recovery is possible through network centrality measures. Optimal recovery strategies may be different per hazard, per community within a network, and for different measures of partial recovery. In addition, topological characterization provides a means for interpreting the comparative performance of proposed recovery strategies. The methods can be directly extended to other Large-Scale Critical Lifeline Infrastructure Networks including transportation, water, energy and communications systems that are threatened by natural or human-induced hazards, including cascading failures. Furthermore, the quantitative framework developed here can generalize across natural, engineered and human systems, offering an actionable and generalizable approach for emergency management in particular as well as for network resilience in general.     

Laminins in basement membrane assembly

The heterotrimeric laminins are a defining component of all basement membranes and self-assemble into a cell-associated network. The three short arms of the cross-shaped laminin molecule form the network nodes, with a strict requirement for one α, one β and one γ arm. The globular domain at the end of the long arm binds to cellular receptors, including integrins, α-dystroglycan, heparan sulfates and sulfated glycolipids. Collateral anchorage of the laminin network is provided by the proteoglycans perlecan and agrin. A second network is then formed by type IV collagen, which interacts with the laminin network through the heparan sulfate chains of perlecan and agrin and additional linkage by nidogen. This maturation of basement membranes becomes essential at later stages of embryo development.     

中国森林生态系统定位研究网络的建设与发展

中国森林生态系统定位研究网络 (CFERN)起步于 2 0世纪 5 0年代末 6 0年代初 ,是国家林业局科技司直接领导和管辖的进行长期生态系统定位观测的大型生态学研究网络。它包含分布于不同气候带的 1 5个生态站 ,其中 6个国家级生态站 ,网络的覆盖面完整 ,基本具备了由北向南以热量驱动和由东向西以水分驱动的生态梯度十字网。网络管理中心负责协调各台站之间的联系、网络通讯的出版发行以及制定各种学术研究活动计划。     

Phylogenomic networks

Phylogenomics is aimed at studying functional and evolutionary aspects of genome biology using phylogenetic analysis of whole genomes. Current approaches to genome phylogenies are commonly founded in terms of phylogenetic trees. However, several evolutionary processes are non tree-like in nature, including recombination and lateral gene transfer (LGT). Phylogenomic networks are a special type of phylogenetic network reconstructed from fully sequenced genomes. The network model, comprising genomes connected by pairwise evolutionary relations, enables the reconstruction of both vertical and LGT events. Modeling genome evolution in the form of a network enables the use of an extensive toolbox developed for network research. The structural properties of phylogenomic networks open up fundamentally new insights into genome evolution.     

Network-Based Study Reveals Potential Infection Pathways of Hepatitis-C Leading to Various Diseases

Protein-protein interaction network-based study of viral pathogenesis has been gaining popularity among computational biologists in recent days. In the present study we attempt to investigate the possible pathways of hepatitis-C virus (HCV) infection by integrating the HCV-human interaction network, human protein interactome and human genetic disease association network. We have proposed quasi-biclique and quasi-clique mining algorithms to integrate these three networks to identify infection gateway host proteins and possible pathways of HCV pathogenesis leading to various diseases. Integrated study of three networks, namely HCV-human interaction network, human protein interaction network, and human proteins-disease association network reveals potential pathways of infection by the HCV that lead to various diseases including cancers. The gateway proteins have been found to be biologically coherent and have high degrees in human interactome compared to the other virus-targeted proteins. The analyses done in this study provide possible targets for more effective anti-hepatitis-C therapeutic involvement.     

Modeling fluctuations in default-mode brain network using a spiking neural network

Recently, numerous attempts have been made to understand the dynamic behavior of complex brain systems using neural network models. The fluctuations in blood-oxygen-level-dependent (BOLD) brain signals at less than 0.1 Hz have been observed by functional magnetic resonance imaging (fMRI) for subjects in a resting state. This phenomenon is referred to as a "default-mode brain network." In this study, we model the default-mode brain network by functionally connecting neural communities composed of spiking neurons in a complex network. Through computational simulations of the model, including transmission delays and complex connectivity, the network dynamics of the neural system and its behavior are discussed. The results show that the power spectrum of the modeled fluctuations in the neuron firing patterns is consistent with the default-mode brain network's BOLD signals when transmission delays, a characteristic property of the brain, have finite values in a given range.     

The abundance effect on network nestedness is stronger for parasitic than herbivory interactions

多度对寄生型网络嵌套结构的影响大于食草型网络 因为物种多度显著影响种间互作频率,分析食物网结构对物种多样性和稳定性影响时,应基于能够反映物种真实偏好的网络(即偏好网),而不是直接观察得到的网络(即观察网)。食草网络中(植物为低营养级)的植物资源多度大于寄生网络中(动物为低营养级)寄主资源多度,因此我们假设：寄生网络的结构比食草网络的结构更易受到多度效应的影响。为验证这一假设,我们从已发表的文献中收集了80 个定量观察网络(包括34个植物-食草昆虫网络和46个寄生网络),应用有效多度模型去除物种多度对观察网络的影响,从而得出偏好网络。然后,我们应用weighted NODF和spectral radius两个嵌套系数表征网络嵌套性,分析了观察网和偏好网的物种链接数分布、相互作用均匀度、加权连通度和稳健性的差异。结果表明,在偏好网中,寄生网络的嵌套程度要低于食草网络,这可能是因为去除多度影响增加了种间作用频率的均匀度。偏好网的加权连通度和稳健性显著高于相应的观察网,表明偏好网比观察网具有更高的网络稳定性。未来的食物网研究不仅应关注互惠和拮抗网络的结构差异,还应该关注食草和寄生等不同类型拮抗食物网的结构差异。     

On the long-run sensitivity of probabilistic Boolean networks

Boolean networks and, more generally, probabilistic Boolean networks, as one class of gene regulatory networks, model biological processes with the network dynamics determined by the logic-rule regulatory functions in conjunction with probabilistic parameters involved in network transitions. While there has been significant research on applying different control policies to alter network dynamics as future gene therapeutic intervention, we have seen less work on understanding the sensitivity of network dynamics with respect to perturbations to networks, including regulatory rules and the involved parameters, which is particularly critical for the design of intervention strategies. This paper studies this less investigated issue of network sensitivity in the long run. As the underlying model of probabilistic Boolean networks is a finite Markov chain, we define the network sensitivity based on the steady-state distributions of probabilistic Boolean networks and call it long-run sensitivity. The steady-state distribution reflects the long-run behavior of the network and it can give insight into the dynamics or momentum existing in a system. The change of steady-state distribution caused by possible perturbations is the key measure for intervention. This newly defined long-run sensitivity can provide insight on both network inference and intervention. We show the results for probabilistic Boolean networks generated from random Boolean networks and the results from two real biological networks illustrate preliminary applications of sensitivity in intervention for practical problems.     

Modularity of trophic network is driven by phylogeny and migration in a steppe ecosystem

Evidence is mounting that the structures of trophic networks are governed by migratory movements of interacting species and also by their phylogenetic relationships. Using the largest available trophic network of a large steppe ecosystem, we tested that steppe trophic networks including migratory species are associated with (i) migratory strategy and (ii) phylogenetic relatedness of interacting species: (1) whole graph-level metrics, estimated as modularity, and (2) species-level network metrics, measured as node degree (number of interacting partners), and centrality metrics. We found that (1) a substantial number of links were established by migrant taxa; (2) the phylogenetic signal in network structure was moderate for both consumer and prey nodes; (3) both consumer and prex phylogenies affected modularity, which was modulated by migration strategy; and (4) all species-level graph properties significantly differed between networks including and excluding migratory taxa. In sum, here we show that the structure of steppe trophic networks is primarily governed by migratory strategies and to a lesser extent, by phylogenetic relatedness, using the largest available food web representative for steppe ecology and migration biology.