arXiv subjects

Kazuhiro Takemoto

Publications and source records attributed to Kazuhiro Takemoto.

29 records · Page 2Linked to original sources

arXiv2013

Modular organization of cancer signaling networks is associated with patient survivability

Molecular signaling networks are believed to determine cancer robustness. Although cancer patient survivability was reported to correlate with the heterogeneous connectivity of the signaling networks inspired by theoretical studies on the increase of network robustness due to the heterogeneous connectivity, other theoretical and data analytic studies suggest an alternative explanation: the impact of modular organization of networks on biological robustness or adaptation to changing environments. In this study, thus, we evaluate whether the modularity--robustness hypothesis is applicable to cancer using network analysis. We focus on 14 specific cancer types whose molecular signaling networks are available in databases, and show that modular organization of cancer signaling networks is associated with the patient survival rate. In particular, the cancers with less modular signaling networks are more curable. This result is consistent with a prediction from the modularity--robustness hypothesis. Furthermore, we show that the network modularity is a better descriptor of the patient survival rate than the heterogeneous connectivity. However, these results do not contradict the importance of the heterogeneous connectivity. Rather, they provide new and different insights into the relationship between cellular networks and cancer behaviors. Despite several limitations of data analysis, these findings enhance our understanding of adaptive and evolutionary mechanisms of cancer cells.

q-bio.MN↗

arXiv2012

Metabolic network modularity arising from simple growth processes

Metabolic networks consist of linked functional components, or modules. The mechanism underlying metabolic network modularity is of great interest not only to researchers of basic science but also to those in fields of engineering. Previous studies have suggested a theoretical model, which proposes that a change in the evolutionary goal (system-specific purpose) increases network modularity, and this hypothesis was supported by statistical data analysis. Nevertheless, further investigation has uncovered additional possibilities that might explain the origin of network modularity. In this work, we propose an evolving network model without tuning parameters to describe metabolic networks. We demonstrate, quantitatively, that metabolic network modularity can arise from simple growth processes, independent of the change in the evolutionary goal. Our model is applicable to a wide range of organisms, and appears to suggest that metabolic network modularity can be more simply determined than previously thought. Nonetheless, our proposition does not serve to contradict the previous model; it strives to provide an insight from a different angle in the ongoing efforts to understand metabolic evolution, with the hope of eventually achieving the synthetic engineering of metabolic networks.

q-bio.MN↗

arXiv2011

Analysis of the impact degree distribution in metabolic networks using branching process approximation

Theoretical frameworks to estimate the tolerance of metabolic networks to various failures are important to evaluate the robustness of biological complex systems in systems biology. In this paper, we focus on a measure for robustness in metabolic networks, namely, the impact degree, and propose an approximation method to predict the probability distribution of impact degrees from metabolic network structures using the theory of branching process. We demonstrate the relevance of this method by testing it on real-world metabolic networks. Although the approximation method possesses a few limitations, it may be a powerful tool for evaluating metabolic robustness.

q-bio.MN↗

arXiv2011

Global architecture of metabolite distributions across species and its formation mechanisms

Living organisms produce metabolites of many types via their metabolisms. Especially, flavonoids, a kind of secondary metabolites, of plant species are interesting examples. Since plant species are believed to have specific flavonoids with respect to diverse environment, elucidation of design principles of metabolite distributions across plant species is important to understand metabolite diversity and plant evolution. In the previous work, we found heterogeneous connectivity in metabolite distributions, and proposed a simple model to explain a possible origin of heterogeneous connectivity. In this paper, we show further structural properties in the metabolite distribution among families inspired by analogy with plant-animal mutualistic networks: nested structure and modular structure. An earlier model represents that these structural properties in bipartite relationships are determined based on traits of elements and external factors. However, we find that the architecture of metabolite distributions is described by simple evolution processes without trait-based mechanisms by comparison between our model and the earlier model. Our model can better predict nested structure and modular structure in addition to heterogeneous connectivity both qualitatively and quantitatively. This finding implies an alternative possible origin of these structural properties, and suggests simpler formation mechanisms of metabolite distributions across plant species than expected.

q-bio.PE↗

arXiv2011

Nested structure acquired through simple evolutionary process

Nested structure, which is non-random, controls cooperation dynamics and biodiversity in plant-animal mutualistic networks. This structural pattern has been explained in a static (non-growth) network models. However, evolutionary processes might also influence the formation of such a structural pattern. We thereby propose an evolving network model for plant-animal interactions and show that non-random patterns such as nested structure and heterogeneous connectivity are both qualitatively and quantitatively predicted through simple evolutionary processes. This finding implies that network models can be simplified by considering evolutionary processes, and also that another explanation exists for the emergence of non-random patterns and might provide more comprehensible insights into the formation of plant-animal mutualistic networks from the evolutionary perspective.

q-bio.PE↗

arXiv2009

Heterogeneous distribution of metabolites across plant species

We investigate the distribution of flavonoids, a major category of plant secondary metabolites, across species. Flavonoids are known to show high species specificity, and were once considered as chemical markers for understanding adaptive evolution and characterization of living organisms. We investigate the distribution among species using bipartite networks, and find that two heterogeneous distributions are conserved among several families: the power-law distributions of the number of flavonoids in a species and the number of shared species of a particular flavonoid. In order to explain the possible origin of the heterogeneity, we propose a simple model with, essentially, a single parameter. As a result, we show that two respective power-law statistics emerge from simple evolutionary mechanisms based on a multiplicative process. These findings provide insights into the evolution of metabolite diversity and characterization of living organisms that defy genome sequence analysis for different reasons.

q-bio.MN↗

arXiv2007

Effects of feedback and feedforward loops on dynamics of transcriptional regulatory model networks

We demonstrate the advantages of feedforward loops using a Boolean network, which is one of the discrete dynamical models for transcriptional regulatory networks. After comparing the dynamical behaviors of network embedded feedback and feedforward loops, we found that feedforward loops can provide higher temporal order (coherence) with lower entropy (randomness) in a temporal program of gene expression. In addition, complexity of the state space that increases with longer length of attractors and greater number of attractors is also reduced for networks with more feedforward loops. Feedback loops show opposite effects on dynamics of the networks. These results suggest that feedforward loops are one of the favorable local structures in biomolecular and neuronal networks.

nlin.CG↗

arXiv2007

Structure of n-clique networks embedded in a complex network

We propose the n-clique network as a powerful tool for understanding global structures of combined highly-interconnected subgraphs, and provide theoretical predictions for statistical properties of the n-clique networks embedded in a complex network using the degree distribution and the clustering spectrum. Furthermore, using our theoretical predictions, we find that the statistical properties are invariant between 3-clique networks and original networks for several observable real-world networks with the scale-free connectivity and the hierarchical modularity. The result implies that structural properties are identical between the 3-clique networks and the original networks.

physics.soc-ph↗

arXiv2006

Local cause of coherence in Boolean networks

We performed a numerical study on random Boolean networks with power-law rank outdegree distributions to find local structural cause for the emergence of high or low degree of coherence in binary state variables. The degree of randomness and coherence of the binary sequence are measured by entropy and mutual information, depending on local structure that consists of a node with a highly connected, called hub and its upstream nodes, and types of Boolean functions for the nodes. With a large number of output connections from a hub, the effects of Boolean function on the hub are more prominent. The local structures that give larger entropy tend to give rise to larger mutual information. Based on the numerical results and structural conditions we derived a time-independent transmission characteristic function of state variables for the local structures. We obtained good relationships between the numerical and analytical results, which indicate that dynamical properties from the whole networks can be inferred from the differences in the local structures.

nlin.CG↗

arXiv2006

Modeling for evolving biological networks with scale-free connectivity, hierarchical modularity, and disassortativity

We propose a growing network model that consists of two tunable mechanisms: growth by merging modules which are represented as complete graphs and a fitness-driven preferential attachment. Our model exhibits the three prominent statistical properties are widely shared in real biological networks, for example gene regulatory, protein-protein interaction, and metabolic networks. They retain three power law relationships, such as the power laws of degree distribution, clustering spectrum, and degree-degree correlation corresponding to scale-free connectivity, hierarchical modularity, and disassortativity, respectively. After making comparisons of these properties between model networks and biological networks, we confirmed that our model has inference potential for evolutionary processes of biological networks.

q-bio.MN↗

arXiv2005

Evolving networks by merging cliques

We propose a model for evolving networks by merging building blocks represented as complete graphs, reminiscent of modules in biological system or communities in sociology. The model shows power-law degree distributions, power-law clustering spectra and high average clustering coefficients independent of network size. The analytical solutions indicate that a degree exponent is determined by the ratio of the number of merging nodes to that of all nodes in the blocks, demonstrating that the exponent is tunable, and are also applicable when the blocks are classical networks such as Erdős-Rényi or regular graphs. Our model becomes the same model as the Barabási-Albert model under a specific condition.

cond-mat.stat-mech↗