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Search Results: 1 - 10 of 78268 matches for " Luonan Chen "
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Chaotic Simulated Annealing by A Neural Network Model with Transient Chaos
Luonan Chen,Kazuyuki Aihara
Physics , 1997,
Abstract: We propose a neural network model with transient chaos, or a transiently chaotic neural network (TCNN) as an approximation method for combinatorial optimization problem, by introducing transiently chaotic dynamics into neural networks. Unlike conventional neural networks only with point attractors, the proposed neural network has richer and more flexible dynamics, so that it can be expected to have higher ability of searching for globally optimal or near-optimal solutions. A significant property of this model is that the chaotic neurodynamics is temporarily generated for searching and self-organizing, and eventually vanishes with autonomous decreasing of a bifurcation parameter corresponding to the "temperature" in usual annealing process. Therefore, the neural network gradually approaches, through the transient chaos, to dynamical structure similar to such conventional models as the Hopfield neural network which converges to a stable equilibrium point. Since the optimization process of the transiently chaotic neural network is similar to simulated annealing, not in a stochastic way but in a deterministically chaotic way, the new method is regarded as chaotic simulated annealing (CSA). Fundamental characteristics of the transiently chaotic neurodynamics are numerically investigated with examples of a single neuron model and the Traveling Salesman Problem (TSP). Moreover, a maintenance scheduling problem for generators in a practical power system is also analysed to verify practical efficiency of this new method.
Chaos and Asymptotical Stability in Discrete-time Neural Networks
Luonan Chen,Kazuyuki Aihara
Physics , 1997, DOI: 10.1016/S0167-2789(96)00302-8
Abstract: This paper aims to theoretically prove by applying Marotto's Theorem that both transiently chaotic neural networks (TCNN) and discrete-time recurrent neural networks (DRNN) have chaotic structure. A significant property of TCNN and DRNN is that they have only one fixed point, when absolute values of the self-feedback connection weights in TCNN and the difference time in DRNN are sufficiently large. We show that this unique fixed point can actually evolve into a snap-back repeller which generates chaotic structure, if several conditions are satisfied. On the other hand, by using the Lyapunov functions, we also derive sufficient conditions on asymptotical stability for symmetrical versions of both TCNN and DRNN, under which TCNN and DRNN asymptotically converge to a fixed point. Furthermore, generic bifurcations are also considered in this paper. Since both of TCNN and DRNN are not special but simple and general, the obtained theoretical results hold for a wide class of discrete-time neural networks. To demonstrate the theoretical results of this paper better, several numerical simulations are provided as illustrating examples.
Transient Resetting: A Novel Mechanism for Synchrony and Its Biological Examples
Chunguang Li ,Luonan Chen,Kazuyuki Aihara
PLOS Computational Biology , 2006, DOI: 10.1371/journal.pcbi.0020103
Abstract: The study of synchronization in biological systems is essential for the understanding of the rhythmic phenomena of living organisms at both molecular and cellular levels. In this paper, by using simple dynamical systems theory, we present a novel mechanism, named transient resetting, for the synchronization of uncoupled biological oscillators with stimuli. This mechanism not only can unify and extend many existing results on (deterministic and stochastic) stimulus-induced synchrony, but also may actually play an important role in biological rhythms. We argue that transient resetting is a possible mechanism for the synchronization in many biological organisms, which might also be further used in the medical therapy of rhythmic disorders. Examples of the synchronization of neural and circadian oscillators as well as a chaotic neuron model are presented to verify our hypothesis.
Stochastic synchronization of genetic oscillator networks
Chunguang Li, Luonan Chen, Kazuyuki Aihara
BMC Systems Biology , 2007, DOI: 10.1186/1752-0509-1-6
Abstract: In this paper, based on systems biology approach, we provide a general theoretical result on the synchronization of genetic oscillators with stochastic perturbations. By exploiting the specific properties of many genetic oscillator models, we provide an easy-verified sufficient condition for the stochastic synchronization of coupled genetic oscillators, based on the Lur'e system approach in control theory. A design principle for minimizing the influence of noise is also presented. To demonstrate the effectiveness of our theoretical results, a population of coupled repressillators is adopted as a numerical example.In summary, we present an efficient theoretical method for analyzing the synchronization of genetic oscillator networks, which is helpful for understanding and testing the synchronization phenomena in biological organisms. Besides, the results are actually applicable to general oscillator networks.Elucidating the collective dynamics of coupled genetic oscillators not only is important for the understanding of the rhythmic phenomena of living organisms, but also has many potential applications in bioengineering areas. So far, many researchers have studied the synchronization in genetic networks from the aspects of experiment, numerical simulation and theoretical analysis. For instance, in [1], the authors experimentally investigated the synchronization of cellular clock in the suprachiasmatic nucleus (SCN); in [2-4], the synchronization are studied in biological networks of identical genetic oscillators; and in [5-7], the synchronization for coupled nonidentical genetic oscillators is investigated. Gene regulation is an intrinsically noisy process, which is subject to intracellular and extracellular noise perturbations and environment fluctuations [8-12,14]. Such cellular noises will undoubtedly affect the dynamics of the networks both quantitatively and qualitatively. In [13], the authors numerically studied the cooperative behaviors of a multicell system w
Synchronization of Coupled Nonidentical Genetic Oscillators
Chunguang Li,Luonan Chen,Kazuyuki Aihara
Quantitative Biology , 2006, DOI: 10.1088/1478-3975/3/1/004
Abstract: The study on the collective dynamics of synchronization among genetic oscillators is essential for the understanding of the rhythmic phenomena of living organisms at both molecular and cellular levels. Genetic oscillators are biochemical networks, which can generally be modelled as nonlinear dynamic systems. We show in this paper that many genetic oscillators can be transformed into Lur'e form by exploiting the special structure of biological systems. By using control theory approach, we provide a theoretical method for analyzing the synchronization of coupled nonidentical genetic oscillators. Sufficient conditions for the synchronization as well as the estimation of the bound of the synchronization error are also obtained. To demonstrate the effectiveness of our theoretical results, a population of genetic oscillators based on the Goodwin model are adopted as numerical examples.
Hubs with Network Motifs Organize Modularity Dynamically in the Protein-Protein Interaction Network of Yeast
Guangxu Jin, Shihua Zhang, Xiang-Sun Zhang, Luonan Chen
PLOS ONE , 2007, DOI: 10.1371/journal.pone.0001207
Abstract: Background It has been recognized that modular organization pervades biological complexity. Based on network analysis, ‘party hubs’ and ‘date hubs’ were proposed to understand the basic principle of module organization of biomolecular networks. However, recent study on hubs has suggested that there is no clear evidence for coexistence of ‘party hubs’ and ‘date hubs’. Thus, an open question has been raised as to whether or not ‘party hubs’ and ‘date hubs’ truly exist in yeast interactome. Methodology In contrast to previous studies focusing on the partners of a hub or the individual proteins around the hub, our work aims to study the network motifs of a hub or interactions among individual proteins including the hub and its neighbors. Depending on the relationship between a hub's network motifs and protein complexes, we define two new types of hubs, ‘motif party hubs’ and ‘motif date hubs’, which have the same characteristics as the original ‘party hubs’ and ‘date hubs’ respectively. The network motifs of these two types of hubs display significantly different features in spatial distribution (or cellular localizations), co-expression in microarray data, controlling topological structure of network, and organizing modularity. Conclusion By virtue of network motifs, we basically solved the open question about ‘party hubs’ and ‘date hubs’ which was raised by previous studies. Specifically, at the level of network motifs instead of individual proteins, we found two types of hubs, motif party hubs (mPHs) and motif date hubs (mDHs), whose network motifs display distinct characteristics on biological functions. In addition, in this paper we studied network motifs from a different viewpoint. That is, we show that a network motif should not be merely considered as an interaction pattern but be considered as an essential function unit in organizing modules of networks.
Bistability and Oscillations in Gene Regulation Mediated by Small Noncoding RNAs
Dengyu Liu,Xiao Chang,Zengrong Liu,Luonan Chen,Ruiqi Wang
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0017029
Abstract: The interplay of small noncoding RNAs (sRNAs), mRNAs, and proteins has been shown to play crucial roles in almost all cellular processes. As key post-transcriptional regulators of gene expression, the mechanisms and roles of sRNAs in various cellular processes still need to be fully understood. When participating in cellular processes, sRNAs mainly mediate mRNA degradation or translational repression. Here, we show how the dynamics of two minimal architectures is drastically affected by these two mechanisms. A comparison is also given to reveal the implication of the fundamental differences. This study may help us to analyze complex networks assembled by simple modules more easily. A better knowledge of the sRNA-mediated motifs is also of interest for bio-engineering and artificial control.
Gene function prediction using labeled and unlabeled data
Xing-Ming Zhao, Yong Wang, Luonan Chen, Kazuyuki Aihara
BMC Bioinformatics , 2008, DOI: 10.1186/1471-2105-9-57
Abstract: In this paper, we present a new technique, namely Annotating Genes with Positive Samples (AGPS), for defining negative samples in gene function prediction. With the defined negative samples, it is straightforward to predict the functions of unknown genes. In addition, the AGPS algorithm is able to integrate various kinds of data sources to predict gene functions in a reliable and accurate manner. With the one-class and two-class Support Vector Machines as the core learning algorithm, the AGPS algorithm shows good performances for function prediction on yeast genes.We proposed a new method for defining negative samples in gene function prediction. Experimental results on yeast genes show that AGPS yields good performances on both training and test sets. In addition, the overlapping between prediction results and GO annotations on unknown genes also demonstrates the effectiveness of the proposed method.One of the main goals in post-genomic era is to predict the biological functions of genes. Recently, with the rapid advance in high-throughput biotechnologies, such as yeast two-hybrid systems [1], protein complex [2,3] and microarray expression profiles [4], a large amount of biological data have been generated. These data are rich sources for deducing and understanding gene functions. For example, protein-protein interaction data are widely exploited for inferring functions of genes with the assumption that interacting proteins have the same or similar functions, i.e. "guilty by association" rule [5-10]. In addition, gene expression data have been widely used for gene function prediction, where genes with similar expression patterns are assumed to have similar functions [11]. In the literature, it has been shown that integration of different kinds of data sources can considerably improve prediction results [12-15]. With various kinds of high-throughput data, the machine learning techniques, especially Support Vector Machines (SVMs), have been used for predicting gene
Biomolecular network querying: a promising approach in systems biology
Shihua Zhang, Xiang-Sun Zhang, Luonan Chen
BMC Systems Biology , 2008, DOI: 10.1186/1752-0509-2-5
Abstract: With the rapid accumulation of 'omic' data from multiple species [1], various models of biological networks are being constructed, such as protein-protein interaction (PPI) networks [2,3], gene regulatory networks [4,5], gene co-expression networks [6-8], transcription regulatory networks [9], and metabolic networks [10,11]. Instead of looking at individual components, studies on those molecular networks provide new opportunities for understanding cellular biology and human health at a system-wide level. Because of the complexity of life, revealing how genes, proteins and small molecules interact to form functional cellular machinery is a major challenge in systems biology. Recent studies have made great progress in this field, which considerably expanded our insight into the organizational principles and cellular mechanisms of biological systems. For example, new insights have been gained regarding topological properties [10-12], modular organization [13], and motif enrichment [14]. In particular, network centrality and connectivity measures have been applied to identify essential genes in lower organisms [15] and cancer-related genes in humans [16].Biological systems differ from each other not only because of differences in their components, but also because of differences in their network architectures. A complicated living organism cannot be fully understood by merely analyzing individual components, and it is the interactions between these components and networks that are ultimately responsible for an organism's form and function. For example, humans and chimpanzees are very similar on the sequence and gene expression level, but show striking differences in the "wiring" of their co-expression networks [17]. It is essential to address the similarities and differences between molecular networks by comparative network analysis, to find conserved regions, discover new biological functions, understand the evolution of protein interactions, and uncover underlying mec
Identification of dysfunctional modules and disease genes in congenital heart disease by a network-based approach
Danning He, Zhi-Ping Liu, Luonan Chen
BMC Genomics , 2011, DOI: 10.1186/1471-2164-12-592
Abstract: In this paper, by modeling the information flow from source disease genes to targets of differentially expressed genes via a context-specific protein-protein interaction network, we extracted dysfunctional modules which were then validated by various types of measurements and independent datasets. Network topology analysis of these modules revealed major and auxiliary pathways and cellular processes in CHD, demonstrating the biological usefulness of the identified modules. We also prioritized a list of candidate CHD genes from these modules using a guilt-by-association approach, which are well supported by various kinds of literature and experimental evidence.We provided a network-based analysis to detect dysfunctional modules and disease genes of CHD by modeling the information transmission from source disease genes to targets of differentially expressed genes. Our method resulted in 12 modules from the constructed CHD subnetwork. We further identified and prioritized candidate disease genes of CHD from these dysfunctional modules. In conclusion, module analysis not only revealed several important findings with regard to the underlying molecular mechanisms of CHD, but also suggested the distinct network properties of causal disease genes which lead to identification of candidate CHD genes.Congenital heart disease (CHD) is among the most common human congenital defects, and is the leading cause of infant morbidity in the world [1,2]. Although CHD is known to arise from abnormal heart development during embryogenesis [3,4], its molecular mechanism remains far from clear. Currently, about 30 different genes have been known to cause CHD. Understanding the molecular functions, molecular interactions and represented pathways implicated in these CHD genes contribute to our knowledge of CHD pathogenesis, and therefore help improve clinical diagnosis and medical care of this disease. Network-based methods are powerful tools of systematic analysis of complex diseases, and id
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