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Systems Biology, IET

Issue 6 • Date Nov. 2009

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Displaying Results 1 - 11 of 11
  • Editorial - Selected papers from the 2nd international symposium on optimization and systems biology

    Publication Year: 2009 , Page(s): 439
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (76 KB)  

    One of the major challenges for post-genomic biology is to understand how genes, proteins and small molecules interact to form cellular systems. It has been recognised that a complicated living organism cannot be fully understood by merely analysing individual components, and that interactions of those components or networks are ultimately responsible for an organism??s form and functions. Instead of analysing individual components or aspects of the organism, systems biology is the study of an organism, viewed as a dynamical and interacting network of biomolecules which give rise to a complicated life. With increasingly accumulated data from high-throughput technologies, molecular networks and their dynamics have been studied extensively from various aspects of living organisms. Many mathematical methods have been adopted in computational systems biology; in particular, optimisation and statistics play a key role in analysing and understanding biological mechanisms from system-wide viewpoints. View full abstract»

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  • Combinatorial regulation: characteristics of dynamic correlations

    Publication Year: 2009 , Page(s): 440 - 452
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (415 KB)  

    A cis-regulatory module often processes multiple regulatory inputs provided by transcription factors that bind to DNA and regulate the expression of the gene in a combinatorial logic manner. Here the authors analyse dynamic cross correlations between the regulator concentrations and the output of the gene using stochastic modelling, and show their characteristics for different logic operations. The authors find that the dynamic cross-correlation curve with respect to the correlation time near the peak close to the point of the zero correlation time is upwards convex in the case of AND logic whereas downwards convex in the case of OR logic, where intrinsic and extrinsic noise plays a distinct role. This result implies that the cross-correlation function is a useful and robust index for distinguishing super- and sub-additive gene regulatory scenarios (molecular analogues of AND and OR logic operations) that faithfully represent gene expression. View full abstract»

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  • Generating probabilistic boolean networks from a prescribed transition probability matrix

    Publication Year: 2009 , Page(s): 453 - 464
    Cited by:  Papers (4)
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (397 KB)  

    Probabilistic Boolean networks (PBNs) have received much attention in modeling genetic regulatory networks. A PBN can be regarded as a Markov chain process and is characterised by a transition probability matrix. In this study, the authors propose efficient algorithms for constructing a PBN when its transition probability matrix is given. The complexities of the algorithms are also analysed. This is an interesting inverse problem in network inference using steady-state data. The problem is important as most microarray data sets are assumed to be obtained from sampling the steady-state. View full abstract»

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  • Distribution and enumeration of attractors in probabilistic boolean networks

    Publication Year: 2009 , Page(s): 465 - 474
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (374 KB)  

    Many mathematical models for gene regulatory networks have been proposed. In this study, the authors study attractors in probabilistic Boolean networks (PBNs). They study the expected number of singleton attractors in a PBN and show that it is (2 - (1/2)L-1)n, where n is the number of nodes in a PBN and L is the number of Boolean functions assigned to each node. In the case of L=2, this number is simplified into 1.5n. It is an interesting result because it is known that the expected number of singleton attractors in a Boolean network (BN) is 1. Then, we present algorithms for identifying singleton and small attractors and perform both theoretical and computational analyses on their average case time complexities. For example, the average case time complexities for identifying singleton attractors of a PBN with L=2 and L=3 are O(1.601n) and O(1.763n), respectively. The results of computational experiments suggest that these algorithms are much more efficient than the naive algorithm that examines all possible 2n states. View full abstract»

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  • Identifying differentially expressed pathways via a mixed integer linear programming model

    Publication Year: 2009 , Page(s): 475 - 486
    Cited by:  Papers (1)
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (376 KB)  

    The identification of genes and pathways involved in biological processes is a central problem in systems biology. Recent microarray technologies and other high-throughput experiments provide information which sheds light on this problem. In this article, the authors propose a new computational method to detect active pathways, or identify differentially expressed pathways via integration of gene expression and interactomic data in a sophisticated and efficient manner. Specifically, by using signal-to-noise ratio to measure the differentially expressed level of networks, this problem is formulated as a mixed integer linear programming problem (MILP). The results on yeast and human data demonstrate that the proposed method is more accurate and robust than existing approaches. View full abstract»

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  • Period-phase map: two-dimensional selection of circadian rhythm-related genes

    Publication Year: 2009 , Page(s): 487 - 495
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (540 KB)  

    Many genes related to the circadian rhythm, especially those involved in phase shifts induced by different environmental stimuli, still remain enigmatic. In this study, the authors monitored the expression of rat genes measured with multiple phase-resetting stimuli, and developed a technique to extract the candidate genes for the changes in circadian rhythm by the stimuli, from microarray data. First, the spectra for the time series of gene expression were estimated by fast Fourier transform, and then two fitting methods, the random period fitting method and the conditional curve fitting method, using the estimated periods as the initial values, were applied to the control and the stimulated expression data to estimate the periods and the phases. Finally, by comparing the two sets of periods and phases, the period change and the phase shift by stimuli were estimated to extract the candidate genes related to the master clock, by mapping the period change and the phase shift on a two-dimensional space, a period-phase map (PPM). As an indirect validation of the genes selected by our method, the significant enrichment of extracted gene clusters on the PPM was further evaluated, in terms of biological function. As a result, the gene clusters related to photoreceptors and neural regulation emerged on the PPM, thus implying the relationships in the stimulus response of the master clock that resides in the brain at the intersection of the optic nerves. Thus, the present approach is a feasible means to explore the oscillatory genes related to stimulus responses. View full abstract»

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  • Dynamics of microRNA-mediated motifs

    Publication Year: 2009 , Page(s): 496 - 504
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (341 KB)  

    In the past, most research on gene regulation has focused on transcriptional regulation and post-translational regulation. Recently, the post-transcriptional regulation by small non-coding RNAs has drawn much attention. This mode of regulation was found in both prokaryotes and eukaryotes. In this study, the authors integrate transcriptional and post-transcriptional regulation into four different kinds of microRNA-mediated motifs and investigate the mechanism of microRNAs regulation from the viewpoints of dynamics. Theoretical analysis and numerical simulations show that all the four motifs exhibit strong robustness to external and stochastic perturbations. View full abstract»

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  • Cross-platform method for identifying candidate network biomarkers for prostate cancer

    Publication Year: 2009 , Page(s): 505 - 512
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (330 KB)  

    Discovering biomarkers using mass spectrometry (MS) and microarray expression profiles is a promising strategy in molecular diagnosis. Here, the authors proposed a new pipeline for biomarker discovery that integrates disease information for proteins and genes, expression profiles in both genomic and proteomic levels, and protein-protein interactions (PPIs) to discover high confidence network biomarkers. Using this pipeline, a total of 474 molecules (genes and proteins) related to prostate cancer were identified and a prostate-cancer-related network (PCRN) was derived from the integrative information. Thus, a set of candidate network biomarkers were identified from multiple expression profiles composed by eight microarray datasets and one proteomics dataset. The network biomarkers with PPIs can accurately distinguish the prostate patients from the normal ones, which potentially provide more reliable hits of biomarker candidates than conventional biomarker discovery methods. View full abstract»

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  • Estimation of metabolic pathway systems from different data sources

    Publication Year: 2009 , Page(s): 513 - 522
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (399 KB)  

    Parameter estimation is the main bottleneck of metabolic pathway modelling. It may be addressed from the bottom up, using information on metabolites, enzymes and modulators, or from the top down, using metabolic time series data, which have become more prevalent in recent years. The authors propose here that it is useful to combine the two strategies and to complement time-series analysis with kinetic information. In particular, the authors investigate how the recent method of dynamic flux estimation (DFE) may be supplemented with other types of estimation. Using the glycolytic pathway in Lactococcus lactis as an illustration example, the authors demonstrate some strategies of such supplementation. View full abstract»

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  • Detecting drug targets with minimum side effects in metabolic networks

    Publication Year: 2009 , Page(s): 523 - 533
    Cited by:  Papers (1)
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (487 KB)  

    High-throughput techniques produce massive data on a genome-wide scale which facilitate pharmaceutical research. Drug target discovery is a crucial step in the drug discovery process and also plays a vital role in therapeutics. In this study, the problem of detecting drug targets was addressed, which finds a set of enzymes whose inhibition stops the production of a given set of target compounds and meanwhile minimally eliminates non-target compounds in the context of metabolic networks. The model aims to make the side effects of drugs as small as possible and thus has practical significance of potential pharmaceutical applications. Specifically, by exploiting special features of metabolic systems, a novel approach was proposed to exactly formulate this drug target detection problem as an integer linear programming model, which ensures that optimal solutions can be found efficiently without any heuristic manipulations. To verify the effectiveness of our approach, computational experiments on both Escherichia coli and Homo sapiens metabolic pathways were conducted. The results show that our approach can identify the optimal drug targets in an exact and efficient manner. In particular, it can be applied to large-scale networks including the whole metabolic networks from most organisms. View full abstract»

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  • Pathway level analysis by augmenting activities of transcription factor target genes

    Publication Year: 2009 , Page(s): 534 - 542
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (989 KB)  

    Many approaches to discovering significant pathways in gene expression profiles have been developed to facilitate biological interpretation and hypothesis generation. In this work, the authors propose a pathway identification scheme integrating the activity of pathway member genes with that of target genes of transcription factors (TFs) in the same pathway by the weighted Z-method. The authors evaluated the integrative scoring scheme in gene expression profiles of essential thrombocythemia patients with JAK2V617F mutation status, primary breast tumour samples with the status of metastasis occurrence, two independent lung cancer expression profiles with their prognosis, and found that our approach identified cancer-type-specific pathways better than gene set enrichment analysis (GSEA) and Tian's method using the original pathways [pathways that have TFs from database] and the extended pathways (including target genes of TFs of the original pathways). The success of our scheme implicates that adding information of transcriptional regulation is better way of utilising mRNA measurements for estimating differential activities of pathways from gene expression profiles more exactly. View full abstract»

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IET Systems Biology covers intra- and inter-cellular dynamics, using systems- and signal-oriented approaches.

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