Systems biology最新文献_第5页

In silico evolution of functional modules in biochemical networks. 生物化学网络中功能模块的计算机进化。

Systems biology Pub Date : 2006-07-01 DOI: 10.1049/ip-syb:20050096

S R Paladugu, V Chickarmane, A Deckard, J P Frumkin, M McCormack, H M Sauro

{"title":"In silico evolution of functional modules in biochemical networks.","authors":"S R Paladugu, V Chickarmane, A Deckard, J P Frumkin, M McCormack, H M Sauro","doi":"10.1049/ip-syb:20050096","DOIUrl":"https://doi.org/10.1049/ip-syb:20050096","url":null,"abstract":"Understanding the large reaction networks found in biological systems is a daunting task. One approach is to divide a network into more manageable smaller modules, thus simplifying the problem. This is a common strategy used in engineering. However, the process of identifying biological modules is still in its infancy and very little is understood about the range and capabilities of motif structures found in biological modules. In order to delineate these modules, a library of functional motifs has been generated via in silico evolution techniques. On the basis of their functional forms, networks were evolved from four broad areas: oscillators, bistable switches, homeostatic systems and frequency filters. Some of these motifs were constructed from simple mass action kinetics, others were based on Michaelis-Menten kinetics as found in protein/protein networks and the remainder were based on Hill equations as found in gene/protein interaction networks. The purpose of the study is to explore the capabilities of different network architectures and the rich variety of functional forms that can be generated. Ultimately, the library may be used to delineate functional motifs in real biological networks.","PeriodicalId":87457,"journal":{"name":"Systems biology","volume":"153 4","pages":"223-35"},"PeriodicalIF":0.0,"publicationDate":"2006-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1049/ip-syb:20050096","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"26262759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 80

Genomes to systems 2006. 基因组系统，2006年。

Systems biology Pub Date : 2006-07-01

引用次数: 0

Unravelling the regulatory structure of biochemical networks using stimulus response experiments and large-scale model selection. 利用刺激反应实验和大规模模型选择揭示生化网络的调节结构。

Systems biology Pub Date : 2006-07-01 DOI: 10.1049/ip-syb:20050089

S A Wahl, M D Haunschild, M Oldiges, W Wiechert

{"title":"Unravelling the regulatory structure of biochemical networks using stimulus response experiments and large-scale model selection.","authors":"S A Wahl, M D Haunschild, M Oldiges, W Wiechert","doi":"10.1049/ip-syb:20050089","DOIUrl":"https://doi.org/10.1049/ip-syb:20050089","url":null,"abstract":"To unravel the complex in vivo regulatory interdependences of biochemical networks, experiments with the living organism are absolutely necessary. Stimulus response experiments (SREs) have become increasingly popular in recent years. The response of metabolite concentrations from all major parts of the central metabolism is monitored over time by modem analytical methods, producing several thousand data points. SREs are applied to determine enzyme kinetic parameters and to find unknown enzyme regulatory mechanisms. Owing to the complex regulatory structure of metabolic networks and the amount of measured data, the evaluation of an SRE has to be extensively supported by modelling. If the enzyme regulatory mechanisms are part of the investigation, a large number of models with different enzyme kinetics have to be tested for their ability to reproduce the observed behaviour. In this contribution, a systematic model-building process for data-driven exploratory modelling is introduced with the aim of discovering essential features of the biological system. The process is based on data pre-processing, correlation-based hypothesis generation, automatic model family generation, large-scale model selection and statistical analysis of the best-fitting models followed by an extraction of common features. It is illustrated by the example of the aromatic amino acid synthesis pathway in Escherichia coli.","PeriodicalId":87457,"journal":{"name":"Systems biology","volume":"153 4","pages":"275-85"},"PeriodicalIF":0.0,"publicationDate":"2006-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1049/ip-syb:20050089","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"26320483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 37

Use and abuse of the quasi-steady-state approximation. 准稳态近似的使用和滥用。

Systems biology Pub Date : 2006-07-01 DOI: 10.1049/ip-syb:20050104

E H Flach, S Schnell

引用次数: 0

Quantifying gene network connectivity in silico: scalability and accuracy of a modular approach. 量化基因网络连通性：模块化方法的可扩展性和准确性。

Systems biology Pub Date : 2006-07-01 DOI: 10.1049/ip-syb:20050090

N Yalamanchili, D E Zak, B A Ogunnaike, J S Schwaber, A Kriete, B N Kholodenko

{"title":"Quantifying gene network connectivity in silico: scalability and accuracy of a modular approach.","authors":"N Yalamanchili, D E Zak, B A Ogunnaike, J S Schwaber, A Kriete, B N Kholodenko","doi":"10.1049/ip-syb:20050090","DOIUrl":"10.1049/ip-syb:20050090","url":null,"abstract":"Large, complex data sets that are generated from microarray experiments, create a need for systematic analysis techniques to unravel the underlying connectivity of gene regulatory networks. A modular approach, previously proposed by Kholodenko and co-workers, helps to scale down the network complexity into more computationally manageable entities called modules. A functional module includes a gene's mRNA, promoter and resulting products, thus encompassing a large set of interacting states. The essential elements of this approach are described in detail for a three-gene model network and later extended to a ten-gene model network, demonstrating scalability. The network architecture is identified by analysing in silico steady-state changes in the activities of only the module outputs, communicating intermediates, that result from specific perturbations applied to the network modules one at a time. These steady-state changes form the system response matrix, which is used to compute the network connectivity or network interaction map. By employing a known biochemical network, the accuracy of the modular approach and its sensitivity to key assumptions are evaluated.","PeriodicalId":87457,"journal":{"name":"Systems biology","volume":"153 4","pages":"236-46"},"PeriodicalIF":0.0,"publicationDate":"2006-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2346590/pdf/nihms43950.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"26320054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

From systems biology to dynamical neuropharmacology: proposal for a new methodology. 从系统生物学到动态神经药理学:一种新方法的建议。

Systems biology Pub Date : 2006-07-01 DOI: 10.1049/ip-syb:20050091

P Erdi, T Kiss, J Tóth, B Ujfalussy, L Zalányi

引用次数: 21

Parameter estimation in stochastic biochemical reactions. 随机生化反应中的参数估计。

Systems biology Pub Date : 2006-07-01 DOI: 10.1049/ip-syb:20050105

S Reinker, R M Altman, J Timmer

{"title":"Parameter estimation in stochastic biochemical reactions.","authors":"S Reinker, R M Altman, J Timmer","doi":"10.1049/ip-syb:20050105","DOIUrl":"https://doi.org/10.1049/ip-syb:20050105","url":null,"abstract":"Gene regulatory, signal transduction and metabolic networks are major areas of interest in the newly emerging field of systems biology. In living cells, stochastic dynamics play an important role; however, the kinetic parameters of biochemical reactions necessary for modelling these processes are often not accessible directly through experiments. The problem of estimating stochastic reaction constants from molecule count data measured, with error, at discrete time points is considered. For modelling the system, a hidden Markov process is used, where the hidden states are the true molecule counts, and the transitions between those states correspond to reaction events following collisions of molecules. Two different algorithms are proposed for estimating the unknown model parameters. The first is an approximate maximum likelihood method that gives good estimates of the reaction parameters in systems with few possible reactions in each sampling interval. The second algorithm, treating the data as exact measurements, approximates the number of reactions in each sampling interval by solving a simple linear equation. Maximising the likelihood based on these approximations can provide good results, even in complex reaction systems.","PeriodicalId":87457,"journal":{"name":"Systems biology","volume":"153 4","pages":"168-78"},"PeriodicalIF":0.0,"publicationDate":"2006-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1049/ip-syb:20050105","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"26262753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 128

Signal propagation in nonlinear stochastic gene regulatory networks. 非线性随机基因调控网络中的信号传播。

Systems biology Pub Date : 2006-05-01 DOI: 10.1049/ip-syb:20050027

S Achimescu, O Lipan

引用次数: 14

Robustness analysis of biochemical network models. 生化网络模型的稳健性分析。

Systems biology Pub Date : 2006-05-01 DOI: 10.1049/ip-syb:20050024

J Kim, D G Bates, I Postlethwaite, L Ma, P A Iglesias

{"title":"Robustness analysis of biochemical network models.","authors":"J Kim, D G Bates, I Postlethwaite, L Ma, P A Iglesias","doi":"10.1049/ip-syb:20050024","DOIUrl":"https://doi.org/10.1049/ip-syb:20050024","url":null,"abstract":"Biological systems that have been experimentally verified to be robust to significant changes in their environments require mathematical models that are themselves robust. In this context, a necessary condition for model robustness is that the model dynamics should not be sensitive to small variations in the model's parameters. Robustness analysis problems of this type have been extensively studied in the field of robust control theory and have been found to be very difficult to solve in general. The authors describe how some tools from robust control theory and nonlinear optimisation can be used to analyse the robustness of a recently proposed model of the molecular network underlying adenosine 3',5'-cyclic monophosphate (cAMP) oscillations observed in fields of chemotactic Dictyostelium cells. The network model, which consists of a system of seven coupled nonlinear differential equations, accurately reproduces the spontaneous oscillations in cAMP observed during the early development of D. discoideum. The analysis by the authors reveals, however, that very small variations in the model parameters can effectively destroy the required oscillatory dynamics. A biological interpretation of the analysis results is that correct functioning of a particular positive feedback loop in the proposed model is crucial to maintaining the required oscillatory dynamics.","PeriodicalId":87457,"journal":{"name":"Systems biology","volume":"153 3","pages":"96-104"},"PeriodicalIF":0.0,"publicationDate":"2006-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1049/ip-syb:20050024","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"26318718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 91

Dealing with gene expression missing data. 基因表达缺失数据处理。

Systems biology Pub Date : 2006-05-01 DOI: 10.1049/ip-syb:20050056

L P Brás, J C Menezes

{"title":"Dealing with gene expression missing data.","authors":"L P Brás, J C Menezes","doi":"10.1049/ip-syb:20050056","DOIUrl":"https://doi.org/10.1049/ip-syb:20050056","url":null,"abstract":"Compared evaluation of different methods is presented for estimating missing values in microarray data: weighted K-nearest neighbours imputation (KNNimpute), regression-based methods such as local least squares imputation (LLSimpute) and partial least squares imputation (PLSimpute) and Bayesian principal component analysis (BPCA). The influence in prediction accuracy of some factors, such as methods' parameters, type of data relationships used in the estimation process (i.e. row-wise, column-wise or both), missing rate and pattern and type of experiment [time series (TS), non-time series (NTS) or mixed (MIX) experiments] is elucidated. Improvements based on the iterative use of data (iterative LLS and PLS imputation--ILLSimpute and IPLSimpute), the need to perform initial imputations (modified PLS and Helland PLS imputation--MPLSimpute and HPLSimpute) and the type of relationships employed (KNNarray, LLSarray, HPLSarray and alternating PLS--APLSimpute) are proposed. Overall, it is shown that data set properties (type of experiment, missing rate and pattern) affect the data similarity structure, therefore influencing the methods' performance. LLSimpute and ILLSimpute are preferable in the presence of data with a stronger similarity structure (TS and MIX experiments), whereas PLS-based methods (MPLSimpute, IPLSimpute and APLSimpute) are preferable when estimating NTS missing data.","PeriodicalId":87457,"journal":{"name":"Systems biology","volume":"153 3","pages":"105-19"},"PeriodicalIF":0.0,"publicationDate":"2006-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1049/ip-syb:20050056","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"26318719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 22