Inferring Broad Regulatory Biology from Time Course Data: Have We Reached an Upper Bound under Constraints Typical of In Vivo Studies?

被引:8
作者
Vashishtha, Saurabh [1 ]
Broderick, Gordon [1 ,2 ,3 ]
Craddock, Travis J. A. [2 ,3 ]
Fletcher, Mary Ann [2 ,4 ]
Klimas, Nancy G. [2 ,4 ]
机构
[1] Univ Alberta, Dept Med, Edmonton, AB, Canada
[2] Nova SE Univ, Inst Neuroimmune Med, Ft Lauderdale, FL 33314 USA
[3] Nova SE Univ, Ctr Psychol Studies, Ft Lauderdale, FL 33314 USA
[4] Miami Vet Affairs Med Ctr, Miami, FL USA
来源
PLOS ONE | 2015年 / 10卷 / 05期
基金
美国国家卫生研究院;
关键词
DYNAMIC BAYESIAN NETWORK; ENGINEERING GENE NETWORKS; IMPROVED RECONSTRUCTION; MUTUAL INFORMATION; COMPOUND-MODE; INFERENCE; EXPRESSION; IDENTIFICATION; ALGORITHM; KNOCKOUT;
D O I
10.1371/journal.pone.0127364
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
There is a growing appreciation for the network biology that regulates the coordinated expression of molecular and cellular markers however questions persist regarding the identifiability of these networks. Here we explore some of the issues relevant to recovering directed regulatory networks from time course data collected under experimental constraints typical of in vivo studies. NetSim simulations of sparsely connected biological networks were used to evaluate two simple feature selection techniques used in the construction of linear Ordinary Differential Equation (ODE) models, namely truncation of terms versus latent vector projection. Performance was compared with ODE-based Time Series Network Identification (TSNI) integral, and the information-theoretic Time-Delay ARACNE (TD-ARACNE). Projection-based techniques and TSNI integral outperformed truncation-based selection and TD-ARACNE on aggregate networks with edge densities of 10-30%, i.e. transcription factor, protein-protein cliques and immune signaling networks. All were more robust to noise than truncation-based feature selection. Performance was comparable on the in silico 10-node DREAM 3 network, a 5-node Yeast synthetic network designed for In vivo Reverse-engineering and Modeling Assessment (IRMA) and a 9-node human HeLa cell cycle network of similar size and edge density. Performance was more sensitive to the number of time courses than to sample frequency and extrapolated better to larger networks by grouping experiments. In all cases performance declined rapidly in larger networks with lower edge density. Limited recovery and high false positive rates obtained overall bring into question our ability to generate informative time course data rather than the design of any particular reverse engineering algorithm.
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页数:27
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