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An algebra-based method for inferring gene regulatory networks. BMC SYSTEMS BIOLOGY 2014; 8:37. [PMID: 24669835 PMCID: PMC4022379 DOI: 10.1186/1752-0509-8-37] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/12/2013] [Accepted: 03/06/2014] [Indexed: 11/10/2022]
Abstract
BACKGROUND The inference of gene regulatory networks (GRNs) from experimental observations is at the heart of systems biology. This includes the inference of both the network topology and its dynamics. While there are many algorithms available to infer the network topology from experimental data, less emphasis has been placed on methods that infer network dynamics. Furthermore, since the network inference problem is typically underdetermined, it is essential to have the option of incorporating into the inference process, prior knowledge about the network, along with an effective description of the search space of dynamic models. Finally, it is also important to have an understanding of how a given inference method is affected by experimental and other noise in the data used. RESULTS This paper contains a novel inference algorithm using the algebraic framework of Boolean polynomial dynamical systems (BPDS), meeting all these requirements. The algorithm takes as input time series data, including those from network perturbations, such as knock-out mutant strains and RNAi experiments. It allows for the incorporation of prior biological knowledge while being robust to significant levels of noise in the data used for inference. It uses an evolutionary algorithm for local optimization with an encoding of the mathematical models as BPDS. The BPDS framework allows an effective representation of the search space for algebraic dynamic models that improves computational performance. The algorithm is validated with both simulated and experimental microarray expression profile data. Robustness to noise is tested using a published mathematical model of the segment polarity gene network in Drosophila melanogaster. Benchmarking of the algorithm is done by comparison with a spectrum of state-of-the-art network inference methods on data from the synthetic IRMA network to demonstrate that our method has good precision and recall for the network reconstruction task, while also predicting several of the dynamic patterns present in the network. CONCLUSIONS Boolean polynomial dynamical systems provide a powerful modeling framework for the reverse engineering of gene regulatory networks, that enables a rich mathematical structure on the model search space. A C++ implementation of the method, distributed under LPGL license, is available, together with the source code, at http://www.paola-vera-licona.net/Software/EARevEng/REACT.html.
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Abstract
The Master equation is considered the gold standard for modeling the stochastic mechanisms of gene regulation in molecular detail, but it is too complex to solve exactly in most cases, so approximation and simulation methods are essential. However, there is still a lack of consensus about the best way to carry these out. To help clarify the situation, we review Master equation models of gene regulation, theoretical approximations based on an expansion method due to N.G. van Kampen and R. Kubo, and simulation algorithms due to D.T. Gillespie and P. Langevin. Expansion of the Master equation shows that for systems with a single stable steady-state, the stochastic model reduces to a deterministic model in a first-order approximation. Additional theory, also due to van Kampen, describes the asymptotic behavior of multistable systems. To support and illustrate the theory and provide further insight into the complex behavior of multistable systems, we perform a detailed simulation study comparing the various approximation and simulation methods applied to synthetic gene regulatory systems with various qualitative characteristics. The simulation studies show that for large stochastic systems with a single steady-state, deterministic models are quite accurate, since the probability distribution of the solution has a single peak tracking the deterministic trajectory whose variance is inversely proportional to the system size. In multistable stochastic systems, large fluctuations can cause individual trajectories to escape from the domain of attraction of one steady-state and be attracted to another, so the system eventually reaches a multimodal probability distribution in which all stable steady-states are represented proportional to their relative stability. However, since the escape time scales exponentially with system size, this process can take a very long time in large systems.
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Affiliation(s)
- Arwen Meister
- Computational Biology Lab, Bio-X Program, Stanford University, Stanford, CA 94305, USA
| | - Chao Du
- Computational Biology Lab, Bio-X Program, Stanford University, Stanford, CA 94305, USA
| | - Ye Henry Li
- Computational Biology Lab, Bio-X Program, Stanford University, Stanford, CA 94305, USA
| | - Wing Hung Wong
- Computational Biology Lab, Bio-X Program, Stanford University, Stanford, CA 94305, USA
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203
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Qabaja A, Jarada T, Elsheikh A, Alhajj R. Prediction of gene-based drug indications using compendia of public gene expression data and PubMed abstracts. J Bioinform Comput Biol 2014; 12:1450007. [PMID: 24969745 DOI: 10.1142/s0219720014500073] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The tremendous research effort on diseases and drug discovery has produced a huge amount of important biomedical information which is mostly hidden in the web. In addition, many databases have been created for the purpose of storing enormous amounts of information and high-throughput experiments related to drugs and diseases' effects on genes. Thus, developing an algorithm to integrate biological data from different sources forms one of the greatest challenges in the field of computational biology. Based on our belief that data integration would result in better understanding for the drug mode of action or the disease pathophysiology, we have developed a novel paradigm to integrate data from three major sources in order to predict novel therapeutic drug indications. Microarray data, biomedical text mining data, and gene interaction data have been all integrated to predict ranked lists of genes based on their relevance to a particular drug or disease molecular action. These ranked lists of genes have finally been used as a raw material for building a disease-drug connectivity map based on the enrichment between the up/down tags of a particular disease signature and the ranked lists of drugs. Using this paradigm, we have reported 13% sensitivity improvement in comparison with using microarray or text mining data independently. In addition, our paradigm is able to predict many clinically validated disease-drug associations that could not be captured using microarray or text mining data independently.
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Affiliation(s)
- Ala Qabaja
- Department of Computer Science, University of Calgary, Calgary, Alberta, Canada
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204
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Zhou X, Liu J. Inferring gene dependency network specific to phenotypic alteration based on gene expression data and clinical information of breast cancer. PLoS One 2014; 9:e92023. [PMID: 24637666 PMCID: PMC3956890 DOI: 10.1371/journal.pone.0092023] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2013] [Accepted: 02/19/2014] [Indexed: 12/19/2022] Open
Abstract
Although many methods have been proposed to reconstruct gene regulatory network, most of them, when applied in the sample-based data, can not reveal the gene regulatory relations underlying the phenotypic change (e.g. normal versus cancer). In this paper, we adopt phenotype as a variable when constructing the gene regulatory network, while former researches either neglected it or only used it to select the differentially expressed genes as the inputs to construct the gene regulatory network. To be specific, we integrate phenotype information with gene expression data to identify the gene dependency pairs by using the method of conditional mutual information. A gene dependency pair (A,B) means that the influence of gene A on the phenotype depends on gene B. All identified gene dependency pairs constitute a directed network underlying the phenotype, namely gene dependency network. By this way, we have constructed gene dependency network of breast cancer from gene expression data along with two different phenotype states (metastasis and non-metastasis). Moreover, we have found the network scale free, indicating that its hub genes with high out-degrees may play critical roles in the network. After functional investigation, these hub genes are found to be biologically significant and specially related to breast cancer, which suggests that our gene dependency network is meaningful. The validity has also been justified by literature investigation. From the network, we have selected 43 discriminative hubs as signature to build the classification model for distinguishing the distant metastasis risks of breast cancer patients, and the result outperforms those classification models with published signatures. In conclusion, we have proposed a promising way to construct the gene regulatory network by using sample-based data, which has been shown to be effective and accurate in uncovering the hidden mechanism of the biological process and identifying the gene signature for phenotypic change.
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Affiliation(s)
| | - Juan Liu
- School of computer, Wuhan University, Wuhan, China
- * E-mail:
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205
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CaSPIAN: a causal compressive sensing algorithm for discovering directed interactions in gene networks. PLoS One 2014; 9:e90781. [PMID: 24622336 PMCID: PMC3951243 DOI: 10.1371/journal.pone.0090781] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2013] [Accepted: 02/05/2014] [Indexed: 11/21/2022] Open
Abstract
We introduce a novel algorithm for inference of causal gene interactions, termed CaSPIAN (Causal Subspace Pursuit for Inference and Analysis of Networks), which is based on coupling compressive sensing and Granger causality techniques. The core of the approach is to discover sparse linear dependencies between shifted time series of gene expressions using a sequential list-version of the subspace pursuit reconstruction algorithm and to estimate the direction of gene interactions via Granger-type elimination. The method is conceptually simple and computationally efficient, and it allows for dealing with noisy measurements. Its performance as a stand-alone platform without biological side-information was tested on simulated networks, on the synthetic IRMA network in Saccharomyces cerevisiae, and on data pertaining to the human HeLa cell network and the SOS network in E. coli. The results produced by CaSPIAN are compared to the results of several related algorithms, demonstrating significant improvements in inference accuracy of documented interactions. These findings highlight the importance of Granger causality techniques for reducing the number of false-positives, as well as the influence of noise and sampling period on the accuracy of the estimates. In addition, the performance of the method was tested in conjunction with biological side information of the form of sparse “scaffold networks”, to which new edges were added using available RNA-seq or microarray data. These biological priors aid in increasing the sensitivity and precision of the algorithm in the small sample regime.
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206
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Dimitrakopoulou K, Dimitrakopoulos GN, Wilk E, Tsimpouris C, Sgarbas KN, Schughart K, Bezerianos A. Influenza A immunomics and public health omics: the dynamic pathway interplay in host response to H1N1 infection. OMICS-A JOURNAL OF INTEGRATIVE BIOLOGY 2014; 18:167-83. [PMID: 24512282 DOI: 10.1089/omi.2013.0062] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Towards unraveling the influenza A (H1N1) immunome, this work aims at constructing the murine host response pathway interactome. To accomplish that, an ensemble of dynamic and time-varying Gene Regulatory Network Inference methodologies was recruited to set a confident interactome based on mouse time series transcriptome data (day 1-day 60). The proposed H1N1 interactome demonstrated significant transformations among activated and suppressed pathways in time. Enhanced interplay was observed at day 1, while the maximal network complexity was reached at day 8 (correlated with viral clearance and iBALT tissue formation) and one interaction was present at day 40. Next, we searched for common interactivity features between the murine-adapted PR8 strain and other influenza A subtypes/strains. For this, two other interactomes, describing the murine host response against H5N1 and H1N1pdm, were constructed, which in turn validated many of the observed interactions (in the period day 1-day 7). The H1N1 interactome revealed the role of cell cycle both in innate and adaptive immunity (day 1-day 14). Also, pathogen sensory pathways (e.g., RIG-I) displayed long-lasting association with cytokine/chemokine signaling (until day 8). Interestingly, the above observations were also supported by the H5N1 and H1N1pdm models. It also elucidated the enhanced coupling of the activated innate pathways with the suppressed PPAR signaling to keep low inflammation until viral clearance (until day 14). Further, it showed that interactions reflecting phagocytosis processes continued long after the viral clearance and the establishment of adaptive immunity (day 8-day 40). Additionally, interactions involving B cell receptor pathway were evident since day 1. These results collectively inform the emerging field of public health omics and future clinical studies aimed at deciphering dynamic host responses to infectious agents.
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207
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Jang IS, Margolin A, Califano A. hARACNe: improving the accuracy of regulatory model reverse engineering via higher-order data processing inequality tests. Interface Focus 2014; 3:20130011. [PMID: 24511376 PMCID: PMC3915831 DOI: 10.1098/rsfs.2013.0011] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023] Open
Abstract
A key goal of systems biology is to elucidate molecular mechanisms associated with physiologic and pathologic phenotypes based on the systematic and genome-wide understanding of cell context-specific molecular interaction models. To this end, reverse engineering approaches have been used to systematically dissect regulatory interactions in a specific tissue, based on the availability of large molecular profile datasets, thus improving our mechanistic understanding of complex diseases, such as cancer. In this paper, we introduce high-order Algorithm for the Reconstruction of Accurate Cellular Network (hARACNe), an extension of the ARACNe algorithm for the dissection of transcriptional regulatory networks. ARACNe uses the data processing inequality (DPI), from information theory, to detect and prune indirect interactions that are unlikely to be mediated by an actual physical interaction. Whereas ARACNe considers only first-order indirect interactions, i.e. those mediated by only one extra regulator, hARACNe considers a generalized form of indirect interactions via two, three or more other regulators. We show that use of higher-order DPI resulted in significantly improved performance, based on transcription factor (TF)-specific ChIP-chip data, as well as on gene expression profile following RNAi-mediated TF silencing.
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Affiliation(s)
- In Sock Jang
- Sage Bionetworks , 1100 Fairview Avenue North, Seattle, WA 98109 , USA
| | - Adam Margolin
- Sage Bionetworks , 1100 Fairview Avenue North, Seattle, WA 98109 , USA
| | - Andrea Califano
- Department of Systems Biology, Biochemistry and Molecular Biophysics, Biomedical Informatics, and Herbert Irving Comprehensive Cancer Center , Columbia University Medical Center , New York, NY 10032 , USA
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208
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Su RQ, Lai YC, Wang X, Do Y. Uncovering hidden nodes in complex networks in the presence of noise. Sci Rep 2014; 4:3944. [PMID: 24487720 PMCID: PMC3909906 DOI: 10.1038/srep03944] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2013] [Accepted: 01/13/2014] [Indexed: 11/10/2022] Open
Abstract
Ascertaining the existence of hidden objects in a complex system, objects that cannot be observed from the external world, not only is curiosity-driven but also has significant practical applications. Generally, uncovering a hidden node in a complex network requires successful identification of its neighboring nodes, but a challenge is to differentiate its effects from those of noise. We develop a completely data-driven, compressive-sensing based method to address this issue by utilizing complex weighted networks with continuous-time oscillatory or discrete-time evolutionary-game dynamics. For any node, compressive sensing enables accurate reconstruction of the dynamical equations and coupling functions, provided that time series from this node and all its neighbors are available. For a neighboring node of the hidden node, this condition cannot be met, resulting in abnormally large prediction errors that, counterintuitively, can be used to infer the existence of the hidden node. Based on the principle of differential signal, we demonstrate that, when strong noise is present, insofar as at least two neighboring nodes of the hidden node are subject to weak background noise only, unequivocal identification of the hidden node can be achieved.
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Affiliation(s)
- Ri-Qi Su
- School of Electrical, Computer, and Energy Engineering, Arizona State University, Tempe, AZ 85287, USA
| | - Ying-Cheng Lai
- 1] School of Electrical, Computer, and Energy Engineering, Arizona State University, Tempe, AZ 85287, USA [2] Department of Physics, Arizona State University, Tempe, AZ 85287, USA
| | - Xiao Wang
- School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ 85287, USA
| | - Younghae Do
- Department of Mathematics, Kyungpook National University, Daegu, 702-701, South Korea
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209
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Christadore LM, Pham L, Kolaczyk ED, Schaus SE. Improvement of experimental testing and network training conditions with genome-wide microarrays for more accurate predictions of drug gene targets. BMC SYSTEMS BIOLOGY 2014; 8:7. [PMID: 24444313 PMCID: PMC3911882 DOI: 10.1186/1752-0509-8-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/29/2012] [Accepted: 11/21/2013] [Indexed: 11/10/2022]
Abstract
Background Genome-wide microarrays have been useful for predicting chemical-genetic interactions at the gene level. However, interpreting genome-wide microarray results can be overwhelming due to the vast output of gene expression data combined with off-target transcriptional responses many times induced by a drug treatment. This study demonstrates how experimental and computational methods can interact with each other, to arrive at more accurate predictions of drug-induced perturbations. We present a two-stage strategy that links microarray experimental testing and network training conditions to predict gene perturbations for a drug with a known mechanism of action in a well-studied organism. Results S. cerevisiae cells were treated with the antifungal, fluconazole, and expression profiling was conducted under different biological conditions using Affymetrix genome-wide microarrays. Transcripts were filtered with a formal network-based method, sparse simultaneous equation models and Lasso regression (SSEM-Lasso), under different network training conditions. Gene expression results were evaluated using both gene set and single gene target analyses, and the drug’s transcriptional effects were narrowed first by pathway and then by individual genes. Variables included: (i) Testing conditions – exposure time and concentration and (ii) Network training conditions – training compendium modifications. Two analyses of SSEM-Lasso output – gene set and single gene – were conducted to gain a better understanding of how SSEM-Lasso predicts perturbation targets. Conclusions This study demonstrates that genome-wide microarrays can be optimized using a two-stage strategy for a more in-depth understanding of how a cell manifests biological reactions to a drug treatment at the transcription level. Additionally, a more detailed understanding of how the statistical model, SSEM-Lasso, propagates perturbations through a network of gene regulatory interactions is achieved.
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Affiliation(s)
| | | | | | - Scott E Schaus
- Department of Chemistry, Boston University, Boston, MA, USA.
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210
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Zheng Z, Christley S, Chiu WT, Blitz IL, Xie X, Cho KWY, Nie Q. Inference of the Xenopus tropicalis embryonic regulatory network and spatial gene expression patterns. BMC SYSTEMS BIOLOGY 2014; 8:3. [PMID: 24397936 PMCID: PMC3896677 DOI: 10.1186/1752-0509-8-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/16/2013] [Accepted: 12/19/2013] [Indexed: 11/10/2022]
Abstract
BACKGROUND During embryogenesis, signaling molecules produced by one cell population direct gene regulatory changes in neighboring cells and influence their developmental fates and spatial organization. One of the earliest events in the development of the vertebrate embryo is the establishment of three germ layers, consisting of the ectoderm, mesoderm and endoderm. Attempts to measure gene expression in vivo in different germ layers and cell types are typically complicated by the heterogeneity of cell types within biological samples (i.e., embryos), as the responses of individual cell types are intermingled into an aggregate observation of heterogeneous cell types. Here, we propose a novel method to elucidate gene regulatory circuits from these aggregate measurements in embryos of the frog Xenopus tropicalis using gene network inference algorithms and then test the ability of the inferred networks to predict spatial gene expression patterns. RESULTS We use two inference models with different underlying assumptions that incorporate existing network information, an ODE model for steady-state data and a Markov model for time series data, and contrast the performance of the two models. We apply our method to both control and knockdown embryos at multiple time points to reconstruct the core mesoderm and endoderm regulatory circuits. Those inferred networks are then used in combination with known dorsal-ventral spatial expression patterns of a subset of genes to predict spatial expression patterns for other genes. Both models are able to predict spatial expression patterns for some of the core mesoderm and endoderm genes, but interestingly of different gene subsets, suggesting that neither model is sufficient to recapitulate all of the spatial patterns, yet they are complementary for the patterns that they do capture. CONCLUSION The presented methodology of gene network inference combined with spatial pattern prediction provides an additional layer of validation to elucidate the regulatory circuits controlling the spatial-temporal dynamics in embryonic development.
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Affiliation(s)
| | | | | | | | | | | | - Qing Nie
- Department of Mathematics, University of California, Irvine, CA 92697, USA.
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211
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Qabaja A, Alshalalfa M, Alanazi E, Alhajj R. Prediction of novel drug indications using network driven biological data prioritization and integration. J Cheminform 2014; 6:1. [PMID: 24397863 PMCID: PMC3896815 DOI: 10.1186/1758-2946-6-1] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2013] [Accepted: 11/28/2013] [Indexed: 11/23/2022] Open
Abstract
Background With the rapid development of high-throughput genomic technologies and the accumulation of genome-wide datasets for gene expression profiling and biological networks, the impact of diseases and drugs on gene expression can be comprehensively characterized. Drug repositioning offers the possibility of reduced risks in the drug discovery process, thus it is an essential step in drug development. Results Computational prediction of drug-disease interactions using gene expression profiling datasets and biological networks is a new direction in drug repositioning that has gained increasing interest. We developed a computational framework to build disease-drug networks using drug- and disease-specific subnetworks. The framework incorporates protein networks to refine drug and disease associated genes and prioritize genes in disease and drug specific networks. For each drug and disease we built multiple networks using gene expression profiling and text mining. Finally a logistic regression model was used to build functional associations between drugs and diseases. Conclusions We found that representing drugs and diseases by genes with high centrality degree in gene networks is the most promising representation of drug or disease subnetworks.
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Affiliation(s)
| | - Mohammed Alshalalfa
- Department of Computer Science, University of Calgary, Calgary, Alberta, Canada.
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212
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Inference of Vohradský's models of genetic networks by solving two-dimensional function optimization problems. PLoS One 2014; 8:e83308. [PMID: 24386175 PMCID: PMC3875442 DOI: 10.1371/journal.pone.0083308] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2013] [Accepted: 11/01/2013] [Indexed: 11/21/2022] Open
Abstract
The inference of a genetic network is a problem in which mutual interactions among genes are inferred from time-series of gene expression levels. While a number of models have been proposed to describe genetic networks, this study focuses on a mathematical model proposed by Vohradský. Because of its advantageous features, several researchers have proposed the inference methods based on Vohradský's model. When trying to analyze large-scale networks consisting of dozens of genes, however, these methods must solve high-dimensional non-linear function optimization problems. In order to resolve the difficulty of estimating the parameters of the Vohradský's model, this study proposes a new method that defines the problem as several two-dimensional function optimization problems. Through numerical experiments on artificial genetic network inference problems, we showed that, although the computation time of the proposed method is not the shortest, the method has the ability to estimate parameters of Vohradský's models more effectively with sufficiently short computation times. This study then applied the proposed method to an actual inference problem of the bacterial SOS DNA repair system, and succeeded in finding several reasonable regulations.
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213
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Melendez J, Patel M, Oakes BL, Xu P, Morton P, McClean MN. Real-time optogenetic control of intracellular protein concentration in microbial cell cultures. Integr Biol (Camb) 2014; 6:366-72. [DOI: 10.1039/c3ib40102b] [Citation(s) in RCA: 58] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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214
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Laubenbacher R, Hinkelmann F, Murrugarra D, Veliz-Cuba A. Algebraic Models and Their Use in Systems Biology. DISCRETE AND TOPOLOGICAL MODELS IN MOLECULAR BIOLOGY 2014. [DOI: 10.1007/978-3-642-40193-0_21] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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215
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Wang Z, Wang Y, Wang N, Wang J, Wang Z, Vallejos CE, Wu R. Towards a comprehensive picture of the genetic landscape of complex traits. Brief Bioinform 2014; 15:30-42. [PMID: 22930650 PMCID: PMC3896925 DOI: 10.1093/bib/bbs049] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2012] [Accepted: 07/09/2012] [Indexed: 12/11/2022] Open
Abstract
The formation of phenotypic traits, such as biomass production, tumor volume and viral abundance, undergoes a complex process in which interactions between genes and developmental stimuli take place at each level of biological organization from cells to organisms. Traditional studies emphasize the impact of genes by directly linking DNA-based markers with static phenotypic values. Functional mapping, derived to detect genes that control developmental processes using growth equations, has proven powerful for addressing questions about the roles of genes in development. By treating phenotypic formation as a cohesive system using differential equations, a different approach-systems mapping-dissects the system into interconnected elements and then map genes that determine a web of interactions among these elements, facilitating our understanding of the genetic machineries for phenotypic development. Here, we argue that genetic mapping can play a more important role in studying the genotype-phenotype relationship by filling the gaps in the biochemical and regulatory process from DNA to end-point phenotype. We describe a new framework, named network mapping, to study the genetic architecture of complex traits by integrating the regulatory networks that cause a high-order phenotype. Network mapping makes use of a system of differential equations to quantify the rule by which transcriptional, proteomic and metabolomic components interact with each other to organize into a functional whole. The synthesis of functional mapping, systems mapping and network mapping provides a novel avenue to decipher a comprehensive picture of the genetic landscape of complex phenotypes that underlie economically and biomedically important traits.
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Affiliation(s)
- Zhong Wang
- Center for Statistical Genetics, The Pennsylvania State University, Hershey, PA 17033, USA.
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216
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Abstract
Modern high-throughput assays yield detailed characterizations of the genomic, transcriptomic, and proteomic states of biological samples, enabling us to probe the molecular mechanisms that regulate hematopoiesis or give rise to hematological disorders. At the same time, the high dimensionality of the data and the complex nature of biological interaction networks present significant analytical challenges in identifying causal variations and modeling the underlying systems biology. In addition to identifying significantly disregulated genes and proteins, integrative analysis approaches that allow the investigation of these single genes within a functional context are required. This chapter presents a survey of current computational approaches for the statistical analysis of high-dimensional data and the development of systems-level models of cellular signaling and regulation. Specifically, we focus on multi-gene analysis methods and the integration of expression data with domain knowledge (such as biological pathways) and other gene-wise information (e.g., sequence or methylation data) to identify novel functional modules in the complex cellular interaction network.
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Affiliation(s)
- Rosemary Braun
- Biostatistics Division, Department of Preventive Medicine and Northwestern Institute on Complex Systems, Northwestern University, 680 N. Lake Shore Dr., Suite 1400, 60611, Chicago, IL, USA,
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217
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Molinelli EJ, Korkut A, Wang W, Miller ML, Gauthier NP, Jing X, Kaushik P, He Q, Mills G, Solit DB, Pratilas CA, Weigt M, Braunstein A, Pagnani A, Zecchina R, Sander C. Perturbation biology: inferring signaling networks in cellular systems. PLoS Comput Biol 2013; 9:e1003290. [PMID: 24367245 PMCID: PMC3868523 DOI: 10.1371/journal.pcbi.1003290] [Citation(s) in RCA: 91] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2012] [Accepted: 08/26/2013] [Indexed: 12/16/2022] Open
Abstract
We present a powerful experimental-computational technology for inferring network models that predict the response of cells to perturbations, and that may be useful in the design of combinatorial therapy against cancer. The experiments are systematic series of perturbations of cancer cell lines by targeted drugs, singly or in combination. The response to perturbation is quantified in terms of relative changes in the measured levels of proteins, phospho-proteins and cellular phenotypes such as viability. Computational network models are derived de novo, i.e., without prior knowledge of signaling pathways, and are based on simple non-linear differential equations. The prohibitively large solution space of all possible network models is explored efficiently using a probabilistic algorithm, Belief Propagation (BP), which is three orders of magnitude faster than standard Monte Carlo methods. Explicit executable models are derived for a set of perturbation experiments in SKMEL-133 melanoma cell lines, which are resistant to the therapeutically important inhibitor of RAF kinase. The resulting network models reproduce and extend known pathway biology. They empower potential discoveries of new molecular interactions and predict efficacious novel drug perturbations, such as the inhibition of PLK1, which is verified experimentally. This technology is suitable for application to larger systems in diverse areas of molecular biology. Drugs that target specific effects of signaling proteins are promising agents for treating cancer. One of the many obstacles facing optimal drug design is inadequate quantitative understanding of the coordinated interactions between signaling proteins. De novo model inference of network or pathway models refers to the algorithmic construction of mathematical predictive models from experimental data without dependence on prior knowledge. De novo inference is difficult because of the prohibitively large number of possible sets of interactions that may or may not be consistent with observations. Our new method overcomes this difficulty by adapting a method from statistical physics, called Belief Propagation, which first calculates probabilistically the most likely interactions in the vast space of all possible solutions, then derives a set of individual, highly probable solutions in the form of executable models. In this paper, we test this method on artificial data and then apply it to model signaling pathways in a BRAF-mutant melanoma cancer cell line based on a large set of rich output measurements from a systematic set of perturbation experiments using drug combinations. Our results are in agreement with established biological knowledge, predict novel interactions, and predict efficacious drug targets that are specific to the experimental cell line and potentially to related tumors. The method has the potential, with sufficient systematic perturbation data, to model, de novo and quantitatively, the effects of hundreds of proteins on cellular responses, on a scale that is currently unreachable in diverse areas of cell biology. In a disease context, the method is applicable to the computational design of novel combination drug treatments.
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Affiliation(s)
- Evan J. Molinelli
- Computational Biology Program, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America
- Tri-Institutional Program for Computational Biology and Medicine, Weill Cornell Medical College, New York, New York, United States of America
| | - Anil Korkut
- Computational Biology Program, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America
| | - Weiqing Wang
- Computational Biology Program, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America
| | - Martin L. Miller
- Computational Biology Program, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America
| | - Nicholas P. Gauthier
- Computational Biology Program, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America
| | - Xiaohong Jing
- Computational Biology Program, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America
| | - Poorvi Kaushik
- Computational Biology Program, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America
- Tri-Institutional Program for Computational Biology and Medicine, Weill Cornell Medical College, New York, New York, United States of America
| | - Qin He
- Computational Biology Program, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America
| | - Gordon Mills
- Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - David B. Solit
- Program in Molecular Pharmacology, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America
- Human Oncology and Pathogenesis Program, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America
| | - Christine A. Pratilas
- Program in Molecular Pharmacology, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America
- Department of Pediatrics, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America
| | - Martin Weigt
- Laboratoire de Génomique des Microorganismes, Université Pierre et Marie Curie, Paris, France
| | - Alfredo Braunstein
- Politecnico di Torino and Human Genetics Foundation, HuGeF, Torino, Italy
| | - Andrea Pagnani
- Politecnico di Torino and Human Genetics Foundation, HuGeF, Torino, Italy
| | - Riccardo Zecchina
- Politecnico di Torino and Human Genetics Foundation, HuGeF, Torino, Italy
| | - Chris Sander
- Computational Biology Program, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America
- * E-mail:
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218
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Ye G, Tang M, Cai JF, Nie Q, Xie X. Low-rank regularization for learning gene expression programs. PLoS One 2013; 8:e82146. [PMID: 24358148 PMCID: PMC3866120 DOI: 10.1371/journal.pone.0082146] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2013] [Accepted: 10/30/2013] [Indexed: 12/25/2022] Open
Abstract
Learning gene expression programs directly from a set of observations is challenging due to the complexity of gene regulation, high noise of experimental measurements, and insufficient number of experimental measurements. Imposing additional constraints with strong and biologically motivated regularizations is critical in developing reliable and effective algorithms for inferring gene expression programs. Here we propose a new form of regulation that constrains the number of independent connectivity patterns between regulators and targets, motivated by the modular design of gene regulatory programs and the belief that the total number of independent regulatory modules should be small. We formulate a multi-target linear regression framework to incorporate this type of regulation, in which the number of independent connectivity patterns is expressed as the rank of the connectivity matrix between regulators and targets. We then generalize the linear framework to nonlinear cases, and prove that the generalized low-rank regularization model is still convex. Efficient algorithms are derived to solve both the linear and nonlinear low-rank regularized problems. Finally, we test the algorithms on three gene expression datasets, and show that the low-rank regularization improves the accuracy of gene expression prediction in these three datasets.
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Affiliation(s)
- Guibo Ye
- Department of Computer Science, University of California Irvine, Irvine, California, United States of America
- Department of Mathematics, University of California Irvine, Irvine, California, United States of America
| | - Mengfan Tang
- Department of Computer Science, University of California Irvine, Irvine, California, United States of America
| | - Jian-Feng Cai
- Department of Mathematics, University of Iowa, Iowa City, Iowa, United States of America
| | - Qing Nie
- Department of Mathematics, University of California Irvine, Irvine, California, United States of America
- Center for Complex Biological Systems, University of California Irvine, Irvine, California, United States of America
| | - Xiaohui Xie
- Department of Computer Science, University of California Irvine, Irvine, California, United States of America
- Center for Complex Biological Systems, University of California Irvine, Irvine, California, United States of America
- * E-mail:
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219
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Iorio F, Saez-Rodriguez J, Bernardo DD. Network based elucidation of drug response: from modulators to targets. BMC SYSTEMS BIOLOGY 2013; 7:139. [PMID: 24330611 PMCID: PMC3878740 DOI: 10.1186/1752-0509-7-139] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/23/2012] [Accepted: 07/19/2013] [Indexed: 11/20/2022]
Abstract
: Network-based drug discovery aims at harnessing the power of networks to investigate the mechanism of action of existing drugs, or new molecules, in order to identify innovative therapeutic treatments. In this review, we describe some of the most recent advances in the field of network pharmacology, starting with approaches relying on computational models of transcriptional networks, then moving to protein and signaling network models and concluding with "drug networks". These networks are derived from different sources of experimental data, or literature-based analysis, and provide a complementary view of drug mode of action. Molecular and drug networks are powerful integrated computational and experimental approaches that will likely speed up and improve the drug discovery process, once fully integrated into the academic and industrial drug discovery pipeline.
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Affiliation(s)
- Francesco Iorio
- European Molecular Biology Laboratory - European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge CB10 1SD, UK
- Cancer Genome Project, Wellcome Trust Sanger Institute, Hinxton CB10 1SA, UK
| | - Julio Saez-Rodriguez
- European Molecular Biology Laboratory - European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge CB10 1SD, UK
| | - Diego di Bernardo
- Telethon Institute of Genetics and Medicine, Naples, Italy
- Deptartment of Electrical Engineering and Information Technology, University of Naples “Federico II”, Naples, Italy
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220
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Stein RR, Bucci V, Toussaint NC, Buffie CG, Rätsch G, Pamer EG, Sander C, Xavier JB. Ecological modeling from time-series inference: insight into dynamics and stability of intestinal microbiota. PLoS Comput Biol 2013; 9:e1003388. [PMID: 24348232 PMCID: PMC3861043 DOI: 10.1371/journal.pcbi.1003388] [Citation(s) in RCA: 382] [Impact Index Per Article: 31.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2013] [Accepted: 10/27/2013] [Indexed: 01/19/2023] Open
Abstract
The intestinal microbiota is a microbial ecosystem of crucial importance to human health. Understanding how the microbiota confers resistance against enteric pathogens and how antibiotics disrupt that resistance is key to the prevention and cure of intestinal infections. We present a novel method to infer microbial community ecology directly from time-resolved metagenomics. This method extends generalized Lotka–Volterra dynamics to account for external perturbations. Data from recent experiments on antibiotic-mediated Clostridium difficile infection is analyzed to quantify microbial interactions, commensal-pathogen interactions, and the effect of the antibiotic on the community. Stability analysis reveals that the microbiota is intrinsically stable, explaining how antibiotic perturbations and C. difficile inoculation can produce catastrophic shifts that persist even after removal of the perturbations. Importantly, the analysis suggests a subnetwork of bacterial groups implicated in protection against C. difficile. Due to its generality, our method can be applied to any high-resolution ecological time-series data to infer community structure and response to external stimuli. Recent advances in DNA sequencing and metagenomics are opening a window into the human microbiome revealing novel associations between certain microbial consortia and disease. However, most of these studies are cross-sectional and lack a mechanistic understanding of this ecosystem's structure and its response to external perturbations, therefore not allowing accurate temporal predictions. In this article, we develop a method to analyze temporal community data accounting also for time-dependent external perturbations. In particular, this method combines the classical Lotka–Volterra model of population dynamics with regression techniques to obtain mechanistically descriptive coefficients which can be further used to construct predictive models of ecosystem dynamics. Using then data from a mouse experiment under antibiotic perturbations, we are able to predict and recover the microbiota temporal dynamics and study the concept of alternative stable states and antibiotic-induced transitions. As a result, our method reveals a group of commensal microbes that potentially protect against infection by the pathogen Clostridium difficile and proposes a possible mechanism how the antibiotic makes the host more susceptible to infection.
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Affiliation(s)
- Richard R. Stein
- Computational Biology Program, Sloan-Kettering Institute, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America
- * E-mail: (RRS); (VB); (JBX)
| | - Vanni Bucci
- Computational Biology Program, Sloan-Kettering Institute, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America
- * E-mail: (RRS); (VB); (JBX)
| | - Nora C. Toussaint
- Immunology Program, Sloan-Kettering Institute, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America
| | - Charlie G. Buffie
- Immunology Program, Sloan-Kettering Institute, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America
| | - Gunnar Rätsch
- Computational Biology Program, Sloan-Kettering Institute, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America
| | - Eric G. Pamer
- Immunology Program, Sloan-Kettering Institute, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America
| | - Chris Sander
- Computational Biology Program, Sloan-Kettering Institute, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America
| | - João B. Xavier
- Computational Biology Program, Sloan-Kettering Institute, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America
- * E-mail: (RRS); (VB); (JBX)
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221
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Abstract
Molecular entities work in concert as a system and mediate phenotypic outcomes and disease states. There has been recent interest in modelling the associations between molecular entities from their observed expression profiles as networks using a battery of algorithms. These networks have proven to be useful abstractions of the underlying pathways and signalling mechanisms. Noise is ubiquitous in molecular data and can have a pronounced effect on the inferred network. Noise can be an outcome of several factors including: inherent stochastic mechanisms at the molecular level, variation in the abundance of molecules, heterogeneity, sensitivity of the biological assay or measurement artefacts prevalent especially in high-throughput settings. The present study investigates the impact of discrepancies in noise variance on pair-wise dependencies, conditional dependencies and constraint-based Bayesian network structure learning algorithms that incorporate conditional independence tests as a part of the learning process. Popular network motifs and fundamental connections, namely: (a) common-effect, (b) three-chain, and (c) coherent type-I feed-forward loop (FFL) are investigated. The choice of these elementary networks can be attributed to their prevalence across more complex networks. Analytical expressions elucidating the impact of discrepancies in noise variance on pairwise dependencies and conditional dependencies for special cases of these motifs are presented. Subsequently, the impact of noise on two popular constraint-based Bayesian network structure learning algorithms such as Grow-Shrink (GS) and Incremental Association Markov Blanket (IAMB) that implicitly incorporate tests for conditional independence is investigated. Finally, the impact of noise on networks inferred from publicly available single cell molecular expression profiles is investigated. While discrepancies in noise variance are overlooked in routine molecular network inference, the results presented clearly elucidate their non-trivial impact on the conclusions that in turn can challenge the biological significance of the findings. The analytical treatment and arguments presented are generic and not restricted to molecular data sets.
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Affiliation(s)
- Radhakrishnan Nagarajan
- Division of Biomedical Informatics, Department of Biostatistics, University of Kentucky, United States of America
- * E-mail:
| | - Marco Scutari
- UCL Genetics Institute, University College London, London, United Kiingdom
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222
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Reconstructing biological gene regulatory networks: where optimization meets big data. EVOLUTIONARY INTELLIGENCE 2013. [DOI: 10.1007/s12065-013-0098-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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223
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Simcha DM, Younes L, Aryee MJ, Geman D. Identification of direction in gene networks from expression and methylation. BMC SYSTEMS BIOLOGY 2013; 7:118. [PMID: 24182195 PMCID: PMC4228359 DOI: 10.1186/1752-0509-7-118] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/17/2012] [Accepted: 10/17/2013] [Indexed: 01/27/2023]
Abstract
BACKGROUND Reverse-engineering gene regulatory networks from expression data is difficult, especially without temporal measurements or interventional experiments. In particular, the causal direction of an edge is generally not statistically identifiable, i.e., cannot be inferred as a statistical parameter, even from an unlimited amount of non-time series observational mRNA expression data. Some additional evidence is required and high-throughput methylation data can viewed as a natural multifactorial gene perturbation experiment. RESULTS We introduce IDEM (Identifying Direction from Expression and Methylation), a method for identifying the causal direction of edges by combining DNA methylation and mRNA transcription data. We describe the circumstances under which edge directions become identifiable and experiments with both real and synthetic data demonstrate that the accuracy of IDEM for inferring both edge placement and edge direction in gene regulatory networks is significantly improved relative to other methods. CONCLUSION Reverse-engineering directed gene regulatory networks from static observational data becomes feasible by exploiting the context provided by high-throughput DNA methylation data.An implementation of the algorithm described is available at http://code.google.com/p/idem/.
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Affiliation(s)
- David M Simcha
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.
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224
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Becker K, Balsa-Canto E, Cicin-Sain D, Hoermann A, Janssens H, Banga JR, Jaeger J. Reverse-engineering post-transcriptional regulation of gap genes in Drosophila melanogaster. PLoS Comput Biol 2013; 9:e1003281. [PMID: 24204230 PMCID: PMC3814631 DOI: 10.1371/journal.pcbi.1003281] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2013] [Accepted: 09/02/2013] [Indexed: 12/19/2022] Open
Abstract
Systems biology proceeds through repeated cycles of experiment and modeling. One way to implement this is reverse engineering, where models are fit to data to infer and analyse regulatory mechanisms. This requires rigorous methods to determine whether model parameters can be properly identified. Applying such methods in a complex biological context remains challenging. We use reverse engineering to study post-transcriptional regulation in pattern formation. As a case study, we analyse expression of the gap genes Krüppel, knirps, and giant in Drosophila melanogaster. We use detailed, quantitative datasets of gap gene mRNA and protein expression to solve and fit a model of post-transcriptional regulation, and establish its structural and practical identifiability. Our results demonstrate that post-transcriptional regulation is not required for patterning in this system, but is necessary for proper control of protein levels. Our work demonstrates that the uniqueness and specificity of a fitted model can be rigorously determined in the context of spatio-temporal pattern formation. This greatly increases the potential of reverse engineering for the study of development and other, similarly complex, biological processes. The analysis of pattern-forming gene networks is largely focussed on transcriptional regulation. However, post-transcriptional events, such as translation and regulation of protein stability also play important roles in the establishment of protein expression patterns and levels. In this study, we use a reverse-engineering approach—fitting mathematical models to quantitative expression data—to analyse post-transcriptional regulation of the Drosophila gap genes Krüppel, knirps and giant, involved in segment determination during early embryogenesis. Rigorous fitting requires us to establish whether our models provide a robust and unique solution. We demonstrate, for the first time, that this can be done in the context of a complex spatio-temporal regulatory system. This is an important methodological advance for reverse-engineering developmental processes. Our results indicate that post-transcriptional regulation is not required for pattern formation, but is necessary for proper regulation of gap protein levels. Specifically, we predict that translation rates must be tuned for rapid early accumulation, and protein stability must be increased for persistence of high protein levels at late stages of gap gene expression.
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Affiliation(s)
- Kolja Becker
- EMBL/CRG Research Unit in Systems Biology, Centre de Regulació Genòmica, and Universitat Pombeu Fabra (UPF), Barcelona, Spain
- Institute of Genetics, Johannes Gutenberg University, Mainz, Germany
| | | | - Damjan Cicin-Sain
- EMBL/CRG Research Unit in Systems Biology, Centre de Regulació Genòmica, and Universitat Pombeu Fabra (UPF), Barcelona, Spain
| | - Astrid Hoermann
- EMBL/CRG Research Unit in Systems Biology, Centre de Regulació Genòmica, and Universitat Pombeu Fabra (UPF), Barcelona, Spain
| | - Hilde Janssens
- EMBL/CRG Research Unit in Systems Biology, Centre de Regulació Genòmica, and Universitat Pombeu Fabra (UPF), Barcelona, Spain
| | | | - Johannes Jaeger
- EMBL/CRG Research Unit in Systems Biology, Centre de Regulació Genòmica, and Universitat Pombeu Fabra (UPF), Barcelona, Spain
- * E-mail:
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225
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Michailidis G, d'Alché-Buc F. Autoregressive models for gene regulatory network inference: sparsity, stability and causality issues. Math Biosci 2013; 246:326-34. [PMID: 24176667 DOI: 10.1016/j.mbs.2013.10.003] [Citation(s) in RCA: 49] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2013] [Revised: 10/09/2013] [Accepted: 10/14/2013] [Indexed: 10/26/2022]
Abstract
Reconstructing gene regulatory networks from high-throughput measurements represents a key problem in functional genomics. It also represents a canonical learning problem and thus has attracted a lot of attention in both the informatics and the statistical learning literature. Numerous approaches have been proposed, ranging from simple clustering to rather involved dynamic Bayesian network modeling, as well as hybrid ones that combine a number of modeling steps, such as employing ordinary differential equations coupled with genome annotation. These approaches are tailored to the type of data being employed. Available data sources include static steady state data and time course data obtained either for wild type phenotypes or from perturbation experiments. This review focuses on the class of autoregressive models using time course data for inferring gene regulatory networks. The central themes of sparsity, stability and causality are discussed as well as the ability to integrate prior knowledge for successful use of these models for the learning task at hand.
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Affiliation(s)
- George Michailidis
- Department of Statistics, University of Michigan, Ann Arbor, MI 48109-1107, USA
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226
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Sławek J, Arodź T. ENNET: inferring large gene regulatory networks from expression data using gradient boosting. BMC SYSTEMS BIOLOGY 2013; 7:106. [PMID: 24148309 PMCID: PMC4015806 DOI: 10.1186/1752-0509-7-106] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/24/2013] [Accepted: 10/17/2013] [Indexed: 01/19/2023]
Abstract
BACKGROUND The regulation of gene expression by transcription factors is a key determinant of cellular phenotypes. Deciphering genome-wide networks that capture which transcription factors regulate which genes is one of the major efforts towards understanding and accurate modeling of living systems. However, reverse-engineering the network from gene expression profiles remains a challenge, because the data are noisy, high dimensional and sparse, and the regulation is often obscured by indirect connections. RESULTS We introduce a gene regulatory network inference algorithm ENNET, which reverse-engineers networks of transcriptional regulation from a variety of expression profiles with a superior accuracy compared to the state-of-the-art methods. The proposed method relies on the boosting of regression stumps combined with a relative variable importance measure for the initial scoring of transcription factors with respect to each gene. Then, we propose a technique for using a distribution of the initial scores and information about knockouts to refine the predictions. We evaluated the proposed method on the DREAM3, DREAM4 and DREAM5 data sets and achieved higher accuracy than the winners of those competitions and other established methods. CONCLUSIONS Superior accuracy achieved on the three different benchmark data sets shows that ENNET is a top contender in the task of network inference. It is a versatile method that uses information about which gene was knocked-out in which experiment if it is available, but remains the top performer even without such information. ENNET is available for download from https://github.com/slawekj/ennet under the GNU GPLv3 license.
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Affiliation(s)
- Janusz Sławek
- Department of Computer Science, Virginia Commonwealth University, Richmond, Virginia
| | - Tomasz Arodź
- Department of Computer Science, Virginia Commonwealth University, Richmond, Virginia
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227
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Puniyani K, Xing EP. GINI: from ISH images to gene interaction networks. PLoS Comput Biol 2013; 9:e1003227. [PMID: 24130465 PMCID: PMC3794902 DOI: 10.1371/journal.pcbi.1003227] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2012] [Accepted: 07/30/2013] [Indexed: 12/22/2022] Open
Abstract
Accurate inference of molecular and functional interactions among genes, especially in multicellular organisms such as Drosophila, often requires statistical analysis of correlations not only between the magnitudes of gene expressions, but also between their temporal-spatial patterns. The ISH (in-situ-hybridization)-based gene expression micro-imaging technology offers an effective approach to perform large-scale spatial-temporal profiling of whole-body mRNA abundance. However, analytical tools for discovering gene interactions from such data remain an open challenge due to various reasons, including difficulties in extracting canonical representations of gene activities from images, and in inference of statistically meaningful networks from such representations. In this paper, we present GINI, a machine learning system for inferring gene interaction networks from Drosophila embryonic ISH images. GINI builds on a computer-vision-inspired vector-space representation of the spatial pattern of gene expression in ISH images, enabled by our recently developed system; and a new multi-instance-kernel algorithm that learns a sparse Markov network model, in which, every gene (i.e., node) in the network is represented by a vector-valued spatial pattern rather than a scalar-valued gene intensity as in conventional approaches such as a Gaussian graphical model. By capturing the notion of spatial similarity of gene expression, and at the same time properly taking into account the presence of multiple images per gene via multi-instance kernels, GINI is well-positioned to infer statistically sound, and biologically meaningful gene interaction networks from image data. Using both synthetic data and a small manually curated data set, we demonstrate the effectiveness of our approach in network building. Furthermore, we report results on a large publicly available collection of Drosophila embryonic ISH images from the Berkeley Drosophila Genome Project, where GINI makes novel and interesting predictions of gene interactions. Software for GINI is available at http://sailing.cs.cmu.edu/Drosophila_ISH_images/ As high-throughput technologies for molecular abundance profiling are becoming more inexpensive and accessible, computational inference of gene interaction networks from such data based on well-founded statistical principles is imperative to advance the understanding of regulatory mechanisms in various biological systems. Reverse engineering of gene networks has traditionally relied on analysis of whole-genome microarray data; here we present a new method, GINI, to infer gene networks from ISH images, thereby enabling exploration of spatial characteristics of gene expressions for network inference. Our method generates a Markov network, which encapsulates globally meaningful statistical-dependencies from vector-valued gene spatial patterns. In other words, we advance the state-of-art in both the usage of richer forms of expression data, and the employment of principled statistical methodology for sound network inference on such new form of data. Our results show that analyzing the spatial distribution of gene expression enables us to capture information not available from microarray data. Such an analysis is especially important in analyzing genes involved in embryonic development of Drosophila to reveal specific spatial patterning that determines the development of the 14 segments of the adult fly.
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Affiliation(s)
- Kriti Puniyani
- School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
| | - Eric P. Xing
- School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
- * E-mail:
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228
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Zhang X, Shao B, Wu Y, Qi O. A reverse engineering approach to optimize experiments for the construction of biological regulatory networks. PLoS One 2013; 8:e75931. [PMID: 24069453 PMCID: PMC3777925 DOI: 10.1371/journal.pone.0075931] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2013] [Accepted: 08/22/2013] [Indexed: 01/15/2023] Open
Abstract
One of the major objectives in systems biology is to understand the relation between the topological structures and the dynamics of biological regulatory networks. In this context, various mathematical tools have been developed to deduct structures of regulatory networks from microarray expression data. In general, from a single data set, one cannot deduct the whole network structure; additional expression data are usually needed. Thus how to design a microarray expression experiment in order to get the most information is a practical problem in systems biology. Here we propose three methods, namely, maximum distance method, trajectory entropy method, and sampling method, to derive the optimal initial conditions for experiments. The performance of these methods is tested and evaluated in three well-known regulatory networks (budding yeast cell cycle, fission yeast cell cycle, and E. coli. SOS network). Based on the evaluation, we propose an efficient strategy for the design of microarray expression experiments.
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Affiliation(s)
- Xiaomeng Zhang
- The State Key Laboratory for Artificial Microstructures and Mesoscopic Physics, School of Physics, Peking University, Beijing, China
- The Center for Quantitative Biology and Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
| | - Bin Shao
- The State Key Laboratory for Artificial Microstructures and Mesoscopic Physics, School of Physics, Peking University, Beijing, China
- The Center for Quantitative Biology and Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
| | - Yangle Wu
- The State Key Laboratory for Artificial Microstructures and Mesoscopic Physics, School of Physics, Peking University, Beijing, China
- The Center for Quantitative Biology and Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
| | - Ouyang Qi
- The State Key Laboratory for Artificial Microstructures and Mesoscopic Physics, School of Physics, Peking University, Beijing, China
- The Center for Quantitative Biology and Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
- * E-mail:
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229
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Shin GW, Na J, Seo M, Chung B, Nam HG, Lee SJ, Jung GY. Precise Expression Profiling by Stuffer-Free Multiplex Ligation-Dependent Probe Amplification. Anal Chem 2013; 85:9383-9. [DOI: 10.1021/ac402314h] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Gi Won Shin
- Institute of Environmental
and Energy Technology, Pohang University of Science and Technology, Pohang, Gyeongbuk 790-784, Korea
| | - Jeongkyeong Na
- School of Interdisciplinary
Bioscience and Bioengineering, Pohang University of Science and Technology, Pohang, Gyeongbuk 790-784, Korea
| | - Mihwa Seo
- School of Interdisciplinary
Bioscience and Bioengineering, Pohang University of Science and Technology, Pohang, Gyeongbuk 790-784, Korea
| | - Boram Chung
- School of Interdisciplinary
Bioscience and Bioengineering, Pohang University of Science and Technology, Pohang, Gyeongbuk 790-784, Korea
| | - Hong Gil Nam
- Department
of New Biology, Daegu Gyeongbuk Institute of Science and Technology, Daegu 711-873, Korea
| | - Seung-Jae Lee
- School of Interdisciplinary
Bioscience and Bioengineering, Pohang University of Science and Technology, Pohang, Gyeongbuk 790-784, Korea
- Department of
Life Sciences, Pohang University of Science and Technology, Pohang, Gyeongbuk 790-784, Korea
- World Class University
Information Technology Convergence Engineering, Pohang University of Science and Technology, Pohang,Gyeongbuk 790-784, Korea
| | - Gyoo Yeol Jung
- School of Interdisciplinary
Bioscience and Bioengineering, Pohang University of Science and Technology, Pohang, Gyeongbuk 790-784, Korea
- Department
of Chemical Engineering, Pohang University of Science and Technology, Pohang, Gyeongbuk 790-784, Korea
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230
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Brouard C, Vrain C, Dubois J, Castel D, Debily MA, d'Alché-Buc F. Learning a Markov Logic network for supervised gene regulatory network inference. BMC Bioinformatics 2013; 14:273. [PMID: 24028533 PMCID: PMC3849013 DOI: 10.1186/1471-2105-14-273] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2012] [Accepted: 09/03/2013] [Indexed: 11/23/2022] Open
Abstract
Background Gene regulatory network inference remains a challenging problem in systems biology despite the numerous approaches that have been proposed. When substantial knowledge on a gene regulatory network is already available, supervised network inference is appropriate. Such a method builds a binary classifier able to assign a class (Regulation/No regulation) to an ordered pair of genes. Once learnt, the pairwise classifier can be used to predict new regulations. In this work, we explore the framework of Markov Logic Networks (MLN) that combine features of probabilistic graphical models with the expressivity of first-order logic rules. Results We propose to learn a Markov Logic network, e.g. a set of weighted rules that conclude on the predicate “regulates”, starting from a known gene regulatory network involved in the switch proliferation/differentiation of keratinocyte cells, a set of experimental transcriptomic data and various descriptions of genes all encoded into first-order logic. As training data are unbalanced, we use asymmetric bagging to learn a set of MLNs. The prediction of a new regulation can then be obtained by averaging predictions of individual MLNs. As a side contribution, we propose three in silico tests to assess the performance of any pairwise classifier in various network inference tasks on real datasets. A first test consists of measuring the average performance on balanced edge prediction problem; a second one deals with the ability of the classifier, once enhanced by asymmetric bagging, to update a given network. Finally our main result concerns a third test that measures the ability of the method to predict regulations with a new set of genes. As expected, MLN, when provided with only numerical discretized gene expression data, does not perform as well as a pairwise SVM in terms of AUPR. However, when a more complete description of gene properties is provided by heterogeneous sources, MLN achieves the same performance as a black-box model such as a pairwise SVM while providing relevant insights on the predictions. Conclusions The numerical studies show that MLN achieves very good predictive performance while opening the door to some interpretability of the decisions. Besides the ability to suggest new regulations, such an approach allows to cross-validate experimental data with existing knowledge.
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Affiliation(s)
- Céline Brouard
- IBISC EA 4526, Université d'Évry-Val d'Essonne, 23 Boulevard de France, 91037, Évry, France.
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231
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Meister A, Li YH, Choi B, Wong WH. Learning a nonlinear dynamical system model of gene regulation: A perturbed steady-state approach. Ann Appl Stat 2013. [DOI: 10.1214/13-aoas645] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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232
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Wang YK, Hurley DG, Schnell S, Print CG, Crampin EJ. Integration of steady-state and temporal gene expression data for the inference of gene regulatory networks. PLoS One 2013; 8:e72103. [PMID: 23967277 PMCID: PMC3743784 DOI: 10.1371/journal.pone.0072103] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2013] [Accepted: 07/05/2013] [Indexed: 01/02/2023] Open
Abstract
We develop a new regression algorithm, cMIKANA, for inference of gene regulatory networks from combinations of steady-state and time-series gene expression data. Using simulated gene expression datasets to assess the accuracy of reconstructing gene regulatory networks, we show that steady-state and time-series data sets can successfully be combined to identify gene regulatory interactions using the new algorithm. Inferring gene networks from combined data sets was found to be advantageous when using noisy measurements collected with either lower sampling rates or a limited number of experimental replicates. We illustrate our method by applying it to a microarray gene expression dataset from human umbilical vein endothelial cells (HUVECs) which combines time series data from treatment with growth factor TNF and steady state data from siRNA knockdown treatments. Our results suggest that the combination of steady-state and time-series datasets may provide better prediction of RNA-to-RNA interactions, and may also reveal biological features that cannot be identified from dynamic or steady state information alone. Finally, we consider the experimental design of genomics experiments for gene regulatory network inference and show that network inference can be improved by incorporating steady-state measurements with time-series data.
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Affiliation(s)
- Yi Kan Wang
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Daniel G. Hurley
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
- Department of Molecular Medicine and Pathology, University of Auckland, Auckland, New Zealand
| | - Santiago Schnell
- Department of Molecular & Integrative Physiology and Department of Computational Medicine & Bioinformatics, University of Michigan Medical School, Ann Arbor, Michigan, United States of America
| | - Cristin G. Print
- Department of Molecular Medicine and Pathology, University of Auckland, Auckland, New Zealand
- New Zealand Bioinformatics Institute, Auckland, New Zealand
- Maurice Wilkins Centre for Molecular Biodiscovery, Auckland, New Zealand
| | - Edmund J. Crampin
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
- Maurice Wilkins Centre for Molecular Biodiscovery, Auckland, New Zealand
- Department of Engineering Science, University of Auckland, Auckland, New Zealand
- Melbourne School of Engineering, The University of Melbourne, Melbourne, Victoria, Australia
- National ICT Australia Victoria Research Lab, Canberra, Victoria, Australia
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233
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Chowdhury AR, Chetty M, Vinh NX. Evaluating influence of microRNA in reconstructing gene regulatory networks. Cogn Neurodyn 2013; 8:251-9. [PMID: 24808933 DOI: 10.1007/s11571-013-9265-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2013] [Revised: 07/23/2013] [Accepted: 07/25/2013] [Indexed: 12/11/2022] Open
Abstract
Gene regulatory network (GRN) consists of interactions between transcription factors (TFs) and target genes (TGs). Recently, it has been observed that micro RNAs (miRNAs) play a significant part in genetic interactions. However, current microarray technologies do not capture miRNA expression levels. To overcome this, we propose a new technique to reverse engineer GRN from the available partial microarray data which contains expression levels of TFs and TGs only. Using S-System model, the approach is adapted to cope with the unavailability of information about the expression levels of miRNAs. The versatile Differential Evolutionary algorithm is used for optimization and parameter estimation. Experimental studies on four in silico networks, and a real network of Saccharomyces cerevisiae called IRMA network, show significant improvement compared to traditional S-System approach.
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Affiliation(s)
- Ahsan Raja Chowdhury
- Gippsland School of Information Technology, Monash University, Victoria, Australia ; National ICT Australia (NICTA), VRL, Melbourne, Australia
| | - Madhu Chetty
- Gippsland School of Information Technology, Monash University, Victoria, Australia ; National ICT Australia (NICTA), VRL, Melbourne, Australia
| | - Nguyen Xuan Vinh
- Gippsland School of Information Technology, Monash University, Victoria, Australia
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234
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Santra T, Kolch W, Kholodenko BN. Integrating Bayesian variable selection with Modular Response Analysis to infer biochemical network topology. BMC SYSTEMS BIOLOGY 2013; 7:57. [PMID: 23829771 PMCID: PMC3726398 DOI: 10.1186/1752-0509-7-57] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/23/2012] [Accepted: 06/28/2013] [Indexed: 12/31/2022]
Abstract
Background Recent advancements in genetics and proteomics have led to the acquisition of large quantitative data sets. However, the use of these data to reverse engineer biochemical networks has remained a challenging problem. Many methods have been proposed to infer biochemical network topologies from different types of biological data. Here, we focus on unraveling network topologies from steady state responses of biochemical networks to successive experimental perturbations. Results We propose a computational algorithm which combines a deterministic network inference method termed Modular Response Analysis (MRA) and a statistical model selection algorithm called Bayesian Variable Selection, to infer functional interactions in cellular signaling pathways and gene regulatory networks. It can be used to identify interactions among individual molecules involved in a biochemical pathway or reveal how different functional modules of a biological network interact with each other to exchange information. In cases where not all network components are known, our method reveals functional interactions which are not direct but correspond to the interaction routes through unknown elements. Using computer simulated perturbation responses of signaling pathways and gene regulatory networks from the DREAM challenge, we demonstrate that the proposed method is robust against noise and scalable to large networks. We also show that our method can infer network topologies using incomplete perturbation datasets. Consequently, we have used this algorithm to explore the ERBB regulated G1/S transition pathway in certain breast cancer cells to understand the molecular mechanisms which cause these cells to become drug resistant. The algorithm successfully inferred many well characterized interactions of this pathway by analyzing experimentally obtained perturbation data. Additionally, it identified some molecular interactions which promote drug resistance in breast cancer cells. Conclusions The proposed algorithm provides a robust, scalable and cost effective solution for inferring network topologies from biological data. It can potentially be applied to explore novel pathways which play important roles in life threatening disease like cancer.
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Affiliation(s)
- Tapesh Santra
- Systems Biology Ireland, Conway Institute of Biomolecular & Biomedical Research, University College Dublin, Belfield, Dublin 4, Ireland.
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235
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Wang Y, Wang N, Wang J, Wang Z, Wu R. Delivering systems pharmacogenomics towards precision medicine through mathematics. Adv Drug Deliv Rev 2013; 65:905-11. [PMID: 23523629 PMCID: PMC3988791 DOI: 10.1016/j.addr.2013.03.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2013] [Revised: 02/13/2013] [Accepted: 03/13/2013] [Indexed: 12/13/2022]
Abstract
The latest developments of pharmacology in the post-genomic era foster the emergence of new biomarkers that represent the future of drug targets. To identify these biomarkers, we need a major shift from traditional genomic analyses alone, moving the focus towards systems approaches to elucidating genetic variation in biochemical pathways of drug response. Is there any general model that can accelerate this shift via a merger of systems biology and pharmacogenomics? Here we describe a statistical framework for mapping dynamic genes that affect drug response by incorporating its pharmacokinetic and pharmacodynamic pathways. This framework is expanded to shed light on the mechanistic and therapeutic differences of drug response based on pharmacogenetic information, coupled with genomic, proteomic and metabolic data, allowing novel therapeutic targets and genetic biomarkers to be characterized and utilized for drug discovery.
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Affiliation(s)
- Yaqun Wang
- Center for Statistical Genetics, Pennsylvania State University, Hershey, PA 17033, USA
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236
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Petri G, Scolamiero M, Donato I, Vaccarino F. Topological Strata of Weighted Complex Networks. PLoS One 2013; 8:e66506. [PMID: 23805226 PMCID: PMC3689815 DOI: 10.1371/journal.pone.0066506] [Citation(s) in RCA: 114] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2013] [Accepted: 05/07/2013] [Indexed: 11/18/2022] Open
Abstract
The statistical mechanical approach to complex networks is the dominant paradigm in describing natural and societal complex systems. The study of network properties, and their implications on dynamical processes, mostly focus on locally defined quantities of nodes and edges, such as node degrees, edge weights and -more recently- correlations between neighboring nodes. However, statistical methods quickly become cumbersome when dealing with many-body properties and do not capture the precise mesoscopic structure of complex networks. Here we introduce a novel method, based on persistent homology, to detect particular non-local structures, akin to weighted holes within the link-weight network fabric, which are invisible to existing methods. Their properties divide weighted networks in two broad classes: one is characterized by small hierarchically nested holes, while the second displays larger and longer living inhomogeneities. These classes cannot be reduced to known local or quasilocal network properties, because of the intrinsic non-locality of homological properties, and thus yield a new classification built on high order coordination patterns. Our results show that topology can provide novel insights relevant for many-body interactions in social and spatial networks. Moreover, this new method creates the first bridge between network theory and algebraic topology, which will allow to import the toolset of algebraic methods to complex systems.
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Affiliation(s)
| | - Martina Scolamiero
- ISI Foundation, Torino, Italy
- Dipartimento di Ingegneria Gestionale e della Produzione, Politecnico di Torino, Torino, Italy
| | - Irene Donato
- ISI Foundation, Torino, Italy
- Dipartimento di Scienze Matematiche, Politecnico di Torino, Torino, Italy
| | - Francesco Vaccarino
- ISI Foundation, Torino, Italy
- Dipartimento di Scienze Matematiche, Politecnico di Torino, Torino, Italy
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237
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Chowdhury AR, Chetty M, Vinh NX. Incorporating time-delays in S-System model for reverse engineering genetic networks. BMC Bioinformatics 2013; 14:196. [PMID: 23777625 PMCID: PMC3839642 DOI: 10.1186/1471-2105-14-196] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2013] [Accepted: 06/07/2013] [Indexed: 11/10/2022] Open
Abstract
Background In any gene regulatory network (GRN), the complex interactions occurring amongst transcription factors and target genes can be either instantaneous or time-delayed. However, many existing modeling approaches currently applied for inferring GRNs are unable to represent both these interactions simultaneously. As a result, all these approaches cannot detect important interactions of the other type. S-System model, a differential equation based approach which has been increasingly applied for modeling GRNs, also suffers from this limitation. In fact, all S-System based existing modeling approaches have been designed to capture only instantaneous interactions, and are unable to infer time-delayed interactions. Results In this paper, we propose a novel Time-Delayed S-System (TDSS) model which uses a set of delay differential equations to represent the system dynamics. The ability to incorporate time-delay parameters in the proposed S-System model enables simultaneous modeling of both instantaneous and time-delayed interactions. Furthermore, the delay parameters are not limited to just positive integer values (corresponding to time stamps in the data), but can also take fractional values. Moreover, we also propose a new criterion for model evaluation exploiting the sparse and scale-free nature of GRNs to effectively narrow down the search space, which not only reduces the computation time significantly but also improves model accuracy. The evaluation criterion systematically adapts the max-min in-degrees and also systematically balances the effect of network accuracy and complexity during optimization. Conclusion The four well-known performance measures applied to the experimental studies on synthetic networks with various time-delayed regulations clearly demonstrate that the proposed method can capture both instantaneous and delayed interactions correctly with high precision. The experiments carried out on two well-known real-life networks, namely IRMA and SOS DNA repair network in Escherichia coli show a significant improvement compared with other state-of-the-art approaches for GRN modeling.
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Affiliation(s)
- Ahsan Raja Chowdhury
- Gippsland School of Information Technology, Monash University, Churchill, Victoria-3842, Australia.
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238
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Csermely P, Korcsmáros T, Kiss HJM, London G, Nussinov R. Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review. Pharmacol Ther 2013; 138:333-408. [PMID: 23384594 PMCID: PMC3647006 DOI: 10.1016/j.pharmthera.2013.01.016] [Citation(s) in RCA: 522] [Impact Index Per Article: 43.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2013] [Accepted: 01/22/2013] [Indexed: 02/02/2023]
Abstract
Despite considerable progress in genome- and proteome-based high-throughput screening methods and in rational drug design, the increase in approved drugs in the past decade did not match the increase of drug development costs. Network description and analysis not only give a systems-level understanding of drug action and disease complexity, but can also help to improve the efficiency of drug design. We give a comprehensive assessment of the analytical tools of network topology and dynamics. The state-of-the-art use of chemical similarity, protein structure, protein-protein interaction, signaling, genetic interaction and metabolic networks in the discovery of drug targets is summarized. We propose that network targeting follows two basic strategies. The "central hit strategy" selectively targets central nodes/edges of the flexible networks of infectious agents or cancer cells to kill them. The "network influence strategy" works against other diseases, where an efficient reconfiguration of rigid networks needs to be achieved by targeting the neighbors of central nodes/edges. It is shown how network techniques can help in the identification of single-target, edgetic, multi-target and allo-network drug target candidates. We review the recent boom in network methods helping hit identification, lead selection optimizing drug efficacy, as well as minimizing side-effects and drug toxicity. Successful network-based drug development strategies are shown through the examples of infections, cancer, metabolic diseases, neurodegenerative diseases and aging. Summarizing >1200 references we suggest an optimized protocol of network-aided drug development, and provide a list of systems-level hallmarks of drug quality. Finally, we highlight network-related drug development trends helping to achieve these hallmarks by a cohesive, global approach.
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Affiliation(s)
- Peter Csermely
- Department of Medical Chemistry, Semmelweis University, P.O. Box 260, H-1444 Budapest 8, Hungary.
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239
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Cai X, Bazerque JA, Giannakis GB. Inference of gene regulatory networks with sparse structural equation models exploiting genetic perturbations. PLoS Comput Biol 2013; 9:e1003068. [PMID: 23717196 PMCID: PMC3662697 DOI: 10.1371/journal.pcbi.1003068] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2012] [Accepted: 03/28/2013] [Indexed: 12/22/2022] Open
Abstract
Integrating genetic perturbations with gene expression data not only improves accuracy of regulatory network topology inference, but also enables learning of causal regulatory relations between genes. Although a number of methods have been developed to integrate both types of data, the desiderata of efficient and powerful algorithms still remains. In this paper, sparse structural equation models (SEMs) are employed to integrate both gene expression data and cis-expression quantitative trait loci (cis-eQTL), for modeling gene regulatory networks in accordance with biological evidence about genes regulating or being regulated by a small number of genes. A systematic inference method named sparsity-aware maximum likelihood (SML) is developed for SEM estimation. Using simulated directed acyclic or cyclic networks, the SML performance is compared with that of two state-of-the-art algorithms: the adaptive Lasso (AL) based scheme, and the QTL-directed dependency graph (QDG) method. Computer simulations demonstrate that the novel SML algorithm offers significantly better performance than the AL-based and QDG algorithms across all sample sizes from 100 to 1,000, in terms of detection power and false discovery rate, in all the cases tested that include acyclic or cyclic networks of 10, 30 and 300 genes. The SML method is further applied to infer a network of 39 human genes that are related to the immune function and are chosen to have a reliable eQTL per gene. The resulting network consists of 9 genes and 13 edges. Most of the edges represent interactions reasonably expected from experimental evidence, while the remaining may just indicate the emergence of new interactions. The sparse SEM and efficient SML algorithm provide an effective means of exploiting both gene expression and perturbation data to infer gene regulatory networks. An open-source computer program implementing the SML algorithm is freely available upon request.
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Affiliation(s)
- Xiaodong Cai
- Department of Electrical and Computer Engineering, University of Miami, Coral Gables, FL, USA.
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240
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McCormick MA, Kennedy BK. Genome-scale studies of aging: challenges and opportunities. Curr Genomics 2013; 13:500-7. [PMID: 23633910 PMCID: PMC3468883 DOI: 10.2174/138920212803251454] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2012] [Revised: 06/08/2012] [Accepted: 07/25/2012] [Indexed: 12/21/2022] Open
Abstract
Whole-genome studies involving a phenotype of interest are increasingly prevalent, in part due to a dramatic increase in speed at which many high throughput technologies can be performed coupled to simultaneous decreases in cost. This type of genome-scale methodology has been applied to the phenotype of lifespan, as well as to whole-transcriptome changes during the aging process or in mutants affecting aging. The value of high throughput discovery-based science in this field is clearly evident, but will it yield a true systems-level understanding of the aging process? Here we review some of this work to date, focusing on recent findings and the unanswered puzzles to which they point. In this context, we also discuss recent technological advances and some of the likely future directions that they portend.
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241
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Tjärnberg A, Nordling TE, Studham M, Sonnhammer EL. Optimal Sparsity Criteria for Network Inference. J Comput Biol 2013; 20:398-408. [DOI: 10.1089/cmb.2012.0268] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Affiliation(s)
- Andreas Tjärnberg
- Stockholm Bioinformatics Center, Science for Life Laboratory, Stockholm, Sweden
- Department of Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden
| | - Torbjörn E.M. Nordling
- Stockholm Bioinformatics Center, Science for Life Laboratory, Stockholm, Sweden
- Automatic Control Lab, School of Electrical Engineering, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Matthew Studham
- Stockholm Bioinformatics Center, Science for Life Laboratory, Stockholm, Sweden
- Department of Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden
| | - Erik L.L. Sonnhammer
- Stockholm Bioinformatics Center, Science for Life Laboratory, Stockholm, Sweden
- Department of Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden
- Swedish eScience Research Center, Stockholm, Sweden
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242
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McLean TI. "Eco-omics": a review of the application of genomics, transcriptomics, and proteomics for the study of the ecology of harmful algae. MICROBIAL ECOLOGY 2013; 65:901-915. [PMID: 23553002 DOI: 10.1007/s00248-013-0220-5] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/03/2012] [Accepted: 03/14/2013] [Indexed: 06/02/2023]
Abstract
The implementation of molecular techniques has been widely adopted throughout the life sciences except in the marine sciences. The latter trend is quickly being reversed as even more cutting-edge molecular platforms, referred to collectively as 'omics-related technologies, are being used in a number of laboratories that study various aspects of life in the marine environment. This review provides a brief overview of just a few representative studies that have used genomics, transcriptomics, or proteomics approaches to deepen our understanding, specifically, about the underlying molecular biology of harmful algae. The examples of the studies described here are particularly relevant in showing how the information gleaned from these technologies can uncover the genetic capacity of harmful algal bloom-forming species, can generate new hypotheses about mechanistic relationships that bridge gene-environment interactions, and can impinge on our understanding surrounding the ecology of these organisms.
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Affiliation(s)
- T I McLean
- The Department of Biological Sciences, The University of Southern Mississippi, 118 College Drive #5018, Hattiesburg, MS 39406-0001, USA.
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243
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Chen BS, Li CW. Analysing microarray data in drug discovery using systems biology. Expert Opin Drug Discov 2013; 2:755-68. [PMID: 23488963 DOI: 10.1517/17460441.2.5.755] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
The innovation of present drug design focuses on new targets. However, compound efficacy and safety in human metabolism, including toxicity and pharmacokinetic profiles, but not target selection, are the criteria that determine which drug candidates enter the clinic. Systems biology approaches to disease are developed from the idea that disease-perturbed regulatory networks differ from their normal counterparts. Microarray data analyses reveal global changes in gene or protein expression in response to genetic and environmental changes and, accordingly, are well suited to construct the normal, disease-perturbed and drug-affected networks, which are useful for drug discovery in the pharmaceutical industry. The integration of modelling, microarray data and systems biology approaches will allow for a true breakthrough in in silico absorption, distribution, metabolism, excretion and toxicity assessment in drug design. Therefore, drug discovery through systems biology by means of microarray analyses could significantly reduce the time and cost of new drug development.
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Affiliation(s)
- Bor-Sen Chen
- National Tsing Hua University, Laboratory of Control and Systems Biology, 101, Sec 2, Kuang Fu Road, Hsinchu, 300, Taiwan
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244
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Li S, Hsu L, Peng J, Wang P. BOOTSTRAP INFERENCE FOR NETWORK CONSTRUCTION WITH AN APPLICATION TO A BREAST CANCER MICROARRAY STUDY. Ann Appl Stat 2013; 7:391-417. [PMID: 24563684 DOI: 10.1214/12-aoas589] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Gaussian Graphical Models (GGMs) have been used to construct genetic regulatory networks where regularization techniques are widely used since the network inference usually falls into a high-dimension-low-sample-size scenario. Yet, finding the right amount of regularization can be challenging, especially in an unsupervised setting where traditional methods such as BIC or cross-validation often do not work well. In this paper, we propose a new method - Bootstrap Inference for Network COnstruction (BINCO) - to infer networks by directly controlling the false discovery rates (FDRs) of the selected edges. This method fits a mixture model for the distribution of edge selection frequencies to estimate the FDRs, where the selection frequencies are calculated via model aggregation. This method is applicable to a wide range of applications beyond network construction. When we applied our proposed method to building a gene regulatory network with microarray expression breast cancer data, we were able to identify high-confidence edges and well-connected hub genes that could potentially play important roles in understanding the underlying biological processes of breast cancer.
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Affiliation(s)
- Shuang Li
- Fred Hutchinson Cancer Research Center, M2-B500, 1100 Fairview Ave N., Seattle, WA 98109, USA
| | - Li Hsu
- Fred Hutchinson Cancer Research Center, M2-B500, 1100 Fairview Ave N., Seattle, WA 98109, USA
| | - Jie Peng
- Department of Statistics, University of California, Davis, Mathematical Sciences Building, One Shields Avenue, Davis, CA 95616
| | - Pei Wang
- Fred Hutchinson Cancer Research Center, M2-B500, 1100 Fairview Ave N., Seattle, WA 98109, USA
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245
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Qi H, Blanchard A, Lu T. Engineered genetic information processing circuits. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2013; 5:273-87. [DOI: 10.1002/wsbm.1216] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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246
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Bo W, Fu G, Wang Z, Xu F, Shen Y, Xu J, Huang Z, Gai J, Vallejos CE, Wu R. Systems mapping: how to map genes for biomass allocation toward an ideotype. Brief Bioinform 2013; 15:660-9. [PMID: 23428353 DOI: 10.1093/bib/bbs089] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
The recent availability of high-throughput genetic and genomic data allows the genetic architecture of complex traits to be systematically mapped. The application of these genetic results to design and breed new crop types can be made possible through systems mapping. Systems mapping is a computational model that dissects a complex phenotype into its underlying components, coordinates different components in terms of biological laws through mathematical equations and maps specific genes that mediate each component and its connection with other components. Here, we present a new direction of systems mapping by integrating this tool with carbon economy. With an optimal spatial distribution of carbon fluxes between sources and sinks, plants tend to maximize whole-plant growth and competitive ability under limited availability of resources. We argue that such an economical strategy for plant growth and development, once integrated with systems mapping, will not only provide mechanistic insights into plant biology, but also help to spark a renaissance of interest in ideotype breeding in crops and trees.
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247
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Faria JP, Overbeek R, Xia F, Rocha M, Rocha I, Henry CS. Genome-scale bacterial transcriptional regulatory networks: reconstruction and integrated analysis with metabolic models. Brief Bioinform 2013; 15:592-611. [DOI: 10.1093/bib/bbs071] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
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248
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Reverse engineering a hierarchical regulatory network downstream of oncogenic KRAS. Mol Syst Biol 2013; 8:601. [PMID: 22864383 PMCID: PMC3421447 DOI: 10.1038/msb.2012.32] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2012] [Accepted: 06/29/2012] [Indexed: 01/20/2023] Open
Abstract
RAS mutations are highly relevant for progression and therapy response of human tumours, but the genetic network that ultimately executes the oncogenic effects is poorly understood. Here, we used a reverse-engineering approach in an ovarian cancer model to reconstruct KRAS oncogene-dependent cytoplasmic and transcriptional networks from perturbation experiments based on gene silencing and pathway inhibitor treatments. We measured mRNA and protein levels in manipulated cells by microarray, RT-PCR and western blot analysis, respectively. The reconstructed model revealed complex interactions among the transcriptional and cytoplasmic components, some of which were confirmed by double pertubation experiments. Interestingly, the transcription factors decomposed into two hierarchically arranged groups. To validate the model predictions, we analysed growth parameters and transcriptional deregulation in the KRAS-transformed epithelial cells. As predicted by the model, we found two functional groups among the selected transcription factors. The experiments thus confirmed the predicted hierarchical transcription factor regulation and showed that the hierarchy manifests itself in downstream gene expression patterns and phenotype.
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Balabanov S, Wilhelm T, Venz S, Keller G, Scharf C, Pospisil H, Braig M, Barett C, Bokemeyer C, Walther R, Brümmendorf TH, Schuppert A. Combination of a proteomics approach and reengineering of meso scale network models for prediction of mode-of-action for tyrosine kinase inhibitors. PLoS One 2013; 8:e53668. [PMID: 23326482 PMCID: PMC3541187 DOI: 10.1371/journal.pone.0053668] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2012] [Accepted: 12/03/2012] [Indexed: 12/19/2022] Open
Abstract
In drug discovery, the characterisation of the precise modes of action (MoA) and of unwanted off-target effects of novel molecularly targeted compounds is of highest relevance. Recent approaches for identification of MoA have employed various techniques for modeling of well defined signaling pathways including structural information, changes in phenotypic behavior of cells and gene expression patterns after drug treatment. However, efficient approaches focusing on proteome wide data for the identification of MoA including interference with mutations are underrepresented. As mutations are key drivers of drug resistance in molecularly targeted tumor therapies, efficient analysis and modeling of downstream effects of mutations on drug MoA is a key to efficient development of improved targeted anti-cancer drugs. Here we present a combination of a global proteome analysis, reengineering of network models and integration of apoptosis data used to infer the mode-of-action of various tyrosine kinase inhibitors (TKIs) in chronic myeloid leukemia (CML) cell lines expressing wild type as well as TKI resistance conferring mutants of BCR-ABL. The inferred network models provide a tool to predict the main MoA of drugs as well as to grouping of drugs with known similar kinase inhibitory activity patterns in comparison to drugs with an additional MoA. We believe that our direct network reconstruction approach, demonstrated on proteomics data, can provide a complementary method to the established network reconstruction approaches for the preclinical modeling of the MoA of various types of targeted drugs in cancer treatment. Hence it may contribute to the more precise prediction of clinically relevant on- and off-target effects of TKIs.
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MESH Headings
- Animals
- Apoptosis/drug effects
- Benzamides/pharmacology
- Benzamides/therapeutic use
- Blotting, Western
- Cell Line, Tumor
- Cluster Analysis
- Drug Resistance, Neoplasm/drug effects
- Humans
- Imatinib Mesylate
- Leukemia, Myelogenous, Chronic, BCR-ABL Positive/drug therapy
- Leukemia, Myelogenous, Chronic, BCR-ABL Positive/pathology
- Mice
- Models, Biological
- Neoplasm Proteins/metabolism
- Piperazines/pharmacology
- Piperazines/therapeutic use
- Protein Kinase Inhibitors/pharmacology
- Protein Kinase Inhibitors/therapeutic use
- Protein-Tyrosine Kinases/antagonists & inhibitors
- Protein-Tyrosine Kinases/metabolism
- Proteomics/methods
- Pyrimidines/pharmacology
- Pyrimidines/therapeutic use
- Signal Transduction/drug effects
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Affiliation(s)
- Stefan Balabanov
- Department of Oncology, Haematology and Bone Marrow Transplantation with Section Pneumology, Hubertus Wald-Tumor Zentrum (UCCH), University Hospital Eppendorf (UKE), Hamburg, Germany
- Division of Hematology, University Hospital Zürich, Zürich, Switzerland
| | - Thomas Wilhelm
- Department of Oncology, Haematology and Bone Marrow Transplantation with Section Pneumology, Hubertus Wald-Tumor Zentrum (UCCH), University Hospital Eppendorf (UKE), Hamburg, Germany
- Department of Biochemistry, University Hospital Aachen (UKA) of the Rheinisch.-Westfälische Technische Hochschule Aachen, Aachen, Germany
| | - Simone Venz
- Department of Medical Biochemistry and Molecular Biology, University of Greifswald, Greifswald, Germany
- Interfacultary Institute of Genetics and Functional Genomics, University of Greifswald, Greifswald, Germany
| | - Gunhild Keller
- Department of Oncology, Haematology and Bone Marrow Transplantation with Section Pneumology, Hubertus Wald-Tumor Zentrum (UCCH), University Hospital Eppendorf (UKE), Hamburg, Germany
| | - Christian Scharf
- Department of Otorhinolaryngology, Head and Neck Surgery, University of Greifswald, Greifswald, Germany
| | - Heike Pospisil
- Bioinformatics, University of Applied Sciences Wildau, Wildau, Germany
| | - Melanie Braig
- Department of Oncology, Haematology and Bone Marrow Transplantation with Section Pneumology, Hubertus Wald-Tumor Zentrum (UCCH), University Hospital Eppendorf (UKE), Hamburg, Germany
| | - Christine Barett
- Department of Oncology, Haematology and Bone Marrow Transplantation with Section Pneumology, Hubertus Wald-Tumor Zentrum (UCCH), University Hospital Eppendorf (UKE), Hamburg, Germany
| | - Carsten Bokemeyer
- Department of Oncology, Haematology and Bone Marrow Transplantation with Section Pneumology, Hubertus Wald-Tumor Zentrum (UCCH), University Hospital Eppendorf (UKE), Hamburg, Germany
| | - Reinhard Walther
- Department of Medical Biochemistry and Molecular Biology, University of Greifswald, Greifswald, Germany
| | - Tim H. Brümmendorf
- Department of Oncology, Haematology and Bone Marrow Transplantation with Section Pneumology, Hubertus Wald-Tumor Zentrum (UCCH), University Hospital Eppendorf (UKE), Hamburg, Germany
- Medizinische Klinik IV - Hämatologie und Onkologie, Universitätsklinikum Aachen (UKA) of the Rheinisch.-Westfälische Technische Hochschule Aachen, Aachen, Germany
| | - Andreas Schuppert
- Aachen Institute for Advanced Study in Computational Engineering Science (AICES), RWTH Aachen University, Aachen, Germany
- * E-mail:
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Abstract
Resistance to drugs that treat infectious disease is a major problem worldwide. The rapid emergence of drug resistance is not well understood. We present two in silico models for the discovery of drug resistance mechanisms and for combating the evolution of resistance, respectively. In the first model, we computationally investigated subgraphs of a biological interaction network that show substantial adaptations when cells transcriptionally respond to a changing environment or treatment. As a case study, we investigated the response of the malaria parasite Plasmodium falciparum to chloroquine and tetracycline treatments. The second model involves a machine learning technique that combines clustering, common distance similarity measurements, and hierarchical clustering to propose new combinations of drug targets.
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Affiliation(s)
- Segun Fatumo
- Department of Computer and Information Sciences, Covenant University, Ota, Nigeria
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