# Reconstruction of *Escherichia coli* transcriptional regulatory networks via regulon-based associations

- Hossein Zare
^{1}, - Dipen Sangurdekar
^{2}, - Poonam Srivastava
^{2}, - Mostafa Kaveh
^{1}and - Arkady Khodursky
^{2}Email author

**3**:39

https://doi.org/10.1186/1752-0509-3-39

© Zare et al; licensee BioMed Central Ltd. 2009

**Received: **17 November 2008

**Accepted: **14 April 2009

**Published: **14 April 2009

## Abstract

### Background

Network reconstruction methods that rely on covariance of expression of transcription regulators and their targets ignore the fact that transcription of regulators and their targets can be controlled differently and/or independently. Such oversight would result in many erroneous predictions. However, accurate prediction of gene regulatory interactions can be made possible through modeling and estimation of transcriptional activity of groups of co-regulated genes.

### Results

Incomplete regulatory connectivity and expression data are used here to construct a consensus network of transcriptional regulation in *Escherichia coli* (*E. coli*). The network is updated via a covariance model describing the activity of gene sets controlled by common regulators. The proposed model-selection algorithm was used to annotate the likeliest regulatory interactions in *E. coli* on the basis of two independent sets of expression data, each containing many microarray experiments under a variety of conditions. The key regulatory predictions have been verified by an experiment and literature survey. In addition, the estimated activity profiles of transcription factors were used to describe their responses to environmental and genetic perturbations as well as drug treatments.

### Conclusion

Information about transcriptional activity of documented co-regulated genes (a core regulon) should be sufficient for discovering new target genes, whose transcriptional activities significantly co-vary with the activity of the core regulon members. Our ability to derive a highly significant consensus network by applying the regulon-based approach to two very different data sets demonstrated the efficiency of this strategy. We believe that this approach can be used to reconstruct gene regulatory networks of other organisms for which partial sets of known interactions are available.

## 1 Background

One of the goals of systems biology is to elucidate functionally relevant regulatory interactions[1, 2]. Since changes in gene expression are in part determined by such interactions between regulators and their target genes, genome-wide expression data can be effectively used to impute regulatory transcriptional networks. One approach, which is based on the assumption that functionally related genes should show similar transcriptional activity across time points or different environmental conditions, uses clustering to identify sets of genes with similar expression profiles [3] and regulator-specific network modules [4]. However, the utility of this pioneering approach was limited by the greedy nature of clustering and lack of a quantitative measure of interactions between genes.

Models of Gene Regulatory Networks

Gene Network Methods | Brief Descriptions |
---|---|

Require time series data, limited to small-scale networks, quantify interactions, associations are based on mRNA levels | |

Require time series data, limited to small scale networks, associations are based on mRNA levels | |

Measure the marginal and conditional dependencies among genes, associations are based on mRNA levels, learning the structure of large scale networks is highly complex | |

Measure the linear or nonlinear correlations among genes, associations are based on mRNA levels and may not be direct. | |

Require complete knowledge of a potential connectivity network, refine and quantify the network using gene expression data | |

Supervised Methods, [35] and this paper | Require partial knowledge of the connectivity network, association are based on activity profile of transcription factors |

Yet another approach to inferring gene regulatory networks is through non-greedy decomposition of gene expression data matrices to uncover hidden, often overlapping, regulatory signals and transcriptional connectivity patterns. Since the data does not have to be a time series, one can collect data for as many different experiments as possible and combine them to increase the sample size and prevent the problem of under-determination. Principal component analysis (PCA) [22] or singular value decomposition [23–25] and independent component analysis (ICA)[26] can be used to determine the low-dimensional representation of the data through decomposing the original data into a few regulatory signals which explain most of the data. However, the orthogonality assumption of PCA and the statistical independency assumption of ICA place methodological constraints on biological signals. Network component analysis (NCA) [27] is another matrix decomposition method, devised specifically for gene expression data, which takes advantage of available knowledge about the connectivity pattern of the network. The NCA method and a two-stage matrix decomposition model in [28] assume that the connectivity matrix is fully known, and, therefore, it does not predict any new interaction among the genes and transcription factors.

In this paper we introduce a new and highly efficient approach to the problem of gene network inference through a simple model selection algorithm. The algorithm uses gene expression data and documented transcriptional connections to predict a more complete structure of the transcriptional network. The method is based on covariance models of the co-regulated gene sets. We demonstrate that this approach can uncover previously uncharacterized regulatory interactions and simultaneously estimate the activity profiles of regulators from the corresponding covariance matrices. Comparison with the relevance network and GGM algorithms on two different data sets demonstrated the advantage of using the "gene"-"regulon" associations (the present method) over the "gene"-"regulator" associations (relevance networks and GGM). The proposed method outperformed both algorithms on both data sets at a very high significance margin with a recall value of more than 62% and precision value of 64%. We confirmed some of the predicted interactions by experiment and literature mining.

## 2 Results and Discussions

### 2.1 Data sets

Microarray gene expression data for more than 100 arrays representing 46 biologically distinct conditions have been used to reconstruct the underlying large scale transcriptional regulatory network of *E. coli*. The conditions covered a spectrum of environmental and genetic perturbations and drug treatments. The environmental perturbations, in addition to those described in [29] (Data set is available at NCBI Gene Expression Omnibus (GEO) with accession number: GSE4357-GSE4380), included different amino acid and nucleotide additions and limitations (NCBI GEO Series accession number: GSE15409). After filtering the collected expression data by a series of different criteria (removing genes with low variance expressions across conditions, with small absolute values and with low entropy profiles across conditions), expression measurements of 3658 genes were used in this study.

A second data set used in our study was published in [17] and was obtained from Many Microbe Microarray database (*M*^{3D}) web site http://m3d.bu.edu. This set contained expression levels of *E. coli* genes across 524 arrays which resolved into 189 different experimental conditions. It should be noted here that not only do these two data sets cover very different genetic or environmental perturbations, but they also have been collected on two different microarray platforms: cDNA microarrays and Affymatrix Genechips.

The connectivity matrix obtained from RegulonDB http://regulondb.ccg.unam.mx[30]. This connectivity matrix contained the connectivity pattern between 1210 genes (we only considered genes having expression measurements in our data set) and 137 transcription factors having more than two known targets.

### 2.2 Algorithm

For each transcription factor, the core regulon, consisting of the set of known targets, is identified from the known connectivity matrix described above. This information is used to learn a covariance model for each transcription factor. The covariance matrices are estimated from the expression data of genes assigned to regulons. The gene expression measurements of the group of *K* genes controlled by a TF are treated as *K* realizations of an independent random variable with the same distribution and, therefore, the weighted sample covariance matrix estimation method was applied to approximate the TF's model covariance matrix. Higher weight is given to those gene samples that are exclusively controlled by the TF. The bootstrap procedure is incorporated to increase the estimation accuracy of covariance model for those TFs with the low number of known targets. Then the algorithm computes the Gene-Regulon association score for each gene-regulon pair using the likelihood function defined in the Method Section. Finally, the activity profiles of transcription factors are estimated using the eigenvalue decomposition procedure. The regulon-based methodology assumes that the expressions of target genes vary with the activity of their regulator, which does not have to be determined solely by its transcript levels but can be a combination of latent factors including abundance, modification status, interaction with low molecular weight effectors or other proteins.

### 2.3 Performance Comparison

To assess the relative performance of our algorithm, we compared our algorithm with the relevance network method developed in [17], and with GGM method presented in [21]. We did not include other relevance network methods such as [16, 18] and Bayesian networks [19] because the method in [17] outperforms them.

Since we were interested in transcriptional regulatory interactions (interactions between transcription factors and their target genes), we built a network by comparing scores for all possible pairs of transcription factor-gene targets. To make a fair comparison, in each method for each gene we ranked the regulators based on their association scores with that gene. A regulator which has the maximum association score with a gene was assigned to that gene. The second regulator was assigned to the gene if the corresponding association score was greater than the minimum of the association scores of the genes assigned to that regulator in the first round. This procedure was repeated and assignments were made, if warranted by the association score, for the lower ranking regulators as well. This procedure is different from those that use a global threshold parameter to select the edges. Assignment of regulators based on a global threshold for all TFs results in a very limited number of predictions, although with a good precision. Due to the large scale of the data, it is reasonable to assume that there should be at least one regulator that can explain the gene expression data of each gene. Although, this association may not be discovered through the dependency between the expression level of the gene and the mRNA level of a gene coding for the transcription factor, it may be discovered using gene-regulon based association, and this is what we would like to demonstrate.

We compared the prediction results of these three algorithms over the set of known interactions in RegulonDB database [30], which was compiled to a binary matrix of interactions between 1210 genes and 137 transcription factors. Because this data set is incomplete and there are no negative controls, the appropriate measures to compare the performance of the algorithms is recall and precision, see [Additional file 1] for the procedural details.

Comparison of recall(Precision) (%), rounded to the closest integer, for the model selection algorithm, relevance network and graphical gaussian model on two large-scale microarrays data sets.

Methods | |||
---|---|---|---|

Data sets | Model selection algorithm (this paper) | Relevance Network | GGM |

Our data set | 44 (43) | 8 (6) | 3(3) |

Data set in [17] | 62 (64) | 20 (16) | 3(2) |

The second reason for outperforming the relevance network algorithm is the capacity of our method to capture condition-specific activity and the co-variance of the gene expression profiles across different conditions. On the other hand, the relevance network algorithm treats gene expression levels across different conditions as ensembles of single random variables to estimate the probability distribution for each gene, and therefore it can not account for condition-specific activity of the genes when the sample size is not large enough. See [Additional file 1] for more information on the comparison between the algorithms.

### 2.4 Network reconstruction

We applied the proposed method to a large-scale gene expression data set to evaluate its capacity to recover known regulatory interactions and predict new ones. We ignored any TF assignments with a very small number (< 5) of known interactions. We assigned to each gene a regulator based on the maximum probability of association calculated using the proposed algorithm presented in the methods section. When we limited the number of associations for each gene to one, the algorithm was able to recover the correct association for 86% of the genes with known interaction, i.e., 1044 genes out of 1210 genes were associated with their known regulators. When two regulators were assigned to genes, an additional 542 known interactions were recovered, which included known interactions for 26 genes that were not in the set of 1044. That increased the percentage of genes with correct associations to 88%, and this implies that for 74% of genes, i.e. for 516 out of 696 genes having two or more regulators, both predicted regulators were correct.

In addition to recovering known interactions, the algorithm discovered new, un-annotated interactions. Some of the discovered interactions could be independently confirmed. For example, the algorithm predicted two targets of ArgR, *hisJ* and *artJ*, which were not among known interactions in the RegulonDB database, but have been recently reported in the literature [31]. For this particular TF, 21 out of 27 known interactions in regulonDB were recovered.

*ompT*,

*dppA*,

*eco*,

*pntA*,

*pntB*,

*csiE*,

*sdaB sdaC*,

*yhjE*and

*ygdH*, most of which were also verified using a qPCR experiment (Table 3). Thus, the algorithm discovered 10

*new*targets of the transcription factor Lrp that could be confirmed by a biological experiment. In addition, limited evidence in the literature suggests that

*sdaBC*and

*pntAB*are also likely Lrp targets [33, 34].

*pntAB*is also among predicted targets for lrp in [17]. Our results also indicated that Lrp controls expression of the

*leuABC*operon. Although according to RegulonDB there is no interaction between Lrp and the

*leuABC*operon, it has been previously reported that Lrp does regulate this operon [33]. Overall, this rate of discovery of true Lrp targets is higher than any previously reported discovery rate of confirmed targets for any regulator by a discovery algorithm.

New targets of Lrp which were confirmed using qPCR (the fold enrichment values with '*' are from ChIP-chip)

Gene name | Fold transcript change | Fold IP enrichment | Lrp Activity | Function |
---|---|---|---|---|

ompT | 8.5 | 2.8 | Positive | DLP12 prophage; outer membrane protease VII |

eco | 1.8 | 2.3 | Negative | ecotin, serine protease inhibitor |

dppA | 1.6 | 3.4 | Positive | dipeptide transporter |

pntA | 1.6 | 2.8 | Positive | pyridine nucleotide transhydrogenase, alpha subunit |

artP | 1.7 | 4 | Negative | arginine periplasmic transport system |

sdaC | - | 1.9 | Positive | predicted serine transporter |

yhjE | 1.5 | 6.9 | Negative | putative transporter |

csiE | - | 2.1* | Negative | stationary phase inducible protein |

ygdH | - | 2* | Positive | unknown |

### 2.5 Activity of regulators

The principal eigenvector of the covariance matrix computed from the set of genes controlled by each regulator was assumed to be a good and biologically sound approximation of the activity profiles of the regulators. We proceeded to evaluate this assumption by examining the estimated activity levels of transcription factors in individual conditions and by comparing the eigenvector-derived profiles with the activity profiles calculated by the Network Component Analysis, a state of the art connectivity matrix decomposition technique proposed in [27]. (Note, the activity levels of TFs are the relative activities of TFs in each condition with respect to a reference sample.)

Several conditions in our data set were expected to elicit transcriptional responses mediated by the activities of known regulators. Indeed, we found that in all conditions with well-studied and understood transcriptional responses, the identity of the most active TF matched our expectations. For example, in the experiment to measure transcriptional response to the addition of the amino acid arginine, transcription factor ArgR appeared to be the most active TF. Similarly, TrpR was the most active TF in the condition when tryptophan was added to the medium, and LexA was the most active TF under conditions of UV and Gamma treatment Fig. 1.

The correlation analysis of TFs activity profiles revealed that the activity profiles of almost half of the transcription factors considered in this study are correlated with one another. Some global regulators such as CRP, IHF, FNR were correlated with more than 10 TFs. However, some local regulators, such as OmpR, PhoP, and EnvY, also showed a high degree of correlation with other TFs. Figure S1 [see Additional file 2] shows the network view of correlation between transcription factors. The existence of the edge between two TFs indicates that the correlation between their activity profiles is above a threshold value of 0.70. Such similarities may indicate a certain degree of regulatory redundancy, i.e. different regulators controlling subsets of overlapping genes. Indeed, when we examined to what extent the correlations between the profiles are indicative of TFs regulating common genes, we observed that transcription factor pairs with high correlation regulate common genes with higher probability than TF pairs with low correlations. 55% of TF pairs with correlation above 0.70 appeared to have common targets, compared to 20% of TF pairs with correlation less than 0.70.

However several transcription factors, including LexA, GcvA, SoxR, DsdC and FadR, did not show high degree of correlation with other TFs, even though they were relatively responsive in a high number of conditions in the study.

### 2.6 Network refinement

Based on the two expression data sets, covering regulatory states for almost all genes in the genome, each gene (operon) was assigned a transcription factor(s) that was likely to control the expression of that gene. To this end, the present algorithm was applied to both data sets. For each data set, every gene was assigned three top-ranking regulators with the highest probabilities of association, calculated using equation (3). If a gene had the same regulator(s) predicted from both sets, the regulatory association between the gene and regulator(s) was deemed true and cataloged for the purpose of network refinement. This resulted in a list of 1719 genes associated with at least one regulator [see Additional file 3]. 779 genes out of 1719 had no previously characterized regulatory interactions. The existence of consensus regulators (predicted using two completely independent data sets) for this number of genes is statistically significant. Only 167 genes would have been expected to have a consensus regulator if the assignments in one of the sets had been done at random.

We incorporated the operon information to further refine the set of regulatory interactions. The consensus regulator for an operon was chosen as a common regulator of genes in the same operon, as predicted on the basis of both data sets. This resulted in a list of potential regulator(s) for each operon [see Additional file 4], with many already confirmed or highly plausible regulatory interactions. For example, *dinI* and *dinP* are known targets of LexA, but not among the known interactions set used in this study. Also, yafNOP, *yebB*, *yebG*, *yigN*, and *yjjB-dnaTC-yjjA* were predicted to be LexA targets. *yafNOP* is a neighboring operon of *dinB* with a weak but significant score for a LexA binding site in its promoter region. Regulatory regions of *yebG*, *yigN* and *yebB* contain high scoring LexA binding sites. The possibility that *dnaT* and *dnaC*, two genes involved in DNA replication, are under LexA control is intriguing, and warrants further experimentation.

Overall, even though the two data compendia appeared to be substantially different as far as dominant activity profiles are concerned, transcriptional profiles of as many as 1407 genes and 773 multigene operons could be explained at least in part by the activity of the same regulator(s) in both data sets. This result implies that, provided a sufficiently diverse collection of experimental conditions, the method will converge on true transcriptional regulators of any given gene in a genome, including regulators themselves [see Additional files 1 and 5].

## 3 Conclusion

Genome-wide transcriptional data allow for systematic analysis of regulatory patterns of gene expression. These patterns are at least in part determined by interactions between transcription factors and their cognate target genes. Accurate prediction of such interactions, which is essential for understanding phenotypic outcomes of genetic and environmental perturbations, depends on the quality of models capturing essential regulatory features and on their underlying assumptions. One such feature is that the transcriptional activity of co-regulated genes should sufficiently absorb in itself the activity of their common regulator. Moreover, the information about transcriptional activity of the known co-regulated genes (a core regulon) should also be sufficient for discovering new target genes, whose transcriptional activity significantly co-vary with the activity of the core regulon members.

We introduced an effective approach to predict interactions between regulators and genes through a simple model selection algorithm. The algorithm takes advantage of both gene expression data and the knowledge of known gene-TF associations to simultaneously discover new interactions between regulators and genes and to estimate the activity profiles of the regulators. The proposed approach, unlike other methods, associates the expression of genes in a regulon with the activity of transcription factors rather than with the expression levels of genes encoding for the transcription factors. We demonstrated that incorporation of information about the activity profiles of transcription factors allows for a reliable identification of many known as well as previously uncharacterized regulatory interactions, which could not be achieved by methods solely relying on gene expression associations. We should mention that the power of a regulon-based association framework presented in this paper and of the supervised inference method [35] relies on the prior information about known interactions between genes and transcription factors: without such information these supervised algorithms are not applicable. However, with the help of ChIP-chip and ChIP-sequencing technology, it should be possible to obtain sufficient amount of data to seed a *de novo* interactivity matrix, which can be used in combination with gene expression data to construct a reliable gene regulatory network by means of these supervised learning methods.

## 4 Methods

**e** represents a vector of gene expression measurements across *m* conditions, **S** is a *m* × *k* matrix of the *k* regulators' activities and **c** is a sparse vector that represents the interaction between the gene and regulators. We assume **S** and **c** are both unknown and *ϵ* is an *m* elements vector of random noise with zero mean and covariance matrix of *σ*^{2}**I**.

The fact that the expression level of genes is controlled by only a few regulatory nodes makes the resulting network, and therefore **c**, sparse. Furthermore, the direct measurement of the regulators' activity is a challenging and expensive, if not an impossible, task. Therefore, one does not have access to **S**, the activity profiles of regulators. The objective is to estimate the activity profiles of the regulators that control transcription (initiation) of groups of genes. At the same time, we also wish to provide for each gene the set of regulators that can explain the observed gene expression data.

_{ f }be a set consisting of the expression vectors of genes known to be controlled by a single regulator

*f*. Then,

**s**

_{ f }, the activity profile of the regulator

*f*, is unknown and

*c*

_{ f }is the interaction coefficient, which is assumed to be a random variable with distribution (

*υ*,

*γ*

^{2}). The likelihood function for

**s**

_{ f }[36] is,

**s**

_{ f }, maximizes

*p*(

**e**

_{ g }|

**s**

_{ f }). On the other hand, to compute

*p*(

**e**

_{ g }|

**s**

_{ f }) the regulatory signals need not be known explicitly and the knowledge of the sample mean (

*μ*

_{ f }) and the covariance matrix (Σ

_{ f }) corresponding to each set Ω

_{ f }is sufficient for model selection. Therefore, one only needs to estimate these sample mean and covariance matrices from the data. The covariance matrix Σ

_{ f }, which is the covariance matrix of gene expression data in Ω

_{ f }, can be estimated using sample covariance matrix. Assume

**E**

_{ f }to be the expression measurements of genes in Ω

_{ f }, then one can estimate the weighted covariance matrix corresponding to regulator

*f*by,

where *N*_{
f
}is the number of samples in Ω_{
f
}, *μ*_{
i
}and *μ*_{
j
}are *i* th and *j* th entries of sample mean, *μ*_{
f
}and *w*_{
k
}'s are weights which sum to one.

_{ f }'s as sets of genes controlled only by one regulator, if

**s**

_{ f }represents the true model, then the covariance matrix Σ

_{ f }can be represented by its first rank approximation using eigenvalue decomposition. Therefore, ignoring the noise terms, the inverse of the covariance matrix can be efficiently computed through its first rank approximation, which not only speeds up the algorithm but also reduces the effects of the noise.

where *λ*_{1} and **u**_{1} are respectively the principal eigenvalue and the principal eigenvector of the covariance matrix. Given data sets of known TF-gene interactions one can form the sets of Ω_{
f
}'s and use equations 4,5 and 3, respectively, to compute *p*(**e**_{
g
}|**s**_{
f
}) for all TFs and assign to each gene *g* the regulator *f* that provides the maximum value.

The knowledge of regulator-gene interactions represents a low-resolution view of molecular interactions inside a cell. It does not provide any details about how and when these interactions occur. Therefore, as a complement to this information, in some biological studies, the question might be which regulators and how they respond under different environmental or genetic perturbations. One way to tackle this problem is to study the activity levels of different transcription factors across different conditions. The principal eigenvector (eigenvector corresponding to the largest eigenvalue) of the sample covariance matrix (Σ_{
f
}) can be viewed as the activity profile of a regulator *f*. Notice that the activity profiles of regulators are principal eigenvectors of different covariance matrices, which necessarily need not be, and, indeed they are not, orthogonal to each other. Therefore, the estimated activity profiles are different from those estimated by algorithms such as Principal component analysis (PCA) [22] or singular value decomposition [23, 24] and independent component analysis (ICA)[26] which decompose the original data into a few regulatory signals that are orthogonal or independent.

## Declarations

### Acknowledgements

This work was supported in part by the University of Minnesota Doctoral Dissertation Fellowship (HZ) and by NIH grant GM066098 (AK). PS was supported by NIH grant R01AI054716 to Robert Blumenthal (University of Toledo College of Medicine, AK-subcontractor). We thank Bree Hamann for comments and corrections.

## Authors’ Affiliations

## References

- Friedman N: Inferring Cellular Networks Using Probabilistic Graphical Models. Science. 2004, 303: 799-805.View ArticlePubMedGoogle Scholar
- Levine M, Davidson EH: Gene regulatory networks for development. Proc Natl Acad Sci U S A. 2005, 102 (14): 4936-4942.PubMed CentralView ArticlePubMedGoogle Scholar
- Eisen MB, Spellman PT, Brown PO, Botstein D: Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci U S A. 1998, 95 (25): 14863-14868.PubMed CentralView ArticlePubMedGoogle Scholar
- Tavazoie S, Hughes JD, Campbell MJ, Cho RJ, Church GM: Systematic determination of genetic network architecture. Nat Genet. 1999, 22: 281-285.View ArticlePubMedGoogle Scholar
- Singer RH, Penman S: Messenger RNA in HeLa cells: kinetics of formation and decay. J Mol Biol. 1973, 78 (2): 321-334.View ArticlePubMedGoogle Scholar
- Chen HC, Lee HC, Lin TY, Li WH, Chen BS: Quantitative characterization of the transcriptional regulatory network in the yeast cell cycle. Bioinformatics. 2004, 20 (12): 1914-1927.View ArticlePubMedGoogle Scholar
- Sasik R, Iranfar N, Hwa T, Loomis WF: Extracting transcriptional events from temporal gene expression patterns during Dictyostelium development. Bioinformatics. 2002, 18 (1): 61-66.View ArticlePubMedGoogle Scholar
- Yamaguchi R, Yoshida R, Imoto S, Higuchi T, Miyano S: Finding module-based gene networks with state-space models – Mining high-dimensional and short time-course gene expression data. Signal Processing Magazine, IEEE. 2007, 24 (1):Google Scholar
- Perrin BE, Ralaivola L, Mazurie A, Bottani S, Mallet J, d'Alch-Buc F: Gene networks inference using dynamic Bayesian networks. Bioinformatics. 2003, 19 (2): 38-48.Google Scholar
- Kim S, Imoto S, Miyano S: Dynamic Bayesian network and nonparametric regression for nonlinear modeling of gene networks from time series gene expression data. Biosystems. 2004, 75 (1–3): 57-65.View ArticlePubMedGoogle Scholar
- Akutsu T, Miyano S, Kuhara S: Inferring qualitative relations in genetic networks and metabolic pathways. Bioinformatics. 2000, 16: 727-743.View ArticlePubMedGoogle Scholar
- Shmulevich I, Dougherty ER, Kim S, Zhang W: Probabilistic Boolean networks: a rule-based uncertainty model for gene regulatory networks. Bioinformatics. 2002, 18 (2): 261-274.View ArticlePubMedGoogle Scholar
- Zhou X, Wang X, Dougherty ER: Construction of genomic networks using mutual-information clustering and reversible-jump Markov-chain-Monte-Carlo predictor design. Signal Processing. 2003, 83 (4): 745-761.View ArticleGoogle Scholar
- Holter NS, Maritan A, Cieplak M, Fedoroff NV, Banavar JR: Dynamic modeling of gene expression data. Proc Natl Acad Sci U S A. 2001, 98 (4): 1693-1698.PubMed CentralView ArticlePubMedGoogle Scholar
- Hoon M, Imoto S, Miyano S: Inferring Gene Regulatory Networks from Time-Ordered Gene Expression Data Using Differential Equations. Lecture Notes in Computer Science. 2002, 283-288. Springer Berlin/HeidelbergGoogle Scholar
- Butte AJ, Kohane IS: Mutual infromation relevance networks: Functional genomic clustering using pairwise entropy measuremens. Pac Symp Biocomput. 2002, 418-429.Google Scholar
- Faith JJ, Hayete B, Thaden JT, Mogno I, Wierzbowski J, Cottarel G, Kasif S, Collins JJ, Gardner TS: Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles. PLOS Biology. 2007, 5 (1): 54-66.View ArticleGoogle Scholar
- Margolin AA, Nemenman I, Basso K, Wiggins C, Stolovitzky G, Dalla Favera R, Califano A: ARACNE: An algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinformatics. 2006, 7 (1):Google Scholar
- Friedman N, Linial M, Nachman I, Pe'er D: Using Bayesian networks to analyze expression data. J Comput Biol. 2000, 7: 601-620.View ArticlePubMedGoogle Scholar
- Hiroyuki T, Katsuhisa H: Inference of a genetic network by a combined approach of cluster analysis and graphical Gaussian modeling. Bioinformatics. 2002, 18 (2): 287-297.View ArticleGoogle Scholar
- Schafer J, Strimmer K: An empirical Bayes approach to inferring large-scale gene association networks. Bioinformatics. 2005, 21 (6): 754-764.View ArticlePubMedGoogle Scholar
- Raychaudhuri S, Stuart JM, Altman RB: Principal components analysis to summarize microarray experiments: application to sporulation time series. Pac Symp Biocomput. 2000, 455-466.Google Scholar
- Alter O, Brown PO, Botstein D: Singular value decomposition for genome-wide expression data processing and modeling. Proc Natl Acad Sci U S A. 2004, 29;97 (18): 10101-10106.Google Scholar
- Holter NS, Mitra M, Maritan A, Cieplak M, Banavar JR, Fedoroff NV: Fundamental patterns underlying gene expression profiles: simplicity from complexity. Proc Natl Acad Sci U S A. 2000, 97 (15): 8409-8414.PubMed CentralView ArticlePubMedGoogle Scholar
- Alter O, Golub GH: Integrative analysis of genome-scale data by using pseudoinverse projection predicts novel correlation between DNA replication and RNA transcription. Proc Natl Acad Sci U S A. 2004, 101 (47): 16577-16582.PubMed CentralView ArticlePubMedGoogle Scholar
- Liebermeister W: Linear modes of gene expression determined by independent component analysis. Bioinformatics. 2002, 18 (1): 51-60.View ArticlePubMedGoogle Scholar
- Liao JC, Boscolo R, Yang YL, Tran LM, Sabatti C, Roychowdhury VP: Network component analysis: reconstruction of regulatory signals in biological systems. Proc Natl Acad Sci U S A. 2003, 100 (26): 15522-15527.PubMed CentralView ArticlePubMedGoogle Scholar
- Li H, Zhan M: Unraveling transcriptional regulatory programs by integrative analysis of microarray and transcription factor binding data. Bioinformatics. 2008, 24 (17): 1874-1880.PubMed CentralView ArticlePubMedGoogle Scholar
- Sangurdekar D, Srienc F, Khodursky A: Classification based framework for quantitative description of large-scale microarray data. Genome Biol. 2006, 7 (4): R32-PubMed CentralView ArticlePubMedGoogle Scholar
- Salgado H, et al.: RegulonDB (version 5.0): Escherichia coli K-12 transcriptional regulatory network, operon organization, and growth conditions. Nucleic Acids Res. 2006, 34: D394-D397.PubMed CentralView ArticlePubMedGoogle Scholar
- Caldara M, Charlier D, Cunin R: The arginine regulon of Escherichia coli: whole-system transcriptome analysis discovers new genes and provides an integrated view of arginine regulation. Microbiology. 2006, 152: 3343-3354.View ArticlePubMedGoogle Scholar
- Tani TH, Khodursky A, Blumenthal RM, Brown PO, Matthews RG: Adaptation to famine: a family of stationary-phase genes revealed by microarray analysis. Proc Natl Acad Sci U S A. 2002, 99 (21): 13471-13476.PubMed CentralView ArticlePubMedGoogle Scholar
- Shao ZQ, Lin RT, Newman EB: Sequencing and characterization of the sdaC gene and identification of the sdaCB operon in E. coli K-12. Eur J Biochem. 1994, 222: 901-907.View ArticlePubMedGoogle Scholar
- DAri R, Lin RT, Newman EB: The leucine responsive regulatory protein: more than a regulator?. Trends Biochem Sci. 1993, 18: 260-263.View ArticleGoogle Scholar
- Mordelet F, Vert JP: SIRENE: supervised inference of regulatory networks. Bioinformatics. 2008, 24 (16): 76-82.View ArticleGoogle Scholar
- Kay Steven: Fundamentals of statistical signal processing: Estimation Theory. 1993, Prentice-Hall, IncGoogle Scholar

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