# Reconstructing Generalized Logical Networks of Transcriptional Regulation in Mouse Brain from Temporal Gene Expression Data

- Mingzhou(Joe) Song
^{1}Email author, - Chris K. Lewis
^{1}, - Eric R. Lance
^{1}, - Elissa J. Chesler
^{2}, - Roumyana Kirova Yordanova
^{3}, - Michael A. Langston
^{4}, - Kerrie H. Lodowski
^{5}and - Susan E. Bergeson
^{6}

**2009**:545176

https://doi.org/10.1155/2009/545176

© Mingzhou (Joe) Song et al. 2009

**Received: **1 June 2008

**Accepted: **12 December 2008

**Published: **27 January 2009

## Abstract

Gene expression time course data can be used not only to detect differentially expressed genes but also to find temporal associations among genes. The problem of reconstructing generalized logical networks to account for temporal dependencies among genes and environmental stimuli from transcriptomic data is addressed. A network reconstruction algorithm was developed that uses statistical significance as a criterion for network selection to avoid false-positive interactions arising from pure chance. The multinomial hypothesis testing-based network reconstruction allows for explicit specification of the false-positive rate, unique from all extant network inference algorithms. The method is superior to dynamic Bayesian network modeling in a simulation study. Temporal gene expression data from the brains of alcohol-treated mice in an analysis of the molecular response to alcohol are used for modeling. Genes from major neuronal pathways are identified as putative components of the alcohol response mechanism. Nine of these genes have associations with alcohol reported in literature. Several other potentially relevant genes, compatible with independent results from literature mining, may play a role in the response to alcohol. Additional, previously unknown gene interactions were discovered that, subject to biological verification, may offer new clues in the search for the elusive molecular mechanisms of alcoholism.

## Keywords

## 1. Introduction

The regulation of transcription occurring in an intriguingly complex biological system involves multiple interacting regulatory processes in gene regulatory networks (GRNs). Modeling transcriptional regulation requires algorithms that retain information about regulatory interactions. The generalized logical network (GLN) is a generative model that can be reconstructed from temporal trajectories, for example, from data collected in time-series studies of gene expression. Because these data capture information on temporal antecedence, the approach can be used to develop stronger hypotheses about casual relations among transcriptional events than one would be able to derive from mere correlation analyses. We designed a GLN reconstruction algorithm that differs from previous approaches because it makes use of hypothesis testing on the multinomial distribution to establish directed connections among genes. Our statistical approach allows explicit control of false positives by specifying a desirable alpha level, while other criteria used in network reconstruction, such as the Bayesian information criterion (BIC) used in dynamic Bayesian networks (DBNs) reconstruction and the coefficient of determination (COD) used in Boolean networks (BNs) reconstruction, do not explicitly enforce false-positive rate control.

GLNs also allow more aspects of systems to be studied than other network models by enabling (1) adaptive description for interactions among variables, (2) nonlinear interaction patterns, and (3) finite steady states, attractor basins, and state transition diagrams. The software CellNetAnalyzer [1] allows a user to draft a GLN from existing knowledge. Our method allows such networks to be reconstructed and derived solely from data-driven approaches. GLNs have the further advantage that they do not require parametric assumptions, unlike stochastic logical networks [2] which discretize differential equations based on strong assumptions. Additionally, our implementation of GLN modeling focuses on network reconstruction from temporal gene expression data, which can be used complementarily with network property analysis algorithms such as the network walking algorithm [3], and literature mining tools such as those reviewed in [4].

- (i)
Temporal probabilistic networks. The dynamic Bayesian network (DBN) is an extension of Bayesian networks, which incorporates time transitions between Bayesian networks. A DBN describes temporal statistical dependencies among genes. DBNs have been successful in extracting probabilistic dependencies among genes in GRNs [5–7]. Certain DBNs can even be converted to probabilistic Boolean networks [8]. However, DBN is an indirect tool to understand system dynamics since it does not explicitly describe temporal relations among entities in a functional form, while a GLN provides immediate functional relationships among variables.

- (ii)
Continuous dynamical system models. Differential equations in both deterministic [9, 10] and stochastic [11] formulations have been used to model interactions in GRNs in continuous time. The E-Cell Project [12, 13] uses differential equations to target knowledge-based reproduction, not data-driven reconstruction, of intracellular biochemical and molecular interactions within a single cell. The stochastic master equations relate state probabilities by differential equations, impractical for biological systems involving many variables because of the computational burden. Recent research has been focusing on improving the scalability of such models [14].

- (iii)
Discrete dynamical system models. The Boolean network (BN) [1, 15–18] and its Markovian [19] or probabilistic [20] extensions, where each variable takes the value of either 0 or 1, are 1st-order special cases of the GLN. The dichotomous nature of a BN seriously limits its capacity to discriminate quantitative differences among continuous random variables. As most biological networks are rarely binary, much information is lost. This can be crucial when such differences are more interesting than the mere information of presence (1) or absence (0). In addition, the coefficient of determination criterion used in BN reconstruction does not address the issue of model complexity and goodness of fit.

To summarize, these temporal probabilistic networks do not explicitly describe system dynamics. Continuous dynamical system models, computationally and data intensive and thus often not data driven, are also inconvenient for visualizing state transitions. BNs cannot capture subtle and nonlinear interactions. Details of these and various other major network reconstruction and modeling algorithms can be found in recent reviews [21, 22].

Temporal dependency may reflect causal interactions among processes in a dynamical system, but not always. System modeling may be further complicated by incomplete observations—a situation that is typical for biological experiments. For example, protein concentrations, post-translational protein modification states, and small molecular messengers are missing in a GRN developed entirely from transcriptome data. However, a consistent temporal dependency must arise from a causal interaction, even with incomplete observations. Therefore, statistically significant temporal dependencies among genes and environmental stimuli may still constitute a basis to establish causalities.

We reconstruct GLNs from trajectories of discrete random variables, the abundance of mRNAs, in order to uncover temporal dependencies among genes and environmental stimuli. Temporal dependencies among key genes in response to alcohol in mice are assessed through GLN modeling. The effects of alcohol on functions of gene products and the corresponding effect on gene expression are an active research area, particularly in the inflammatory and neural plasticity processes that result in lasting brain changes in response to alcohol. We believe that the GLN approach will provide highly relevant clues to discover biologically important gene interactions involved in the molecular mechanisms of brain changes in alcoholism. The resulting network model demonstrates the tremendous potential for GLN modeling to provide insight into the diverse molecular mechanisms underlying clinical phenomena such as alcoholism.

The paper is organized into eight sections. The GLN is defined in Section 2. A procedure is given in Section 3 to determine the statistical power of reconstructing a GLN given an experimental design. An algorithm for reconstruction of GLNs based on multinomial testing is described in Section 4. Comparisons of reconstruction accuracy between GLN and DBN modeling are made in Section 5. A microarray experiment for the influence of alcohol on mouse brain gene expression is recounted in Section 6. The GLN modeling result of the GRN in the mouse brain in response to alcohol is discussed in Section 7. Finally, conclusions and future work are given in Section 8.

## 2. The Generalized Logical Network

As a discrete-time and discrete-value dynamical system model, a GLN of nodes is a directed graph with a gtt attached to each node. Each abstract node can represent information about a molecule, a cell, a species, or a stimulus. The gtt allows a discrete variable to take more than two possible values and to reflect subtle but crucial changes, and encodes precisely the biological mechanisms that the nodes use to interact with each other.

With parents, the size of is , exponential in and posing a memory problem. The generalized logical decision diagram is a space efficient data structure to store a gtt by removing fictitious variables and redundancies, extending the binary decision diagram [23].

The following is an example showing the gtt of of 3 levels with two parents of 2 and 3 levels, respectively.

Synchronous th order GLNs allow modeling of variable time delays abundant in biological systems. Let be the initial states of a GLN. A trajectory of length is defined as . Our discussion is restricted to synchronous and first-order GLNs.

## 3. Statistical Power for GLN Reconstruction

Given the number of time points on a trajectory and the sample size per time point, one is statistically limited in detecting true interactions in a GLN beyond a certain network complexity by the statistical power. The gtts, distributions of each variable, sample size (number of replicas and time points), Type I error, and effect size together determine the statistical power. Power is independent of the computational approach used to reconstruct a GLN from observed trajectories. With estimation of statistical power, one can answer the question of whether the amount of data in the trajectory can statistically support any GLN for certain complexity at all.

## 4. GLN Reconstruction through Multinomial Tests

A GLN can be reconstructed from observed trajectories of a system under perturbed conditions. There are two important issues in GLN reconstruction. The first one is how to search efficiently for the best among feasible GLN candidates. This issue depends on how one handles the combinatorial computational cost, generally *NP*-hard, incurred by reconstructing a GLN. The second issue is how to determine the false-positive rate that the best candidate arises out of randomness caused by noise and sampling errors in a network where no nodes interact, recently gaining attention such as in BN fitting [25]. Various criteria for goodness of fit have been used in reconstruction of a GLN from observed trajectories. Mutual information among variables has been employed in interaction graphs [26]; likelihood and BIC are used to determine network structure for Bayesian networks [27] and DBNs; the coefficient of determination has been used for BNs [20]. These measures, however, do not control the false-positive rate directly.

By performing multinomial tests on the transition tables at each node, we are able to resolve simultaneously both issues above in one framework. The network topology inference reduces to selecting the parents for each node through multiple applications of the same multinomial test. The false-positive control is achieved by setting an -level, which can be adjusted for multiple comparisons, for the tests at each node, instead of always keeping a parent selection with the best value of criterion as in all other approaches mentioned above. Our criterion is the statistical significance of each test. Thus, we move forward from existing network topology inference approaches by assessing the probability of false-positive interactions arising by chance in GLN reconstruction.

*Null Hypothesis.*

*Alternative Hypothesis.*

A -value can be computed for to indicate the statistical significance of a GLN model. The -value provides a means to tradeoff between goodness of fit and complexity. Therefore, GLN reconstruction is to find a GLN with the minimum -value. Since the statistics for the transition tables at each node are independent of each other, minimization of the overall -value reduces to minimizing the -values for individual transition tables at each node.

where is the maximum quantization level of all nodes.

**Algorithm 1:** Reconstruct-GLN (A collection of observed trajectories,
-level,
).

**For** each node **do**

**For** each possible selection of
parents **do**

Accumulate a transition table from given trajectories

Compute -value by performing multinomial test on the transition table

**if**
-value is smaller than the current minimum
-value for the current node **then**

Record the current transition table

Replace previous parents with the current selection of parents

**end if**

**end for**

**end for**

Perform -value adjustment for multiple comparisons involved in parent selection

**if** the adjusted
-value is less than the given
-level **then**

Convert the transition table with the minimum -value to a gtt by maximum likelihood

estimation of multinomial parameters

**else**

Declare that the current node has no parents

**end if**

**end for**

Compute the overall -value for the reconstructed GLN

Return the reconstructed GLN, the associated -values for each node, and the overall -value

## 5. Accuracy of GLN versus DBN Reconstruction

As GLN modeling is proposed as a potential alternative to DBN modeling, it is important to assess the performance of GLN relative to DBN modeling in terms of their abilities to recover the topology of the underlying networks. We use Hamming distance, false positives, and false negatives to evaluate the difference between a reconstructed network and the original ground-truth network. The Hamming distance is defined by the total number of different directed edges between two networks of the same set of nodes. A false positive is an incidence of a directed edge in the reconstructed network but not in the original ground-truth network; a false negative is an incidence of a directed edge in the original network but not in the reconstructed network. The definitions imply that the Hamming distance is the sum of false positives and false negatives. We have chosen to use a simulated data set over a real biological data set, such as the yeast cell cycle gene expression data set, to do the performance evaluation. This is because many factors in a biological data set may contribute to the reconstruction performance in addition to the algorithm difference. For example, the ground truth GRN in yeast may not contain all active interactions; it may also include additional interactions that are inactive in the particular experiments. This makes the comparison of algorithm performance less certain. In a simulated example, one has control of all potential variations.

is often evaluated to balance maximum likelihood estimation with the number of parameters in each conditional distribution. In contrast, the statistic is used in GLN modeling, as opposed to the likelihood in DBN modeling; the tradeoff with model complexity in GLN modeling is incorporated into the degrees of freedom of the distribution, as opposed to the term in the BIC in DBN modeling. Additionally, GLN modeling allows the user to control false-positive rate by specifying the size for type I error, while DBN modeling does not facilitate such an option.

For each trajectory, we applied increasing levels of noise with . When , the noise is the strongest in terms of network topology reconstruction. When , it is the same as as far as the topology is concerned.

GLN modeling is built on statistical hypothesis testing, while DBN modeling on information theory. We are curious at a more theoretical level why the GLN reconstruction has shown a consistently superior performance over the DBN reconstruction in the simulation study. We plan to address this remaining issue in our future work.

## 6. Temporal Gene Expression in Mice Exposed to Alcohol

Thirty-five adult DBA/2J (D2) mice were housed on a 12:12 light:dark cycle and given food and water ad libitum. The mice were habituated for three days to i.p. injections of saline and on the forth day were injected with 20% alcohol in saline in a total dose of 4 g/kg. D2 mice are exquisitely sensitive to alcohol dependence, and at this dose show physical signs consistent with dependence from about 4–10 hours after injection. Brains were removed, and anterior cortex tissue was dissected at 2, 7, 12, and 24 hours following the alcohol injection with 7 biological replicates at each time point. All animals were housed and treated according to the National Institutes of Health guidelines for the use and care of laboratory animals [28] and an approved Institutional Animal Care and Use Committee protocol.

cDNA fragments, that had undergone PCR from clones, were printed on poly-L-lysine-coated (Sigma, Mo, USA) microscope slides (Erie Scientific, Portsmouth, NH, USA) using a custom-built robotic arrayer as described in [29]. The clones were from several cDNA libraries, including ESTs cloned in the laboratory of S.E.B., Research Genetics/Invitrogen clone sets Brain Molecular Anatomy Project and Sequence Verified, and the National Institute on Aging (3) clone sets 7.4 K and 15 K. cDNA microarrays were hybridized using the 3DNA array 900 microarray labeling kit according to the manufacturer's protocol (Genisphere, Hatfield, Pa, USA). Total RNA samples were reverse transcribed, labeled with Cyanine-3 (Cy-3), and hybridized against a common reference RNA labeled with Cy-5. The common reference is whole-brain RNA extracted from 100 male B6 mice. All arrays contained the same reference RNA in the Cy-5 channel and were normalized by using within-print tips Lowess nonlinear normalization [30]. Normalized array data were stored in the longhorn array database (LAD) [31] and then standardized by using the red channel (common reference RNA) as the baseline standard with software developed in the laboratory of S.E.B. (These PERL programs are available upon request.) Data were loaded into an in-house database used for sorting by various statistics.

## 7. GLN Modeling of Transcription Regulation in the Mouse Brain

We demonstrate a GRN reconstructed using GLN modeling from a microarray study of temporal gene expression microarrays in mouse brains following acute exposure to alcohol to uncover transcription interactions of involved genes. The microarray data were normalized, quantized, formed to trajectories, and used to reconstruct a GLN. We illustrate the significant interactions we identified, their agreement with the literature, as well as the dynamic behavior of the GRN in response to alcohol.

Through post hoc -tests, partial least squares, and one-way ANOVA (fixed effect only and without multiple testing correction) across time course analyses, a total of 392 differentially expressed genes were selected because they exhibit both temporal and alcohol related expression variation. Missing gene expression values were imputed using the R software package PAMR [32]. Those genes not selected for inclusion do not have strong evidence from this experiment to be on any path from the alcohol node.

These selected genes were entered into the GLN model as candidate GLN components that connect to the alcohol treatment node through gene expression on a directed path.

The alcohol node is assigned based on the experimental condition: 1 for alcohol-injected samples and 0 for control samples. The quantization was implemented in Java and compiled to native code on SuSE Linux using the GCJ compiler. It took about 5 hours to finish the quantization on a 2.8 GHz Pentium dual-core processor computer with 4 GB RAM running SuSE Linux.

*Antxr1*and

*MGC40675*) respond to the injection of alcohol sharply after 2 hours of injection. However, they both return to normal levels after 24 hours of exposure. Although the predicted trajectories cannot capture all subtle changes in the original time courses, the prediction agrees with the overall trend in the observation. This suggests that the model fitting preserved the dynamics in both genes.

*Idh3g, Smarce1, 1700029I01Rik, Gm740, MGC40675, Fosb, Ckap1*, and

*Camk2b*are the most influential gene nodes. It should be noted that not all of the genes that were identified as network members are part of the conventional transcriptional regulatory system. The genomic approach employed in these studies enables detection of broader modifiers of transcription, including those genes which are involved in neuronal processes which in turn result in altered transcriptional activity. In fact, major neural pathways are represented. The interactions with alcohol for

*Smarce1*[35],

*Fosb*[36], and

*Camk2b*[37] are biologically verified. In addition, nine out of the 19 nodes in our GLN (Figure 9) have been identified as interacting with alcohol from biology literature by PathwayArchitect (Stratagene, La Jolla, Calif, USA). From another literature database tool Ingenuity Pathway Analysis (INGENUITY SYSTEMS, Redwood City, Calif, USA), we have found nine genes,

*Antxr1, Thbs4, Rorb, Smarce1, Nsd1, Bc055107, Camk2B, Gla*, and

*Fosb*, on the major canonical hepatic cholestasis, PPAR signaling, and xenobiotic metabolism signaling (e.g.,

*Camk2b*) pathways. The PPAR pathway is involved in the alcoholic metabolism. This indicates that our approach was indeed successful in capturing significant causal interactions through temporal dependencies. More importantly, however, new hypotheses for several genes that had never before been implicated in alcoholism were generated. Without a model which has the ability to detect statistically significant interactions, these would not otherwise have gained attention. Some of these putative network members and relations may be false positives. The molecular mechanisms of alcoholism are complex. Alcohol is a dirty drug, meaning that it acts on a diverse range of neurological processes. Its mechanisms of action are still poorly understood at the gene expression level, as this is a relatively new and active area of investigation in the alcohol research field. Most of the genes we report have not been associated with alcohol responses to date. The ability to contribute novel data-driven hypotheses to this research area will facilitate the planning of future studies, for example, in prioritizing which of over 45,000 proposed new knock-out mice [38] to rederive and test for phenotypic effects related to alcohol response. Ultimately, confirmatory validation experiments and convergent evidence from other high throughput molecular analyses are essential. These results demonstrated that our algorithm can generate and prioritize new hypotheses for understanding complex traits such as alcoholism.

## 8. Conclusions and Future Work

Derived from a statistical property regarding the summation of independent chi-squares, our GLN reconstruction algorithm identifies significant dynamic associations among a subset of genes to a target gene by performing the multinomial test. Thus, we have offered a unique framework to reconstruct GLNs to characterize temporal interactions from time-course gene expression data. Results from our application of this technique to the study of alcohol's influence on gene expression in mouse brains reveal both consistently observed associations and novel hypotheses that remain an open problem for current biological investigation. Based on these results, there appears to be significant potential to inspect the temporal patterns in gene expression through GLN reconstruction. In this paper, we have demonstrated the value of GLN modeling for extracting the underlying causal interactions among genes involved in response to alcohol. Some of the inferences made on temporal dependencies corroborate present knowledge on gene regulation in mouse. The other inferences will be subject to more extensive in vivo biological verification.

Preselection of a subset of interesting genes to render a model computable is a challenge for GRN modeling from microarray data. Approaches which filter genes or gene-gene relations have been applied. While this leads to the improved signal in the data, it also introduces a problem of false-negative results, neglecting extensive information on highly relevant genes which exhibit subtle variation in the same temporal patterns as other connected genes. Rather than filtering based on statistical effects, one could develop GLN models from known pathways and evaluate how they respond and interact with pharmacological perturbations. This strategy can be implemented by reconstructing GLNs from GRNs established by literature mining such as Ingenuity Pathways Knowledge Base (size Ingenuity Systems, Redwood City, Calif, USA) and PathAssist (size JusticeTrax Inc., Mesa, Ariz, USA). This will possibly allow the modeling to begin at a more realistic starting point, and will reserve statistical power for the strong plausible relations that are previously reported.

A more diverse set of nodes can also be incorporated into the GLN modeling. The biological relevance of a reconstructed GLN can be substantially improved if simultaneous measurements of the proteome, the metabolome, and the transcriptome are available, without major modifications to the current algorithms. Once data are properly scaled, the method is highly generalizable and has significant potential for inferring temporal relations among widely diverse biological processes. The illustration of the validity of our results from a small time-course gene expression study indicates substantial potential for denser sampling, and for the incorporation of additional data representing other aspects of the neurobiological response to alcohol, including neurohormonal, physiological, and behavioral measures.

## Declarations

### Acknowledgments

A previous version of this paper was presented at the 2nd Foundations of Systems Biology in Engineering at Stuttgart, Germany, in September 2007. M. Song, C. K. Lewis, and E. R. Lance were supported by the joint National Science Foundation (NSF)—Department of Energy (DOE) Faculty and Student Team program under Grant NSF HRD-0420407. M. Song was also supported in part by the National Research Initiative of the USDA Cooperative State Research, Education and Extension Service, Grant no. 2006-35504-17359, and a Grant no. 5U54CA132383 from the National Cancer Institute. R. K. Yordanova was supported by BISTI. M. A. Langston was supported in part by the National Institutes of Health (NIH) under Grants 1-P01-DA-015027-01, 5-U01-AA-013512, and 1-R01-MH-074460-01, by the DOE under the EPSCoR Laboratory Partnership Program, by the Australian Research Council, and by the European Commission under the Sixth Framework Program. Additionally, E. J. Chesler and M. A. Langston were supported by NIH/NIAAA INIA Bioinformatics Core and Pilot U01AA13499, U24AA13513; E. J. Chesler, M. A. Langston, and R. K. Yordanova by NICHD. S. E. Bergeson was supported by NIH Grants AA013182, AA013403, and AA013475.

## Authors’ Affiliations

## References

- Klamt S, Saez-Rodriguez J, Lindquist JA, Simeoni L, Gilles ED: A methodology for the structural and functional analysis of signaling and regulatory networks.
*BMC Bioinformatics*2006, 7, article 56: 1-26.Google Scholar - Wilczyński B, Tiuryn J: Regulatory network reconstruction using stochastic logical networks. In
*Proceedings of the International Conference on Computational Methods in Systems Biology (CMSB '06), Trento, Italy, October 2006, Lecture Notes in Computer Science*Edited by: Priami C. 4210: 142-154.Google Scholar - Chen Y, Wei T, Yan L,
*et al*.: Developing and applying a gene functional association network for anti-angiogenic kinase inhibitor activity assessment in an angiogenesis co-culture model.*BMC Genomics*2008, 9, article 264: 1-16.Google Scholar - Jensen LJ, Saric J, Bork P: Literature mining for the biologist: from information retrieval to biological discovery.
*Nature Reviews Genetics*2006, 7(2):119-129. 10.1038/nrg1768View ArticleGoogle Scholar - Ong IM, Glasner JD, Page D: Modelling regulatory pathways in
*E. coli*from time series expression profiles.*Bioinformatics*2002, 18(90001):S241-S248.View ArticleGoogle Scholar - Imoto S, Kim S, Goto T,
*et al*.: Bayesian network and nonparametric heteroscedastic regression for nonlinear modeling of genetic network.*Journal of Bioinformatics and Computational Biology*2003, 1(2):231-252. 10.1142/S0219720003000071View ArticleGoogle Scholar - Friedman N: Inferring cellular networks using probabilistic graphical models.
*Science*2004, 303(5659):799-805. 10.1126/science.1094068View ArticleGoogle Scholar - Lähdesmäki H, Hautaniemi S, Shmulevich I, Yli-Harja O: Relationships between probabilistic Boolean networks and dynamic Bayesian networks as models of gene regulatory networks.
*Signal Processing*2006, 86(4):814-834. 10.1016/j.sigpro.2005.06.008View ArticleMATHGoogle Scholar - Meir E, Munro EM, Odell GM, von Dassow G: Ingeneue: a versatile tool for reconstituting genetic networks, with examples from the segment polarity network.
*Journal of Experimental Zoology Part B*2002, 294(3):216-251. 10.1002/jez.10187View ArticleGoogle Scholar - Guthke R, Möller U, Hoffman M, Thies F, Töpfer S: Dynamic network reconstruction from gene expression data applied to immune response during bacterial infection.
*Bioinformatics*2005, 21(8):1626-1634. 10.1093/bioinformatics/bti226View ArticleGoogle Scholar - van Kampen N:
*Stochastic Processes in Physics and Chemistry*. Elsevier, Amsterdam, The Netherlands; 1997.Google Scholar - Tomita M, Hashimoto K, Takahashi K,
*et al*.: E-CELL: software environment for whole-cell simulation.*Bioinformatics*1999, 15(1):72-84. 10.1093/bioinformatics/15.1.72View ArticleGoogle Scholar - Takahashi K, Vel Arjunan SN, Tomita M: Space in systems biology of signaling pathways—towards intracellular molecular crowding in silico.
*FEBS Letters*2005, 579(8):1783-1788. 10.1016/j.febslet.2005.01.072View ArticleGoogle Scholar - Bongard J, Lipson H: Automated reverse engineering of nonlinear dynamical systems.
*Proceedings of the National Academy of Sciences of the United States of America*2007, 104(24):9943-9948. 10.1073/pnas.0609476104View ArticleMATHGoogle Scholar - Liang S, Fuhrman S, Somogyi R: Reveal, a general reverse engineering algorithm for inference of genetic network architectures.
*Pacific Symposium on Biocomputing*1998, 3: 18-29.Google Scholar - Akutsu T, Kuhara S, Maruyama O, Miyano S: Identification of genetic networks by strategic gene disruptions and gene overexpressions under a Boolean model.
*Theoretical Computer Science*2003, 298(1):235-251. 10.1016/S0304-3975(02)00425-5View ArticleMathSciNetMATHGoogle Scholar - Pal R, Ivanov I, Datta A, Bittner ML, Dougherty ER: Generating Boolean networks with a prescribed attractor structure.
*Bioinformatics*2005, 21(21):4021-4025. 10.1093/bioinformatics/bti664View ArticleGoogle Scholar - Garg A, Xenarios I, Mendoza L, DeMicheli G: An efficient method for dynamic analysis of gene regulatory networks and in silico gene perturbation experiments.
*Proceedings of the 11th Annual International Conference on Research in Computational Molecular Biology (RECOMB '07), Oakland, Calif, USA, April 2007, Lecture Notes in Computer Science*4453: 62-76.Google Scholar - Richardson M, Domingos P: Markov logical networks.
*Machine Learning*2006, 62(1-2):107-136. 10.1007/s10994-006-5833-1View ArticleGoogle 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. 10.1093/bioinformatics/18.2.261View ArticleGoogle Scholar - de Jong H: Modeling and simulation of genetic regulatory systems: a literature review.
*Journal of Computational Biology*2002, 9(1):67-103. 10.1089/10665270252833208View ArticleGoogle Scholar - Bansal M, Belcastro V, Ambesi-Impiombato A, di Bernardo D: How to infer gene networks from expression profiles.
*Molecular Systems Biology*2007, 3, article 78: 1-10.Google Scholar - Bryant RE: Graph-based algorithms for Boolean function manipulation.
*IEEE Transactions on Computers*1986, 35(8):677-691.View ArticleMATHGoogle Scholar - Cohen J:
*Statistical Power Analysis for the Behavioral Sciences*. Lawrence Erlbaum Associates, Hillsdale, NJ, USA; 1988.MATHGoogle Scholar - Kim H, Lee JK, Park T: Boolean networks using the chi-square test for inferring large-scale gene regulatory networks.
*BMC Bioinformatics*2007, 8, article 37: 1-15.Google Scholar - Margolin AA, Wang K, Lim WK, Kustagi M, Nemenman I, Califano A: Reverse engineering cellular networks.
*Nature Protocols*2006, 1(2):662-671. 10.1038/nprot.2006.106View ArticleGoogle Scholar - Friedman N, Goldszmidt M: Discretizing continuous attributes while learning Bayesian networks.
*Proceedings of the 13th International Conference on Machine Learning (ICML '96), Bari, Italy, July 1996*157-165.Google Scholar - National Research Council :
*Guide for the Care and Use of Laboratory Animals*. National Research Council, Washington, DC, USA; 1996.Google Scholar - Schena M, Shalon D, Davis RW, Brown PO: Quantitative monitoring of gene expression patterns with a complementary DNA microarray.
*Science*1995, 270(5235):467-470. 10.1126/science.270.5235.467View ArticleGoogle Scholar - Workman C, Jensen LJ, Jarmer H,
*et al*.: A new non-linear normalization method for reducing variability in DNA microarray experiments.*Genome Biology*2002, 3(9):1-16.View ArticleGoogle Scholar - Killion PJ, Sherlock G, Iyer VR: The Longhorn Array Database (LAD): an open-source, MIAME compliant implementation of the Stanford Microarray Database (SMD).
*BMC Bioinformatics*2003, 4, article 32: 1-6.Google Scholar - Troyanskaya O, Cantor M, Sherlock G,
*et al*.: Missing value estimation methods for DNA microarrays.*Bioinformatics*2001, 17(6):520-525. 10.1093/bioinformatics/17.6.520View ArticleGoogle Scholar - Song M, Lance ER, Lewis CK, Chesler EJ, Kirova R, Bergeson SE: Maximum likelihood quantization and logical networks for modeling biological interactions.
*Proceedings of the 11th Annual International Conference on Research in Computational Molecular Biology (RECOMB '07), Oakland, Calif, USA, April 2007*(Poster and abstract)Google Scholar - Palmer SD, Song M: Quantization of multivariate continuous random variables by sequential dynamic programming. In
*Proceedings of the 3rd Annual Meeting on Computing Alliance of Hispanic-Serving Institutions (CAHSI '09), Mountain View, Calif, USA, January 2009*. Google Headquarters; 43-46.Google Scholar - Ozimek P, Lahtchev K, Kiel JAKW, Veenhuis M, van der Klei IJ:
*Hansenula polymorpha*Swi1p and Snf2p are essential for methanol utilisation.*FEMS Yeast Research*2004, 4(7):673-682. 10.1016/j.femsyr.2004.01.009View ArticleGoogle Scholar - Bachtell RK, Wang Y-M, Freeman P, Risinger FO, Ryabinin AE: Alcohol drinking produces brain region-selective changes in expression of inducible transcription factors.
*Brain Research*1999, 847(2):157-165. 10.1016/S0006-8993(99)02019-3View ArticleGoogle Scholar - Winston NJ, Maro B: Calmodulin-dependent protein kinase II is activated transiently in ethanol-stimulated mouse oocytes.
*Developmental Biology*1995, 170(2):350-352. 10.1006/dbio.1995.1220View ArticleGoogle Scholar - Austin CP, Battey JF, Bradley A,
*et al*.: The knockout mouse project.*Nature Genetics*2004, 36(11):921-924.View ArticleGoogle Scholar

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