Proceedings of the 9th International Conference on Info~ation Technology andApplications in Biomedicine, ITAB 2009, Larnaca, cyprus, 5-7 November 2009

ANN-based Simulation of Transcriptional Networks in Yeast

Maria E. Manioudaki and Panayiota Poirazi

Abstract- Artificial Neural Networks (ANNs) have recentlybeen used to quantitatively model stress-induced transcriptionalchanges in gene regulatory networks, based on gene expressionand DNA-binding information. Here, we extend this approachto study the MSN2/4 regulatory networks in Yeast, which areknown to be involved in the stress response. We also refine theANN models by incorporating the dynamics of transcriptionalregulation and test our method on three networks involvingYAPI. For the latter we make an extensive search in order toidentify potential latencies between transcriptional activationand corresponding changes in the expression of targeted genes.Finally, we test our model's ability to replicate gene-deletionfindings in the YAPI networks. We find that our models canaccurately capture the regulatory effect of differenttranscription factors, under both normal and gene knockoutconditions and that incorporating latencies in the ANN modelsresults in significantly higher performance. Overall, we showthat ANNs can be used to provide quantitative predictions aboutthe expression profile of targeted genes during the stressresponse in Yeast.

Index Terms-Gene modules, artificial neural networks, yeaststress, transcriptional latencies, gene knockouts

I. INTRODUCTION

TRANSCRIPTIONAL regulatory networks represent, to

some extent, the master control system of the cell thatorchestrates the differential expression of all genes [1, 2]coordinated by a group of proteins known as transcriptionfactors (TFs) [3]. This network exhibits a hierarchicalstructure and can be represented as a directed graph in whichthe nodes represent the genes and the edges are theregulatory relationships directed from a gene that encodesfor a transcription factor to a gene that is transcriptionallyregulated by this TF. This regulatory organization of genescan be extended in multiple layers [4] and is partitioned intofunctionally coherent groups, the so-called functional

Manuscript received June 30, 2009; revised September 29,2009. This workwas funded by the action 8.3.1 (Reinforcement Pro-gram of HumanResearch Manpower) of the operational program "competitiveness" of theGreek General Secretariat for Research and Technology (PENED 2003­03ED842) and partly by a Marie Curie Outgoing Fellowship (PIOF-GA­2008-219622) of the European Commission.M. E. Manioudaki is with the Department of Chemistry, University ofCrete, Heraklion, Crete, Greece and the Institute of Molecular Biology andBiotechnology (IMBB), Foundation for Research and Technology-Hellas(FORTH), Heraklion, Crete, Greece. (e-mail: mmanioud@imbb.forth.gr).P. Poirazi, is with the Institute of Molecular Biology and Biotechnology(IMBB), Foundation for Research and Technology-Hellas (FORTH),Heraklion, Crete, Greece. (e-mail: poirazi@imbb.forth.gr).

978-1-4244-5379-5/09/$26.00 ©2009 IEEE

modules [5], entities that are recognized as the basicstructural unit in any biological system [6]. A plethora ofcomputational tools exist to infer biologically significantmodules via the integration of different data types and theuse of various algorithmic procedures [7]. Since an accurateestimation of the quality of a given module remains unclear(apart from the experimental validation) and each algorithmposes its own biases in the results [7], the degree of overlapamong different algorithms could be used as an indication ofthe algorithm's accuracy. GRAM [8] is one of thesealgorithms that combines large-scale ChiP-chip andmicroarray data to identify modules of co-expressed and co­regulated genes. The tool is user-friendly and its predictedmodules over different datasets appear to overlapsignificantly with those of other existing tools [7, 9] such asGeneXpress [10] and SAMBA [11]. In previous work [12],we used GRAM to infer the regulatory modules as well astheir regulators for the response of yeast cells to variousenvironmental stimuli. Subsequently, regulatory cascadeswith three layers were built for modules with at least tworegulators. The last layer consisted of a gene in a givenmodule, the middle layer of the gene's direct regulators(transcription factors, TFs) and the upper layer contained theregulators for the TFs. ANN models with the structuralcharacteristics of the biological cascades were developedand tested for their ability to correctly predict the expressionof target genes [12]. In many of the simulated networks,ANN performance was very high, suggesting that aquantitative description of transcriptional effects during thestress response might be possible. Important limitations ofthat work included the overdependence of resulting cascadeson co-expression of genes, which may disregard specificbiological processes of interest. Moreover, the dynamics ofeach TF activation and resulting effects on the expression oftargeted genes was not taken into account. When multipleproteins are responsible for a regulatory activity, they maybe combined in a combinatorial way over time in order toresult in a concerted action [13]. In previous studies,attempts to fmd a correlation between the expression profileof the transcription factors and the expression of the genethat they directly regulate gave promising results only whendifferent activities for each transcription factor wereconsidered [14]. In a three-layer cascade such as those inour study, delays between the upper layer transcriptionfactors and the target gene as well as among the TFsthemselves are expected to be frequent and they should betaken into consideration.

In this study we investigate whether ANNs can be used to:(a) model transcriptional regulation in previously identifiednetworks where well-established biological data have been

incorporated (b) account for potential latencies betweentranscriptional activation and its effects on targeted genesand (c) replicate findings about the deletion of keytranscription factors. Towards these goals, we analyze theregulatory cascades involving the MSN2/MSN4 TFs, whichare ubiquitous in the response of yeast to various stresses[15, 16]. This is an interesting case because although bothTFs have a similar function, only MSN4 is activated bystress while MSN2 is expressed constitutively [17], similarlyto cases with transcription factor dimerization [18] We alsostudy the regulatory networks involving YAPI since theavailability of expression data for the deletion of this TFunder stress conditions [19] would allow us to test thevalidity of our models.

II. METHODS

Data, module identification and regulatory networkmodeling

Microarray gene expression data from yeast cells inresponse to different environmental stresses and knockoutexperiments [19] were downloaded from http://genome­www.stanford.edu/yeast stress/. The regulators of bothMSN2/MSN4 were found from the database ofhttp://www.yeastract.com/. The dataset was divided in fourmain categories, each consisting of 4 to 6 conditions withvarious time-points (Table I). Only the direct regulators wereincluded in our analysis. The regulatory modules and thenetwork cascades of genes responding to different stresseswere found in previous work [20].

TABLE ISTRESS CONDITIONS ORGANIZED INTO FOUR CATEGORIES

Category A Category B Category Cl Category C2(heat shock) (starvation)

Heat shock Amino acid H2Q2 treatment H2Q2 treatmentfrom 25C to 37C starvation

Various Nitrogen source Menadione Menadione

temperatures depletion exposure exposure

t037C Oiauxic shift Oiamide OiamideSteady-state treatment treatmenttemperature

Heat shock Stationary phase OTTexposure OTTexposurefrom 37C to 25C

Heat shock at Steady-state on Hyper-osmoticvariable alternative shockosmolarity carbon sources

Constant Hypo-osmotictemperature shockgrowth

The structure of ANN models was designed as described in[20], using the GRAM algorithm to infer the output (targetgene) and hidden layer (regulators) nodes and bibliographicaldata to fmd the input nodes (upstream regulators). Modelswere trained, validated and tested within the Matlabenvironment using the Neural Network Toolbox with theback-propagation algorithm. For each model, training wasdone using 50% of the experimental conditions and theremaining 50% was used for validation and testing.Validation during training was done using 25% of the data inorder to avoid over-fitting while the rest 25% was used as atest set. This procedure was repeated 100 times and the

training/validation/test data sets were randomly selected foreach repetition. The correlation coefficient (CC) between themodel predictions and the desired output for the test set wasestimated for each run and the performance was assessed asthe average correlation coefficient taken over the test set over100 runs. A network's performance was considered good ifthe average CC value was higher than 0.7.

Generating MSN2/4 cascades

According to the GRAM algorithm, MSN4 appears as aunique regulator in all stress categories shown in TabIe I.Since MSN2 is necessary for MSN4-mediated regulationunder stress conditions [21, 22], we built networks accordingto the MSN4-regulated module, where MSN2 appears as asecond node in the middle layer and is regulated by itsrespective TFs that are induced in the upper layer.

Incorporating transcriptional latencies

To investigate the possibility that different transcriptionfactors exert their regulatory action with variable delays,depending on the stress category and/or the targeted gene,we tested the YAPI networks against all combinations oftime-shifted expression data, for each regulatorindependently, relative to the target gene. This was achievedby shifting the expression profile of the upstream TFs k­steps backwards, where k ranges from zero to n-l (n is thetotal number of time points for the condition with the leastnumber of time-points in the corresponding category), foreach TF independently. For example, given an ANN in acategory with 14 conditions, with 3 TFs in the input layerand 5 time points for the condition with the minimal numberof time points, we test all 64 combinations of possible time­shifts (43

) . For example, for the combination (4, 3, 2), theexpression of the fITst, second and third TFs at time point 0is associated with the expression of the target gene at timepoints 4, 3 and 2, respectively. In other words, the regulatoryeffect of the first, second and third TF on the expression ofthe target gene is evident after 4, 3 and 2 time steps,respectively. All resulting time-shifted ANNs were trained,validated and tested as previously described. The goal ofthese experiments was to find the optimal set of TF time­delays for which the network had the highest performance.To assess the statistical significance of the models'prediction accuracy, all ANNs were also trained usingrandomly shuffled data in the expression profile of theoutput gene.

Deletion experiments

Knockout expression data were available for the TFYAPl, under two stress conditions: (a) transition to 37°Cafter 20 minutes (heat shock) and (b) addition of 0.32mMH20 2 after 20 minutes (oxidative stress) [19]. To investigatewhether our ANN models can reproduce expression changesassociated with the knockout of YAP1, we set its expressionprofile to zero on trained networks with significantcorrelation values (>0.7) and measured the resultingexpression of the target gene. The performance of the

To further test the robustness of our models, we usedperturbation experiments whereby the expression of

Fig 2. The three-layer regulatory cascades involving the YAPI TF. Inknockout experiments, YAPI was deleted and this was simulated by settingits expression profile as well as its node weight to zero. (A) The networkcorresponding to gene YLRI80W (8) The network corresponding to geneYLR063W

CAD1YDR423C

=~r® YAP1YDR41\ YMlOO7W

G LRD83v!:)

B

To assess the accuracy of our ANN models in predicting theexpression profile of downstream genes we shuffled theexpression values of the target gene and re-trained thenetworks. We found that for all cases reported here, thecorrelation coefficient for the shuffled data in all networksdropped dramatically, indicating that the performance of ourANN models is far from random chance.

A

All time-shifted ANNs were trained, validated and tested asdescribed previously. We found a significant increase in thenetworks' performance for time-shifted as opposed to zero­delay models. Table II shows the top performance(correlation coefficient) for both zero-delayed and time­shifted ANN models corresponding to the three biologicalcascades tested.

expression profile of each upstream TF (input layer) wasshifted k-steps backwards and all possible combinations fordifferent values of k per TF were tested, as described in theMethods section. We focused our analysis on two biologicalcascades involving the YAP I transcription factor, for whichknockout data are also available. As shown in Figure 2,YAP1 (along with CAD1) regulates the expression ofYLR180W and YLR063W genes. YLR180W (SAMl) isknown to interact with proteins that participate in theoxidative stress response [28] and displays alternateexpression after various stress signals, including oxidizingagents [29], temperature up-shift [30] and desiccation­rehydration [31]. YLR063W encodes for a protein whosefunction has not, to our knowledge, been deciphered so far,but the gene's expression changes in response to temperature[30] and oxidative stress [29]. Note that one of the cascadeswas activated in two categories, and its activity was modeledseparately resulting in a total of three ANN models.

III. RESULTS

y~J-----4

~Fig 1. The three-layer regulatory network involving MSN2/4. Modulescontaining genes that are co-expressed and co-regulated by MSN2/4 werefirst identified using the GRAM algorithm. Upstream regulators of MSN2/4were then added to the network based on bibliographical information.

Following training, validation and testing (as described in theMethods session), we selected the ANNs that exhibited agood performance. Specifically, for Category A we identified13 networks out of 24 with a correlation coefficient largerthan 0.7. A lower threshold of 0.6 was used for Categories Band C2, for which we identified 9 (out of 23) and 5 (out of16) networks respectively. Note that the 13 ANNs in categoryA had a very good performance (mean CC = 0.85). This isconsistent with previous studies where MSN2/4 appear tohave a more significant role in pathways activated during theresponse to heat shock [23]. Two of the high performanceANNs correspond to genes IMP3IYHR148W andRPL15AIYLR029C (r = 0.85 and 0.84 respectively), both ofwhich were previously shown to be differentially expressedin response to a plethora of stress factors including ethanol[24], arsenic [25], oxidative factors [26] and alteredtemperature [27].

perturbed network was validated against the expression dataof the real knockout experiments. In cases where YAP1appeared as a middle-layer TF the weight of the YAPI nodewas also set to zero.

Modeling transcriptional regulation by MSN2/4

We used ANN models to model a total of 63 regulatorycascades involving the MSN2/MSN4 transcription factors.Modules regulated by the transcription factor MSN4 wereidentified in all four stress categories using the GRAMalgorithm. To account for the synergistic regulatory role ofMSN2 in these modules, we added MSN2 as a secondregulator. Using bibliographical information, we identifiedthe upstream transcription factors for MSN2 and MSN4 asdescribed in [20] and built ANN networks like the onedepicted in Figure 1.

Identifying latency and knockout effects in transcriptionalregulation by YAP1

Since transcriptional regulation is a process that couldrequire a significant amount of time (tens of minutes) beforechanges are seen in the expression of a target gene -mostlydue to the differences in activity that are exhibited by eachTF-, we next considered time-delay cases. Specifically, the

YLR180W and YLR063W was assessed under simulatedknockout conditions for YAPI (see Methods). This was doneby setting YAP 1's expression profile as well as its nodeweight to zero in the optimal latent ANNs and measuring thepredicted expression of the target gene, in comparison withthe experimentally provided value. As shown in Table III,computational predictions are very close to the actual valuesfor both genes across all three stress conditions, indicatingthat our models can simulate quantitatively the contributionof different TFs to the expression profile of each targetedgene, under various stress conditions.

TABLE IIRESULTS FOR ZERO-DELAY AND TIME-SHIFTED ANN MODELS

FOR THE THREE YAPI CASCADESCorrelation

Zero-delay Time-shifted Optimal latencyTopCC TopCC combinations

Category A 0.76 0.90 0/1/0/1/0/1/1/0/1YlR180WCategoryC2 0.75 0.85 1/1/1/0/0/0/0/0/0YlR063WCategoryC2 0.69 0.86 1/0/1/0/0/0/0/1/1YlR180W

TABLE IIIPERFORMANCE ON KNOCKOUT DATA

YLR180W YLR063WHeat shock Oxidative stress

Zero I Time Zero ITime Zero lTimedelay shifted delay shifted delay shiftedCC CC cc cc cc CC

Experimental -1.6 -0.81 -0.93Predicted -1.5 I -2.21 -0.64 I -0.71 -2.18 I -0.52Std. Dev. 0.26 I 0.15 0.23 I 0.18 0.20 I 0.16

IV. CONCLUSIONS

In this work we tested the ability of structurallyconstrained and/or latent ANN models to simulate thetranscriptional regulation process in a set of biologicalnetworks which are activated under stress conditions. UsingDNA-binding and gene expression data, we first identifiedmodules of co-expressed and co-regulated genes and thenadded a third layer of regulation based on bibliographicalinformation. Resulting three-layer cascades were thensimulated using ANN models.

We tested our approach on regulatory cascades involving theMSN4 transcription factors, by adding the MSN2 TF whichis essential for the stress response but whose expression isstable and thus would not have been identified by themodule finding approach alone. We found that ANN modelscould accurately model 27/63 regulatory cascades involvingthese TFs, under 4 stress categories. This semi­computational approach gave an indication for the biologicalprocess in which these regulatory cascades are moreimportant based on high values of correlation coefficients.

We also tested our model's performance while considering

possible latencies between TF activation and its impact onthe expression level of targeted genes. We found that ourmodels can accurately capture the regulatory effect ofdifferent transcription factors, under both normal and geneknockout conditions and that incorporating latencies in theANN models results in significantly higher performance forthe YAPI cascades.

In conclusion, this work shows that module identificationcombined with ANN models can be used to infer truebiological cascades. The need for incorporation of previousbiological knowledge and perturbation methods isfundamental in cases where this is possible and leads to newinsights for the results. The method can also easily beapplied to model regulatory networks that include dimers ofTFs of which one TF is regulated transcriptionally while theexpression of the other remains constant and is subjected topost-transcriptional modifications [18]. It can also beextended to other intracellular processes such as cell cycle,where the expression profile of genes over time and underdifferent conditions is ofparticular interest.

ACKNOWLEDGMENT

The authors would like to thank members of our lab for useful discussionsduring the preparation of this manuscript.

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