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232 Reverse Engineering – Recent Advances and Applications were trained to select for all 85 transcription factors the downstream targets from the 34 gene modules. The parameter settings of GA-PSO algorithm are set as described in our previous work (Ressom et al. 2006). Our inference method mapped all 85 transcription factors to the target gene modules and inferred the most likely NMs. We evaluated the predicted gene regulatory modules for the following eight well known cell cycle related transcription factors: SWI4, SWI5, FKH1, NDD1, ACE2, KAR4, MET28 and RAP1. Since the “true” GRN is not available, the accuracy of putative regulatory relationship was determined by searching known gene connections in databases. Based on the results of the NM module prediction, we collected literature evidences from SGD (Cherry et al. 1997) and BIND (Bader et al. 2003) databases. We examined the inferred relationships for each of the eight transcription factors. An inferred relationship is assumed to be biologically significant if the transcription factors are correlated with the biological functions associated with the critical downstream cluster(s). Figure 8 lists the significant relationships; the eight transcription factors yielded an average precision of 82.9%. NMs for four of these transcription factors were identified in Chiang et al. (Chiang and Chao 2007) together with other four transcription factors. The eight transcription factors in Chiang et al. yielded an average precision of 80.1%. The regulatory relationships inferred by the proposed method are expected to correspond more closely to biologically meaningful regulatory systems and naturally lead themselves to optimum experimental design methods. The results presented in Figure 8 are verified from previous biological evidences. For example, FKH1 is a gene whose protein product is a fork head family protein with a role in the expression of G2/M phase genes. It negatively regulates transcriptional elongation, and regulates donor preference during switching. To further investigate the possibilities that the predicted downstream gene clusters are truly regulated by FKH1, we applied the motif discovery tool, WebMOTIFS (Romer et al. 2007) to find shared motifs in these gene clusters (data not shown). The results revealed that a motif called Fork_head, GTAAACAA, is identified as the most significant motif among these gene clusters (Weigel and Jackle 1990). This finding strongly supports our NM inference results. Another example is the FFL involving SWI5, GAT3 and Gene Cluster 10. SWI5 has been identified as the upstream regulator of GAT3 (Simon et al. 2001; Lee et al. 2002; Harbison et al. 2004). Genes in cluster 10 are mostly involved in DNA helicase activity and mitotic recombination, both of which are important biological steps in the regulation of cell cycle. Although no biological evidences have shown that SWI5 and GAT3 are involved in these processes, there are significant numbers of genes in cluster 10 which are characterized (according to yeastract.com) as genes regulated by both transcription factors (24 for GAT3 and 23 for SWI5 out of 44 genes in cluster 10, respectively). 4.2 Human hela cell cycle dataset 4.2.1 Data sources Gene expression dataset: the human Hela cell cycle dataset (Whitfield et al. 2002) consists of five time courses (114 total arrays). RNA samples were collected for points (typically every 1-2 h) for 30 h (Thy- Thy1), 44 h (Thy-Thy2), 46 h (Thy-Thy3), 36 h (Thy-Noc), or 14 h (shake) after the synchronous arrest. The cell-cycle related gene set contains 1,134 clones corresponding to 874 UNIGENE clusters (UNIGENE build 143). Of these, 1,072 have corresponding Entrez gene IDs, among which 226 have more than one mapping to clones. In
Reverse Engineering Gene Regulatory Networks by Integrating Multi-Source Biological Data total, 846 genes were used for GRN inference. We chose the Thy-Thy3 time course gene expression pattern for 846 genes, since it has the largest number of time points (47). Missing values in the data were imputed using KNN imputation (Troyanskaya et al. 2001). The expression pattern of each gene was standardized between 0 and 1. Molecular interaction data: Protein-protein interactions were extracted from twelve publicly available large-scale protein interaction maps, seven of which are based on information from scientific literature literature-based, three on orthology information, and two on results of RBPSUH HMGB2 E2F2 C2 E2F1 C4 E2F2 BRCA1 E2F1 BRCA1 C34 C34 C5 PCNA E2F2 PCNA C20 E2F1 E2F2 C5 E2F1 PCNA SP1 PCNA C12 C12 C11 E2F1 BRCA1 E2F1 SP1 Fig. 9. Predicted gene regulatory modules from known human cell cycle dependent genes. The left panel presents the four gene regulatory modules, and the right panel depicts inferred transcription factor-target gene relationships for eight cell cycle dependent transcription factors. E2F1 C18 C12 BRCA1 E2F2 E2F2 C30 C34 STAT1 STAT1 C36 233
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REVERSE ENGINEERING - RECENT ADVANC
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Contents Preface IX Part 1 Software
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Preface Introduction In the recent
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Preface XI and con’s related to t
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Preface XIII the step-by-step appli
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Part 1 Software Reverse Engineering
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4 Reverse Engineering - Recent Adva
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10 Reverse Engineering - Recent Adv
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58 2. Model driven architecture: An
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62 Fig. 1. Model, metamodels and tr
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108 Table 1. Number of smoothers an
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1. Introduction 60 Surface Reconstr
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Surface Reconstruction from Unorgan
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1. Introduction A Systematic Approa
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A Systematic Approach for Geometric
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A Review on Shape Engineering and D
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