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The carob moth, Ectomyelois ceratoniae Zeller, is the most important pest on pomegranate fruits and has several overlapping generations per year in Iran. This paper investigated the use of a mechanistic model for describing the effect of the control strategy on the size of the total population of the carob moth. The analytical solution of mechanistic model was written in the form of a logistic probability density function by Matis et al. (2006). The logistic probability density function fits this carob moth data very well for control and treatment fields (r2=0.91 and 0.95). The parameters of the model are the predicted peak size, Nmax, the predicted time of peak, tmax, and an approximate per capita birth and death rate, all of which are important in practical application. In general, this paper demonstrates the utility of analyzing carob moth population data using mechanistic models and their underlying rate parameters. &nbsp

Additional file 1.

Additional file 1.

Additional file 1: Table S1. Common core miRNAs among all enriched pathways. In addition, all miRNAs are depicted with color scales from Green for more replicated miRNAs to Yellow for less replicated miRNAs. For example, hsa-miR-107 is common between five pathways: cell cycle, ERBB signaling, p53 signaling, VEGF signaling, and non-small cell lung cancer pathways, thus is highlighted with dark green, or hsa-miR-203 is shared between two pathways: cell cycle and non -small cell lung cancer pathways which is specified with light yellow. Table S2. Down-regulated enriched pathways in COPD. Also, the size of pathways based on the number of contributed features (SIZE), pathways’ enrichment scores before and after running enrichment peak (ES and NES), percentage of miRNA list before running enrichment peak (Mir%), and enrichment signal strength are represented in the columns. Moreover, the strength of NESs for all pathways is depicted by color-scaled column, which means that the red NES is more meaningful pathway in biology than the green one. Table S3. Up-regulated enriched pathways in NSCLC. Also, the size of pathways based on the number of contributed features (SIZE), pathways’ enrichment scores before and after running enrichment peak (ES and NES), percentage of miRNA list before running enrichment peak (Mir%), and enrichment signal strength are represented in the columns. Moreover, the strength of NESs for all pathways is depicted by color-scaled column, which means that the red NES is more meaningful pathway in biology than the green one.

Additional file 2:Plots showing curvature, CpG and repeats for the Markov-permutations for Arabidopsis and rice first chromosomes. (DOC 152 KB)

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Additional file of Genome-scale computational analysis of DNA curvature and repeats in Arabidopsis and rice uncovers plant-specific genomic properties

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Additional file 4:Plots showing repeat number vs. curvature average and CpG length in 20 kilobase windows for Arabidopsis and rice first chromosomes. (DOC 141 KB)

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Additional file 5 Selecting the optimal β parameter for consensus networks.

Additional file 1:Plots showing curvature. CpG and repeats for all chromosomes of Arabidopsis and rice. (DOC 634 KB)

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Additional file 2 Selecting the optimal β parameter for disease-specific networks.

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Additional file 3:Plots showing curvature. CpG and repeats for all chromosomes of yeast and mouse. (DOC 1 MB)

Table S1. The list of genes present in the turquoise module. Table S2 The list of genes present in the pink module. Table S3. The list of genes present in the brown module. Table S4. The list of genes present in the red module. (DOCX 11 kb)

Frequency of TOM connectivity in the network. (PDF 262Â kb)

Functional analysis of the â light yellowâ module genes. Over-represented GO/pathway terms were grouped based on kappa statistics [62]. The size of each category within a pie chart represents the number of included terms. Only the most significant GO/ terms within groups were labeled. GO/pathway terms are represented as nodes, and the node size represents the term enrichment significance, while the edges represent significant similarity between categories. (A) Representative biological processes interactions among module genes. (B) Representative molecular function interactions among module genes. (C) Representative Reactome analysis interactions among module genes. (PDF 1482Â kb)

Frequency of TOM connectivity in the network. (PDF 262Â kb)

Functional analysis of the â light yellowâ module genes. Over-represented GO/pathway terms were grouped based on kappa statistics [62]. The size of each category within a pie chart represents the number of included terms. Only the most significant GO/ terms within groups were labeled. GO/pathway terms are represented as nodes, and the node size represents the term enrichment significance, while the edges represent significant similarity between categories. (A) Representative biological processes interactions among module genes. (B) Representative molecular function interactions among module genes. (C) Representative Reactome analysis interactions among module genes. (PDF 1482Â kb)

Functional analysis of the â saddle brownâ module genes. Over-represented GO/pathway terms were grouped based on kappa statistics [62]. The size of each category within a pie chart represents the number of included terms. Only the most significant GO/ terms within groups were labeled. GO/pathway terms are represented as nodes, and the node size represents the term enrichment significance, while the edges represent significant similarity between categories. (A) Representative biological processes interactions among module genes. (B) Representative Reactome analysis interactions among module genes. (PDF 1302Â kb)

Functional analysis of the â saddle brownâ module genes. Over-represented GO/pathway terms were grouped based on kappa statistics [62]. The size of each category within a pie chart represents the number of included terms. Only the most significant GO/ terms within groups were labeled. GO/pathway terms are represented as nodes, and the node size represents the term enrichment significance, while the edges represent significant similarity between categories. (A) Representative biological processes interactions among module genes. (B) Representative Reactome analysis interactions among module genes. (PDF 1302Â kb)

Functional analysis of the â blueâ module genes. Over-represented GO/pathway terms were grouped based on kappa statistics [62]. The size of each category within a pie chart represents the number of included terms. Only the most significant GO/ terms within groups were labeled. GO/pathway terms are represented as nodes, and the node size represents the term enrichment significance, while the edges represent significant similarity between categories. (A) Representative biological processes interactions among module genes. (B) Representative molecular function interactions among module genes. (C) Representative Reactome analysis interactions among module genes. (PDF 2680Â kb)

Functional analysis of the â blueâ module genes. Over-represented GO/pathway terms were grouped based on kappa statistics [62]. The size of each category within a pie chart represents the number of included terms. Only the most significant GO/ terms within groups were labeled. GO/pathway terms are represented as nodes, and the node size represents the term enrichment significance, while the edges represent significant similarity between categories. (A) Representative biological processes interactions among module genes. (B) Representative molecular function interactions among module genes. (C) Representative Reactome analysis interactions among module genes. (PDF 2680Â kb)

Functional analysis of the red module. Over-represented GO/pathway terms were grouped based on kappa statistics. The size of each category within a pie chart represents the number of included terms. Only the most significant GO/terms within groups were labeled. GO/pathway terms are represented as nodes, and the node size represents the term enrichment significance, while the edges represent significant similarity between categories. (A) Representative biological processes interactions among module genes. (B) Representative molecular function interactions among module genes. (C) Representative Ractome analysis interactions among module genes. (PDF 2606Â kb)

(A) Frequency of combined interaction scores from the String database [21] and (B) Frequency of TOM connectivity in BGCN. (PDF 648Â kb)

Functional analysis of the light green module genes. Over-represented GO/pathway terms were grouped based on kappa statistics. The size of each category within a pie chart represents the number of included terms. Only the most significant GO/terms within groups were labeled. GO/pathway terms are represented as nodes, and the node size represents the term enrichment significance, while the edges represent significant similarity between categories. (A) Representative biological processes interactions among module genes. (B) Representative molecular function interactions among module genes. (C) Representative Ractome analysis interactions among module genes. (PDF 2605Â kb)

Functional analysis of the red module. Over-represented GO/pathway terms were grouped based on kappa statistics. The size of each category within a pie chart represents the number of included terms. Only the most significant GO/terms within groups were labeled. GO/pathway terms are represented as nodes, and the node size represents the term enrichment significance, while the edges represent significant similarity between categories. (A) Representative biological processes interactions among module genes. (B) Representative molecular function interactions among module genes. (C) Representative Ractome analysis interactions among module genes. (PDF 2606Â kb)

Functional analysis of the light green module genes. Over-represented GO/pathway terms were grouped based on kappa statistics. The size of each category within a pie chart represents the number of included terms. Only the most significant GO/terms within groups were labeled. GO/pathway terms are represented as nodes, and the node size represents the term enrichment significance, while the edges represent significant similarity between categories. (A) Representative biological processes interactions among module genes. (B) Representative molecular function interactions among module genes. (C) Representative Ractome analysis interactions among module genes. (PDF 2605Â kb)

Functional analysis of the red module genes. Over-represented GO/pathway terms were grouped based on kappa statistics. The size of each category within a pie chart represents the number of included terms. Only the most significant GO/terms within groups were labeled. GO/pathway terms are represented as nodes, and the node size represents the term enrichment significance, while the edges represent significant similarity between categories. (A) Representative biological processes interactions among module genes. (B) Representative molecular function interactions among module genes. (C) Representative Ractome analysis interactions among module genes. (PDF 3753Â kb)

(A) Frequency of combined interaction scores from the String database [21] and (B) Frequency of TOM connectivity in BGCN. (PDF 648Â kb)

EGENES is a knowledge-based database for efficient analysis of plant expressed sequence tags (ESTs) that was recently added to the KEGG suite of databases. It links plant genomic information with higher order functional information in a single database. It also provides gene indices for each genome. The genomic information in EGENES is a collection of EST contigs constructed from assembly of ESTs. Due to the extremely large genomes of plant species, the bulk collection of data such as ESTs is a quick way to capture a complete repertoire of genes expressed in an organism. Using ESTs for reconstructing metabolic pathways is a new expansion in KEGG and provides researchers with a new resource for species in which only EST sequences are available. Functional annotation in EGENES is a process of linking a set of genes/transcripts in each genome with a network of interacting molecules in the cell. EGENES is a multispecies, integrated resource consisting of genomic, chemical, and network information containing a complete set of building blocks (genes and molecules) and wiring diagrams (biological pathways) to represent cellular functions. Using EGENES, genome-based pathway annotation and EST-based annotation can now be compared and mutually validated. The ultimate goals of EGENES will be to: bring new plant species into KEGG by clustering and annotating ESTs; abstract knowledge and principles from large-scale plant EST data; and improve computational prediction of systems of higher complexity. EGENES will be updated at least once a year. EGENES is publicly available and is accessible by the following link or by KEGG's navigation system (http://www.genome.jp/kegg-bin/create_kegg_menu?category=plants_egenes).

The complexity of cancer progression can manifests itself on at least three scales that can be described using mathematical models, namely microscopic, mesoscopic and macroscopic scales. Multiscale cancer models have proven to be advantageous in this context because they can simultaneously incorporate the many different characteristics and scales of complex diseases such as cancer. This has driven the expansion of more predictive data-driven models, coupled to experimental and clinical data. These models are defining the foundations that facilitate the forthcoming design of patient specific cancer therapy. This should be considered as a great leap toward the era of personalized medicine. Consequently, further improvements in mathematical modeling of cancer will lead to the design of more sophisticated cancer therapy approaches.