Target genes and corresponding factors in networks 1.

<p>This table lists the target genes and factors that regulate them. The regression coefficients are listed on the right side.</p

Regulatory networks found by our model.

<p>The first column is differentially expressed isoform groups in our cases, the second and third columns are the Transcription factors and splicing factors predicted by our regression model. Some cells are blank, which means no corresponding factors for that co-expressed group.</p

Names and description of splicing factors used in our model.

<p>This table contains 22 splicing factors which are selected to predict the expression levels of differentially expressed isoforms. This table lists their names and some related references. Most of these details are from SpliceAid.</p

Pseudocode of LARS algorithm.

<p>Pseudocode of LARS algorithm.</p

Results of enrichment analysis using GO database.

<p>Three networks enriched in some GO biological processes. This table lists the details and the P values.</p

Expression ratio of SRSF11’s three isoforms (A), motifs in SRSF11’s isoforms and classical SR proteins (B), RT-PCR results (C) and protein Expression of SRSF11.

<p>(A). Expression ratio of SRSF11’s three isoforms in seven disease sample and control: uc009wbj.1 (light green), uc001deu.2 (light blue) and uc001.dev.3 (light red). They have almost the same total expression levels but very different ratios in MDS (four ) and control (average of five controls), which means the splicing patterns of SRSF11 are switched. (B). This figure demonstrates motifs in SRSF11’s isoforms and classical SR proteins. Different motifs have different bio-function. (C). Three isoforms that are over-expressed in our disease samples are picked up for RT-PCR validation. They are isoforms of three splicing factor, one isoform (uc001deu.2, refseq ID: NM_001190987) of SRSF11, one isoform (uc001xlp.3, refseq ID: NM_006925) of SRSF5 and one isoform (uc003jun.2, refseq ID:NM_080743) of SRSF12. Validation demonstrated that their expression levels in MDS disease are higher than in control. (D). Isofrom uc001deu.2 is translated into protein Q05519 and Q05519 is highly expressed in blood disease according to the Model Organism Protein Expression Database (MOPED); COPD: Chronic obstructive pulmonary disease.</p

Results of enrichment analysis using KEGG database.

<p>This table lists top enriched KEGG pathways and corresponding networks number.</p

Flowchart of proposed method for constructing regulatory networks.

<p>Flowchart of proposed method for constructing regulatory networks: (A). Process raw RNA-seq data, find out deferentially expressed isoforms using Tophat and Cufflinks and cluster these isoforms to get gene cluster that may be regulated by same TFs and SFs. (B). Construct two dataset, promote region data (PRD) and exon-intron data (EID), for mining the interaction strength of the TF-isoform interactions and SF-isoform interaction. (C). Use interaction strength to predict the expression levels of isoforms in a co-expressed group. (D). Link model-selected TFs and SFs with their target genes.</p

The effects of MIC-BMSC interaction inhibition on tumor growth and the outputs of cytotoxic chemotherapy.

<p>(A) The growing trends of tumor cells under MBMSC or NBMSC environment with or without niche inhibitor (AMD3100). (B) The growing trends of MIC cells under MBMSC or NBMSC environment with or without niche inhibitor (AMD3100). (C) The responses of myeloma tumor to cytotoxic drugs under MBMSC or NBMSC environment with or without niche inhibitor (AMD3100). (D) The responses of MICs to cytotoxic drugs under MBMSC or NBMSC environment with or without niche inhibitor (AMD3100). Myeloma-associated or normal BMSC niches were denoted in red or green color, and the effects of the niche inhibitors on tumor growth (A and B) or cytotoxic drug efficacy (C and D) were labeled in solid lines and their controls in dashed lines, respectively.</p

The stochastic simulation of cell behaviors using rule implementation, dice-rolling-based decision-making, and Markov chain Monte Carlo approaches.

<p>The stochastic simulation of cell behaviors using rule implementation, dice-rolling-based decision-making, and Markov chain Monte Carlo approaches.</p