Revealing dynamic regulations and the related key proteins of myeloma-initiating cells by integrating experimental data into a systems biological model

Abstract

Motivation The growth and survival of myeloma cells are greatly affected by their surrounding microenvironment. To understand the molecular mechanism and the impact of stiffness on the fate of myeloma-initiating cells (MICs), we develop a systems biological model to reveal the dynamic regulations by integrating reverse-phase protein array data and the stiffness-associated pathway. Results We not only develop a stiffness-associated signaling pathway to describe the dynamic regulations of the MICs, but also clearly identify three critical proteins governing the MIC proliferation and death, including FAK, mTORC1 and NFκB, which are validated to be related with multiple myeloma by our immunohistochemistry experiment, computation and manually reviewed evidences. Moreover, we demonstrate that the systematic model performs better than widely used parameter estimation algorithms for the complicated signaling pathway. Availability and implementation We can not only use the systems biological model to infer the stiffness-associated genetic signaling pathway and locate the critical proteins, but also investigate the important pathways, proteins or genes for other type of the cancer. Thus, it holds universal scientific significance. Contact zhangle06@scu.edu.cn or lnchen@sibs.ac.cn Supplementary information Supplementary data are available at Bioinformatics online

Artificial intelligence in cancer target identification and drug discovery

“…Although the decision tree, random forest and ADTree 86 – 88 , 158 demonstrate the tendency to identify such proteins that are well annotated and studied for cancer, these methods are subject to producing local optimal solutions. Therefore, Chen et al 143 proposed using the decision tree classifier based on particle swarm optimization 166 to avoid falling into the trap of local minima by adding randomness to optimize the number of features and detection accuracy of cancer treatment targets. Furthermore, the gradient boosting decision tree 167 is a very flexible and scalable method to classify network nodes for future study.…”

An integrated platform for Brucella with knowledge graph technology: From genomic analysis to epidemiological projection

“…The established approach for streamline analysis is well recognized. First, bacterial genomic DNA is extracted and subjected to sequencing according to the guidelines of the commercial platform; then, the obtained results are analyzed using bioinformatics software ( Zhang et al, 2019a ; Zhang et al, 2019d ; Lei et al, 2020 ; Wu et al, 2020 ; You et al, 2020 ; Zhang et al, 2021a ; Gao et al, 2021 ; Lei Zhang et al, 2021 ) or suitable platform databases ( Xiao et al, 2020 ; Zhang et al, 2021b ; Xiao et al, 2021 ) for the assembly and annotation of genomes, determination of SNPs, identification of antimicrobial resistance genes ( Lv et al, 2021 ) and virulence-associated factors, building phylogenetic trees, etc. ( Seemann, 2014 ; Johansen et al, 2018 ; Yasuhiro et al, 2018 ; Ledwaba et al, 2021 ; Liu et al, 2021 ).…”

2019nCoVAS: Developing the Web Service for Epidemic Transmission Prediction, Genome Analysis, and Psychological Stress Assessment for 2019-nCoV

“…The COVID-19 epidemic, caused by the pathogenic virus 2019-nCoV, is still expanding around the world and poses a serious threat to human life and health [1] . Thus, it is critical for us to carry out epidemic transmission prediction [2] , [3] , genome sequence analysis [4] , [5] , and public psychological stress assessments [6] , [7] for 2019-nCoV.…”

“…Although previously well-developed SIR [13] or SEIR [14] models can estimate the basic reproduction number (R 0 ), neither SIR nor SEIR consider the factors of suspected patient quarantine. Furthermore, the most commonly used web services of 2019-nCoV [5] , [15] , [16] only focus on the statistical analysis of real epidemic data, and there are only a few online predictive services with different epidemic transmission models.…”