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Efficient and accurate recognition of protein–DNA interactions is vital for understanding the molecular mechanisms of related biological processes and further guiding drug discovery. Although the current experimental protocols are the most precise way to determine protein–DNA binding sites, they tend to be labor-intensive and time-consuming. There is an immediate need to design efficient computational approaches for predicting DNA-binding sites. Here, we proposed ULDNA, a new deep-learning model, to deduce DNA-binding sites from protein sequences. This model leverages an LSTM-attention architecture, embedded with three unsupervised language models that are pre-trained on large-scale sequences from multiple database sources. To prove its effectiveness, ULDNA was tested on 229 protein chains with experimental annotation of DNA-binding sites. Results from computational experiments revealed that ULDNA significantly improves the accuracy of DNA-binding site prediction in comparison with 17 state-of-the-art methods. In-depth data analyses showed that the major strength of ULDNA stems from employing three transformer language models. Specifically, these language models capture complementary feature embeddings with evolution diversity, in which the complex DNA-binding patterns are buried. Meanwhile, the specially crafted LSTM-attention network effectively decodes evolution diversity-based embeddings as DNA-binding results at the residue level. Our findings demonstrated a new pipeline for predicting DNA-binding sites on a large scale with high accuracy from protein sequence alone. [ABSTRACT FROM AUTHOR]

Synthetic jet has shown promising applications in active flow control in recent years. Tomographic particle image velocity (Tomo-PIV) is an emerging flow field measurement, which can obtain three-dimensional flow field with a high spatial and temporal resolution to help us better understand the evolution of synthetic jet. Therefore, this paper uses a time-resolved Tomo-PIV system to measure and analyze the three-dimensional flow evolution of a submerged two-orifice synthetic jet. The measurement is conducted for stroke length L = 1.9, 2.6, and 3.0 while keeping the orifice diameter D and the distance between the two orifices s constant. Research results reveal that the three-dimensional flow evolution can be described as follows: first, two independent vortex rings form at the outlet of the orifice plate; next, these two vortex rings interact with each other and merge into a non-circular vortex ring, which then undergoes axis switching and vortex reconnection, with the tendency of splitting into two vortex rings. Furthermore, the position where the axis-switching finishes coincides with the location of the maximum mean streamwise velocity. When the stroke length of the synthetic jet is 2.6, the non-circular vortex ring undergoes a collision of vortices after the completion of the axis switching, resulting in the phenomenon of vortex ring bifurcation. However, the non-circular vortex ring fails to split into two vortex rings for stroke lengths of 1.9 and 3. Moreover, the entrainment of the synthetic jet increases with the increase in stroke length. [ABSTRACT FROM AUTHOR]

Most life activities in organisms are regulated through protein complexes, which are mainly controlled via Protein–Protein Interactions (PPIs). Discovering new interactions between proteins and revealing their biological functions are of great significance for understanding the molecular mechanisms of biological processes and identifying the potential targets in drug discovery. Current experimental methods only capture stable protein interactions, which lead to limited coverage. In addition, expensive cost and time consuming are also the obvious shortcomings. In recent years, various computational methods have been successfully developed for predicting PPIs based only on protein homology, primary sequences of protein or gene ontology information. Computational efficiency and data complexity are still the main bottlenecks for the algorithm generalization. In this study, we proposed a novel computational framework, HNSPPI, to predict PPIs. As a hybrid supervised learning model, HNSPPI comprehensively characterizes the intrinsic relationship between two proteins by integrating amino acid sequence information and connection properties of PPI network. The experimental results show that HNSPPI works very well on six benchmark datasets. Moreover, the comparison analysis proved that our model significantly outperforms other five existing algorithms. Finally, we used the HNSPPI model to explore the SARS-CoV-2-Human interaction system and found several potential regulations. In summary, HNSPPI is a promising model for predicting new protein interactions from known PPI data. [ABSTRACT FROM AUTHOR]

In the present study, elastic body wave propagation in fluid-saturated porous media is investigated, and the analytical model is solved in terms of the model of soil mechanics. The potential function expressions of elastic body waves in fluid-saturated porous media with different permeability (i.e., finite, zero, and infinity) are derived by using potential functions of solid- and liquid-phase displacements. Then, the elastic wave dispersion equations are obtained by using the complex forms of the plane harmonic wave. Furthermore, in the case of finite, zero, and infinite permeability, the plane wave solutions of the elastic waves are derived and the analytical formulations of propagation velocities and attenuation coefficients are calculated. Finally, numerical examples are carried out to analyze the dispersion and attenuation characteristics of three waves (fast compressional wave P1, low compressional wave P2, and shear wave S) in two-phase media. In addition, the effects of various properties (i.e., dynamic permeability coefficient, dissipation coefficient, porosity, and Poisson ratio) on the propagation characteristics of three waves are studied numerically. This study is of value to the wave propagation phenomenon as well as other related disciplines. [ABSTRACT FROM AUTHOR]

Sleep staging is a key process for evaluating sleep quality and diagnosing somnipathy-related diseases. Psychologists are required to do the traditional sleep stages identification. The manual work by these experts is often time-consuming and error-prone. In order to improve the performance of such a process, automatic Electroencephalography (EEG) signal analysis using machine learning approaches is often used. In this paper, a transitions-optimized Hidden Markov Model (HMM) model is proposed to improve the accuracy of prediction. Our proposed framework includes 4 key modules: feature extraction, feature selection, classification, and transition optimization. By applying, transition optimization process after a general GMM-HMM classification, our experimental results show a convincing improvement in the accuracy of classification. [ABSTRACT FROM AUTHOR]

The geological activity in Sichuan and Yunnan of China is intense, and large earthquakes such as the Tonghai earthquake in 1970 have caused huge casualties and economic losses. Therefore, it is significant to reduce the long-period earthquake damage of dams and high-rise buildings to reproduce the near-fault long-period ground motion characteristics of the Tonghai earthquake. In this paper, the finite difference method is used to predict the long-period characteristics of the Tonghai Ms 7.8 earthquake. Considering the uncertainty of the initial fracture point, number of asperities, and slip angle, 18 seismic scenarios are established. The predicted results are verified by comparing intensity, attenuation relation and pulse probability distribution. At the same time, based on the velocity pulse recognition method, the pulse probability distribution of the near-field is obtained. We selected six seismic scenarios with high probability from 18 seismic scenarios. Our results show that the spatial distribution and pulse probability distribution of near-field simulated ground motion is significantly affected by the initial rupture positions. After the characteristics of the largest asperity on the fault are determined, the remaining asperities have limited influence on the spatial distribution of simulated ground motion. This study expands the application scope of the finite difference method, which can simulate the seismic intensity characteristics of destructive earthquakes in areas with few seismic records. [ABSTRACT FROM AUTHOR]

One avian H3N2 influenza virus, providing its PB1 and HA segments, reassorted with one human H2N2 virus and caused a pandemic outbreak in 1968, killing over 1 million people. After its introduction to humanity, the pandemic H3N2 virus continued adapting to humans and has resulted in epidemic outbreaks every influenza season. To understand the functional roles of the originally avian PB1 gene in the circulating strains of human H3N2 influenza viruses, we analyzed the evolution of the PB1 gene in all human H3N2 isolates from 1968 to 2019. We found several specific residues dramatically changed around 2002–2009 and remained stable through to 2019. Then, we verified the functions of these PB1 mutations in the genetic background of the early pandemic virus, A/Hong Kong/1/1968(HK/68), as well as a recent seasonal strain, A/Jiangsu/34/2016 (JS/16). The PB1 V709I or PB1 V113A/K586R/D619N/V709I induced higher polymerase activity of HK/68 in human cells. And the four mutations acted cooperatively that had an increased replication capacity in vitro and in vivo at an early stage of infection. In contrast, the backward mutant, A113V/R586K/N619D/I709V, reduced polymerase activity in human cells. The PB1 I709V decreased viral replication in vitro, but this mutant only showed less effect on mice infection experiment, which suggested influenza A virus evolved in human host was not always consisted with highly replication efficiency and pathogenicity in other mammalian host. Overall, our results demonstrated that the identified PB1 mutations contributed to the viral evolution of human influenza A (H3N2) viruses. [ABSTRACT FROM AUTHOR]

Plants are often attacked by various pathogens during their growth, which may cause environmental pollution, food shortages, or economic losses in a certain area. Integration of high throughput phenomics data and computer vision (CV) provides a great opportunity to realize plant disease diagnosis in the early stage and uncover the subtype or stage patterns in the disease progression. In this study, we proposed a novel computational framework for plant disease identification and subtype discovery through a deep-embedding image-clustering strategy, Weighted Distance Metric and the t-stochastic neighbor embedding algorithm (WDM-tSNE). To verify the effectiveness, we applied our method on four public datasets of images. The results demonstrated that the newly developed tool is capable of identifying the plant disease and further uncover the underlying subtypes associated with pathogenic resistance. In summary, the current framework provides great clustering performance for the root or leave images of diseased plants with pronounced disease spots or symptoms. [ABSTRACT FROM AUTHOR]

Artificial intelligence-based air quality index (AQI) forecasting is a hot research topic in the fields of sustainable and smart industrial environment design. There are mainly two obstacles that hinder the existing machine learning (ML) and deep learning (DL) technologies providing accurate forecasting results to protect the environment, which include the intercorrelation between different AQI components and the highly volatile AQI pattern changes. In this article, a novel DL framework combining multiple nested long short term memory networks (MTMC-NLSTM) is proposed for accurate AQI forecasting enlightened with the federated learning. The performance of the proposed MTMC-NLSTM model is compared with conventional ML models, DL methods, as well as hybrid DL models. The experimental results show that the performance of the proposed method is superior to those of all compared models. [ABSTRACT FROM AUTHOR]

Anomalies in time series, also called "discord," are the abnormal subsequences. The occurrence of anomalies in time series may indicate that some faults or disease will occur soon. Therefore, development of novel computational approaches for anomaly detection (discord search) in time series is of great significance for state monitoring and early warning of real-time system. Previous studies show that many algorithms were successfully developed and were used for anomaly classification, e.g., health monitoring, traffic detection, and intrusion detection. However, the anomaly detection of time series was not well studied. In this paper, we proposed a long short-term memory- (LSTM-) based anomaly detection method (LSTMAD) for discord search from univariate time series data. LSTMAD learns the structural features from normal (nonanomalous) training data and then performs anomaly detection via a statistical strategy based on the prediction error for observed data. In our experimental evaluation using public ECG datasets and real-world datasets, LSTMAD detects anomalies more accurately than other existing approaches in comparison. [ABSTRACT FROM AUTHOR]

Prostate cancer (PCa) is the most commonly diagnosed malignancy and the second leading cause of cancer-related death in American men. Androgen deprivation therapy (ADT) has become a standard treatment strategy for advanced PCa. Although a majority of patients initially respond to ADT well, most of them will eventually develop castration-resistant PCa (CRPC). Previous studies suggest that ADT-induced changes in the immune microenvironment (mE) in PCa might be responsible for the failures of various therapies. However, the role of the immune system in CRPC development remains unclear. To systematically understand the immunity leading to CRPC progression and predict the optimal treatment strategy in silico, we developed a 3D Hybrid Multi-scale Model (HMSM), consisting of an ODE system and an agent-based model (ABM), to manipulate the tumor growth in a defined immune system. Based on our analysis, we revealed that the key factors (e.g. WNT5A, TRAIL, CSF1, etc.) mediated the activation of PC-Treg and PC-TAM interaction pathways, which induced the immunosuppression during CRPC progression. Our HMSM model also provided an optimal therapeutic strategy for improving the outcomes of PCa treatment. [ABSTRACT FROM AUTHOR]

The extreme learning machine (ELM) is famous for its single hidden-layer feed-forward neural network which results in much faster learning speed comparing with traditional machine learning techniques. Moreover, extensions of ELM achieve stable classification performances for imbalanced data. In this paper, we introduce a hybrid method combining the extended Kalman filter (EKF) with cost-sensitive dissimilar ELM (CS-D-ELM). The raw data are preprocessed by EKF to produce inputs for the CS-D-ELM classifier. Experimental results show that the proposed method is more suitable for real-time fault diagnosis of air handling units than traditional approaches. [ABSTRACT FROM AUTHOR]

Previous studies showed that individuals' traits could be used to explain the similarity of behavioral patterns across different occasions. Such studies have typically focused on personality traits, and have not been extended to psychological needs. Our study used a large dataset of 1,715,078 anonymous users' App usage records to examine whether the individual's needs-based profiles of App usage were consistent across different situations (as indexed by categories of App functions). Results showed a high level of consistency across situations in a user's choice of Apps based on the needs the Apps could satisfy. These results provide clear evidence in support of cross-category App recommendation systems. [ABSTRACT FROM AUTHOR]

Tunnel settlement commonly occurs during the tunnel construction processes in large cities. Existing forecasting methods for tunnel settlements include model-based approaches and artificial intelligence (AI) enhanced approaches. Compared with traditional forecasting methods, artificial neural networks can be easily implemented, with high performance efficiency and forecasting accuracy. In this study, an extended machine learning framework is proposed combining particle swarm optimization (PSO) with support vector regression (SVR), back-propagation neural network (BPNN), and extreme learning machine (ELM) to forecast the surface settlement for tunnel construction in two large cities of China P.R. Based on real-world data verification, the PSO-SVR method shows the highest forecasting accuracy among the three proposed forecasting algorithms. [ABSTRACT FROM AUTHOR]

Cadmium is one of the major heavy metal pollutions in coastal waters, and it is well known that cadmium at trace concentration is toxic to macroalgae. Change in marine carbonate system and ocean acidification caused by elevated atmospheric CO2 also alter physiological characteristics of macroalgae. However, less research is focused on the combined impacts of elevated CO2 and cadmium pollution on the growth and physiology in macroalgae. In this study, the maricultivated macroalga Pyropia haitanensis (Rhodophyta) was cultured at three levels of Cd2+ (control, 4 and 12 mg L−1) and two concentrations of CO2, the ambient CO2 (AC, 410 ppm) and elevated CO2 (HC, 1100 ppm). The results showed that 12 mg L−1 Cd2+ significantly suppressed the relative growth rate and superoxide dismutase activity in AC-grown P. haitanensis, while such inhibition extents by Cd2+ were alleviated in HC-grown algae. Cd2+ had no effects on efficiency of electron transport (α) and maximum electron transport rate (ETRmax), but α was increased by elevated CO2. Cd2+ dramatically suppressed the maximum net photosynthesis oxygen evolution rate (NPRm) and the minimum saturation irradiance (Ik) when the algal thalli were grown at AC, while such suppression of NPRm by Cd2+ was much decreased when the thalli were grown at HC. Collectively, our results suggested that elevated CO2 would alleviate Cd2+ toxicity on P. haitanensis. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is the most common cause of dementia. Although genome-wide association study (GWAS) have reported hundreds of single-nucleotide polymorphisms (SNPs) and genes linked to AD, the mechanisms about how these SNPs modulate the development of AD remain largely unknown. In this study, we performed GWAS for three traits in cerebrospinal fluid (CSF) and one clinical trait in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. Our analysis identified five most significant AD related SNPs (FDR < 0.05) within or proximal to APOE, APOC1, and TOMM40. One of the SNPs was co-inherited with APOE allele 4, which is the most important genetic risk factor for AD. Three of the five SNPs were located in promoter or enhancer regions, and transcription factor (TF) binding affinity calculations showed dramatic changes (| Log2FC| > 2) of three TFs (PLAG1, RREB1, and ZBTB33) for two motifs containing SNPs rs2075650 and rs157580. In addition, our GWAS showed that both rs2075650 and rs157580 were significantly associated with the poliovirus receptor-related 2 (PVRL2) gene (FDR < 0.25), which is involved in spreading of herpes simplex virus (HSV). The altered regulation of PVRL2 may increase the susceptibility AD patients to HSV and other virus infections of the brain. Our work suggests that AD is a type of immune disorder driven by viral or microbial infections of the brain during aging. [ABSTRACT FROM AUTHOR]

Background/Aims: MicroRNAs (miRNAs) are promising biomarkers for pancreatic cancer (PaCa). However, systemic and unified evaluations of the diagnostic value of miRNAs are lacking. Therefore, we performed a systematic evaluation based on miRNA expression profiling studies. Methods: We obtained miRNA expression profiling studies from Gene Expression Omnibus (GEO) and ArrayExpress (AE) databases and calculated the pooled sensitivity, specificity, and summary area under a receiver operating characteristic (ROC) curve for every miRNA. According to the area under the curve (AUC), we identified the miRNAs with diagnostic potentiality and validated their prognostic role in The Cancer Genome Atlas (TCGA) data. Gene Ontology (GO) annotations and pathway enrichments of the target genes of the miRNAs were evaluated using bioinformatics tools. Results: Ten miRNA expression profiling studies including 958 patients were used in this diagnostic meta-analysis. A total of 693 miRNAs were measured in more than 9 studies. The top 50 miRNAs with high predictive values for PaCa were identified. Among them, miR-130b had the best predictive value for PaCa (pooled sensitivity: 0.73 [95% confidence intervals (CI) 0.44-0.91], specificity: 0.81 [95% CI 0.59–0.93], and AUC: 0.84 [95% CI 0.73–0.95]). We identified nine miRNAs (miR-23a, miR-30a, miR-125a, miR-129-1, miR-181b-1, miR-203, miR-221, miR-222, and miR-1301) associated with overall survival in PaCa patients by combining our results with TCGA data. The results of a Cox model revealed that two miRNAs (miR-30a [hazard ratio (HR)=2.43, 95% CI 1.05-5.59; p=0.037] and miR-203 [HR=3.14, 95% CI 1.28-7.71; p=0.012]) were independent risk factors for prognosis in PaCa patients. In total, 405 target genes of the nine miRNAs were enriched with Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and cancer-associated pathways such as Ras signaling pathways, phospholipase D signaling pathway, and AMP-activated protein kinase (AMPK) signaling pathway were revealed among the top 20 enriched pathways. There were significant negative correlations between miR-181b-1 and miR-125a expression levels and the methylation status of their promoter region. Conclusion: Our study performed a systematic evaluation of the diagnostic value of miRNAs based on miRNA expression profiling studies. We identified that miR-23a, miR-30a, miR-125a, miR-129-1, miR-181b-1, miR-203, miR-221, miR-222, and miR-1301 had moderate diagnostic value for PaCa and predicted overall survival in PaCa patients. [ABSTRACT FROM AUTHOR]

This study proposes a novel approach, namely, skin flow complex algorithm (SFCA), to decompose the molecular skin surface into topological disks. The main contributions of SFCA include providing a simple decomposition and fast calculation of the molecular skin surface. Unlike most existing works which partition the molecular skin surface into sphere and hyperboloid patches, SFCA partitions the molecular skin surface into triangular quadratic patches and rectangular quadratic patches. Each quadratic patch is proven to be a topological disk and rendered by a rational Bézier patch. The skin surface is constructed by assembling all rational Bézier patches. Experimental results show that the SFCA is more efficient than most existing algorithms, and produces a triangulation of molecular skin surface which is decomposable, deformable, smooth, watertight and feature-preserved. [ABSTRACT FROM AUTHOR]

Molecular skin surface (MSS), proposed by Edelsbrunner, is a C2 continuous smooth surface modeling approach of biological macromolecules. Compared to the traditional methods of molecular surface representations (e.g., the solvent exclusive surface), MSS has distinctive advantages including having no self-intersection and being decomposable and transformable. For further promoting MSS to the field of bioinformatics, transformation between different MSS representations mimicking the macromolecular dynamics is demanded. The transformation process helps biologists understand the macromolecular dynamics processes visually in the atomic level, which is important in studying the protein structures and binding sites for optimizing drug design. However, modeling the transformation between different MSSs suffers from high computational cost while the traditional approaches reconstruct every intermediate MSS from respective intermediate union of balls. In this study, we propose a novel computational framework named general MSS transformation framework (GMSSTF) between two MSSs without the assistance of union of balls. To evaluate the effectiveness of GMSSTF, we applied it on a popular public database PDB (Protein Data Bank) and compared the existing MSS algorithms with and without GMSSTF. The simulation results show that the proposed GMSSTF effectively improves the computational efficiency and is potentially useful for macromolecular dynamic simulations. [ABSTRACT FROM AUTHOR]

The biological process and molecular functions involved in the cancer progression remain difficult to understand for biologists and clinical doctors. Recent developments in high-throughput technologies urge the systems biology to achieve more precise models for complex diseases. Computational and mathematical models are gradually being used to help us understand the omics data produced by high-throughput experimental techniques. The use of computational models in systems biology allows us to explore the pathogenesis of complex diseases, improve our understanding of the latent molecular mechanisms, and promote treatment strategy optimization and new drug discovery. Currently, it is urgent to bridge the gap between the developments of high-throughput technologies and systemic modeling of the biological process in cancer research. In this review, we firstly studied several typical mathematical modeling approaches of biological systems in different scales and deeply analyzed their characteristics, advantages, applications, and limitations. Next, three potential research directions in systems modeling were summarized. To conclude, this review provides an update of important solutions using computational modeling approaches in systems biology. [ABSTRACT FROM AUTHOR]

MicroRNAs (miRNAs) family, which is involved in cancer development, proliferation, apoptosis, and drug resistance, is a group of noncoding RNAs that modulate the expression of oncogenes and antioncogenes. Doxorubicin is an active cytotoxic agent for breast cancer treatment, but the acquisition of doxorubicin resistance is a common and critical limitation to cancer therapy. The aim of this study was to investigate whether miR-193b mediated the resistance of breast cancer cells to doxorubicin by targeting myeloid cell leukemia-1 (MCL-1). In this study, we found that miR-193b levels were significantly lower in doxorubicin-resistant MCF-7 (MCF-7/DOXR) cells than in the parental MCF-7 cells. We observed that exogenous miR-193b significantly suppressed the ability of MCF-7/DOXR cells to resist doxorubicin. It demonstrated that miR-193b directly targeted MCL-1 3'-UTR (3'-Untranslated Regions). Further studies indicated that miR-193b sensitized MCF-7/DOXR cells to doxorubicin through a mechanism involving the downregulation of MCL-1. Together, our findings provide evidence that the modulation of miR-193b may represent a novel therapeutic target for the treatment of breast cancer. [ABSTRACT FROM AUTHOR]

Proteins and their interactions lie at the heart of most underlying biological processes. Consequently, correct detection of protein-protein interactions (PPIs) is of fundamental importance to understand the molecular mechanisms in biological systems. Although the convenience brought by high-throughput experiment in technological advances makes it possible to detect a large amount of PPIs, the data generated through these methods is unreliable and may not be completely inclusive of all possible PPIs. Targeting at this problem, this study develops a novel computational approach to effectively detect the protein interactions. This approach is proposed based on a novel matrix-based representation of protein sequence combined with the algorithm of support vector machine (SVM), which fully considers the sequence order and dipeptide information of the protein primary sequence. When performed on yeast PPIs datasets, the proposed method can reach 90.06% prediction accuracy with 94.37% specificity at the sensitivity of 85.74%, indicating that this predictor is a useful tool to predict PPIs. Achieved results also demonstrate that our approach can be a helpful supplement for the interactions that have been detected experimentally. [ABSTRACT FROM AUTHOR]

The Library of Integrated Network-Based Cellular Signatures (LINCS) project aims to create a network-based understanding of biology by cataloging changes in gene expression and signal transduction that occur when cells are exposed to a variety of perturbations. It is helpful for understanding cell pathways and facilitating drug discovery. Here, we developed a novel approach to infer cell-specific pathways and identify a compound's effects using gene expression and phosphoproteomics data under treatments with different compounds. Gene expression data were employed to infer potential targets of compounds and create a generic pathway map. Binary linear programming (BLP) was then developed to optimize the generic pathway topology based on the mid-stage signaling response of phosphorylation. To demonstrate effectiveness of this approach, we built a generic pathway map for the MCF7 breast cancer cell line and inferred the cell-specific pathways by BLP. The first group of 11 compounds was utilized to optimize the generic pathways, and then 4 compounds were used to identify effects based on the inferred cell-specific pathways. Cross-validation indicated that the cell-specific pathways reliably predicted a compound's effects. Finally, we applied BLP to re-optimize the cell-specific pathways to predict the effects of 4 compounds (trichostatin A, MS-275, staurosporine, and digoxigenin) according to compound-induced topological alterations. Trichostatin A and MS-275 (both HDAC inhibitors) inhibited the downstream pathway of HDAC1 and caused cell growth arrest via activation of p53 and p21; the effects of digoxigenin were totally opposite. Staurosporine blocked the cell cycle via p53 and p21, but also promoted cell growth via activated HDAC1 and its downstream pathway. Our approach was also applied to the PC3 prostate cancer cell line, and the cross-validation analysis showed very good accuracy in predicting effects of 4 compounds. In summary, our computational model can be used to elucidate potential mechanisms of a compound's efficacy. [ABSTRACT FROM AUTHOR]

Substantial effort in recent years has been devoted to analyzing data based large-scale biological networks, which provide valuable insight into the topologies of complex biological networks but are rarely context specific and cannot be used to predict the responses of cell signaling proteins to specific ligands or compounds. In this work, we proposed a novel strategy to investigate kinase inhibitor induced pathway signatures by integrating multiplex data in Library of Integrated Network-based Cellular Signatures (LINCS), e.g. KINOMEscan data and cell proliferation/mitosis imaging data. Using this strategy, we first established a PC9 cell line specific pathway model to investigate the pathway signatures in PC9 cell line when perturbed by a small molecule kinase inhibitor GW843682. This specific pathway revealed the role of PI3K/AKT in modulating the cell proliferation process and the absence of two anti-proliferation links, which indicated a potential mechanism of abnormal expansion in PC9 cell number. Incorporating the pathway model for side effects on primary human hepatocytes, it was used to screen 27 kinase inhibitors in LINCS database and PF02341066, known as Crizotinib, was finally suggested with an optimal concentration 4.6 uM to suppress PC9 cancer cell expansion while avoiding severe damage to primary human hepatocytes. Drug combination analysis revealed that the synergistic effect region can be predicted straightforwardly based on a threshold which is an inherent property of each kinase inhibitor. Furthermore, this integration strategy can be easily extended to other specific cell lines to be a powerful tool for drug screen before clinical trials. [ABSTRACT FROM AUTHOR]

Interaction of myeloma cells with osteoclasts (OC) can enhance tumor cell expansion through activation of complex signaling transduction networks. Both cells reside in the bone marrow, a hypoxic niche. How OC-myeloma interaction in a hypoxic environment affects myeloma cell growth and their response to drug treatment is poorly understood. In this study, we i) cultured myeloma cells in the presence/absence of OCs under normoxia and hypoxia conditions and did protein profiling analysis using reverse phase protein array; ii) computationally developed an Integer Linear Programming approach to infer OC-mediated myeloma cell-specific signaling pathways under normoxic and hypoxic conditions. Our modeling analysis indicated that in the presence OCs, (1) cell growth-associated signaling pathways, PI3K/AKT and MEK/ERK, were activated and apoptotic regulatory proteins, BAX and BIM, down-regulated under normoxic condition; (2) β1 Integrin/FAK signaling pathway was activated in myeloma cells under hypoxic condition. Simulation of drug treatment effects by perturbing the inferred cell-specific pathways showed that targeting myeloma cells with the combination of PI3K and integrin inhibitors potentially (1) inhibited cell proliferation by reducing the expression/activation of NF-κB, S6, c-Myc, and c-Jun under normoxic condition; (2) blocked myeloma cell migration and invasion by reducing the expression of FAK and PKC under hypoxic condition. [ABSTRACT FROM AUTHOR]