Systemic modeling myeloma-osteoclast interactions under normoxic/hypoxic condition using a novel computational approach

Deer antler renewal gives insights into mammalian epimorphic regeneration

Deer antlers are the only known mammalian organ that, once lost, can fully grow back naturally. Hence, the antler offers a unique opportunity to learn how nature has solved the problem of mammalian epimorphic regeneration (EpR). Comprehensive comparisons amongst different types of EpR reveal that antler renewal is fundamentally different from that in lower vertebrates such as regeneration of the newt limb. Surprisingly, antler renewal is comparable to wound healing over a stump of regeneration-incompetent digit/limb, bone fracture repair, and to a lesser extent to digit tip regeneration in mammals. Common to all these mammalian cases of reaction to the amputation/mechanical trauma is the response of the periosteal cells at the distal end/injury site with formation of a circumferential cartilaginous callus (CCC). Interestingly, whether the CCC can proceed to the next stage to transform to a blastema fully depends on the presence of an interactive partner. The actual form of the partner can vary in different cases with the nail organ in digit tip EpR, the opposing callus in bone fracture repair, and the closely associated enveloping skin in antler regeneration. Due to absence of such an interactive partner, the CCC of a mouse/rat digit/limb stump becomes involuted gradually. Based on these discoveries, we created an interactive partner for the rat digit/limb stump through surgically removal of the interposing layers of loose connective tissue and muscle between the resultant CCC and the enveloping skin after amputation and by forcefully bonding two tissue types tightly together. In so doing partial regeneration of the limb stump occurred. In summary, if EpR in humans is to be realized, then I envisage that it would be more likely in a manner akin to antler regeneration rather to that of lower vertebrates such as newt limbs.

A Review on Artificial Intelligence Enabled Design, Synthesis, and Process Optimization of Chemical Products for Industry 4.0

With the development of Industry 4.0, artificial intelligence (AI) is gaining increasing attention for its performance in solving particularly complex problems in industrial chemistry and chemical engineering. Therefore, this review provides an overview of the application of AI techniques, in particular machine learning, in chemical design, synthesis, and process optimization over the past years. In this review, the focus is on the application of AI for structure-function relationship analysis, synthetic route planning, and automated synthesis. Finally, we discuss the challenges and future of AI in making chemical products.

Redox integration of signaling and metabolism in a head and neck cancer model of radiation resistance using COSMRO

Redox metabolism is increasingly investigated in cancer as driving regulator of tumor progression, response to therapies and long-term patients' quality of life. Well-established cancer therapies, such as radiotherapy, either directly impact redox metabolism or have redox-dependent mechanisms of action defining their clinical efficacy. However, the ability to integrate redox information across signaling and metabolic networks to facilitate discovery and broader investigation of redox-regulated pathways in cancer remains a key unmet need limiting the advancement of new cancer therapies. To overcome this challenge, we developed a new constraint-based computational method (COSMro) and applied it to a Head and Neck Squamous Cell Cancer (HNSCC) model of radiation resistance. This novel integrative approach identified enhanced capacity for H2S production in radiation resistant cells and extracted a key relationship between intracellular redox state and cholesterol metabolism; experimental validation of this relationship highlights the importance of redox state in cellular metabolism and response to radiation.

Bioinformatics analysis to screen for genes related to myocardial infarction

Myocardial infarction (MI) is an acute and persistent myocardial ischemia caused by coronary artery disease. This study screened potential genes related to MI. Three gene expression datasets related to MI were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were screened using the MetaDE package. Afterward, the modules and genes closely related to MI were screened and a gene co-expression network was constructed. A support vector machine (SVM) classification model was then constructed based on the GSE61145 dataset using the e1071 package in R. A total of 98 DEGs were identified in the MI samples. Next, three modules associated with MI were screened and an SVM classification model involving seven genes was constructed. Among them, BCL6, CEACAM8, and CUGBP2 showed co-interactions in the gene co-expression network. Therefore, ACOX1, BCL6, CEACAM8, and CUGBP2, in addition to GPX7, might be feature genes related to MI.

Identification of signaling pathways associated with achaete-scute homolog 1 in glioblastomas through ChIP-seq data bioinformatics

Background: Achaete-scute homolog 1 transcription factors were important in the differentiation of neuronal-like glioblastoma (GBM) cancer stem cells (CSCs). To gain a better understanding of the role of ASCL1 in GBM, chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) data can be analyzed to construct their gene transcription regulation network.

Methods: GSE87618 was downloaded from the Gene Expression Omnibus, which is a famous database, in the field of biology. The filtered clean reads were mapped to the human genome utilizing the software of bowtie2. Then, differential peak analysis was performed by diffbind. Finally, the annotated gene functions and signaling pathways were investigated by Gene ontology function and kyoto encyclopedia of genes genomes (KEGG) pathway enrichment analysis. Moreover, the protein–protein interaction network (PPI) analysis of genes obtained from ASCL1 was carried out to explore the hub genes influenced by ASCL1.

Results: A total of 516 differential peaks were selected. GO analysis of functions revealed that promoter, untranslated region (UTR), exon, intron, and intergenic genes were mainly enriched in biological pathways such as keratinization, regulation of cAMP metabolic process, blood coagulation, fibrin clot formation, midgut development, and synapse assembly. Genes were mainly enriched in KEGG pathways including pentose phosphate pathway, glycosphingolipid biosynthesis—globo and isoglobo series, ECM–receptor interaction, and adherens junction. In total, 244 nodes and 475 interaction pairs were included in the PPI network with the hub genes including EGFR, CTNNB1, and SPTAN1.

Conclusion: EGFR, SPTAN1, and CTNN1B might be the potential down-stream genes of ASCL1 in GBM development, and CTNN1B might make contributions to GBM progression on regulating the cAMP pathway.

A survey on protein–DNA-binding sites in computational biology

Transcription factors are important cellular components of the process of gene expression control. Transcription factor binding sites are locations where transcription factors specifically recognize DNA sequences, targeting gene-specific regions and recruiting transcription factors or chromatin regulators to fine-tune spatiotemporal gene regulation. As the common proteins, transcription factors play a meaningful role in life-related activities. In the face of the increase in the protein sequence, it is urgent how to predict the structure and function of the protein effectively. At present, protein–DNA-binding site prediction methods are based on traditional machine learning algorithms and deep learning algorithms. In the early stage, we usually used the development method based on traditional machine learning algorithm to predict protein–DNA-binding sites. In recent years, methods based on deep learning to predict protein–DNA-binding sites from sequence data have achieved remarkable success. Various statistical and machine learning methods used to predict the function of DNA-binding proteins have been proposed and continuously improved. Existing deep learning methods for predicting protein–DNA-binding sites can be roughly divided into three categories: convolutional neural network (CNN), recursive neural network (RNN) and hybrid neural network based on CNN–RNN. The purpose of this review is to provide an overview of the computational and experimental methods applied in the field of protein–DNA-binding site prediction today. This paper introduces the methods of traditional machine learning and deep learning in protein–DNA-binding site prediction from the aspects of data processing characteristics of existing learning frameworks and differences between basic learning model frameworks. Our existing methods are relatively simple compared with natural language processing, computational vision, computer graphics and other fields. Therefore, the summary of existing protein–DNA-binding site prediction methods will help researchers better understand this field.

Phage_UniR_LGBM: Phage Virion Proteins Classification with UniRep Features and LightGBM Model

Phage, the most prevalent creature on the planet, serves a variety of critical roles. Phage's primary role is to facilitate gene-to-gene communication. The phage proteins can be defined as the virion proteins and the nonvirion ones. Nowadays, experimental identification is a difficult process that necessitates a significant amount of laboratory time and expense. Considering such situation, it is critical to design practical calculating techniques and develop well-performance tools. In this work, the Phage_UniR_LGBM has been proposed to classify the virion proteins. In detailed, such model utilizes the UniRep as the feature and the LightGBM algorithm as the classification model. And then, the training data train the model, and the testing data test the model with the cross-validation. The Phage_UniR_LGBM was compared with the several state-of-the-art features and classification algorithms. The performances of the Phage_UniR_LGBM are 88.51% in Sp,89.89% in Sn, 89.18% in Acc, 0.7873 in MCC, and 0.8925 in F1 score.

Hypertension-Related Drug Activity Identification Based on Novel Ensemble Method

Hypertension is a chronic disease and major risk factor for cardiovascular and cerebrovascular diseases that often leads to damage to target organs. The prevention and treatment of hypertension is crucially important for human health. In this paper, a novel ensemble method based on a flexible neural tree (FNT) is proposed to identify hypertension-related active compounds. In the ensemble method, the base classifiers are Multi-Grained Cascade Forest (gcForest), support vector machines (SVM), random forest (RF), AdaBoost, decision tree (DT), Gradient Boosting Decision Tree (GBDT), KNN, logical regression, and naïve Bayes (NB). The classification results of nine classifiers are utilized as the input vector of FNT, which is utilized as a nonlinear ensemble method to identify hypertension-related drug compounds. The experiment data are extracted from hypertension-unrelated and hypertension-related compounds collected from the up-to-date literature. The results reveal that our proposed ensemble method performs better than other single classifiers in terms of ROC curve, AUC, TPR, FRP, Precision, Specificity, and F1. Our proposed method is also compared with the averaged and voting ensemble methods. The results reveal that our method could identify hypertension-related compounds more accurately than two classical ensemble methods.

Early Prediction of Organ Failures in Patients with Acute Pancreatitis Using Text Mining

It is of great significance to establish an assessment model for organ failures in the early stage of admission in acute pancreatitis (AP). And the clinical notes are underutilized. To predict organ failures for AP patients using early clinical notes in hospital, early text features obtained from the pretrained Chinese Bidirectional Encoder Representations from Transformers model and attention-based LSTM were combined with early structured features (laboratory tests, vital signs, and demographic characteristics) to predict organ failures (respiratory, cardiovascular, and renal) in 12,748 AP inpatients in West China Hospital, Sichuan University, from 2008 to 2018. The text plus structured features fusion model was used to predict organ failures, compared to the baseline model with only structured features. The performance of the model with text features added is superior to the model that only includes structured features.

Drug Signature Detection Based on L1000 Genomic and Proteomic Big Data

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. L1000 big datasets provide gene expression profiles induced by over 10,000 compounds, shRNAs, and kinase inhibitors using L1000 platform. We developed a systematic compound signature discovery pipeline named csNMF, which covers from raw L1000 data processing to drug screening and mechanism generation. The discovered compound signatures of breast cancer were consistent with the LINCS KINOMEscan data and were clinically relevant. In this way, the potential mechanisms of compounds' efficacy are elucidated by our computational model.

DiscMLA: An Efficient Discriminative Motif Learning Algorithm over High-Throughput Datasets

The transcription factors (TFs) can activate or suppress gene expression by binding to specific sites, hence are crucial regulatory elements for transcription. Recently, series of discriminative motif finders have been tailored to offering promising strategy for harnessing the power of large quantities of accumulated high-throughput experimental data. However, in order to achieve high speed, these algorithms have to sacrifice accuracy by employing simplified statistical models during the searching process. In this paper, we propose a novel approach named Discriminative Motif Learning via AUC (DiscMLA) to discover motifs on high-throughput datasets. Unlike previous approaches, DiscMLA tries to optimize with a more comprehensive criterion (AUC) during motifs searching. In addition, based on an experimental observation of motif identification on large-scale datasets, some novel procedures are designed to accelerate DiscMLA. The experimental results on 52 real-world datasets demonstrate that our approach substantially outperforms previous methods on discriminative motif learning problems. DiscMLA' stability, discriminability, and validity will help to exploit high-throughput datasets and answer many fundamental biological questions.

A linear programming computational framework integrates phosphor-proteomics and prior knowledge to predict drug efficacy

Background

In recent years, the integration of ‘omics’ technologies, high performance computation, and mathematical modeling of biological processes marks that the systems biology has started to fundamentally impact the way of approaching drug discovery. The LINCS public data warehouse provides detailed information about cell responses with various genetic and environmental stressors. It can be greatly helpful in developing new drugs and therapeutics, as well as improving the situations of lacking effective drugs, drug resistance and relapse in cancer therapies, etc.

Results

In this study, we developed a Ternary status based Integer Linear Programming (TILP) method to infer cell-specific signaling pathway network and predict compounds’ treatment efficacy. The novelty of our study is that phosphor-proteomic data and prior knowledge are combined for modeling and optimizing the signaling network. To test the power of our approach, a generic pathway network was constructed for a human breast cancer cell line MCF7; and the TILP model was used to infer MCF7-specific pathways with a set of phosphor-proteomic data collected from ten representative small molecule chemical compounds (most of them were studied in breast cancer treatment). Cross-validation indicated that the MCF7-specific pathway network inferred by TILP were reliable predicting a compound’s efficacy. Finally, we applied TILP to re-optimize the inferred cell-specific pathways and predict the outcomes of five small compounds (carmustine, doxorubicin, GW-8510, daunorubicin, and verapamil), which were rarely used in clinic for breast cancer. In the simulation, the proposed approach facilitates us to identify a compound’s treatment efficacy qualitatively and quantitatively, and the cross validation analysis indicated good accuracy in predicting effects of five compounds.

Conclusions

In summary, the TILP model is useful for discovering new drugs for clinic use, and also elucidating the potential mechanisms of a compound to targets.

Electronic supplementary material

The online version of this article (10.1186/s12918-017-0501-6) contains supplementary material, which is available to authorized users.

Classification of Protein Structure Classes on Flexible Neutral Tree

Accurate classification on protein structural is playing an important role in Bioinformatics. An increase in evidence demonstrates that a variety of classification methods have been employed in such a field. In this research, the features of amino acids composition, secondary structure's feature, and correlation coefficient of amino acid dimers and amino acid triplets have been used. Flexible neutral tree (FNT), a particular tree structure neutral network, has been employed as the classification model in the protein structures' classification framework. Considering different feature groups owing diverse roles in the model, impact factors of different groups have been put forward in this research. In order to evaluate different impact factors, Impact Factors Scaling (IFS) algorithm, which aim at reducing redundant information of the selected features in some degree, have been put forward. To examine the performance of such framework, the 640, 1189, and ASTRAL datasets are employed as the low-homology protein structure benchmark datasets. Experimental results demonstrate that the performance of the proposed method is better than the other methods in the low-homology protein tertiary structures.

Nonconvex Penalty Based Low-Rank Representation and Sparse Regression for eQTL Mapping

This paper addresses the problem of accounting for confounding factors and expression quantitative trait loci (eQTL) mapping in the study of SNP-gene associations. The existing convex penalty based algorithm has limited capacity to keep main information of matrix in the process of reducing matrix rank. We present an algorithm, which use nonconvex penalty based low-rank representation to account for confounding factors and make use of sparse regression for eQTL mapping (NCLRS). The efficiency of the presented algorithm is evaluated by comparing the results of 18 synthetic datasets given by NCLRS and presented algorithm, respectively. The experimental results or biological dataset show that our approach is an effective tool to account for non-genetic effects than currently existing methods.

A biosystems approach to identify the molecular signaling mechanisms of TMEM30A during tumor migration

Understanding the molecular mechanisms underlying cell migration, which plays an important role in tumor growth and progression, is critical for the development of novel tumor therapeutics. Overexpression of transmembrane protein 30A (TMEM30A) has been shown to initiate tumor cell migration, however, the molecular mechanisms through which this takes place have not yet been reported. Thus, we propose the integration of computational and experimental approaches by first predicting potential signaling networks regulated by TMEM30A using a) computational biology methods, b) our previous mass spectrometry results of the TMEM30A complex in mouse tissue, and c) a number of migration-related genes manually collected from the literature, and subsequently performing molecular biology experiments including the in vitro scratch assay and real-time quantitative polymerase chain reaction (qPCR) to validate the reliability of the predicted network. The results verify that the genes identified in the computational signaling network are indeed regulated by TMEM30A during cell migration, indicating the effectiveness of our proposed method and shedding light on the regulatory mechanisms underlying tumor migration, which facilitates the understanding of the molecular basis of tumor invasion.

WSMD: weakly-supervised motif discovery in transcription factor ChIP-seq data

Although discriminative motif discovery (DMD) methods are promising for eliciting motifs from high-throughput experimental data, due to consideration of computational expense, most of existing DMD methods have to choose approximate schemes that greatly restrict the search space, leading to significant loss of predictive accuracy. In this paper, we propose Weakly-Supervised Motif Discovery (WSMD) to discover motifs from ChIP-seq datasets. In contrast to the learning strategies adopted by previous DMD methods, WSMD allows a "global" optimization scheme of the motif parameters in continuous space, thereby reducing the information loss of model representation and improving the quality of resultant motifs. Meanwhile, by exploiting the connection between DMD framework and existing weakly supervised learning (WSL) technologies, we also present highly scalable learning strategies for the proposed method. The experimental results on both real ChIP-seq datasets and synthetic datasets show that WSMD substantially outperforms former DMD methods (including DREME, HOMER, XXmotif, motifRG and DECOD) in terms of predictive accuracy, while also achieving a competitive computational speed.

Mathematical and Computational Modeling in Complex Biological Systems

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.