Prostate cancer patients often have increased levels of psychological stress or anxiety, but the molecular mechanisms underlying the interaction between psychological stress and prostate cancer as well as therapy resistance have been rarely studied and remain poorly understood. Recent reports show that stress inhibits apoptosis in prostate cancer cells via epinephrine/beta2 adrenergic receptor/PKA/BAD pathway. In this study, we used experimental data on the signaling pathways that control BAD phosphorylation to build a dynamic network model of apoptosis regulation in prostate cancer cells. We then compared the predictive power of two different models with or without the role of Mcl-1, which justified the role of Mcl-1 stabilization in anti-apoptotic effects of emotional stress. Based on the selected model, we examined and quantitatively evaluated the induction of apoptosis by drug combination therapies. We predicted that the combination of PI3K inhibitor LY294002 and inhibition of BAD phosphorylation at S112 would produce the best synergistic effect among 8 interventions examined. Experimental validation confirmed the effectiveness of our predictive model. Moreover, we found that epinephrine signaling changes the synergism pattern and decreases efficacy of combination therapy. The molecular mechanisms responsible for therapeutic resistance and the switch in synergism were explored by analyzing a network model of signaling pathways affected by psychological stress. These results provide insights into the mechanisms of psychological stress signaling in therapy-resistant cancer, and indicate the potential benefit of reducing psychological stress in designing more effective therapies for prostate cancer patients.
Psychological stress and anxiety are often experienced by prostate cancer patients, but the underlying mechanisms of interactions between psychological stress and cancer development, as well as drug resistance, are unclear. Here, we employed a systems biology approach to study interactions between stress-activated epinephrine/beta2 adrenergic receptor/protein kinase A signaling and a regulatory network that controls apoptosis in prostate cancer cells. We developed a dynamic network model of signaling pathways that control apoptosis in prostate cancer cells and quantitatively evaluated the effects of stress-activated signaling on apoptosis induced by drug combinations. Experimental data were used to guide modeling, to fit the unknown parameters and validate the model. Based on our model we found that epinephrine/beta2 adrenergic receptor/protein kinase A signaling can decrease drug efficiency, and can shift the effect of drug combination from synergy to antagonism. We also predicted that in addition to BAD phosphorylation Mcl-1 expression could be upregulated by stress/epinephrine signaling to inhibit apoptosis. This study provides insights into the mechanisms of psychological stress signaling in therapy-resistant cancer, and suggests that reducing psychological stress could help to make prostate cancer treatment more effective.
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.
Motivation: It becomes widely accepted that human cancer is a disease involving dynamic changes in the genome and that the missense mutations constitute the bulk of human genetic variations. A multitude of computational algorithms, especially the machine learning-based ones, has consequently been proposed to distinguish missense changes that contribute to the cancer progression (‘driver’ mutation) from those that do not (‘passenger’ mutation). However, the existing methods have multifaceted shortcomings, in the sense that they either adopt incomplete feature space or depend on protein structural databases which are usually far from integrated.
Results: In this article, we investigated multiple aspects of a missense mutation and identified a novel feature space that well distinguishes cancer-associated driver mutations from passenger ones. An index (DX score) was proposed to evaluate the discriminating capability of each feature, and a subset of these features which ranks top was selected to build the SVM classifier. Cross-validation showed that the classifier trained on our selected features significantly outperforms the existing ones both in precision and robustness. We applied our method to several datasets of missense mutations culled from published database and literature and obtained more reasonable results than previous studies.
Availability: The software is available online at http://www.methodisthealth.com/software and https://sites.google.com/site/drivermutationidentification/.
Supplementary information: Supplementary data are available at Bioinformatics online.
More and more transcription factors and their motifs have been reported and linked to specific gene expression levels. However, focusing only on transcription is not sufficient for mechanism research. Most genes, especially in eukaryotes, are alternatively spliced to different isoforms. Some of these isoforms increase the biodiversity of proteins. From this viewpoint, transcription and splicing are two of important mechanisms to modulate expression levels of isoforms. To integrate these two kinds of regulation, we built a linear regression model to select a subset of transcription factors and splicing factors for each co-expressed isoforms using least-angle regression approach. Then, we applied this method to investigate the mechanism of myelodysplastic syndromes (MDS), a precursor lesion of acute myeloid leukemia. Results suggested that expression levels of most isoforms were regulated by a set of selected regulatory factors. Some of the detected factors, such as EGR1 and STAT family, are highly correlated with progression of MDS. We discovered that the splicing factor SRSF11 experienced alternative splicing switch, and in turn induced different amino acid sequences between MDS and controls. This splicing switch causes two different splicing mechanisms. Polymerase Chain Reaction experiments also confirmed that one of its isoforms was over-expressed in MDS. We analyzed the regulatory networks constructed from the co-expressed isoforms and their regulatory factors in MDS. Many of these networks were enriched in the herpes simplex infection pathway which involves many splicing factors, and pathways in cancers and acute or chronic myeloid leukemia.
The long-term performance of tissue-engineered bone grafts is determined by a dynamic balance between bone regeneration and resorption. We proposed using embedded cytokine slow-releasing hydrogels to tune this balance toward a desirable final bone density. In this study we established a systems biology model, and quantitatively explored the combinatorial effects of delivered cytokines from hydrogels on final bone density. We hypothesized that: 1) bone regeneration was driven by transcription factors Runx2 and Osterix, which responded to released cytokines, such as Wnt, BMP2, and TGFβ, drove the development of osteoblast lineage, and contributed to bone mass generation; and 2) the osteoclast lineage, on the other hand, governed the bone resorption, and communications between these two lineages determined the dynamics of bone remodeling. In our model, Intracellular signaling pathways were represented by ordinary differential equations, while the intercellular communications and cellular population dynamics were modeled by stochastic differential equations. Effects of synergistic cytokine combinations were evaluated by Loewe index and Bliss index. Simulation results revealed that the Wnt/BMP2 combinations released from hydrogels showed best control of bone regeneration and synergistic effects, and suggested optimal dose ratios of given cytokine combinations released from hydrogels to most efficiently control the long-term bone remodeling. We revealed the characteristics of cytokine combinations of Wnt/BMP2 which could be used to guide the design of in vivo bone scaffolds and the clinical treatment of some diseases such as osteoporosis.
Bone tissue engineering; Bone remodeling; Cytokine combination therapy; Systems biology; Osteogenic differentiation; Signaling pathway
This paper proposes a nonlinear regression model to predict soft tissue deformation after maxillofacial surgery. The feature which served as input in the model is extracted with Finite Element Model (FEM). The output in the model is the facial deformation calculated from the preoperative and postoperative 3D data. After finding the relevance between feature and facial deformation by using the regression model, we establish a general relationship which can be applied to all the patients. As a new patient comes, we predict his/her facial deformation by combining the general relationship and the new patient’s biomechanical properties. Thus, our model is biomechanical relevant and statistical relevant. Validation on eleven patients demonstrates the effectiveness and efficiency of our method.
kernel ridge regression; finite element model; maxillofacial surgery; soft tissue deformation
Recent advances in cone-beam computed tomography (CBCT) have rapidly enabled widepsread applications of dentomaxillofacial imaging and orthodontic practices in the past decades due to its low radiation dose, high spatial resolution, and accessibility. However, low contrast resolution in CBCT image has become its major limitation in building skull models. Intensive hand-segmentation is usually required to reconstruct the skull models. One of the regions affected by this limitation the most is the thin bone images. This paper presents a novel segmentation approach based on wavelet density model (WDM) for a particular interest in the outer surface of anterior wall of maxilla. Nineteen CBCT datasets are used to conduct two experiments. This mode-based segmentation approach is validated and compared with three different segmentation approaches. The results show that the performance of this model-based segmentation approach is better than those of the other approaches. It can achieve 0.25 ± 0.2mm of surface error from ground truth of bone surface.
3D segmentation; Active shape model (ASM); Statistical shape model (SSM); Craniomaxillofacial (CMF) surgeries; Cone-beam computed tomography (CBCT)
The rapid development of next generation sequencing (NGS) technology provides a novel avenue for genomic exploration and research. Single nucleotide variants (SNVs) inferred from next generation sequencing are expected to reveal gene mutations in cancer. However, NGS has lower sequence coverage and poor SNVs detection capability in the regulatory regions of the genome. Post probabilistic based methods are efficient for detection of SNVs in high coverage regions or sequencing data with high depth. However, for data with low sequencing depth, the efficiency of such algorithms remains poor and needs to be improved.
A new tool SNVHMM basing on a discrete hidden Markov model (HMM) was developed to infer the genotype for each position on the genome. We incorporated the mapping quality of each read and the corresponding base quality on the reads into the emission probability of HMM. The context information of the whole observation as well as its confidence were completely utilized to infer the genotype for each position on the genome in study. Therefore, more probability power can be gained over the Bayes based methods, which is very useful for SNVs detection for data with low sequencing depth. Moreover, our model was verified by testing against two sets of lobular breast tumor and Myelodysplastic Syndromes (MDS) data each. Comparing against a recently published SNVs calling algorithm SNVMix2, our model improved the performance of SNVMix2 largely when the sequencing depth is low and also outperformed SNVMix2 when SNVMix2 is well trained by large datasets.
SNVHMM can detect SNVs from NGS cancer data efficiently even if the sequence depth is very low. The training data size can be very small for SNVHMM to work. SNVHMM incorporated the base quality and mapping quality of all observed bases and reads, and also provides the option for users to choose the confidence of the observation for SNVs prediction.
Recently, melanoma has become the most malignant and commonly occurring skin cancer. Melanoma is not only the major source (75%) of deaths related to skin cancer, but also it is hard to be treated by the conventional drugs. Recent research indicated that angiogenesis is an important factor for tumor initiation, expansion, and response to therapy. Thus, we proposed a novel multi-scale agent-based computational model that integrates the angiogenesis into tumor growth to study the response of melanoma cancer under combined drug treatment.
Our multi-scale agent-based model can simulate the melanoma tumor growth with angiogenesis under combined drug treatment. The significant synergistic effects between drug Dox and drug Sunitinib demonstrated the clinical potential to interrupt the communication between melanoma cells and its related vasculatures. Also, the sensitivity analysis of the model revealed that diffusivity related to the micro-vasculatures around tumor tissues closely correlated with the spread, oscillation and destruction of the tumor.
Simulation results showed that the 3D model can represent key features of melanoma growth, angiogenesis, and its related micro-environment. The model can help cancer researchers understand the melanoma developmental mechanism. Drug synergism analysis suggested that interrupting the communications between melanoma cells and the related vasculatures can significantly increase the drug efficacy against tumor cells.
Microenvironment; Drug synergism; Agent-based model; Multi-scale; Melanoma; Anti-angiogenesis
Gene fusions, which result from abnormal chromosome rearrangements, are a pathogenic factor in cancer development. The emerging RNA-Seq technology enables us to detect gene fusions and profile their features.
In this paper, we proposed a novel fusion detection tool, FusionQ, based on paired-end RNA-Seq data. This tool can detect gene fusions, construct the structures of chimerical transcripts, and estimate their abundances. To confirm the read alignment on both sides of a fusion point, we employed a new approach, “residual sequence extension”, which extended the short segments of the reads by aggregating their overlapping reads. We also proposed a list of filters to control the false-positive rate. In addition, we estimated fusion abundance using the Expectation-Maximization algorithm with sparse optimization, and further adopted it to improve the detection accuracy of the fusion transcripts. Simulation was performed by FusionQ and another two stated-of-art fusion detection tools. FusionQ exceeded the other two in both sensitivity and specificity, especially in low coverage fusion detection. Using paired-end RNA-Seq data from breast cancer cell lines, FusionQ detected both the previously reported and new fusions. FusionQ reported the structures of these fusions and provided their expressions. Some highly expressed fusion genes detected by FusionQ are important biomarkers in breast cancer. The performances of FusionQ on cancel line data still showed better specificity and sensitivity in the comparison with another two tools.
FusionQ is a novel tool for fusion detection and quantification based on RNA-Seq data. It has both good specificity and sensitivity performance. FusionQ is free and available at http://www.wakehealth.edu/CTSB/Software/Software.htm.
Fusion detection; chimerical transcripts quantification; EM algorithm
In this editorial preface, I briefly review cancer bioinformatics and introduce the four articles in this special issue highlighting important applications of the field: detection of chromatin states; detection of SNP-containing motifs and association with transcription factor-binding sites; improvements in functional enrichment modules; and gene association studies on aging and cancer. We expect this issue to provide bioinformatics scientists, cancer biologists, and clinical doctors with a better understanding of how cancer bioinformatics can be used to identify candidate biomarkers and targets and to conduct functional analysis.
Chromatin sates; SNP-containing motifs; functional enrichment analysis; gene association
Myelodysplastic syndromes have increased in frequency and incidence in the American population, but patient prognosis has not significantly improved over the last decade. Such improvements could be realized if biomarkers for accurate diagnosis and prognostic stratification were successfully identified. In this study, we propose a method that associates two state-of-the-art array technologies—single nucleotide polymorphism (SNP) array and gene expression array—with gene motifs considered transcription factor-binding sites (TFBS). We are particularly interested in SNP-containing motifs introduced by genetic variation and mutation as TFBS. The potential regulation of SNP-containing motifs affects only when certain mutations occur. These motifs can be identified from a group of co-expressed genes with copy number variation. Then, we used a sliding window to identify motif candidates near SNPs on gene sequences. The candidates were filtered by coarse thresholding and fine statistical testing. Using the regression-based LARS-EN algorithm and a level-wise sequence combination procedure, we identified 28 SNP-containing motifs as candidate TFBS. We confirmed 21 of the 28 motifs with ChIP-chip fragments in the TRANSFAC database. Another six motifs were validated by TRANSFAC via searching binding fragments on co-regulated genes. The identified motifs and their location genes can be considered potential biomarkers for myelodysplastic syndromes. Thus, our proposed method, a novel strategy for associating two data categories, is capable of integrating information from different sources to identify reliable candidate regulatory SNP-containing motifs introduced by genetic variation and mutation.
Association study; genetic variation and mutation; transcription factor-binding sites; myelodysplastic syndromes
By altering the electrostatic charge of histones or providing binding sites to protein recognition molecules, Chromatin marks have been proposed to regulate gene expression, a property that has motivated researchers to link these marks to Cis-regulatory elements. With the help of next generation sequencing technologies, we can now correlate one specific chromatin mark with regulatory elements (e.g. enhancers or promoters) and also build tools, such as hidden Markov models, to gain insight into mark combinations. However, hidden Markov models have limitation for their character of generative models and assume that a current observation depends only on a current hidden state in the chain. Here, we employed two graphical probabilistic models, namely the linear conditional random field model and multivariate hidden Markov model, to mark gene regions with different states based on recurrent and spatially coherent character of these eight marks. Both models revealed chromatin states that may correspond to enhancers and promoters, transcribed regions, transcriptional elongation, and low-signal regions. We also found that the linear conditional random field model was more effective than the hidden Markov model in recognizing regulatory elements, such as promoter-, enhancer-, and transcriptional elongation-associated regions, which gives us a better choice.
Epigenetics; histone modification; conditional random field; regulatory elements
Multiple intergenic single-nucleotide polymorphisms (SNPs) near hedgehog interacting protein (HHIP) on chromosome 4q31 have been strongly associated with pulmonary function levels and moderate-to-severe chronic obstructive pulmonary disease (COPD). However, whether the effects of variants in this region are related to HHIP or another gene has not been proven. We confirmed genetic association of SNPs in the 4q31 COPD genome-wide association study (GWAS) region in a Polish cohort containing severe COPD cases and healthy smoking controls (P = 0.001 to 0.002). We found that HHIP expression at both mRNA and protein levels is reduced in COPD lung tissues. We identified a genomic region located ∼85 kb upstream of HHIP which contains a subset of associated SNPs, interacts with the HHIP promoter through a chromatin loop and functions as an HHIP enhancer. The COPD risk haplotype of two SNPs within this enhancer region (rs6537296A and rs1542725C) was associated with statistically significant reductions in HHIP promoter activity. Moreover, rs1542725 demonstrates differential binding to the transcription factor Sp3; the COPD-associated allele exhibits increased Sp3 binding, which is consistent with Sp3's usual function as a transcriptional repressor. Thus, increased Sp3 binding at a functional SNP within the chromosome 4q31 COPD GWAS locus leads to reduced HHIP expression and increased susceptibility to COPD through distal transcriptional regulation. Together, our findings reveal one mechanism through which SNPs upstream of the HHIP gene modulate the expression of HHIP and functionally implicate reduced HHIP gene expression in the pathogenesis of COPD.
Avian tembusu virus (TMUV), which was first identified in eastern China, is an emerging virus causing serious economic losses in the Chinese poultry industry. Here, we report the complete genome sequence of goose tembusu virus strain JS804, isolated from Jiangnan white geese with severe neurological signs. The genome of JS804 is 10,990 nucleotides (nt) in length and contains a single open reading frame encoding a putative polyprotein of 3,425 amino acids. Research of the whole sequence of tembusu virus will help us to understand further the molecular and evolutionary characteristics and pathogenesis of this virus.
S-glutathionylation, the reversible formation of mixed disulfides between glutathione(GSH) and cysteine residues in proteins, is a specific form of post-translational modification that plays important roles in various biological processes, including signal transduction, redox homeostasis, and metabolism inside cells. Experimentally identifying S-glutathionylation sites is labor-intensive and time consuming, whereas bioinformatics methods provide an alternative way to this problem by predicting S-glutathionylation sites in silico. The bioinformatics approaches give not only candidate sites for further experimental verification but also bio-chemical insights into the mechanism of S-glutathionylation. In this paper, we firstly collect experimentally determined S-glutathionylated proteins and their corresponding modification sites from the literature, and then propose a new method for predicting S-glutathionylation sites by employing machine learning methods based on protein sequence data. Promising results are obtained by our method with an AUC (area under ROC curve) score of 0.879 in 5-fold cross-validation, which demonstrates the predictive power of our proposed method. The datasets used in this work are available at http://csb.shu.edu.cn/SGDB.
Despite successful approaches to preserve organs, tissues, and isolated cells, the maintenance of stem cell viability and function in body fluids during storage for cell distribution and transportation remains unexplored. The aim of this study was to characterize urine-derived stem cells (USCs) after optimal preservation of urine specimens for up to 24 hours. A total of 415 urine specimens were collected from 12 healthy men (age range 20–54 years old). About 6×104 cells shed off from the urinary tract system in 24 hours. At least 100 USC clones were obtained from the stored urine specimens after 24 hours and maintained similar biological features to fresh USCs. The stored USCs had a “rice grain” shape in primary culture, and expressed mesenchymal stem cell surface markers, high telomerase activity, and normal karyotypes. Importantly, the preserved cells retained bipotent differentiation capacity. Differentiated USCs expressed myogenic specific proteins and contractile function when exposed to myogenic differentiation medium, and they expressed urothelial cell-specific markers and barrier function when exposed to urothelial differentiation medium. These data demonstrated that up to 75% of fresh USCs can be safely persevered in urine for 24 hours and that these cells stored in urine retain their original stem cell properties, indicating that preserved USCs could be available for potential use in cell-based therapy or clinical diagnosis.
RNA interference (RNAi) becomes an increasingly important and effective genetic tool to study the function of target genes by suppressing specific genes of interest. This system approach helps identify signaling pathways and cellular phase types by tracking intensity and/or morphological changes of cells. The traditional RNAi screening scheme, in which one siRNA is designed to knockdown one specific mRNA target, needs a large library of siRNAs and turns out to be time-consuming and expensive.
In this paper, we propose a conceptual model, called compressed sensing RNAi (csRNAi), which employs a unique combination of group of small interfering RNAs (siRNAs) to knockdown a much larger size of genes. This strategy is based on the fact that one gene can be partially bound with several small interfering RNAs (siRNAs) and conversely, one siRNA can bind to a few genes with distinct binding affinity. This model constructs a multi-to-multi correspondence between siRNAs and their targets, with siRNAs much fewer than mRNA targets, compared with the conventional scheme. Mathematically this problem involves an underdetermined system of equations (linear or nonlinear), which is ill-posed in general. However, the recently developed compressed sensing (CS) theory can solve this problem. We present a mathematical model to describe the csRNAi system based on both CS theory and biological concerns. To build this model, we first search nucleotide motifs in a target gene set. Then we propose a machine learning based method to find the effective siRNAs with novel features, such as image features and speech features to describe an siRNA sequence. Numerical simulations show that we can reduce the siRNA library to one third of that in the conventional scheme. In addition, the features to describe siRNAs outperform the existing ones substantially.
This csRNAi system is very promising in saving both time and cost for large-scale RNAi screening experiments which may benefit the biological research with respect to cellular processes and pathways.
The epidermal growth factor receptor (EGFR) signaling pathway and angiogenesis in brain cancer act as an engine for tumor initiation, expansion and response to therapy. Since the existing literature does not have any models that investigate the impact of both angiogenesis and molecular signaling pathways on treatment, we propose a novel multi-scale, agent-based computational model that includes both angiogenesis and EGFR modules to study the response of brain cancer under tyrosine kinase inhibitors (TKIs) treatment.
The novel angiogenesis module integrated into the agent-based tumor model is based on a set of reaction–diffusion equations that describe the spatio-temporal evolution of the distributions of micro-environmental factors such as glucose, oxygen, TGFα, VEGF and fibronectin. These molecular species regulate tumor growth during angiogenesis. Each tumor cell is equipped with an EGFR signaling pathway linked to a cell-cycle pathway to determine its phenotype. EGFR TKIs are delivered through the blood vessels of tumor microvasculature and the response to treatment is studied.
Our simulations demonstrated that entire tumor growth profile is a collective behaviour of cells regulated by the EGFR signaling pathway and the cell cycle. We also found that angiogenesis has a dual effect under TKI treatment: on one hand, through neo-vasculature TKIs are delivered to decrease tumor invasion; on the other hand, the neo-vasculature can transport glucose and oxygen to tumor cells to maintain their metabolism, which results in an increase of cell survival rate in the late simulation stages.
Multi-scale; Agent-based modeling; EGFR signaling pathway; Angiogenesis; TKI treatment
In Traditional Chinese Medicine (TCM), the lip diagnosis is an important diagnostic method which has a long history and is applied widely. The lip color of a person is considered as a symptom to reflect the physical conditions of organs in the body. However, the traditional diagnostic approach is mainly based on observation by doctor’s nude eyes, which is non-quantitative and subjective. The non-quantitative approach largely depends on the doctor’s experience and influences accurate the diagnosis and treatment in TCM. Developing new quantification methods to identify the exact syndrome based on the lip diagnosis of TCM becomes urgent and important. In this paper, we design a computer-assisted classification model to provide an automatic and quantitative approach for the diagnosis of TCM based on the lip images.
A computer-assisted classification method is designed and applied for syndrome diagnosis based on the lip images. Our purpose is to classify the lip images into four groups: deep-red, red, purple and pale. The proposed scheme consists of four steps including the lip image preprocessing, image feature extraction, feature selection and classification. The extracted 84 features contain the lip color space component, texture and moment features. Feature subset selection is performed by using SVM-RFE (Support Vector Machine with recursive feature elimination), mRMR (minimum Redundancy Maximum Relevance) and IG (information gain). Classification model is constructed based on the collected lip image features using multi-class SVM and Weighted multi-class SVM (WSVM). In addition, we compare SVM with k-nearest neighbor (kNN) algorithm, Multiple Asymmetric Partial Least Squares Classifier (MAPLSC) and Naïve Bayes for the diagnosis performance comparison. All displayed faces image have obtained consent from the participants.
A total of 257 lip images are collected for the modeling of lip diagnosis in TCM. The feature selection method SVM-RFE selects 9 important features which are composed of 5 color component features, 3 texture features and 1 moment feature. SVM, MAPLSC, Naïve Bayes, kNN showed better classification results based on the 9 selected features than the results obtained from all the 84 features. The total classification accuracy of the five methods is 84%, 81%, 79% and 81%, 77%, respectively. So SVM achieves the best classification accuracy. The classification accuracy of SVM is 81%, 71%, 89% and 86% on Deep-red, Pale Purple, Red and lip image models, respectively. While with the feature selection algorithm mRMR and IG, the total classification accuracy of WSVM achieves the best classification accuracy. Therefore, the results show that the system can achieve best classification accuracy combined with SVM classifiers and SVM-REF feature selection algorithm.
A diagnostic system is proposed, which firstly segments the lip from the original facial image based on the Chan-Vese level set model and Otsu method, then extracts three kinds of features (color space features, Haralick co-occurrence features and Zernike moment features) on the lip image. Meanwhile, SVM-REF is adopted to select the optimal features. Finally, SVM is applied to classify the four classes. Besides, we also compare different feature selection algorithms and classifiers to verify our system. So the developed automatic and quantitative diagnosis system of TCM is effective to distinguish four lip image classes: Deep-red, Purple, Red and Pale. This study puts forward a new method and idea for the quantitative examination on lip diagnosis of TCM, as well as provides a template for objective diagnosis in TCM.
Traditional chinese medicine; Computer-assisted lip diagnosis; Image analysis; Feature selection; Support vector machine
Recent research in cancer biology has suggested the hypothesis that tumors are initiated and driven by a small group of cancer stem cells (CSCs). Furthermore, cancer stem cell niches have been found to be essential in determining fates of CSCs, and several signaling pathways have been proven to play a crucial role in cellular behavior, which could be two important factors in cancer development. To better understand the progression, heterogeneity and treatment response of breast cancer, especially in the context of CSCs, we propose a mathematical model based on the cell compartment method. In this model, three compartments of cellular subpopulations are constructed: CSCs, progenitor cells (PCs), and terminal differentiated cells (TCs). Moreover, 1) the cancer stem cell niche is, considered by modeling its effect on division patterns (symmetric or asymmetric) of CSCs, and 2) the EGFR signaling pathway is integrated by modeling its role in cell proliferation, apoptosis. Our simulation results indicate that 1) a higher probability for symmetric division of CSC may result in a faster expansion of tumor population, and for a larger number of niches, the tumor grows at a slower rate, but the final tumor volume is larger; 2) higher EGFR expression correlates to tumors with larger volumes while a saturation function is observed, and 3) treatments that inhibit tyrosine kinase activity of EGFR may not only repress the tumor volume, but also decrease the CSCs percentages by shifting CSCs from symmetric divisions to asymmetric divisions. These findings suggest that therapies should be designed to effectively control or eliminate the symmetric division of CSCs and to reduce or destroy the CSC niches.
mathematical model; compartment method; signaling pathway; breast cancer; tyrosine kinase inhibitors
Multiscale agent-based modeling (MABM) has been widely used to simulate Glioblastoma Multiforme (GBM) and its progression. At the intracellular level, the MABM approach employs a system of ordinary differential equations to describe quantitatively specific intracellular molecular pathways that determine phenotypic switches among cells (e.g. from migration to proliferation and vice versa). At the intercellular level, MABM describes cell-cell interactions by a discrete module. At the tissue level, partial differential equations are employed to model the diffusion of chemoattractants, which are the input factors of the intracellular molecular pathway. Moreover, multiscale analysis makes it possible to explore the molecules that play important roles in determining the cellular phenotypic switches that in turn drive the whole GBM expansion. However, owing to limited computational resources, MABM is currently a theoretical biological model that uses relatively coarse grids to simulate a few cancer cells in a small slice of brain cancer tissue. In order to improve this theoretical model to simulate and predict actual GBM cancer progression in real time, a graphics processing unit (GPU)-based parallel computing algorithm was developed and combined with the multi-resolution design to speed up the MABM. The simulated results demonstrated that the GPU-based, multi-resolution and multiscale approach can accelerate the previous MABM around 30-fold with relatively fine grids in a large extracellular matrix. Therefore, the new model has great potential for simulating and predicting real-time GBM progression, if real experimental data are incorporated.
Chronic lymphocytic leukemia deletion gene 7 (Clld7) is a candidate tumor suppressor on chromosome 13q14. Clld7 encodes an evolutionarily conserved protein that contains an RCC1 domain plus broad complex, tramtrack, bric-a-brac (BTB) and POZ domains. In this study, we investigated the biological functions of Clld7 protein in inducible osteosarcoma cell lines. Clld7 induction inhibited cell growth, decreased cell viability, and increased gamma-H2AX staining under conditions of caspase inhibition, indicating activation of the DNA damage/repair pathway. Real-time PCR analysis in tumor cells and normal human epithelial cells revealed Clld7 target genes that regulate DNA repair responses. Furthermore, depletion of Clld7 in normal human epithelial cells conferred resistance to apoptosis triggered by DNA damage. Taken together, the biological actions of Clld7 are consistent with those of a tumor suppressor.
tumor suppressor; DNA damage/repair pathway; 13q14; primary human keratinocyte; apoptosis
Motivation: High-throughput protein interaction data, with ever-increasing volume, are becoming the foundation of many biological discoveries, and thus high-quality protein–protein interaction (PPI) maps are critical for a deeper understanding of cellular processes. However, the unreliability and paucity of current available PPI data are key obstacles to the subsequent quantitative studies. It is therefore highly desirable to develop an approach to deal with these issues from the computational perspective. Most previous works for assessing and predicting protein interactions either need supporting evidences from multiple information resources or are severely impacted by the sparseness of PPI networks.
Results: We developed a robust manifold embedding technique for assessing the reliability of interactions and predicting new interactions, which purely utilizes the topological information of PPI networks and can work on a sparse input protein interactome without requiring additional information types. After transforming a given PPI network into a low-dimensional metric space using manifold embedding based on isometric feature mapping (ISOMAP), the problem of assessing and predicting protein interactions is recasted into the form of measuring similarity between points of its metric space. Then a reliability index, a likelihood indicating the interaction of two proteins, is assigned to each protein pair in the PPI networks based on the similarity between the points in the embedded space. Validation of the proposed method is performed with extensive experiments on densely connected and sparse PPI network of yeast, respectively. Results demonstrate that the interactions ranked top by our method have high-functional homogeneity and localization coherence, especially our method is very efficient for large sparse PPI network with which the traditional algorithms fail. Therefore, the proposed algorithm is a much more promising method to detect both false positive and false negative interactions in PPI networks.
Availability: MATLAB code implementing the algorithm is available from the web site http://home.ustc.edu.cn/∼yzh33108/Manifold.htm.
Supplementary information: Supplementary data are available at Bioinformatics online.
High content neuron image processing is considered as an important method for quantitative neurobiological studies. The main goal of analysis in this paper is to provide automatic image processing approaches to process neuron images for studying neuron mechanism in high content screening. In the nuclei channel, all nuclei are segmented and detected by applying the gradient vector field based watershed. Then the neuronal nuclei are selected based on the soma region detected in neurite channel. In neurite images, we propose a novel neurite centerline extraction approach using the improved line-pixel detection technique. The proposed neurite tracing method can detect the curvilinear structure more accurately compared with the current existing methods. An interface called NeuriteIQ based on the proposed algorithms is developed finally for better application in high content screening.
High content screening; Microscopy image; Nuclei segmentation; Neurite outgrowth; Line-pixel detection; Branch area