Intrinsic properties of Boolean dynamics in complex networks

We study intrinsic properties of attractor in Boolean dynamics of complex networks with scale-free topology, comparing with those of the so-called Kauffman's random Boolean networks. We numerically study both frozen and relevant nodes in each attractor in the dynamics of relatively small networks (20?N?200). We investigate numerically robustness of an attractor to a perturbation. An attractor with cycle length of ?_c in a network of size N consists of ?_c states in the state space of 2^N states; each attractor has the arrangement of N nodes, where the cycle of attractor sweeps ?_c states. We define a perturbation as a flip of the state on a single node in the attractor state at a given time step. We show that the rate between unfrozen and relevant nodes in the dynamics of a complex network with scale-free topology is larger than that in Kauffman's random Boolean network model. Furthermore, we find that in a complex scale-free network with fluctuation of the in-degree number, attractors are more sensitive to a state flip for a highly connected node (i.e. input-hub node) than to that for a less connected node. By some numerical examples, we show that the number of relevant nodes increases, when an input-hub node is coincident with and/or connected with an output-hub node (i.e. a node with large output-degree) one another.     

Layered Signaling Regulatory Networks Analysis of Gene Expression Involved in Malignant Tumorigenesis of Non-Resolving Ulcerative Colitis via Integration of Cross-Study Microarray Profiles

Background

Ulcerative colitis (UC) was the most frequently diagnosed inflammatory bowel disease (IBD) and closely linked to colorectal carcinogenesis. By far, the underlying mechanisms associated with the disease are still unclear. With the increasing accumulation of microarray gene expression profiles, it is profitable to gain a systematic perspective based on gene regulatory networks to better elucidate the roles of genes associated with disorders. However, a major challenge for microarray data analysis is the integration of multiple-studies generated by different groups.

Methodology/Principal Findings

In this study, firstly, we modeled a signaling regulatory network associated with colorectal cancer (CRC) initiation via integration of cross-study microarray expression data sets using Empirical Bayes (EB) algorithm. Secondly, a manually curated human cancer signaling map was established via comprehensive retrieval of the publicly available repositories. Finally, the co-differently-expressed genes were manually curated to portray the layered signaling regulatory networks.

Results

Overall, the remodeled signaling regulatory networks were separated into four major layers including extracellular, membrane, cytoplasm and nucleus, which led to the identification of five core biological processes and four signaling pathways associated with colorectal carcinogenesis. As a result, our biological interpretation highlighted the importance of EGF/EGFR signaling pathway, EPO signaling pathway, T cell signal transduction and members of the BCR signaling pathway, which were responsible for the malignant transition of CRC from the benign UC to the aggressive one.

Conclusions

The present study illustrated a standardized normalization approach for cross-study microarray expression data sets. Our model for signaling networks construction was based on the experimentally-supported interaction and microarray co-expression modeling. Pathway-based signaling regulatory networks analysis sketched a directive insight into colorectal carcinogenesis, which was of significant importance to monitor disease progression and improve therapeutic interventions.     

Integrative Genomics Reveals Novel Molecular Pathways and Gene Networks for Coronary Artery Disease

The majority of the heritability of coronary artery disease (CAD) remains unexplained, despite recent successes of genome-wide association studies (GWAS) in identifying novel susceptibility loci. Integrating functional genomic data from a variety of sources with a large-scale meta-analysis of CAD GWAS may facilitate the identification of novel biological processes and genes involved in CAD, as well as clarify the causal relationships of established processes. Towards this end, we integrated 14 GWAS from the CARDIoGRAM Consortium and two additional GWAS from the Ottawa Heart Institute (25,491 cases and 66,819 controls) with 1) genetics of gene expression studies of CAD-relevant tissues in humans, 2) metabolic and signaling pathways from public databases, and 3) data-driven, tissue-specific gene networks from a multitude of human and mouse experiments. We not only detected CAD-associated gene networks of lipid metabolism, coagulation, immunity, and additional networks with no clear functional annotation, but also revealed key driver genes for each CAD network based on the topology of the gene regulatory networks. In particular, we found a gene network involved in antigen processing to be strongly associated with CAD. The key driver genes of this network included glyoxalase I (GLO1) and peptidylprolyl isomerase I (PPIL1), which we verified as regulatory by siRNA experiments in human aortic endothelial cells. Our results suggest genetic influences on a diverse set of both known and novel biological processes that contribute to CAD risk. The key driver genes for these networks highlight potential novel targets for further mechanistic studies and therapeutic interventions.     

The Wisdom of Networks: A General Adaptation and Learning Mechanism of Complex Systems

I hypothesize that re‐occurring prior experience of complex systems mobilizes a fast response, whose attractor is encoded by their strongly connected network core. In contrast, responses to novel stimuli are often slow and require the weakly connected network periphery. Upon repeated stimulus, peripheral network nodes remodel the network core that encodes the attractor of the new response. This “core‐periphery learning” theory reviews and generalizes the heretofore fragmented knowledge on attractor formation by neural networks, periphery‐driven innovation, and a number of recent reports on the adaptation of protein, neuronal, and social networks. The core‐periphery learning theory may increase our understanding of signaling, memory formation, information encoding and decision‐making processes. Moreover, the power of network periphery‐related “wisdom of crowds” inventing creative, novel responses indicates that deliberative democracy is a slow yet efficient learning strategy developed as the success of a billion‐year evolution. Also see the video abstract here: https://youtu.be/IIjP7zWGjVE .     

Artificial mammalian gene regulation networks-novel approaches for gene therapy and bioengineering

Recently developed strategies for targeted molecular interventions in mammalian cells have created novel opportunities in biotechnological and biomedical research with huge economic and therapeutic impact: the design of mammalian cells with desired phenotypes for biopharmaceutical manufacturing, tissue engineering and gene therapy. These advances have been enabled by constructing artificial gene regulation systems with control modalities similar to those evolved in key regulatory networks of mammalian cells. This review highlights recurring cellular regulation strategies and artificial gene regulation technology currently in use for rational reprogramming of cellular key events including metabolism, growth, differentiation and cell death to achieve sophisticated bioprocess and therapeutic goals.     

A Novel Phosphopeptide Microarray Based Interactome Map in Breast Cancer Cells Reveals Phosphoprotein-GRB2 Cell Signaling Networks

The architecture of cellular proteins connected to form signaling pathways in response to internal and external cues is much more complex than a group of simple protein-protein interactions. Post translational modifications on proteins (e.g., phosphorylation of serine, threonine and tyrosine residues on proteins) initiate many downstream signaling events leading to protein-protein interactions and subsequent activation of signaling cascades leading to cell proliferation, cell differentiation and cell death. As evidenced by a rapidly expanding mass spectrometry database demonstrating protein phosphorylation at specific motifs, there is currently a large gap in understanding the functional significance of phosphoproteins with respect to their specific protein connections in the signaling cascades. A comprehensive map that interconnects phospho-motifs in pathways will enable identification of nodal protein interactions that are sensitive signatures indicating a disease phenotype from the physiological hemostasis and provide clues into control of disease. Using a novel phosphopeptide microarray technology, we have mapped endogenous tyrosine-phosphoproteome interaction networks in breast cancer cells mediated by signaling adaptor protein GRB2, which transduces cellular responses downstream of several RTKs through the Ras-ERK signaling cascade. We have identified several previously reported motif specific interactions and novel interactions. The peptide microarray data indicate that various phospho-motifs on a single protein are differentially regulated in various cell types and shows global downregulation of phosphoprotein interactions specifically in cells with metastatic potential. The study has revealed novel phosphoprotein mediated signaling networks, which warrants further detailed analysis of the nodes of protein-protein interaction to uncover their biomarker or therapeutic potential.     

Punicalagin promotes the apoptosis in human cervical cancer (ME-180) cells through mitochondrial pathway and by inhibiting the NF-kB signaling pathway

Increasing attention of plant derived therapeutic agents against cancer, investigating the anti-proliferative efficiency of plant derived chemicals have achieved increasing momentum for the design of anticancer drug. Punicalagin, dietary phytochemical altered the various cell signal transduction pathways associated with cell apoptosis and proliferation. This investigation was intended to examine the efficiency of punicalagin lying on cell viability so as to examine the molecular based punicalagin mechanism stimulated apoptosis via exploring the expression of Bcl-2 family proteins, and caspases also the cell cycle regulatory proteins p53 and NF-κB signaling in human cervical cancer cells. We also analyzed the morphological characteristic changes through mitochondrial membrane depolarization, reactive oxygen species (ROS) generation, TUNEL assay, AO/EtBr analysis in cervical cancer cells. Our findings demonstrated that punicalagin repressed the viability of cervical cancer cells in a dosereliant mode via stimulating mitochondrial mediated apoptosis. Moreover, our this study demonstrated that punicalagin blocked cervical cancer cell proliferation and stimulated cell apoptosis by suppressing NF-kappa B activity. Hence our study suggested that punicalagin exhibits opposing actions on NF-kappa B signaling networks to block cancer cell progression acts as a classical candidate for anticancer drug designing.     

Liver development and cancer formation in zebrafish

Liver is the largest organ in the human body, and it regulates many physiological processes. Many studies on liver development in different model organisms have demonstrated that the mechanism of hepatogenesis is conserved in vertebrates. The identification of the genes and regulatory pathways involved in liver formation provides a basis for the diagnosis of liver diseases and therapeutic interventions. Hepatocellular carcinoma is the third leading cause of mortality worldwide. In the last decade, genetic alterations, which include the gain and loss of DNA, as well as mutations and epigenomic changes, have been identified as important factors in liver cancer. Many genetic pathways are dysregulated during carcinogenesis. Here, we review the gene regulatory networks that underlie liver organogenesis and the dysregulation of these pathways in liver cancer. The genes and pathways involved in hepatogenesis and liver cancer are largely conserved between zebrafish and humans, making this an ideal model organism for the study of this disease. A better understanding of liver development may aid in the development of new diagnostic and therapeutic approaches to liver cancer.     

MicroRNA replacement therapy in cancer

Despite the recent progress in cancer management approaches, the mortality rate of cancer is still growing and there are lots of challenges in the clinics in terms of novel therapeutics. MicroRNAs (miRNA) are regulatory small noncoding RNAs and are already confirmed to have a great role in regulating gene expression level by targeting multiple molecules that affect cell physiology and disease development. Recently, miRNAs have been introduced as promising therapeutic targets for cancer treatment. Regulatory potential of tumor suppressor miRNAs, which enables regulation of entire signaling networks within the cells, makes them an interesting option for developing cancer therapeutics. In this regard, over recent decades, scientists have aimed at developing powerful and safe targeting approaches to restore these suppressive miRNAs in cancerous cells. The present review summarizes the function of miRNAs in tumor development and presents recent findings on how miRNAs have served as therapeutic agents against cancer, with a special focus on tumor suppressor miRNAs (mimics). Moreover, the latest investigations on the therapeutic strategies of miRNA delivery have been presented.     

Loss of Connectivity in Cancer Co-Expression Networks

Differential gene expression profiling studies have lead to the identification of several disease biomarkers. However, the oncogenic alterations in coding regions can modify the gene functions without affecting their own expression profiles. Moreover, post-translational modifications can modify the activity of the coded protein without altering the expression levels of the coding gene, but eliciting variations to the expression levels of the regulated genes. These considerations motivate the study of the rewiring of networks co-expressed genes as a consequence of the aforementioned alterations in order to complement the informative content of differential expression. We analyzed 339 mRNAomes of five distinct cancer types to find single genes that presented co-expression patterns strongly differentiated between normal and tumor phenotypes. Our analysis of differentially connected genes indicates the loss of connectivity as a common topological trait of cancer networks, and unveils novel candidate cancer genes. Moreover, our integrated approach that combines the differential expression together with the differential connectivity improves the classic enrichment pathway analysis providing novel insights on putative cancer gene biosystems not still fully investigated.     

On the long-run sensitivity of probabilistic Boolean networks

Boolean networks and, more generally, probabilistic Boolean networks, as one class of gene regulatory networks, model biological processes with the network dynamics determined by the logic-rule regulatory functions in conjunction with probabilistic parameters involved in network transitions. While there has been significant research on applying different control policies to alter network dynamics as future gene therapeutic intervention, we have seen less work on understanding the sensitivity of network dynamics with respect to perturbations to networks, including regulatory rules and the involved parameters, which is particularly critical for the design of intervention strategies. This paper studies this less investigated issue of network sensitivity in the long run. As the underlying model of probabilistic Boolean networks is a finite Markov chain, we define the network sensitivity based on the steady-state distributions of probabilistic Boolean networks and call it long-run sensitivity. The steady-state distribution reflects the long-run behavior of the network and it can give insight into the dynamics or momentum existing in a system. The change of steady-state distribution caused by possible perturbations is the key measure for intervention. This newly defined long-run sensitivity can provide insight on both network inference and intervention. We show the results for probabilistic Boolean networks generated from random Boolean networks and the results from two real biological networks illustrate preliminary applications of sensitivity in intervention for practical problems.     

miRNA and TMPRSS2-ERG do not mind their own business in prostate cancer cells

Oncogenic fusion proteins belong to an important class that disrupts gene expression networks in a cell. Astonishingly, fusion-positive prostate cancer cells enable the multi-gene regulatory capability of miRNAs to remodel the signal transduction landscape, enhancing or antagonizing the transmission of information to downstream effectors. Accumulating evidence substantiates the fact that miRNAs translate into dose-dependent responsiveness of cells to signaling regulators in transmembrane protease serine 2:ETS-related gene (TMPRSS2-ERG)-positive cells. Wide ranging signaling proteins are the targets for the degree of quantitative fluctuations imposed by miRNAs. miRNA signatures are aberrantly expressed in fusion-positive cancer cells, suggesting that they have a cumulative effect on tumor aggressiveness. It seems attractive to note that TMPRSS2:ERG fusion has a stronger effect as tumors positive for the oncogenic TMPRSS2:ERG have dysregulated oncomirs and tumor suppressor miRNA signature. It is undeniable that a comprehensive analysis of the prostate cancer microRNAome is necessary to uncover novel microRNAs and pathways associated with prostate cancer. Moreover, the identification and validation of miRNA signature in TMPRSS2-ERG-positive prostate cancer cells may help to identify novel molecular targets and pathways for personalized therapy.     

Characterization of stem cells and cancer cells on the basis of gene expression profile stability, plasticity, and robustness: dynamical systems theory of gene expressions under cell-cell interaction explains mutational robustness of differentiated cells and suggests how cancer cells emerge

Here I present and discuss a model that, among other things, appears able to describe the dynamics of cancer cell origin from the perspective of stable and unstable gene expression profiles. In identifying such aberrant gene expression profiles as lying outside the normal stable states attracted through development and normal cell differentiation, the hypothesis explains why cancer cells accumulate mutations, to which they are not robust, and why these mutations create a new stable state far from the normal gene expression profile space. Such cells are in strong contrast with normal cell types that appeared as an attractor state in the gene expression dynamical system under cell-cell interaction and achieved robustness to noise through evolution, which in turn also conferred robustness to mutation. In complex gene regulation networks, other aberrant cellular states lacking such high robustness are expected to remain, which would correspond to cancer cells.     

A scale-free structure prior for graphical models with applications in functional genomics

The problem of reconstructing large-scale, gene regulatory networks from gene expression data has garnered considerable attention in bioinformatics over the past decade with the graphical modeling paradigm having emerged as a popular framework for inference. Analysis in a full Bayesian setting is contingent upon the assignment of a so-called structure prior-a probability distribution on networks, encoding a priori biological knowledge either in the form of supplemental data or high-level topological features. A key topological consideration is that a wide range of cellular networks are approximately scale-free, meaning that the fraction, , of nodes in a network with degree is roughly described by a power-law with exponent between and . The standard practice, however, is to utilize a random structure prior, which favors networks with binomially distributed degree distributions. In this paper, we introduce a scale-free structure prior for graphical models based on the formula for the probability of a network under a simple scale-free network model. Unlike the random structure prior, its scale-free counterpart requires a node labeling as a parameter. In order to use this prior for large-scale network inference, we design a novel Metropolis-Hastings sampler for graphical models that includes a node labeling as a state space variable. In a simulation study, we demonstrate that the scale-free structure prior outperforms the random structure prior at recovering scale-free networks while at the same time retains the ability to recover random networks. We then estimate a gene association network from gene expression data taken from a breast cancer tumor study, showing that scale-free structure prior recovers hubs, including the previously unknown hub SLC39A6, which is a zinc transporter that has been implicated with the spread of breast cancer to the lymph nodes. Our analysis of the breast cancer expression data underscores the value of the scale-free structure prior as an instrument to aid in the identification of candidate hub genes with the potential to direct the hypotheses of molecular biologists, and thus drive future experiments.     

Reverse Engineering Boolean Networks: From Bernoulli Mixture Models to Rule Based Systems

A Boolean network is a graphical model for representing and analyzing the behavior of gene regulatory networks (GRN). In this context, the accurate and efficient reconstruction of a Boolean network is essential for understanding the gene regulation mechanism and the complex relations that exist therein. In this paper we introduce an elegant and efficient algorithm for the reverse engineering of Boolean networks from a time series of multivariate binary data corresponding to gene expression data. We call our method ReBMM, i.e., reverse engineering based on Bernoulli mixture models. The time complexity of most of the existing reverse engineering techniques is quite high and depends upon the indegree of a node in the network. Due to the high complexity of these methods, they can only be applied to sparsely connected networks of small sizes. ReBMM has a time complexity factor, which is independent of the indegree of a node and is quadratic in the number of nodes in the network, a big improvement over other techniques and yet there is little or no compromise in accuracy. We have tested ReBMM on a number of artificial datasets along with simulated data derived from a plant signaling network. We also used this method to reconstruct a network from real experimental observations of microarray data of the yeast cell cycle. Our method provides a natural framework for generating rules from a probabilistic model. It is simple, intuitive and illustrates excellent empirical results.