The Forkhead box transcription factor FOXM1 is required for the maintenance of cell proliferation and protection against oxidative stress in human embryonic stem cells

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ChIP-Array 2: integrating multiple omics data to construct gene regulatory networks

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Real-time experimental implementation of predictive control schemes in a small-scale pasteurization plant

Model predictive control (MPC) is one of the most used optimization-based control strategies for large-scale systems, since this strategy allows to consider a large number of states and multi-objective cost functions in a straightforward way. One of the main issues in the design of multi-objective MPC controllers, which is the tuning of the weights associated to each objective in the cost function, is treated in this work. All the possible combinations of weights within the cost function affect the optimal result in a given Pareto front. Furthermore, when the system has time-varying parameters, e.g., periodic disturbances, the appropriate weight tuning might also vary over time. Moreover, taking into account the computational burden and the selected sampling time in the MPC controller design, the computation time to find a suitable tuning is limited. In this regard, the development of strategies to perform a dynamical tuning in function of the system conditions potentially improves the closed-loop performance. In order to adapt in a dynamical way the weights in the MPC multi-objective cost function, an evolutionary-game approach is proposed. This approach allows to vary the prioritization weights in the proper direction taking as a reference a desired region within the Pareto front. The proper direction for the prioritization is computed by only using the current system values, i.e., the current optimal control action and the measurement of the current states, which establish the system cost function over a certain point in the Pareto front. Finally, some simulations of a multi-objective MPC for a real multi-variable case study show a comparison between the system performance obtained with static and dynamical tuning.Peer ReviewedPostprint (author's final draft

An adaptive control system to deliver Interactive Virtual Environment content to handheld devices

Wireless communication advances have enabled emerging video streaming applications to mobile handheld devices. For example, it is possible to display and interact with complex 3D virtual environments on mobile devices that don't have enough computational and storage capabilities (e.g. smart phones, PDAs) through remote rendering techniques, where a server renders 3D data and streams the corresponding image flow to the client. However, due to fluctuations in bandwidth characteristics and limited mobile device CPU capabilities, it is extremely challenging to design effective systems for streaming interactive multimedia over wireless networks. This paper presents a novel approach based on a controller that can automatically adjust streaming parameters basing on feedback measures from the client device. Experimental results prove the effectiveness of the proposed solution in coping with bandwidth changes, thus providing high Quality of Service (QoS) in remote visualization

ProteoMirExpress: inferring microRNA-centered regulatory networks from high-throughput proteomic and transcriptome data

MicroRNAs (miRNAs) regulate gene expression through translational repression and RNA degradation. Recently developed high-throughput proteomic methods measure gene expression changes at protein levels, and therefore can reveal the direct effects of miRNAs’ translational repression. Here, we present a web server, ProteoMirExpress that integrates proteomic and mRNA expression data together to infer miRNA-centered regulatory networks. With both high throughput data from the users, ProteoMirExpress is able to discover not only miRNA targets that have mRNA decreased, but also subgroups of targets whose proteins are suppressed but mRNAs are not significantly changed or whose mRNAs are decreased but proteins are not significantly changed, which were usually ignored by most current methods. Furthermore, both direct and indirect targets of miRNAs can be detected. Therefore ProteoMirExpress provides more comprehensive miRNA-centered regulatory networks. We use several published data to assess the quality of our inferred networks and prove the value of our server. ProteoMirExpress is available at http://jjwanglab.org/ProteoMirExpress, with free access to academic users.postprin

PTHGRN: unraveling post-translational hierarchical gene regulatory networks using PPI, ChIP-seq and gene expression data

Interactions among transcriptional factors (TFs), cofactors and other proteins or enzymes can affect transcriptional regulatory capabilities of eukaryotic organisms. Post-translational modifications (PTMs) cooperate with TFs and epigenetic alterations to constitute a hierarchical complexity in transcriptional gene regulation. While clearly implicated in biological processes, our understanding of these complex regulatory mechanisms is still limited and incomplete. Various online software have been proposed for uncovering transcriptional and epigenetic regulatory networks, however, there is a lack of effective web-based software capable of constructing underlying interactive organizations between post-translational and transcriptional regulatory components. Here, we present an open web server, post-translational hierarchical gene regulatory network (PTHGRN) to unravel relationships among PTMs, TFs, epigenetic modifications and gene expression. PTHGRN utilizes a graphical Gaussian model with partial least squares regression-based methodology, and is able to integrate protein-protein interactions, ChIP-seq and gene expression data and to capture essential regulation features behind high-throughput data. The server provides an integrative platform for users to analyze ready-to-use public high-throughput Omics resources or upload their own data for systems biology study. Users can choose various parameters in the method, build network topologies of interests and dissect their associations with biological functions. Application of the software to stem cell and breast cancer demonstrates that it is an effective tool for understanding regulatory mechanisms in biological complex systems. PTHGRN web server is publically available at web site http://www.byanbioinfo.org/pthgrn.published_or_final_versio

An integrative method to decode regulatory logics in gene transcription

Modeling of transcriptional regulatory networks (TRNs) has been increasingly used to dissect the nature of gene regulation. Inference of regulatory relationships among transcription factors (TFs) and genes, especially among multiple TFs, is still challenging. In this study, we introduced an integrative method, LogicTRN, to decode TF-TF interactions that form TF logics in regulating target genes. By combining cis-regulatory logics and transcriptional kinetics into one single model framework, LogicTRN can naturally integrate dynamic gene expression data and TF-DNA binding signals in order to identify the TF logics and to reconstruct the underlying TRNs. We evaluated the newly developed methodology using simulation, comparison and application studies, and the results not only show their consistence with existing knowledge, but also demonstrate its ability to accurately reconstruct TRNs in biological complex systems.published_or_final_versio

ERCC1 expression and RAD51B activity correlate with cell cycle response to platinum drug treatment not DNA repair

Background: The H69CIS200 and H69OX400 cell lines are novel models of low-level platinum-drug resistance. Resistance was not associated with increased cellular glutathione or decreased accumulation of platinum, rather the resistant cell lines have a cell cycle alteration allowing them to rapidly proliferate post drug treatment. Results: A decrease in ERCC1 protein expression and an increase in RAD51B foci activity was observed in association with the platinum induced cell cycle arrest but these changes did not correlate with resistance or altered DNA repair capacity. The H69 cells and resistant cell lines have a p53 mutation and consequently decrease expression of p21 in response to platinum drug treatment, promoting progression of the cell cycle instead of increasing p21 to maintain the arrest. Conclusion: Decreased ERCC1 protein and increased RAD51B foci may in part be mediating the maintenance of the cell cycle arrest in the sensitive cells. Resistance in the H69CIS200 and H69OX400 cells may therefore involve the regulation of ERCC1 and RAD51B independent of their roles in DNA repair. The novel mechanism of platinum resistance in the H69CIS200 and H69OX400 cells demonstrates the multifactorial nature of platinum resistance which can occur independently of alterations in DNA repair capacity and changes in ERCC1

DLX1 acts as a crucial target of FOXM1 to promote ovarian cancer aggressiveness by enhancing TGF-β/SMAD4 signaling

Recent evidence from a comprehensive genome analysis and functional studies have revealed that FOXM1 is a crucial metastatic regulator that drives cancer progression. However, the regulatory mechanism by which FOXM1 exerts its metastatic functions in cancer cells remains obscure. Here, we report that DLX1 acts as a FOXM1 downstream target, exerting pro-metastatic function in ovarian cancers. Both FOXM1 isoforms (FOXM1B or FOXM1C) could transcriptionally upregulate DLX1 through two conserved binding sites, located at +61 to +69bp downstream (TFBS1) and -675 to -667bp upstream (TFBS2) of the DLX1 promoter, respectively. This regulation was further accentuated by the significant correlation between the nuclear expression of FOXM1 and DLX1 in high-grade serous ovarian cancers. Functionally, the ectopic expression of DLX1 promoted ovarian cancer cell growth, cell migration/invasion and intraperitoneal dissemination of ovarian cancer in mice, whereas small interfering RNA-mediated DLX1 knockdown in FOXM1-overexpressing ovarian cancer cells abrogated these oncogenic capacities. In contrast, depletion of FOXM1 by shRNAi only partially attenuated tumor growth and exerted almost no effect on cell migration/invasion and the intraperitoneal dissemination of DLX1-overexpressing ovarian cancer cells. Furthermore, the mechanistic studies showed that DLX1 positively modulates TGF- signaling by upregulating PAI-1 and JUNB through direct interaction with SMAD4 in the nucleus upon TGF-1 induction. Taken together, these data strongly suggest that DLX1 plays a pivotal role in FOXM1 signaling to promote cancer aggressiveness through intensifying TGF-/SMAD4 signaling in high-grade serous ovarian cancer cells.published_or_final_versio