A model-based strategy to investigate the role of microRNA regulation in cancer signalling networks

Computer simulation models as a tool to investigate the role of microRNAs in osteoarthritis

The aim of this study was to show how computational models can be used to increase our understanding of the role of microRNAs in osteoarthritis (OA) using miR-140 as an example. Bioinformatics analysis and experimental results from the literature were used to create and calibrate models of gene regulatory networks in OA involving miR-140 along with key regulators such as NF-κB, SMAD3, and RUNX2. The individual models were created with the modelling standard, Systems Biology Markup Language, and integrated to examine the overall effect of miR-140 on cartilage homeostasis. Down-regulation of miR-140 may have either detrimental or protective effects for cartilage, indicating that the role of miR-140 is complex. Studies of individual networks in isolation may therefore lead to different conclusions. This indicated the need to combine the five chosen individual networks involving miR-140 into an integrated model. This model suggests that the overall effect of miR-140 is to change the response to an IL-1 stimulus from a prolonged increase in matrix degrading enzymes to a pulse-like response so that cartilage degradation is temporary. Our current model can easily be modified and extended as more experimental data become available about the role of miR-140 in OA. In addition, networks of other microRNAs that are important in OA could be incorporated. A fully integrated model could not only aid our understanding of the mechanisms of microRNAs in ageing cartilage but could also provide a useful tool to investigate the effect of potential interventions to prevent cartilage loss.

RNA Systems Biology for Cancer: From Diagnosis to Therapy

It is due to the advances in high-throughput omics data generation that RNA species have re-entered the focus of biomedical research. International collaborate efforts, like the ENCODE and GENCODE projects, have spawned thousands of previously unknown functional non-coding RNAs (ncRNAs) with various but primarily regulatory roles. Many of these are linked to the emergence and progression of human diseases. In particular, interdisciplinary studies integrating bioinformatics, systems biology, and biotechnological approaches have successfully characterized the role of ncRNAs in different human cancers. These efforts led to the identification of a new tool-kit for cancer diagnosis, monitoring, and treatment, which is now starting to enter and impact on clinical practice. This chapter is to elaborate on the state of the art in RNA systems biology, including a review and perspective on clinical applications toward an integrative RNA systems medicine approach. The focus is on the role of ncRNAs in cancer.

A systems' biology approach to study microRNA-mediated gene regulatory networks

MicroRNAs (miRNAs) are potent effectors in gene regulatory networks where aberrant miRNA expression can contribute to human diseases such as cancer. For a better understanding of the regulatory role of miRNAs in coordinating gene expression, we here present a systems biology approach combining data-driven modeling and model-driven experiments. Such an approach is characterized by an iterative process, including biological data acquisition and integration, network construction, mathematical modeling and experimental validation. To demonstrate the application of this approach, we adopt it to investigate mechanisms of collective repression on p21 by multiple miRNAs. We first construct a p21 regulatory network based on data from the literature and further expand it using algorithms that predict molecular interactions. Based on the network structure, a detailed mechanistic model is established and its parameter values are determined using data. Finally, the calibrated model is used to study the effect of different miRNA expression profiles and cooperative target regulation on p21 expression levels in different biological contexts.

Computational analysis of target hub gene repression regulated by multiple and cooperative miRNAs

MicroRNA (miRNA) target hubs are genes that can be simultaneously targeted by a comparatively large number of miRNAs, a class of non-coding RNAs that mediate post-transcriptional gene repression. Although the details of target hub regulation remain poorly understood, recent experiments suggest that pairs of miRNAs can cooperate if their binding sites reside in close proximity. To test this and other hypotheses, we established a novel approach to investigate mechanisms of collective miRNA repression. The approach presented here combines miRNA target prediction and transcription factor prediction with data from the literature and databases to generate a regulatory map for a chosen target hub. We then show how a kinetic model can be derived from the regulatory map. To validate our approach, we present a case study for p21, one of the first experimentally proved miRNA target hubs. Our analysis indicates that distinctive expression patterns for miRNAs, some of which interact cooperatively, fine-tune the features of transient and long-term regulation of target genes. With respect to p21, our model successfully predicts its protein levels for nine different cellular functions. In addition, we find that high abundance of miRNAs, in combination with cooperativity, can enhance noise buffering for the transcription of target hubs.

Network-based identification of novel connections among apoptotic signaling pathways in cancer

MicroRNAs (miRNAs), highly conserved, non-coding endogenous RNA and nearly ~22 nucleotides (nt) in length, are well-known to regulate several apoptotic pathways in cancer. In this study, we computationally constructed the initial human apoptotic PPI network by several online databases, and further integrated these high-throughput datasets into a Naïve Bayesian model to predict protein functional connections. Based on the modified apoptotic network, we identified several apoptotic hub proteins such as TP53, SRC, M3K3/5/8, cyclin-dependent kinase2/6, TNFR16/19, and TGF-β receptor 1/2. Subsequently, we identified some microRNAs that could target the aforementioned apoptotic hub proteins by using TargetScan, PicTar, and Diana-MicroH. In conclusion, these results demonstrate the PPI network-based identification of new connections amongst apoptotic pathways in cancer, which may shed new light on the intricate relationships between core apoptotic pathways and some targeted miRNAs in human cancers.

Modeling miRNA regulation in cancer signaling systems: miR-34a regulation of the p53/Sirt1 signaling module

MicroRNAs (miRNAs) are a family of small regulatory RNAs whose function is to regulate the activity and stability of specific messenger RNA targets through posttranscriptional regulatory mechanisms. Most of the times signaling systems involving miRNA modulation are not linear pathways in which a certain transcription factor activate the expression of miRNAs that posttranscriptionally represses targeting proteins, but complex regulatory structures involving a variety of feedback-loop architectures.In this book chapter, we define, discuss, and apply a Systems Biology approach to investigate dynamical features of miRNA regulation, based on the integration of experimental evidences, hypotheses, and quantitative data through mathematical modeling. We further illustrate the approach using as case study the signaling module composed by the proteins p53, Sirt1, and the regulatory miRNA miR-34a. The model was used not only to investigate different possible designs of the silencing mechanism exerted by miR-34a on Sirt1 but also to simulate the dynamics of the system under conditions of (pathological) deregulation of its compounds.