Model-based investigation of the transcriptional activity of p53 and its feedback loop regulation via 14-3-3σ

The role of cooperativity in a p53-miR34 dynamical mathematical model

The objective of this study is to evaluate the role of cooperativity, captured by the Hill coefficient, in a minimal mathematical model describing the interactions between p53 and miR-34a. The model equations are analyzed for negative, none and normal cooperativity using a specific version of bifurcation theory and they are solved numerically. Special attention is paid to the sign of so-called first Lyapunov value. Interpretations of the results are given, both according to dynamic theory and in biological terms. In terms of cell signaling, we propose the hypothesis that when the outgoing signal of a system spends a physiologically significant amount of time outside of its equilibrium state, then the value of that signal can be sampled at any point along the trajectory towards that equilibrium and indeed, at multiple points. Coupled with non-linear behavior, such as that caused by cooperativity, this feature can account for a complex and varied response, which p53 is known for. From dynamical point of view, we found that when cooperativity is negative, the system has only one stable equilibrium point. In the absence of cooperativity, there is a single unstable equilibrium point with a critical boundary of stability. In the case with normal cooperativity, the system can have one, two, or three steady states with both, bi-stability and bi-instability occurring.

System-based strategies for p53 recovery

The authors have proposed a systems theory-based novel drug design approach for the p53 pathway. The pathway is taken as a dynamic system represented by ordinary differential equations-based mathematical model. Using control engineering practices, the system analysis and subsequent controller design is performed for the re-activation of wild-type p53. p53 revival is discussed for both modes of operation, i.e. the sustained and oscillatory. To define the problem in control system paradigm, modification in the existing mathematical model is performed to incorporate the effect of Nutlin. Attractor point analysis is carried out to select the suitable domain of attraction. A two-loop negative feedback control strategy is devised to drag the system trajectories to the attractor point and to regulate cellular concentration of Nutlin, respectively. An integrated framework is constituted to incorporate the pharmacokinetic effects of Nutlin in the cancerous cells. Bifurcation analysis is also performed on the p53 model to see the conditions for p53 oscillation.

Identification of miRNA-mRNA Modules in Colorectal Cancer Using Rough Hypercuboid Based Supervised Clustering

Differences in the expression profiles of miRNAs and mRNAs have been reported in colorectal cancer. Nevertheless, information on important miRNA-mRNA regulatory modules in colorectal cancer is still lacking. In this regard, this study presents an application of the RH-SAC algorithm on miRNA and mRNA expression data for identification of potential miRNA-mRNA modules. First, a set of miRNA rules was generated using the RH-SAC algorithm. The mRNA targets of the selected miRNAs were identified using the miRTarBase database. Next, the expression values of target mRNAs were used to generate mRNA rules using the RH-SAC. Then all miRNA-mRNA rules have been integrated for generating networks. The RH-SAC algorithm unlike other existing methods selects a group of co-expressed miRNAs and mRNAs that are also differentially expressed. In total 17 miRNAs and 141 mRNAs were selected. The enrichment analysis of selected mRNAs revealed that our method selected mRNAs that are significantly associated with colorectal cancer. We identified novel miRNA/mRNA interactions in colorectal cancer. Through experiment, we could confirm that one of our discovered miRNAs, hsa-miR-93-5p, was significantly up-regulated in 75.8% CRC in comparison to their corresponding non-tumor samples. It could have the potential to examine colorectal cancer subtype specific unique miRNA/mRNA interactions.

On p53 revival using system oriented drug dosage design

We propose a new paradigm in the drug design for the revival of the p53 pathway in cancer cells. It is shown that the current strategy of using small molecule based Mdm2 inhibitors is not enough to adequately revive p53 in cancerous cells, especially when it comes to the extracting pulsating behavior of p53. This fact has come to notice when a novel method for the drug dosage design is introduced using system oriented concepts. As a test case, small molecule drug Mdm2 repressor Nutlin 3a is considered. The proposed method determines the dose of Nutlin to revive p53 pathway functionality. For this purpose, PBK dynamics of Nutlin have also been integrated with p53 pathway model. The p53 pathway is the focus of researchers for the last thirty years for its pivotal role as a frontline cancer suppressant protein due to its effect on cell cycle checkpoints and cell apoptosis in response to a DNA strand break. That is the reason for finding p53 being absent in more than 50% of tumor cancers. Various drugs have been proposed to revive p53 in cancer cells. Small molecule based drugs are at the foremost and are the subject of advanced clinical trials. The dosage design of these drugs is an important issue. We use control systems concepts to develop the drug dosage so that the cancer cells can be treated in appropriate time. We investigate by using a computational model how p53 protein responds to drug Nutlin 3a, an agent that interferes with the MDM2-mediated p53 regulation. The proposed integrated model describes in some detail the regulation network of p53 including the negative feedback loop mediated by MDM2 and the positive feedback loop mediated by Mdm2 mRNA as well as the reversible represses of MDM2 caused by Nutlin. The reported PBK dynamics of Nutlin 3a are also incorporated to see the full effect. It has been reported that p53 response to stresses in two ways. Either it has a sustained (constant) p53 response, or there are oscillations in p53 concentration. The claimed dosage strategy achieves the p53 response in the first case. However, for the induction of oscillations, it is shown through bifurcation analysis that to achieve oscillating behavior of p53 inhibition of Mdm2 is not enough, rather antirepression of the p53-Mdm2 complex is also needed which leads to the need of a new drug design paradigm.

Hybrid modeling of the crosstalk between signaling and transcriptional networks using ordinary differential equations and multi-valued logic

A decade of successful results indicates that systems biology is the appropriate approach to investigate the regulation of complex biochemical networks involving transcriptional and post-transcriptional regulations. It becomes mandatory when dealing with highly interconnected biochemical networks, composed of hundreds of compounds, or when networks are enriched in non-linear motifs like feedback and feedforward loops. An emerging dilemma is to conciliate models of massive networks and the adequate description of non-linear dynamics in a suitable modeling framework. Boolean networks are an ideal representation of massive networks that are humble in terms of computational complexity and data demand. However, they are inappropriate when dealing with nested feedback/feedforward loops, structural motifs common in biochemical networks. On the other hand, models of ordinary differential equations (ODEs) cope well with these loops, but they require enormous amounts of quantitative data for a full characterization of the model. Here we propose hybrid models, composed of ODE and logical sub-modules, as a strategy to handle large scale, non-linear biochemical networks that include transcriptional and post-transcriptional regulations. We illustrate the construction of this kind of models using as example a regulatory network centered on E2F1, a transcription factor involved in cancer. The hybrid modeling approach proposed is a good compromise between quantitative/qualitative accuracy and scalability when considering large biochemical networks with a small highly interconnected core, and module of transcriptionally regulated genes that are not part of critical regulatory loops. This article is part of a Special Issue entitled: Computational Proteomics, Systems Biology & Clinical Implications. Guest Editor: Yudong Cai.

Cancer omics: from regulatory networks to clinical outcomes

Current limitation in cancer genomic studies is a lack of the integration of various omics data generated through next generation sequencing technologies, as well as a lack of the sounding and comprehensive epigenomic and genomic information about a particular cancer cell type. In this review, we will discuss main aspects of current genomics research with its application in cancer topics. We will first overview the next-generation sequencing technologies, then outline the major computational approaches, particularly focusing on ChIP-based omics data, and list several remaining open questions facing computational biologists, further present regulatory network analysis inferred from the ChIP-based omics data; finally implicate the clinical outcomes from the network and pathway analysis.

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.