Computational modeling of signal transduction networks without kinetic parameters: Petri net approaches

More and more computational techniques have been applied to model biological systems, especially signaling pathways in medical systems. Due to the large number of experimental data driven by high-throughput technologies, new computational concepts have been developed. Nevertheless, often the necessary kinetic data cannot be determined in sufficient number and quality because of experimental complexity or ethical reasons. At the same time, the number of qualitative data drastically increased, for example, gene expression data, protein-protein interaction data, and imaging data. Especially for large-scale models, the application of kinetic modeling techniques can fail. On the other hand, many large-scale models have been constructed applying qualitative and semiquantitative techniques, for example, logical models or Petri net models. These techniques make it possible to explore system's dynamics without knowing kinetic parameters. Here, we summarize the work of the last 10 years for modeling signal transduction pathways in medical applications applying Petri net formalism. We focus on analysis techniques based on system's invariants without any kinetic parameters and show predictions of all signaling pathways of the system. We start with an intuitive introduction into Petri nets and system's invariants. We illustrate the main concepts using the tumor necrosis factor receptor 1 (TNFR1)-induced nuclear factor κ-light-chain-enhancer of activated B cells (NF-κB) pathway as a case study. Summarizing recent models, we discuss the advantages and challenges of Petri net applications to medical signaling systems. In addition, we provide exemplarily interesting Petri net applications to model signaling in medical systems of the last years that use the well-known stochastic and kinetic concepts developed about 50 years ago.

Evaluation of the cytotoxic anticancer effect of polysaccharide of Nepeta septemcrenata

Background

Promoting cancer cells apoptosis is one of the effective methods to treat cancer. Human hepatocellular carcinoma (HepG2) and colorectal cancer (HCT-116) cell lines were used in the present study to evaluate the cytotoxic and anticancer properties of Nepeta septemcrenata Polysaccharide (NSP).

Result

Treatment of the two examined cells with NSP displayed a significant cytotoxicity towards HepG2 in a dose-dependent manner; meanwhile, its effect on HCT-116 was obtained under the influence of low doses. The quantitative real- time PCR (QRT-PCR) investigation revealed that NSP significantly up-regulated the expression levels of p53, p16, Fas, Fas-L, Bax, caspases-3, caspase-9, and TNF-α in association with down-regulation of cyclin D1, TERT, and BCL2. These findings declare the apoptotic characteristic of NSP.NSP, can also inhibit the development of cancer cells through the down-regulation of TGF-β and VEGF.

Conclusions

Our results suggested that the polysaccharides isolated from N. septemcrenata possess anticancer properties that could be explored for the development of novel anticancer agents.

Comparison of modelling approaches demonstrated for p16-mediated signalling pathway in higher eukaryotes

Quantitative modelling of biological systems using Petri net technologies has experienced renaissance in the past couple of decades. The overwhelming majority of these models is deterministic though underlying biological systems are usually at the mesoscopic level and small, rather than large, and employ sparse molecular structure. Sparse biological systems are accompanied by randomness due to low molecular density, intrinsic random nature of phenomena and noise in an experiment. On the other hand, biochemical reactions are inherently uncertain due to imprecision and vagueness of kinetic parameters. Stochastic methods are used to cope with randomness while fuzzy methods are developed to deal with uncertainty of biological systems, but there is lack of common voice among researchers regarding the best choice of modelling approach for a particular biological system. The main issues addressed in this paper are the choice between deterministic, stochastic and fuzzy parameters and aspects; that is, which modelling approach to follow to reach the realistic approximation of an underlying biological system, and how to measure parallels and discrepancies between different quantitative paradigms. To this end, we use Petri nets with hybrid, stochastic and fuzzy parameters to create quantitative model of p16-mediated signalling pathway in higher eukaryotes, perform deterministic, pure stochastic and fuzzy stochastic simulations to predict the behaviour of major molecular regulators of p16-mediated pathway. In the meanwhile, we show how uncertain kinetic parameters can be precisely approximated in terms of α cuts. Then we perform statistical analysis of simulation results to measure similarity between the three modelling approaches. The statistical analysis reveals significant deviations between deterministic, pure stochastic and fuzzy stochastic approaches for most of the biological components. Due to rather small size of underlying biological system, it turns out that fuzzy stochastic approach is the most appropriate for modelling of p16-mediated signalling pathway because it successfully deals with both randomness and uncertainty and produces quantitative results with biological relevance.

Hepatoprotective effects of hydroxysafflor yellow A in D-galactose-treated aging mice

Hydroxysafflor yellow A (HSYA) is an effective chemical component isolated from Chinese herb Carthamus tinctorius L. In present study, we aimed to evaluate the effects of HSYA on D-galactose- (D-gal-) induced aging in mice, and to elucidate the underlying mechanism. Male C57BL/6 mice were intraperitoneal injection of D-gal and HSYA for 8 weeks. The body weight gain, spleen and thymus coefficients were determined. Levels of super dismutase (SOD), catalase (CAT), glutathione peroxidase (GSH-Px) and malondialdehyde (MDA) in serum and liver were measured using commercial kits. Pathological changes and the SA-β-Gal activity in liver tissues were detected by hematoxylin and eosin and SA-β-Gal staining. The expression levels of p16, CDK4, CDK6 and phosphorylation levels of Retinoblastoma (Rb) were detected by immunohistochemistry and western blot analysis. mRNA levels of genes regulated by p16-Rb pathway were determined by quantitative real-time PCR. In vivo, HSYA improved the aging changes including body weight, organ index and antioxidant status such as activities of SOD, CAT, GSH-Px and MDA in D-gal treated aging mice. HSYA also dramatically attenuated pathologic changes of aging liver tissues induced by D-gal. Furthermore, HSYA significantly decreased the mRNA and protein level of cyclin-dependent kinase inhibitor p16, followed by increasing CDK4/6 protein expression and decreasing the phosphorylation of Retinoblastoma (pRb) which up-regulated the expression of downstream genes CCNE1, CCNA2, P107 and MCM4. Collectively, these data indicated that HSYA could ameliorate aging, especially hepatic replicative senescence resulting from D-gal, the mechanism could be associated with the suppression of p16-Rb pathway.

Modelling and Simulation of Biochemical Processes Using Petri Nets

Systems composed of many components which interact with each other and lead to unpredictable global behaviour, are considered as complex systems. In a biological context, complex systems represent living systems composed of a large number of interacting elements. In order to study these systems, a precise mathematical modelling was typically used in this context. However, this modelling has limitations in the structural understanding and the behavioural study. In this sense, formal computational modelling is an approach that allows to model and to simulate dynamical properties of these particular systems. In this paper, we use Hybrid Functional Petri Net (HFPN), a Petri net extension dedicated to study and verify biopathways, to model and study the Methionine metabolic pathway. Methionine and its derivatives play significant roles in human bodies. We propose a set of simulations for the purpose of studying and analysing the Methionine pathway’s behaviour. Our simulation results have shown that several important abnormalities in this pathway are related to sever diseases such as Alzheimer’s disease, cardiovascular disease, cancers and others.

Raltitrexed Inhibits HepG2 Cell Proliferation via G0/G1 Cell Cycle Arrest

Raltitrexed (RTX) is an antimetabolite drug used as a chemotherapeutic agent for treating colorectal cancer, malignant mesothelioma, and gastric cancer. The antitumor capacity of RTX is attributed to its inhibitory activity on thymidylate synthase (TS), a key enzyme in the synthesis of DNA precursors. The current study is aimed at investigating the potential antitumor effects of RTX in liver cancer. Using the HepG2 cell line as an in vitro model of liver cancer, we evaluated the effects of RTX on cell proliferation employing both a WST-8 assay and a clone formation efficiency assay. In addition, we monitored the ultrastructure changes of HepG2 cells in response to RTX with transmission electric microscopy. To investigate the mechanism underlying the regulation of cell proliferation by RTX, we analyzed cell cycle using cell flow cytometry. Moreover, real-time PCR and Western blot analyses were conducted to examine expression levels of cell cycle regulatory proteins cyclin A and cyclin-dependent kinase 2 (CDK2), as well as their mediators tumor suppressor genes p53 and p16. Our results demonstrate that RTX inhibits HepG2 proliferation by arresting the cell cycle at G₀/G₁. This cell cycle arrest function was mediated via downregulation of cyclin A and CDK2. The observed elevated expression of p53 and p16 by RTX may contribute to the reduction of cyclin A/CDK2. Our study indicates that RTX could serve as a potential chemotherapeutic agent in the treatment of hepatocellular carcinoma.

Target-based drug discovery for [Formula: see text]-globin disorders: drug target prediction using quantitative modeling with hybrid functional Petri nets

Recent molecular studies provide important clues into treatment of [Formula: see text]-thalassemia, sickle-cell anaemia and other [Formula: see text]-globin disorders revealing that increased production of fetal hemoglobin, that is normally suppressed in adulthood, can ameliorate the severity of these diseases. In this paper, we present a novel approach for drug prediction for [Formula: see text]-globin disorders. Our approach is centered upon quantitative modeling of interactions in human fetal-to-adult hemoglobin switch network using hybrid functional Petri nets. In accordance with the reverse pharmacology approach, we pose a hypothesis regarding modulation of specific protein targets that induce [Formula: see text]-globin and consequently fetal hemoglobin. Comparison of simulation results for the proposed strategy with the ones obtained for already existing drugs shows that our strategy is the optimal as it leads to highest level of [Formula: see text]-globin induction and thereby has potential beneficial therapeutic effects on [Formula: see text]-globin disorders. Simulation results enable verification of model coherence demonstrating that it is consistent with qPCR data available for known strategies and/or drugs.