Mosaic gene network modelling identified new regulatory mechanisms in HCV infection

Abstract

Modelling of gene networks is widely used in systems biology to study the functioning of complex biological systems. Most of the existing mathematical modelling techniques are useful for analysis of well-studied biological processes, for which information on rates of reactions is available. However, complex biological processes such as those determining the phenotypic traits of organisms or pathological disease processes, including pathogen-host interactions, involve complicated cross-talk between interacting networks. Furthermore, the intrinsic details of the interactions between these networks are often missing. In this study, we developed an approach, which we call mosaic network modelling, that allows the combination of independent mathematical models of gene regulatory networks and, thereby, description of complex biological systems. The advantage of this approach is that it allows us to generate the integrated model despite the fact that information on molecular interactions between parts of the model (so-called mosaic fragments) might be missing. To generate a mosaic mathematical model, we used control theory and mathematical models, written in the form of a system of ordinary differential equations (ODEs). In the present study, we investigated the efficiency of this method in modelling the dynamics of more than 10,000 simulated mosaic regulatory networks consisting of two pieces. Analysis revealed that this approach was highly efficient, as the mean deviation of the dynamics of mosaic network elements from the behaviour of the initial parts of the model was less than 10%. It turned out that for construction of the control functional, data on perturbation of one or two vertices of the mosaic piece are sufficient. Further, we used the developed method to construct a mosaic gene regulatory network including hepatitis C virus (HCV) as the first piece and the tumour necrosis factor (TNF)-induced apoptosis and NF-κB induction pathways as the second piece. Thus, the mosaic model integrates the model of HCV subgenomic replicon replication with the model of TNF-induced apoptosis and NF-κB induction. Analysis of the mosaic model revealed that the regulation of TNF-induced signaling by the HCV network is crucially dependent on the RIP1, TRADD, TRAF2, FADD, IKK, IκBα, c-FLIP, and BAR genes. Overall, the developed mosaic gene network modelling approach demonstrated good predictive power and allowed the prediction of new regulatory nodes in HCV action on apoptosis and the NF-κB pathway. Those theoretical predictions could be a basis for further experimental verification.

Novel candidate genes important for asthma and hypertension comorbidity revealed from associative gene networks

“…There are a number of resources in the world that allow reconstruction of such associative gene networks, for example, MetaCore [27], Ingenuity [28] and ANDSystem [29,30]. In particular, using the developed by us ANDSystem tool, the following studies were performed: analysis of proteomic data on Helicobacter pylori infection [31]; analysis of the urine proteomic profile in control and under the influence of space flight factors [32]; analysis of tissue-specific gene knockout effect and the search for potential drug targets [33]; analysis of hepatitis C virus life cycle gene networks [34]; analysis of comorbid relations of bronchial asthma and tuberculosis [35], preeclampsia, diabetes and obesity [36], glaucoma [37]; search for novel candidate genes of susceptibility to tuberculosis [38].…”

ANDDigest: a new web-based module of ANDSystem for the search of knowledge in the scientific literature

“…In particular, using the ANDSystem tool, a number of studies were performed, including an analysis of data from high-performance proteomic experiments on the study of Helicobacter pylori and their relationship with the development of gastritis and tumours of the stomach [53]; an analysis of the proteomic urine profile of a healthy person under normal conditions and under the influence of space flight factors [54,55]; analysis of the tissue-specific effects of gene knockout and the search for potential targets for drugs [56]; analysis of the molecular mechanisms of comorbidity of disease [57][58][59]; the search for novel tuberculosis susceptibility candidate genes [60]; analysis of the interactions of hepatitis C virus proteins with human proteins [61]; reconstruction and analysis of mosaic gene networks [62], etc. Moreover, with the prioritization methods of the ANDSystem tool, the key genes involved in the formation of a comorbid state of asthma and hypertension were identified [59], followed by experimental confirmation of their role in pathology [63].…”

FunGeneNet: a web tool to estimate enrichment of functional interactions in experimental gene sets

“…Interactions were subdivided into physical interactions, catalytic reactions, chemical transformations, associations, regulation of expression, activity, transport/release, stability/degradation, etc. The ANDSystem was used to solve a wide range of tasks related to the reconstruction of gene networks – in particular, for the interpretation of data of proteomic experiments [ 20 – 22 ], the analysis of the tissue-specific effect of gene knockout [ 23 ], the analysis of the hepatitis C virus interaction [ 24 , 25 ], the identification of genes susceptibility to tuberculosis [ 26 ] and analysis of molecular mechanisms of comorbidity of diseases [ 27 , 28 ].…”