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The mammalian target of rapamycin (mTOR) is a central regulator of cell growth and proliferation. mTOR signaling is frequently dysregulated in oncogenic cells, and thus an attractive target for anticancer therapy. Using CellDesigner, a modeling support software for graphical notation, we present herein a comprehensive map of the mTOR signaling network, which includes 964 species connected by 777 reactions. The map complies with both the systems biology markup language (SBML) and graphical notation (SBGN) for computational analysis and graphical representation, respectively. As captured in the mTOR map, we review and discuss our current understanding of the mTOR signaling network and highlight the impact of mTOR feedback and crosstalk regulations on drug‐based cancer therapy. This map is available on the Payao platform, a Web 2.0 based community‐wide interactive process for creating more accurate and information‐rich databases. Thus, this comprehensive map of the mTOR network will serve as a tool to facilitate systems‐level study of up‐to‐date mTOR network components and signaling events toward the discovery of novel regulatory processes and therapeutic strategies for cancer.

IntroductionCeliac disease is an autoimmune disease that occurs in people with genetic predisposition, where gluten ingestion causes damage to the small intestines.When people with celiac disease eat gluten (a protein found in wheat, ryeand barley), their body has an immune response that attacks the small intestine resulting in damage to the villi in small intestine. When the villi gets damaged, nutrients cannot be properly absorbed into the body. So it is very important to know what happens in the cells after introducing gluten into organism. Lately, it has been recognised that systems biology tools have potential to increase understanding of how nutrition influences metabolic pathways and homeostasis. In this work the effect of the diet of paediatric patients on the celiac disease immune response was analysed using nutritional system biology approach.Materials and MethodsCeliac disease immune response mathematical model was constructed and analysed using CellDesigner 4.0 (Systems Biology Institute (SBI), Tokyo, Japan). Analysed mathematical model in the form of ordinary differential equations describes processes that take place in two intestinal compartments (lumen and lamina propria) and incorporates 16 variables and 34 processes, which correspond to 34 reaction rates. The effect of the different concentrations of gluten daily intake between paediatric patients on the antibodies level changes was analysed. Western diet consists of 10–20 grams of gluten per day and as ‘safe’ is considered anything under 10 mg per day what is an equivalent to 1/350 of a piece of bread. Atypicalgluten-free dietwillconsistanywherebetween 6 milligramsand 10 mg ofglutenperday but ‘gluten-free’ diet is rarely 100% without gluten i.e. proteins of plant origin from oats, rye, barley and wheat.ResultsMathematical simulations of the celiac disease immune response showed the differences in antibodies levels changes depending on the amount of gluten consumed by paediatric patients. The profile of the antibodies levels decrease after of changing diet form gluten containing to gluten free was also presented.ConclusionsApplication of the nutritional systems biology approach in diet planning ensures detail insight in metabolic process and simple control of the metabolic reaction influenced by nutrient intake.

Lipid metabolism is a complex process and it is extremely helpful to simulate its performance with different models that explain all the biological processes that comprise it, which then enables its better understanding as well as understanding the kinetics of the process itself. Typically, kinetic parameters are obtained from a number of sources under specific experimental conditions, and they are a source of uncertainty. Sensitivity analysis is a useful technique for controlling the uncertainty of model parameters. It evaluates a model’s dependence on its input variables. In this work, hepatic lipid metabolism was mathematically simulated and analyzed. Simulations of the model were performed using different initial plasma glucose (GB) and plasma triacylglyceride (TAG) concentrations according to proposed menus for different meals (breakfast, lunch, snack and dinner). A non-stationary Fourier amplitude sensitivity test (FAST) was applied to analyze the effect of 78 kinetic parameters on 24 metabolite concentrations and 45 reaction rates of the biological part of the hepatic lipid metabolism model at five time points (tf = 10, 50, 100, 250 and 500 min). This study examined the total influence of input parameter uncertainty on the variance of metabolic model predictions. The majority of the propagated uncertainty is due to the interactions of numerous factors rather than being linear from one parameter to one result. Obtained results showed differences in the model control regarding the different initial concentrations and also the changes in the model control over time. The aforementioned knowledge enables dietitians and physicians, working with patients who need to regulate fat metabolism due to illness and/or excessive body mass, to better understand the problem.

Background Public repositories of biological pathways and networks have greatly expanded in recent years. Such databases contain many pathways that facilitate the analysis of high-throughput experimental work and the formulation of new biological hypotheses to be tested, a fundamental principle of the systems biology approach. However, large-scale molecular maps are not always easy to mine and interpret. Results We have developed BiNoM (Biological Network Manager), a Cytoscape plugin, which provides functions for the import-export of some standard systems biology file formats (import from CellDesigner, BioPAX Level 3 and CSML; export to SBML, CellDesigner and BioPAX Level 3), and a set of algorithms to analyze and reduce the complexity of biological networks. BiNoM can be used to import and analyze files created with the CellDesigner software. BiNoM provides a set of functions allowing to import BioPAX files, but also to search and edit their content. As such, BiNoM is able to efficiently manage large BioPAX files such as whole pathway databases (e.g. Reactome). BiNoM also implements a collection of powerful graph-based functions and algorithms such as path analysis, decomposition by involvement of an entity or cyclic decomposition, subnetworks clustering and decomposition of a large network in modules. Conclusions Here, we provide an in-depth overview of the BiNoM functions, and we also detail novel aspects such as the support of the BioPAX Level 3 format and the implementation of a new algorithm for the quantification of pathways for influence networks. At last, we illustrate some of the BiNoM functions on a detailed biological case study of a network representing the G1/S transition of the cell cycle, a crucial cellular process disturbed in most human tumors.

Glaucoma is a group of disease characterized by progressive optic nerve degeneration and retinal ganglion cells (RGCs). The RGCs and evaluation elevated intraocular pressure are the most common cause for glaucoma. In this study, RGC death pathway of glaucoma was modeled to predict the response of the protein receptor, ligand, inhibitor and other regulatory units, which are involved in RGC death pathway in glaucoma. In the pathway modeling six aspects were considered, namely extrinsic pathway, intrinsic pathway, endoplasmic reticulum stress, neurotrophins signaling response, oxidative stress response and calpain activation induced RGC degeneration. The pathway has been designed a compressive pathway of molecular interaction on a cellular level based on published literature for analyzing the expression of the species. The CellDesigner software was used to designing the pathway and store it systems biology markup language (SBML). The SBML squeezer plugin is used to apply the kinetic equation such as a general mass action equation, Michaelis–Menten equation, and Hill equation for pathway simulation. The pathway of glaucoma was showed the over/down expression of the protein species.

Computational modeling techniques and tools are playing increasingly important roles in advancing a system-level mechanistic understanding of biological processes. The in silico experimentation can help shape and guide experimental and clinical efforts. There are an array of tools that can be used to accelerate creation of mathematical models and therefore facilitate generation of in silico simulations. In this chapter, we will briefly review some of these tools and provide hands-on modeling examples. Finally, the chapter proposes an examination of the relationship between model complexity, reliability and knowledge discovery, and model-driven hypothesis generation.