On the calculation of signal transduction ability of signaling transduction pathways in intracellular communication: systematic approach

Mathematical model of TGF-βsignalling: feedback coupling is consistent with signal switching

Background

Transforming growth factor β (TGF-β) signalling regulates the development of embryos and tissue homeostasis in adults. In conjunction with other oncogenic changes, long-term perturbation of TGF-β signalling is associated with cancer metastasis. Although TGF-β signalling can be complex, many of the signalling components are well defined, so it is possible to develop mathematical models of TGF-β signalling using reduction and scaling methods. The parameterization of our TGF-β signalling model is consistent with experimental data.

Results

We developed our mathematical model for the TGF-β signalling pathway, i.e. the RF- model of TGF-β signalling, using the “rapid equilibrium assumption” to reduce the network of TGF-β signalling reactions based on the time scales of the individual reactions. By adding time-delayed positive feedback to the inherent time-delayed negative feedback for TGF-β signalling. We were able to simulate the sigmoidal, switch-like behaviour observed for the concentration dependence of long-term (> 3 hours) TGF-β stimulation. Computer simulations revealed the vital role of the coupling of the positive and negative feedback loops on the regulation of the TGF-β signalling system. The incorporation of time-delays for the negative feedback loop improved the accuracy, stability and robustness of the model. This model reproduces both the short-term and long-term switching responses for the intracellular signalling pathways at different TGF-β concentrations. We have tested the model against experimental data from MEF (mouse embryonic fibroblasts) WT, SV40-immortalized MEFs and Gp130 ^F/F MEFs. The predictions from the RF- model are consistent with the experimental data.

Conclusions

Signalling feedback loops are required to model TGF-β signal transduction and its effects on normal and cancer cells. We focus on the effects of time-delayed feedback loops and their coupling to ligand stimulation in this system. The model was simplified and reduced to its key components using standard methods and the rapid equilibrium assumption. We detected differences in short-term and long-term signal switching. The results from the RF- model compare well with experimental data and predict the dynamics of TGF-β signalling in cancer cells with different mutations.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-017-0421-5) contains supplementary material, which is available to authorized users.

Measuring information flow in cellular networks by the systems biology method through microarray data

In general, it is very difficult to measure the information flow in a cellular network directly. In this study, based on an information flow model and microarray data, we measured the information flow in cellular networks indirectly by using a systems biology method. First, we used a recursive least square parameter estimation algorithm to identify the system parameters of coupling signal transduction pathways and the cellular gene regulatory network (GRN). Then, based on the identified parameters and systems theory, we estimated the signal transductivities of the coupling signal transduction pathways from the extracellular signals to each downstream protein and the information transductivities of the GRN between transcription factors in response to environmental events. According to the proposed method, the information flow, which is characterized by signal transductivity in coupling signaling pathways and information transductivity in the GRN, can be estimated by microarray temporal data or microarray sample data. It can also be estimated by other high-throughput data such as next-generation sequencing or proteomic data. Finally, the information flows of the signal transduction pathways and the GRN in leukemia cancer cells and non-leukemia normal cells were also measured to analyze the systematic dysfunction in this cancer from microarray sample data. The results show that the signal transductivities of signal transduction pathways change substantially from normal cells to leukemia cancer cells.

Systems biology as an integrated platform for bioinformatics, systems synthetic biology, and systems metabolic engineering

Systems biology aims at achieving a system-level understanding of living organisms and applying this knowledge to various fields such as synthetic biology, metabolic engineering, and medicine. System-level understanding of living organisms can be derived from insight into: (i) system structure and the mechanism of biological networks such as gene regulation, protein interactions, signaling, and metabolic pathways; (ii) system dynamics of biological networks, which provides an understanding of stability, robustness, and transduction ability through system identification, and through system analysis methods; (iii) system control methods at different levels of biological networks, which provide an understanding of systematic mechanisms to robustly control system states, minimize malfunctions, and provide potential therapeutic targets in disease treatment; (iv) systematic design methods for the modification and construction of biological networks with desired behaviors, which provide system design principles and system simulations for synthetic biology designs and systems metabolic engineering. This review describes current developments in systems biology, systems synthetic biology, and systems metabolic engineering for engineering and biology researchers. We also discuss challenges and future prospects for systems biology and the concept of systems biology as an integrated platform for bioinformatics, systems synthetic biology, and systems metabolic engineering.