Frontiers in systems biology最新文献

Background: The regulatory mechanisms guiding CD4 T cell differentiation are complex and are further influenced by intrinsic cell variability along with that of microenvironmental cues, such as cytokine and nutrient availability.

Objective: This study aims to expand our understanding of CD4 T cell differentiation by examining the influence of intrinsic noise on cell fate.

Methodology: A model based on a complex regulatory network of early signaling events involved in CD4 T cell activation and differentiation was described in terms of a set of stochastic differential equation to assess the effect of noise intensity on differentiation efficiency to the Th1, Th2, Th17, Treg, and ${\text{T}}_{FH}$ effector phenotypes under defined cytokine and nutrient conditions.

Results: The increase of noise intensity decreases differentiation efficiencies. In a microenvironment of Th1-inducing cytokines and optimal nutrient conditions, noise levels of 3 $\%$ , 5 $\%$ and 10 $\%$ render Th1 differentiation efficiencies of 0.87, 0.76 and 0.62, respectively, underscoring the sensitivity of the network to random variations. Further increments of noise reveal that the network is relatively stable until noise levels of 20 $\%$ , where the resulting cell phenotypes becomes heterogeneous. Notably, Treg differentiation showed the highest robustness to noise perturbations. A combined Th1-Th2 cytokine environment with optimal nutrient levels induces a dominant Th1 phenotype; however, removal of glutamine shifts the balance towards the Th2 phenotype at all noise levels, with an efficiency similar to that obtained under Th2-only cytokine conditions. Similarly, combinations of Th1/Treg and Treg/Th17-inducing cytokines along with the removal of either tryptophan or oxygen shift the dominant Th1 and Treg phenotypes towards Treg and Th17 respectively. Model results are consistent with differentiation efficiency patterns obtained under well-controlled experimental settings reported in the literature.

Conclusion: The stochastic CD4 T cell mathematical model presented here demonstrates a noise-dependent modulation of T cell differentiation induced by cytokines and nutrient availability. Modeling results can be explained by the network topology, which assures that the system will arrive at stable states of cell functionality despite variable levels of biological intrinsic noise. Moreover, the model provides insights into the robustness of the T cell differentiation process.

Both machine learning and mechanistic modelling approaches have been used independently with great success in systems biology. Machine learning excels in deriving statistical relationships and quantitative prediction from data, while mechanistic modelling is a powerful approach to capture knowledge and infer causal mechanisms underpinning biological phenomena. Importantly, the strengths of one are the weaknesses of the other, which suggests that substantial gains can be made by combining machine learning with mechanistic modelling, a field referred to as Scientific Machine Learning (SciML). In this review we discuss recent advances in combining these two approaches for systems biology, and point out future avenues for its application in the biological sciences.

The COVID-19 pandemic has tested the technical, scientific, and industrial resources of all countries worldwide. Faced with the absence of pharmacological strategies against the disease, an effective plan for vaccinating against SARS-CoV-2 has been essential. Due to the lack of production means and necessary infrastructure, only a few nations could adequately confront this pathogen with a production, storage, and distribution scheme in place. This disease has become endemic in many countries, especially in those that are developing, thus necessitating solutions tailored to their reality. In this paper, we propose an in silico method to guide the design towards a thermally stable, universal, efficient, and safe COVID-19 vaccine candidate against SARS-CoV-2 using bioinformatics, immunoinformatics, and molecular modeling approaches for the selection of antigens with higher immunogenic potential, incorporating them into the surface of the M13 phage. Our work focused on using phagemid display as peptide array for neutralizing antibodies (PdPANA). This alternative approach might be useful during the vaccine development process, since it could bring improvements in terms of cost-effectiveness in production, durability, and ease of distribution of the vaccine under less stringent thermal conditions compared to existing methods. Our results suggest that in the heavily glycosylated region of SARS-CoV-2 Spike protein (aa 344-583), from its inter-glycosylated regions, useful antigenic peptides can be obtained to be used in M13 phagemid display system. PdPANA, our proposed method might be useful to overcome the classic shortcoming posed by the phage-display technique (i.e., the time-consuming task of in vitro screening through great sized libraries with non-useful recombinant proteins) and obtain the most ideal recombinant proteins for vaccine design purposes.