Our model is divided into two parts: bioinformatics analysis and mathematical model.
We firstly applied bioinformatics methods to evaluate LINC00857 as a potential biomarker for diagnosis and detection of prognosis within a variety of cancers. We analysed its expression levels in various cancers through TCGA RNA-seq and exoRBase2.0. We also analyzed the relationship between LINC00857 expression and tumor pathological staging using the GEPIA2 tool, which suggests that stage-specific expression changes in LINC00857 expression in many tumor types. The results revealed its potential biomarker function for detection. Through TCGA RNA-seq data, we found that the expression of LINC00857 had a high accuracy in diagnosing cancers. Then, we further linked LINC00857 expression with clinical data to construct models for detection of prognosis within a variety of cancers. The results showed that LINC00857 expression was closely related to patient survival. Furthermore, we used bioinformatics methods to building a Machine Learning Model in selecting potential diagnostic lncRNA markers for atherosclerosis patients. The results showed that the lncRNAs we selected had a high accuracy in diagnosing atherosclerosis.
Our Dynamics Modeling section ran throughout the project. Corresponding to the four parts of the project design, we built four sub-models: reverse transcription isothermal amplification model, enzyme kinetic model for trans cleavage of CRISPR-Cas system, Model of starch hydrolysis by γ-Amylase and Electrical signal conversion model for glucose concentration. Each sub-model was connected using upstream sub-model outputs as the downstream sub-model inputs. Finally, we simulated the kinetics behavior of the entire detection system, based on ODE equations and experimental data. Simulation output and wet lab experiment data reached a R-squared greater than 0.98. Mathematical models were solved using R as a software tool.
We also performed molecular dynamics simulation for six kinds of CasΦ mutants collected from literature and wet lab design. The RMSD of different CasΦ mutants were calculated using gmx rms module in the Gromacs package. We analyzed the changes in the skeleton carbon atom of the protein and the central structure of the entire protein.
Bioinformatics Analysis Mathematical Model
