)devices such as single-layer nanoribbons (GNRs) and multilayer graphene nanoribbons (MLGNRs), graphene oxide, multilayer graphene (MLG), and carbon nanotubes (CNT) are exceptionally promising materials as a channel of FET for nanoelectronic biosensors [3; 4]. Recent advances in GFET-based biosensors have improved the detection and diagnosis of different diseases such as SARS-CoV-2 (COVID-19), bacteria infections, cancer cell disorders, and so on. Unfortunately, the accurate design of GFET-based biosensors needs nanoscale experimental equipment despite its high price. This shortcoming creates a challenge to overcome this deficiency in the design of the GFET biosensor by modeling it and testing new materials and different structures to improve biosensor parameters such as the limit of detection, LOD, dynamic range, selectivity, and sensitivity. Although excellent efforts have been accomplished experimentally, the modeling of the GFET operation is essential to advance development and optimization for different applications. There are only a few reports on the modeling and simulation of GFET-based biosensors. Within the previously proposed models, the surface graphene response with DNA molecules has been modeled by the carrier mobility, transfer characteristics, surface capacitance, and conductivity of graphene [5; 6; 7; 8; 9; 10; 11; 12; 13]. In [5], doping effects of graphene surface functionalization were investigated. In the proposed method, the PBASE (1-pyrenebutanoic acid succinimidyl ester) was immobilized on a graphene surface, and two solvents, dimethyl formamide (DMF) and methanol (CH3 OH), were used to dissolve PBASE. Raman spectra analysis and electrical measurement revealed that PBASE imposes a p-doping effect while DMF and CH3 OH impose an n-doping effect. In [6], the incremental Support Vector Regression, ISVR, algorithm was used to detect interferon-gamma by the aptamer-functionalized GFETs, that the shift of neutral point voltage was mathematically modeled and simulated. In the proposed model, a GFET-based biosensor was employed for tuberculosis susceptibility detection by its interferon-gamma biomarker. In the proposed method, the graphene surface carrier concentration and drain-source current would change when the interferon-gamma molecules attach to the surface of graphene. To create a pattern for drain-source current, an ISVR algorithm was employed that shows an acceptable agreement between outcomes of ISVR and experimental data. Recently, the modeling of GFETs for the detection of DNA hybridization has been employed [7; 8]. In [7], a quantum capacitance-sensitive model for a GFET was established, which shows more than 97% accuracy. In the proposed method, a theoretical parametric model for quantum capacitance has been constructed; then, the unknown parameters are estimated by an ant-colony optimization (ACO) algorithm to decrease error with experimental data. In [8], the source-drain current versus gate-source voltage was modeled by a parabolic parametric function of DNA concentration with three parameters. In the proposed method, three parameters are estimated via experimental data by particle swarm optimization (PSO), where the graphene channel of FET was functionalized by single-stranded DNA and was exposed to the complementary DNA. In [9], an efficient numerical approach was proposed for modeling of transport of armchair graphene ribbon. This method is based on an envelope function in the reciprocal space and a recursive matrix approach that the computation time was decreased with respect to the finite difference method. Additionally, references [10] and [11] for Escherichia coli detection employed G-FETs and functionalized it by antibody and aptamer sensing probes, respectively. In the proposed method, GFETs were experimentally modified with PBASE 1-pyrenebutanoic acid succinimidyl ester and _E. coli_ antibodies [10] pyrene-tagged aptamer [11]. The results show the electrical response depends on Escherichia coli concentration.