Makale Koleksiyonu
https://hdl.handle.net/20.500.12511/4505
Article Collection2024-03-29T00:30:52ZModeling tumor growth using fractal calculus: Insights into tumor dynamics
https://hdl.handle.net/20.500.12511/11869
Modeling tumor growth using fractal calculus: Insights into tumor dynamics
Golmankhaneh, Amirreza Khalili; Tunç, Sümeyye; Schlichtinger, Agnieszka Matylda; Asanza, Dachel Martinez; Golmankhaneh, Alireza Khalili
Important concepts like fractal calculus and fractal analysis, the sum of squared residuals, and Aikaike's information criterion must be thoroughly understood in order to correctly fit cancer-related data using the proposed models. The fractal growth models employed in this work are classified in three main categories: Sigmoidal growth models (Logistic, Gompertz, and Richards models), Power Law growth model, and Exponential growth models (Exponential and Exponential-Lineal models)”. We fitted the data, computed the sum of squared residuals, and determined Aikaike's information criteria using Matlab and the web tool WebPlotDigitizer. In addition, the research investigates “double-size cancer” in the fractal temporal dimension with respect to various mathematical models.
2024-01-01T00:00:00ZFurin and the adaptive mutation of SARS-COV2: A computational framework
https://hdl.handle.net/20.500.12511/10216
Furin and the adaptive mutation of SARS-COV2: A computational framework
Sohail, Ayesha; Tunç, Sümeyye; Nutini, Alessandro; Arif, Robia
SARS-2 virus has reached its most harmful mutated form and has damaged the world's economy, integrity, health system and peace to a limit. An open problem is to address the release of antibodies after the infection and after getting the individuals vaccinated against the virus. The viral fusion process is linked with the furin enzyme and the adaptation is linked with the mutation, called D614G mutation. The cell-protein studies are extremely challenging. We have developed a mathematical model to address the process at the cell-protein level and the delay is linked with this biological process. Genetic algorithm is used to approximate the parametric values. The mathematical model proposed during this research consists of virus concentration, the infected cells count at different stages and the effect of interferon. To improve the understanding of this model of SARS-CoV2 infection process, the action of interferon (IFN) is quantified using a variable for the non-linear mathematical model, that is based on a degradation parameter gamma. This parameter is responsible for the delay in the dynamics of this viral action. We emphasize that this delay responds to the evasion by SARS-CoV2 via antagonizing IFN production, inhibiting IFN signaling and improving viral IFN resistance. We have provided videos to explain the modeling scheme.
2022-01-01T00:00:00ZForecasting the action of CAR-T cells against SARS-corona virus-II infection with branching process
https://hdl.handle.net/20.500.12511/10211
Forecasting the action of CAR-T cells against SARS-corona virus-II infection with branching process
Al-Utaibi, Khaled A.; Nutini, Alessandro; Sohail, Ayesha; Arif, Robia; Tunç, Sümeyye; Sait, Sadiq M.
The CAR-T cells are the genetically engineered T cells, designed to work specifically for the virus antigens (or other antigens, such as tumour specific antigens). The CAR-T cells work as the living drug and thus provides an adoptive immunotherapy strategy. The novel corona virus treatment and control designs are still under clinical trials. One of such techniques is the injection of CAR-T cells to fight against the COVID-19 infection. In this manuscript, the hypothesis is based on the CAR-T cells, that are suitably engineered towards SARS-2 viral antigen, by the N protein. The N protein binds to the SARS-2 viral RNA and is found in abundance in this virus, thus for the engineered cell research, this protein sequence is chosen as a potential target. The use of the sub-population of T-reg cells is also outlined. Mathematical modeling of such complex line of action can help to understand the dynamics. The modeling approach is inspired from the probabilistic rules, including the branching process, the Moran process and kinetic models. The Moran processes are well recognized in the fields of artificial intelligence and data science. The model depicts the infectious axis “virus—CAR-T cells—memory cells”. The theoretical analysis provides a positive therapeutic action; the delay in viral production may have a significant impact on the early stages of infection. Although it is necessary to carefully evaluate the possible side effects of therapy. This work introduces the possibility of hypothesizing an antiviral use by CAR-T cells.
2022-01-01T00:00:00ZModeling the crossover behavior of the bacterial infection with the COVID-19 epidemics
https://hdl.handle.net/20.500.12511/9598
Modeling the crossover behavior of the bacterial infection with the COVID-19 epidemics
Yu, Zhenhua; Sohail, Ayesha; Arif, Robia; Nutini, Alessandro; Nofal, Taher A.; Tunç, Sümeyye
To explore the crossover linkage of the bacterial infections resulting from the viral infection, within the host body, a computational framework is developed. It analyzes the additional pathogenic effect of Streptococcus pneumonia, one of the bacteria that can trigger the super-infection mechanism in the COVID-19 syndrome and the physiological effects of innate immunity for the control or eradication of this bacterial infection. The computational framework, in a novel manner, takes into account the action of pro-inflammatory and anti-inflammatory cytokines in response to the function of macrophages. A hypothetical model is created and is transformed to a system of non-dimensional mathematical equations. The dynamics of three main parameters (macrophages sensitivity κ, sensitivity to cytokines η and bacterial sensitivity ϵ), analyzes a “threshold value” termed as the basic reproduction number R0 which is based on a sub-model of the inflammatory state. Piece-wise differentiation approach is used and dynamical analysis for the inflammatory response of macrophages is studied in detail. The results shows that the inflamatory response, with high probability in bacterial super-infection, is concomitant with the COVID-19 infection. The mechanism of action of the anti-inflammatory cytokines is discussed during this research and it is observed that these cytokines do not prevent inflammation chronic, but only reduce its level while increasing the activation threshold of macrophages. The results of the model quantifies the probable deficit of the biological mechanisms linked with the anti-inflammatory cytokines. The numerical results shows that for such mechanisms, a minimal action of the pathogens is strongly amplified, resulting in the “chronicity” of the inflammatory process.
2022-01-01T00:00:00Z