سخنرانی عمومی در شهریور

Dear all,

It is a pleasure to announce our next online colloquium Monday 4 Sept. (13 Shahrivar), 6pm by Dr. Suzan Farhang of

University of Manitoba, Canada. More information can be found in the textbox below.

Everybody is welcome to attend.

With kind regards,

M.M. Sheikh-Jabbari

Speaker

Dr Suzan Farhang-Sardroodi

Affiliation

Department of Mathematics, University of Manitoba, Winnipeg, Manitoba, Canada

Title of talk

Modeling Humoral Immune Response to SARS-CoV-2 and Machine Learning for Discriminating COVID-19 and Influenza Infection: An Application Approach

عنوان: مدل‌سازی پاسخ مصونیت با پادتن به سارس-کوید-۲ و یادگیری ماشین برای تمیز دادن ابتلا به کوید-۱۹ و آنفولانزا: یک رهیافت کاربردی

Date and time,

Monday 13 Shahrivar 1402 (4 Sept., 2023), 6 pm (Note the unusual day)

Link

https://www.skyroom.online/ch/schoolofphysics/colloquium

Abstract

Mechanistic modelling approaches have become essential in systems biology, enabling the description of known physiological processes and filling gaps in our understanding of complex interactions driving host-pathogen responses. These models provide valuable insights for public health planning and infectious disease control.

In this colloquium, I will first present our mathematical model to investigate humoral (antibody-mediated) immunity. B cells and their antibodies play a crucial role in protecting against COVID-19. However, the decline of antibodies following natural infection or vaccination results in reduced defence against subsequent SARS-CoV-2 infections. To comprehend the dynamics of antibody production from B cells, we constructed a computational biology model that incorporates B cells, IgG-neutralizing antibodies, and host-pathogen interactions. This model provides insights into the kinetic processes and mechanisms that drive the humoral response to SARS-CoV-2, including the initiation of B cell responses, differentiation into germinal center cells, long-lived plasma cells, and memory cells. It enhances our understanding of antibody production in primary and secondary reactions. Next, I will present our recent work that centers around applying mathematical modelling to generate synthetic data of influenza and COVID-19 patients, enabling differentiation between the two infections. Here, we developed and validated a supervised machine-learning model utilizing mechanistic models of viral infection. Our investigation showcases the effectiveness of machine learning models in accurately discerning between these diseases by leveraging essential factors related to viral infection and immune response. This model has the potential to serve as a cost-effective classification system, eliminating the need for expensive virus typing procedures and relying solely on viral load and interferon measurements.