Dr. Fajar Adi Kusumo, a lecturer from the Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada (FMIPA UGM), was officially inaugurated as a professor in the field of dynamical systems on Tuesday, Jul. 1, 2025, at the UGM Campus.
During the inauguration ceremony held at the Senate Hall, Professor Kusumo delivered a speech titled Dynamical Systems and Their Role in Cancer Prognosis.
Professor Kusumo shared that he has been conducting research in the field of dynamical systems since 2001, with its application in cancer prognosis modeling beginning in 2010.
Dynamical systems are a mathematical formalism used to describe deterministic processes.
This system can be applied to population growth, disease transmission within a population, and other processes that can be represented mathematically.
“Dynamical systems can be applied in the healthcare field, particularly for cancer prognosis. This system allows us to model deterministic processes and predict the progression of a disease or a patient’s medical condition,” he explained.
As is widely known, cancer is one of the leading causes of death worldwide.
According to WHO data (2022), cancer causes more than 10 million deaths annually, accounting for one in six global deaths, making it the second leading cause of death globally.
One type of therapy currently being developed to treat cancer is oncolytic virotherapy, which aims to induce apoptosis (cell death) in cancer cells infected by oncolytic viruses.
By utilizing dynamical systems, the characteristics of virotherapy can be better understood, as well as the likelihood of treatment success, particularly for malignant cancers.
“With this model, various scenarios of virotherapy using oncolytic viruses can be studied mathematically by analyzing the existence and stability of the model’s solutions,” he said.
Moreover, dynamical systems can be used to study the progression patterns and characteristics of specific cancer types.
One prevalent type in Indonesia is nasopharyngeal cancer, which is associated with the Epstein-Barr Virus (EBV).
Professor Kusumo explained that two mathematical modeling approaches can help in understanding this cancer.
The first is a tissue-level interaction model involving normal cells, lesion cells, low-dysplastic cells, high-dysplastic cells, EBV-infected cells, and invasive carcinoma cells.
The second model focuses on protein and biomarker interactions involved in regulating cell repair.
By using the second model, researchers found that two patients with seemingly similar diagnostic profiles may experience different disease progression patterns and respond differently to treatments.
“This finding suggests, from a mathematical perspective, that cancer treatment should ideally be personalized,” he added.
Research into cancer modeling continues to evolve. Professor Kusumo expressed hope that mathematical modeling in cancer prognosis will not only aid treatment and care but also help prevent the onset of cancer itself.
“This research is expected to contribute not only to cancer treatment and management but also to early detection and other preventive measures to combat cancer in the future,” he concluded.
Author: Rafif Rusmana
Editor: Gusti Grehenson
Post-editor: Lintang Andwyna
Photographer: Firsto