AdobeStock_120910914.png

Excursion Information

The conference

Announcements(10)

Message from Organizer

Committee Members

Floor map

JSMB Prize Winners & Lectures

Call for Participation

Venue Reception

Presentation Guidelines

Online Registration

Timetable

Mon. Jul 7, 2025

Tue. Jul 8, 2025

Wed. Jul 9, 2025

Thu. Jul 10, 2025

Fri. Jul 11, 2025

Plenary Speakers

Program

Sessions

Search presentations

Social Events

Welcome Reception

Excursion

Banquet

ACMB2005 head 1000-100_re.png

Profile management

Announcements

Bookmark

Download PDF for print

Program Type

Login required to view

Password required to view

Presentation Information

Session Details

The password has been sent to those who have completed payment.

To browse or post comments, you must log in.

You cannot post comments as it is outside the comment posting period.

This comment has been deleted. You cannot reply to this comment.

Complete Flux Scheme for Time-Fractional Order Model of Glioblastoma Growth

Recent trends in modeling and simulation of cancer have increasingly integrated Artificial Intelligence (AI) and Machine Learning (ML), Systems Biology, and Partial Differential Equation (PDE)-based models to better understand tumor dynamics and optimize treatment strategies. AI/ML methods are transforming cancer research by analyzing large-scale datasets, such as genomic data, medical imaging, and patient histories. These techniques can predict cancer progression, identify biomarkers, and recommend personalized treatments. Machine learning models, like neural networks and decision trees, can assist in early diagnosis by detecting patterns in medical images and genomic sequences that may be difficult for humans to discern. Systems biology approaches integrate molecular, cellular, and environmental data to simulate complex interactions within tumor ecosystems. This holistic perspective allows for modeling the multi-scale nature of cancer, including gene regulation, signaling pathways, and microenvironmental interactions. Systems biology helps uncover how tumors evolve, metastasize, and develop resistance to treatments, guiding the development of targeted therapies. PDE-based models are crucial for simulating the spatiotemporal evolution of tumors. These models describe how factors like nutrient supply, drug diffusion, and cell proliferation spread through tissue. Coupled with computational methods, PDE models allow for the simulation of tumor growth, angiogenesis, and response to therapy under various conditions. By incorporating feedback loops and spatial heterogeneity, these models offer deeper insights into tumor behavior and help predict treatment outcomes. In combination, AI/ML, systems biology, and PDE-based models are revolutionizing cancer research, enabling more precise, data-driven approaches to diagnosis, treatment planning, and understanding cancer progression.

Recent Trends in Modeling & Simulation of Cancer

Joint Meeting of Asian Conference for Mathematical Biology and Annual Meeting of Japanese Society for Mathematical Biology (ACMB-JSMB2025)

Presentation Information

[SS24-04]Complete Flux Scheme for Time-Fractional Order
Model of Glioblastoma Growth

Keywords:

Password required to view

Presentation Information

[SS24-04]Complete Flux Scheme for Time-Fractional OrderModel of Glioblastoma Growth

Keywords:

Password required to view

[SS24-04]Complete Flux Scheme for Time-Fractional Order
Model of Glioblastoma Growth