Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.2
4.6
Journals
Bioengineering
Special Issues
Mathematical and Computational Modeling of Cancer Progression
share announcement

Mathematical and Computational Modeling of Cancer Progression

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biomedical Engineering and Biomaterials".

Deadline for manuscript submissions: 31 August 2024 | Viewed by 2405

Special Issue Editor

Dr. Leili Shahriyari

E-Mail Website
Guest Editor
Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, MA 01003, USA
Interests: mathematical biology; bioinformatics; mechanistic modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We invite submissions of manuscripts to this Special Issue, ‘Mathematical and Computational Modeling of Cancer Progression,’ which aims to showcase cutting-edge research highlighting the intersection of mathematics, computational biology, and oncology, with the goal of advancing our understanding of the intricate processes driving cancer progression.

Cancer progression is a multifaceted phenomenon involving intricate interactions between genetic, molecular, and cellular components. Mathematical and computational models offer powerful tools to dissect these complexities, aiding in the identification of critical biomarkers, treatment optimization, and therapeutic development. We encourage submissions that address, but are not limited to, the following topics:

  • Spatial and temporal modeling of tumor growth;
  • Patient-specific modeling and treatment strategies;
  • Computational approaches for tumor microenvironment analysis;
  • Modeling metastasis and therapeutic resistance;
  • Multi-scale modeling in cancer biology;
  • Integration of omics data in cancer modeling;
  • Impact of heterogeneity on disease progression;
  • Machine learning and AI applications in mechanistic modeling of cancer.

Dr. Leili Shahriyari
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Bioengineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • computational oncology
  • cancer progression
  • cancer treatments
  • mechanistic modeling of cancer
  • integration of machine learning and mechanistic modeling

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

14 pages, 2518 KiB  
Article
Modeling the Synergistic Impact of Yttrium 90 Radioembolization and Immune Checkpoint Inhibitors on Hepatocellular Carcinoma
by Minah Kang, Yerim Shin, Yeseul Kim, Sangseok Ha and Wonmo Sung
Bioengineering 2024, 11(2), 106; https://doi.org/10.3390/bioengineering11020106 - 23 Jan 2024
Viewed by 1057
Abstract
The impact of yttrium 90 radioembolization (Y90-RE) in combination with immune checkpoint inhibitors (ICIs) has recently gained attention. However, it is unclear how sequencing and dosage affect therapeutic efficacy. The purpose of this study was to develop a mathematical model to simulate the [...] Read more.
The impact of yttrium 90 radioembolization (Y90-RE) in combination with immune checkpoint inhibitors (ICIs) has recently gained attention. However, it is unclear how sequencing and dosage affect therapeutic efficacy. The purpose of this study was to develop a mathematical model to simulate the synergistic effects of Y90-RE and ICI combination therapy and find the optimal treatment sequences and dosages. We generated a hypothetical patient cohort and conducted simulations to apply different treatments to the same patient. The compartment of models is described with ordinary differential equations (ODEs), which represent targeted tumors, non-targeted tumors, and lymphocytes. We considered Y90-RE as a local treatment and ICIs as a systemic treatment. The model simulations show that Y90-RE and ICIs administered simultaneously yield greater benefits than subsequent sequential therapy. In addition, applying Y90-RE before ICIs has more benefits than applying ICIs before Y90-RE. Moreover, we also observed that the median PFS increased up to 31~36 months, and the DM rates at 3 years decreased up to 36~48% as the dosage of the two drugs increased (p < 0.05). The proposed model predicts a significant benefit of Y90-RE with ICIs from the results of the reduced irradiated tumor burden and the associated immune activation and suppression. Our model is expected to help optimize complex strategies and predict the efficacy of clinical trials for HCC patients. Full article
(This article belongs to the Special Issue Mathematical and Computational Modeling of Cancer Progression)
►▼ Show Figures

Figure 1

Review

Jump to: Research

14 pages, 661 KiB  
Review
Mathematical and Machine Learning Models of Renal Cell Carcinoma: A Review
by Dilruba Sofia, Qilu Zhou and Leili Shahriyari
Bioengineering 2023, 10(11), 1320; https://doi.org/10.3390/bioengineering10111320 - 16 Nov 2023
Viewed by 975
Abstract
This review explores the multifaceted landscape of renal cell carcinoma (RCC) by delving into both mechanistic and machine learning models. While machine learning models leverage patients’ gene expression and clinical data through a variety of techniques to predict patients’ outcomes, mechanistic models focus [...] Read more.
This review explores the multifaceted landscape of renal cell carcinoma (RCC) by delving into both mechanistic and machine learning models. While machine learning models leverage patients’ gene expression and clinical data through a variety of techniques to predict patients’ outcomes, mechanistic models focus on investigating cells’ and molecules’ interactions within RCC tumors. These interactions are notably centered around immune cells, cytokines, tumor cells, and the development of lung metastases. The insights gained from both machine learning and mechanistic models encompass critical aspects such as signature gene identification, sensitive interactions in the tumors’ microenvironments, metastasis development in other organs, and the assessment of survival probabilities. By reviewing the models of RCC, this study aims to shed light on opportunities for the integration of machine learning and mechanistic modeling approaches for treatment optimization and the identification of specific targets, all of which are essential for enhancing patient outcomes. Full article
(This article belongs to the Special Issue Mathematical and Computational Modeling of Cancer Progression)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop