DDMB-MS14

Numerical methods in biomedical sciences

Wednesday, June 16 at 11:30am (PDT)
Wednesday, June 16 at 07:30pm (BST)
Thursday, June 17 03:30am (KST)

SMB2021 Follow Wednesday (Thursday) during the "MS14" time block.

Note: this minisymposia has multiple sessions. The second session is MS13-DDMB (click here).

Yifan Wang (University of California, Irvine, USA), Pejman Sanaei (New York Institute of Technology, USA)

Description:

Mathematical modeling and numerical methods play an important role in biomedical sciences nowadays. A diversity of mathematical models and techniques ranging from solving partial differential equations and stochastic modeling to applying machine learning algorithms to image and data analysis has been tackled and explored, with many interesting applications such as improving the medical imaging to identify pathological tissue better, studying patient RNA-sequencing data to facilitate disease diagnosis, computational analysis to design patient-specific treating plans to improve the treatment outcomes and so on. This mini-symposium plans to gather mathematicians and field experts with various biomedical research interests to share their modeling techniques, discuss the associated challenges, stimulate new research collaborations, and connect different applications that similar mathematical approaches may apply.

Sudhir Pathak

(skpathak@pitt.edu, USA)

"Computational Modeling of the human brain tissue, Estimation and Quantifying tissue type"

MR imaging is a versatile technique that is used to image the anatomical micro-architecture of biological tissue, clinically affected regions such as traumatic injury, blood clot, tumor lesion, and tissue degeneration. In particular, diffusion MR imaging of the human brain can provide the connectivity pattern of the brain regions. In this presentation, I am going to talk about the characterization of the human brain tissue using diffusion MRI. Diffusion MRI is a novel technique that can be used to characterize the diffusion pattern of the micro-environment of the tissue. From these diffusion patterns, one can characterize geometrical and micro-compartmental information of both healthy and pathological tissues. A volume element of diffusion MR images of human brain tissue contains diffusion signals from free, hindered, and restricted water pools. Using mathematical models and proper MR sequences, these water pools can be used to infer diseases and brain connectivity. In the talk, I will present four such mathematical models, DTI, CHARMED, NODDI, and SMT. I will present the assumptions, (dis)advantage, and feasibility of these mathematical models in a clinical setting. These models can be key to providing important information in clinical diagnosis, presurgical planning and possibly used in deciding treatment.

Yuchi Qiu

(Michigan State University, USA)

"Learning biomolecules in mutagenesis via topological and geometric modeling"

Mutagenesis is widely used to understand the structure and function of biomolecules. Relying on emerging large mutation datasets in recent years, machine learning methods provide economic approaches to examine function of new mutant biomolecules in silico. The high geometric dimensionality, which usually contains thousands of atoms for one protein, is the main challenge for machine learning models to learn the three-dimensional biomolecules data. Topological and geometric modeling provide informative geometric simplification and scalable representation of the 3D data. In this talk, we develop a multi-scale method utilizing Poincare-Hopf theorem and Morse theory to analyze protein structure. We apply this method to predict mutation induced protein stability changes and it outperforms other existing methods.

Yu (Andy) Huang

(Memorial Sloan Kettering Cancer Center, USA)

"Computational Models of Transcranial Electrical Stimulation: Methodology, Optimization and Validations"

Transcranial electrical stimulation (TES) has been shown as a promising neurological therapy for a number of diseases. Nowadays, design of electrode montages and interpretation of experimental results for TES heavily rely on computational models, which predict the current-flow distribution inside the head. In this talk I will show you methodological details in building individualized TES models from structural magnetic resonance images of human heads, including image segmentation, electrode placement, finite element modeling, and numerical optimization for targeted stimulation. Model validations using intracranial in vivo recordings will also be discussed. I will also briefly talk about translational efforts that convert TES models into neuromodulation software, either open-source or proprietary, that are used for clinical research on stroke recovery

Mac Hyman

(Tulane University, USA)

"A Bipartite Network Sexual Transmission Model to Inform Public Health Efforts for Controlling the Spread of Chlamydia Trachomatis"

Chlamydia trachomatis (Ct) is the most commonly reported sexually transmitted infection in the USA and causes important reproductive morbidity in women. We created an individual-based heterosexual network model to simulate a realistic chlamydia epidemic on sexual contact networks for a synthetic population. The model is calibrated to the ongoing routine screening among sexually active men and women in New Orleans. The Centers for Disease Control and Prevention recommend routine screening of sexually active women under age 25 but not among men. Despite three decades of screening women, chlamydia prevalence in women remains high. Untested and untreated men can serve as a reservoir of infection in women, and increased screening of both men and women can be an effective strategy to reduce infection in women. We assessed the impact of screening men on the Ct prevalence in women. We used sensitivity analysis to quantify the relative importance of each intervention component. The model suggested the importance of intervention components ranked from high to low as venue-based screening, expedited index treatment, expedited partner treatment, and rescreening. The findings indicated that male screening can substantially reduce the prevalence among women in high-prevalence communities. Joint research with Zhuolin Qu, Asma Azizi, and Patty Kissinger.

Hosted by SMB2021 Follow

Virtual conference of the Society for Mathematical Biology, 2021.