Data-driven modeling and model calibration in biology

Wing-Cheong Lo

City University of Hong Kong (Mathematics)

"Modeling COVID-19 transmission dynamics with self-learning population behavioral change"

Many regions observed recurrent outbreaks of COVID-19 cases after relaxing social distancing measures. It suggests that maintaining sufficient social distancing is important for limiting the spread of COVID-19. The change of population behavior responding to the social distancing measures becomes an important factor for the pandemic prediction. In this study, we develop a SEAIR model for studying the dynamics of COVID-19 transmission with population behavioral change. In our model, the population is divided into several groups with their own social behavior in response to the delayed information about the number of the infected population. The transmission rate depends on the behavioral changes of all the population groups, forming a feedback loop to affect the COVID-19 dynamics. Based on the data of Hong Kong, our simulations demonstrate how the perceived cost after infection and the information delay affect the level and the time period of the COVID-19 waves. This is joint work with Tsz-Lik Chan (University of California Riverside) and Hsiang-Yu Yuan (City University of Hong Kong).

---

Wenrui Hao

Penn State University (Mathematics)

"data driven modeling of Alzheimer’s disease"

With over 5 million individuals affected by Alzheimer’s disease (AD) in the US alone, personalized treatment plans have emerged as a promising approach to managing this complex neurological disorder. However, this approach requires sophisticated analysis of electronic brain data. This talk proposes a mathematical modeling approach to describe the progression of AD clinical biomarkers and integrate patient data for personalized prediction and optimal treatment. The proposed model is validated on a multi-institutional dataset of AD biomarkers to provide personalized predictions, and optimal controls are added to enable personalized therapeutic simulations for AD patients.

---

Kang-Ling Liao

University of Manitoba (Mathematics)

"A simple in-host model for Covid-19 with treatments-model prediction and calibration"

We provide a simple ODEs model with a generic nonlinear incidence rate function and incorporate two treatments, blocking the virus binding and inhibiting the virus replication to investigate the SARS-CoV-2 infection dynamics. We derive conditions of the infection eradication for the long-term dynamics using the basic reproduction number, and to complement the characterization of the dynamics at short-time, the resilience and reactivity of the virus-free equilibrium are considered to inform on the average time of recovery and sensitivity to perturbations in the initial virus free stage. Then, we calibrate the treatment model to clinical datasets for viral load in mild and severe cases and immune cells in severe cases. Combining analytical and numerical results, we explore the impact of calibration on model predictions.
Additional authors: Isam Al-Darabsah (no affiliations); Stephanie Portet (University of Manitoba)

---

Xiaojun Tian

Arizona State University (School of Biological and Health Systems Engineering)

"Modeling Emergent Dynamics in Engineering Synthetic Gene Circuits"

The interplay between synthetic gene circuits and their host organisms, such as growth feedback and resource competition, can give rise to unexpected dynamics. In this presentation, I will discuss our latest research to use mathematical modeling to quantitatively understand and predict the impact of network topology, host physiology, and resource competition on the functional behaviors of gene circuits. Furthermore, I will highlight how resource competition affects the circuit noise behavior and present practical control strategies to engineer more robust gene circuits.

---