… where we talk: Big pharma, In silico trials, and Sciencing for fun.

Marissa earned a PhD in Mathematics from The Ohio State University in 2018. She was then a postdoctoral research fellow at University of Michigan until 2021, when she joined Applied BioMath as a full time QSP modeler, then GSK in July 2024.

She does applied work with her mathematician parents’ full approval.

Follow Marissa’s progress on her LinkedIn page: linkedin.com/in/marissa-renardy. 

Find out more about SMB on: 

• Twitter: @smb_mathbiology 
• Bluesky: @smbmathbiology.bsky.social 
• Facebook: @smb.org 
• Linkedin: @smb_mathbiology 
• The Bulletin of Mathematical Biology

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Estimating Hidden Cholera Burden and Intervention Effectiveness

by Murshed Ahmed Ovi, Andrei Afilipoaei, and Hao Wang

Cholera remains a significant health issue in many developing nations. Understanding the role of asymptomatic and under-reported symptomatic cases is vital in controlling outbreaks. Our model, incorporating indirect transmission, shows these cases greatly influence spread through bacterial shedding. Key challenges include Vibrio cholerae ingestion and bacterial shedding from reported cases. Model fitting in countries like Haiti, Kenya, Malawi, and Zimbabwe estimates over 88.5% of cases are inapparent, with up to 11 weeks before detection. In high-compliance settings, sanitation and handwashing are most effective; in low-compliance areas, vaccination and safe fecal disposal are preferable for reducing cholera risk.

Estimating Hidden Cholera Burden and Intervention Effectiveness.

The effect of antibodies in the presence of syncytia during viral infections

by Isabelle Beach and Hana M. Dobrovolny

Many viruses can spread by fusing cells into multi-nucleated cells called syncytia. This model examines how the effectiveness of antibodies is affected by the presence of syncytia, finding that spreading through syncytia formation protects the virus from elimination by antibodies.

A mathematical model of syncytia-forming virus with an antibody response. 

… where we talk: Hawks and Doves, social media and the PhD comics.

Dan Cooney is a mathematical biologist interested in modeling evolutionary dynamics across scales and exploring collective behavior in complex biological and social systems.

He also enjoys reading, traveling, and "applied game theory" to baseball and chess.

Find out more about Dan’s work on his website:https://publish.illinois.edu/danielbcooney, 

or follow him on Bluesky:@danielcooney1.bsky.social

Find out more about SMB on: 

• Twitter: @smb_mathbiology 
• Bluesky: @smbmathbiology.bsky.social 
• Facebook: @smb.org 
• Linkedin: @smb_mathbiology 
• The Bulletin of Mathematical Biology

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A Framework for Parameter Estimation and Uncertainty Quantification in Systems Biology Using Quantile Regression and Physics-Informed Neural Networks

by Haoran Hu, Qianru Cheng, Shuli Guo, Huifang Wen, Jing Zhang, Yongqi Song, Kaiqun Wang, Di Huang, Hui Zhang, Chaofeng Zhang, and Yanhun Shan

This study introduced a novel framework integrating quantile regression with Physics-Informed Neural Networks to enhance parameter estimation and uncertainty quantification in systems biology models. The approach demonstrated superior accuracy in parameter estimation, stronger correlation between uncertainty and noise levels, and moderate computational costs. By efficiently addressing data limitations and noise, this framework offers a powerful tool for advancing predictive biological modeling, with potential applications in drug development, disease modeling, and understanding complex biological interactions. Its scalability and reliability position is as a promising solution for real-world systems biology challenges. 

The illustration of the PINN-based quantile regresion approach.

… where we talk changing cancer cells, awards and the Indian Institutes of Technology.

Mohit leads an interdisciplinary research team working on elucidating the dynamics of metastasis and drug resistance, through developing multi-scale mathematical models in close collaboration with experimental and clinical colleagues.

He and his group do this work powered by many cups of chai.

Find out more about Mohit’s group and their work on the following website: https://be.iisc.ac.in/~mkjolly/.

Find out more about SMB on: 

• Twitter: @smb_mathbiology 
• Bluesky: @smbmathbiology.bsky.social 
• Facebook: @smb.org 
• Linkedin: @smb_mathbiology 
• The Bulletin of Mathematical Biology

Apple Link      Spotify Link     Read the full transcript

Approximate solutions of a general stochastic velocity-jump model subject to discrete-time noisy observations

by Arianna Ceccarelli, Alexander P. Browning, and Ruth E. Baker

We analyse velocity-jump models for single-agent motion in one spatial dimension, in which the agent transitions between n states, each with a set velocity and fixed switching rates to other states. Since the agent’s true state cannot be observed, computing the exact distributions of discrete-time noisy data is generally intractable. Therefore, we derive approximations for the observed data distributions, both for a single measurement and considering the correlation between locations of a tracked agent, and validate them through simulations of four model structures. These approximations enable fast predictions, guide experimental design, and can be used as likelihoods for inference and model selection.

Solutions of an example three-state model. The three-state model (panel B) comprises a forward-movement state, a backward-movement state and a stationary state. A short data track is shown in panel A. A location increment y is defined. as the difference between subsequently measured locations. Panel C compares the empirical distribution, generated with in-silico data, to one of the approximations obtained in the manuscript for single location increments. Panel D compares the empirical distribution for two subsequent location increments to the joint approximation obtained in the manuscript, and to the marginal approximation which does not take into account the correlation between subsequent location increments.

Modeling Innate Immunity Causing Chronic Inflammation and Tissue Damage

by Kosei Matsuo and Yoh Iwasa

We examine a simple dynamical model of innate immunity. The analysis indicates that when an infection occurs, it triggers inflammation, which activates the innate immune system and initiates the activation cycle. Consequently, pathogens may be eradicated, leaving behind persistent chronic inflammation. Alternatively, the pathogens may not be eradicated, with their abundance either stabilizing at a positive level or oscillating indefinitely. When innate immunity is activated in the absence of inflammation, pathogens are eradicated more easily, and the likelihood of oscillations in inflammation, immune responses, and pathogen abundance is reduced.

Phase plane showing three different behaviors.

Modeling the Impact of Seasonality on Mosquito Population Dynamics: Insights for Vector Control Strategies

by Joseph Baafi and Amy Hurford

Population models are tools for studying the dynamics of organisms and their interactions with the environment, but oversimplifying these models can lead to inaccurate predictions. Our research highlights this challenge by developing a model that incorporates seasonal temperature and rainfall patterns. We demonstrate how environmental factors significantly influence mosquito abundance. Neglecting these factors may lead to biased predictions, while our approach highlights regional differences in mosquito dynamics. For instance, the optimal timing, duration, and strategies for vector control may vary across regions due to differences in seasonal weather. We identified key parameters that drive model outcomes using sensitivity analysis.

Mathematical model illustrating regional variations in mosquito dynamics and peak abundance timing, emphasizing the importance of region-specific control strategies.