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Ensemble Optimal Control for Managing Drug Resistance in Cancer Therapies

by Alessandro Scagliotti; Federico Scagliotti; Laura Deborah Locati; Federico Sottotetti

Cancer treatment often relies on the highest dose a patient can tolerate, but this may not be the best way to control tumors over time. This study uses mathematical modeling to explore a different approach: managing the balance between drug-sensitive and drug-resistant cancer cells. By simulating prostate cancer treated with androgen deprivation therapy, the authors show how treatment timing could be adapted rather than kept continuously high. The proposed “Off-On” adaptive therapy starts with observation and introduces treatment only when needed, aiming to keep the disease under long-term control. The work suggests that smarter schedules, not simply more drug, may improve chronic cancer management.

Image Description: Left: Ensemble optimal control approach. Center: On-Off Adaptive Therapy. Right: Off-On Adaptive Therapy.

Revisiting Turing's Chemical Basis of Morphogenesis

by John J. Tyson

Although Alan Turing’s 1952 paper on the chemical basis of morphogenesis is a classic of theoretical biology, it is notoriously difficult to read. His chemical examples are unusual, his linear stability analysis of the homogeneous steady state seems unnecessarily complex, and his numerical simulations are mysterious. To make Turing’s paper more accessible, I pose his reaction-diffusion equations in dimensionless form, place his linear stability analysis in the context of later approaches, revise his models in more chemically realistic terms, and provide help for computing Turing patterns in one- or two spatial dimensions. I also discuss how Turing’s stationary patterns relate to traveling waves in reaction-diffusion equations.

Does Timing Matter? Exploring the Effects of Measurement Error on Models

by Brock D. Sherlock; Marko A.A. Boon; Maria Vlasiou; Adelle C.F. Coster

Measurement error is inevitable in experimental data collection. Mathematical biologists typically account for dependent variable errors, while independent variable errors are less commonly considered. This work investigates how independent-variable measurement error affects parameter inference in biological systems and reviews statistical methods to address it. We find that parameter inference is often robust to measurement errors, even without explicitly accounting for them. However, some systems are susceptible to these errors, leading to biased parameter estimates. We evaluate correction methods, focusing on their assumptions and data requirements, to guide researchers in selecting appropriate approaches for their specific contexts.

Image Description: Illustration of the effects of measurement error in the independent variable. An oscillating model, the amplitude is estimated from synthetic data. The experimental protocol prescribes independent variable values for data collection. These designated times align with the peaks and troughs of the oscillation. However, the independent variable is subject to error and the true values at which measurements are taken are distributed about the prescribed values. When the true independent variable values are recorded (top) naïve parameter estimation can recover the amplitude that generated the data. However, when only the protocol prescribed values are recorded, as opposed to the true independent variable value at measurement, (bottom) naive parameter estimation leads to a biased estimate.

Damage-Driven Irreversibility and Emergent Senescence in Age–Structured Cell Populations

by Koffi Enakoutsa

A transient stress episode is sufficient to cause a lasting increase in the senescent cell fraction — even after the stressor is completely removed.

Article Summary and Graphical Abstract

A Hallmark-Integrated, Agent-Based Framework for Intratumor Heterogeneity in Melanoma Evolution

by Khola Jamshad, Trachette L. Jackson

We introduce a computational model that uses biologically informed cell behaviors to simulate how genetic diversity within a single tumor shapes its growth and structure over time. The model accounts for mutation-specific advantages, and interactions between tumor and immune cells. Focusing on melanoma, we find that simulated tumors can develop three distinct levels of heterogeneity, influenced by how frequently new mutations arise and by the tumor’s ability to attract immune cells. We also show that tumor cell movement is necessary to reproduce the complex tumor shapes observed in patients. Together, these findings provide a framework for building patient-specific tumor models that connect genetic information to tumor behavior.

A scheme for the BEP-HIM agent-based model for tumor evolution with key findings for melanoma.

Mono- and Polyauxic Growth Kinetics: A Semi-Mechanistic Framework for Complex Biological Dyanmics

by Gustavo Mockaitis

Understanding how microbes grow in complex mixtures, like those in bioenergy and waste valorization, is tricky. Current math models are either too basic or demand impractical amounts of data. This study introduces a smart, open-source tool that bridges the gap. It breaks down messy, multi-phase growth curves into clear, overlapping steps. By using automated algorithms to filter bad data and find the best fit, it pulls real biological insights, such as true growth rates and delay times, straight from standard, easy-to-collect observations. It’s a reliable way to turn everyday reactor data into deep, actionable understanding.

Unified semi-mechanistic framework for polyauxic microbial growth analysis. Experimental biomass-versus-time data, illustrated with a chemostat context, are processed through a modeling pipeline that reformulates canonical sigmoidal equations, estimates parameters by global and local optimization, and performs model selection. The output is an overall fitted curve decomposed into individual growth phases, yielding interpretable phase-specific kinetic parameters such as maximum growth rate and lag time.

Emergence of Bursting and Delay-Induced Spiral Patterns in Eco-Epidemiological Systems

by Namrata Mani Tripathi

Understanding the spatio-temporal dynamics of interacting populations is crucial for ecological systems. We develop an eco-epidemic model with susceptible and infected prey and predators, incorporating carryover $(f_1)$, fear $(f_2)$, and recovery $(\gamma)$. Existence, boundedness, and Hopf bifurcation are established. Without delays, $f_1$ stabilizes while $f_2$ destabilizes dynamics, and recovery affects populations. With delays, chaotic oscillations and bursting arise in unstable regimes, while sufficient recovery suppresses delay effects. Spatial analysis shows Turing patterns, where delays and recovery shape spirals and clusters, influencing ecosystem stability.

Delay-driven eco-epidemic dynamics illustrating how fear, carryover, and recovery generate chaotic oscillations and spiral pattern formation in space.

Final-size solutions for SIRI models with vaccination

by Maria A. Gutierrez and Julia R. Gog

This work extends the deterministic SIR epidemic model to allow reinfections of individuals in the recovered compartment. Hosts with prior immunity, elicited from vaccination or a past infection, are less susceptible to the disease. We interpret partial host immunity as either all-or-none or leaky. For both interpretations, we find final-size solutions for the cumulative number of reinfections and primary infections across a transient epidemic wave. These analytical expressions depend on the vaccination coverage of the host population, the vaccine efficacy on naive hosts, the relative susceptibility to reinfection, and the basic reproduction number (R0). If R0 is above a reinfection threshold, the leaky model has an endemic equilibrium.

Graphical abstract

Observer-Based Source Localization in Tree Infection Networks via Laplace Transforms

by Graham Kesler O’Connor, Julia M. Jess, Devlin Costello, Manuel E. Lladser

Pinpointing "patient zero" in an outbreak, whether a biological disease, a computer virus, or rumor is notoriously difficult. Our paper introduces two new statistical methods based on Laplace transforms to trace the origin of an infection in tree networks when only a subset of nodes report their infection times. This makes our methods suitable for any situation in which a susceptible-infected (SI) infection spreads through a network without loops, with infected nodes infecting susceptible neighbors after random, independent delays, with explicit Laplace transforms. In particular, our methods provide public health, cybersecurity, and intelligence officials with a general tool for tracing and containing outbreaks.

Middle: Formulation of the observers' infection times using Laplace transforms of the edge delays, alongside a proposed source estimator derived from the empirical Laplace transform of the observers. Left: Source localization on a linear network with node 0 as the sole observer, as the infection source shifts from node 1 to node 10. Right: Source localization performance along the Thukela River basin, where the leftmost node is the true source, estimated using the simulated infection times of three downstream observers selected at random.

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