Biology subjects

Wilkie, K. P.

Publications and source records attributed to Wilkie, K. P..

5 recordsLinked to original sources

bioRxiv2026

Toroidal Search Algorithm: A Topology-Inspired Metaheuristic with Applications to ODE Parameterization in Mathematical Oncology

We present the Toroidal Search Algorithm (TSA), a novel population-based metaheuristic optimization method inspired by the topology of a torus. Conventional metaheuristics frequently suffer from boundary stagnation, a phenomenon that severely degrades performance in bounded and high-dimensional search spaces. TSA addresses this limitation by embedding the search domain into a toroidal geometry, thereby eliminating artificial boundaries and enabling continuous cyclic exploration. Beyond boundary handling, TSA uses winding numbers to capture the history of agent movement across periodic dimensions, which are exploited to adaptively refine local search. A modified sigmoid control function regulates the transition between global and local search. Performance of TSA is evaluated on a collection of unimodal and multimodal benchmark functions at various dimensions. It consistently outperforms established metaheuristics. Notably, TSA demonstrates exceptional robustness to increasing dimensionality, maintaining fast convergence and low variance where competing methods deteriorate. To assess real-world applicability, we apply TSA to an inverse problem from mathematical oncology. With both synthetic and clinical data, TSA reliably recovers physiologically plausible parameters with greater stability and predictive accuracy than competing algorithms. These results demonstrate that TSA is a powerful and robust tool for large-scale global optimization in computational modelling applications. Striking Image O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/709766v1_ufig1.gif" ALT="Figure 1"> View larger version (131K): org.highwire.dtl.DTLVardef@bb604aorg.highwire.dtl.DTLVardef@1ae9cb3org.highwire.dtl.DTLVardef@b1c126org.highwire.dtl.DTLVardef@1ffa938_HPS_FORMAT_FIGEXP M_FIG C_FIG Image generated with Google Gemini.

systems biology↗

bioRxiv2025

Delaying Cancer Progression by Integrating Toxicity Constraints in a Model of Adaptive Therapy

Cancer therapies often fail when intolerable toxicity or drug-resistant cancer cells undermine otherwise effective treatment strategies. Over the past decade, adaptive therapy has emerged as a promising approach to postpone emergence of resistance by altering dose timing based on tumor burden thresholds. Despite encouraging results, these protocols often overlook the crucial role of toxicity-induced treatment breaks, which may permit tumor regrowth. Herein, we explore the following question: would incorporating toxicity feedback improve or hinder the efficacy of adaptive therapy? To address this question, we propose a mathematical framework for incorporating toxic feedback into treatment design. We find that the degree of competition between sensitive and resistant populations, along with the growth rate of resistant cells, critically modulates the impact of toxicity feedback on time to progression. Further, our model identifies circumstances where strategic treatment breaks, which may be based on either tumor size or toxicity, can mitigate overtreatment and extend time to progression, both at the baseline parameterization and across a heterogeneous virtual population. Taken together, these findings highlight the importance of integrating toxicity considerations into the design of adaptive therapy.

systems biology↗

bioRxiv2022

Approaches to generating virtual patient cohorts with applications in oncology

Virtual clinical trials (VCTs) have gained popularity for their ability to rationalize the drug development process using mathematical and computational modelling, and to provide key insights into the mechanisms regulating patient responses to treatment. In this chapter, we cover approaches for generating virtual cohorts with applications in cancer biology and treatment. VCTs are an effective tool for predicting clinical responses to novel therapeutics and establishing effective treatment strategies. These VCTs allow us to capture inter-individual variability (IIV) which can lead to diversity in patient drug responses. Here we discuss three main methodologies for capturing IIV with a VCT. First, we highlight the use of population pharmacokinetic (PopPK) models, which extrapolate from empirical data population PK parameters that best fits the individual variability seen in drug disposition using non-linear mixed effects models. Next, we show how virtual patients may be sampled from a normal distribution with mean and standard deviation informed from experimental data to estimate parameters in a mechanistic model that regulates drug PKs. Lastly, we show how optimization techniques can be used to calibrate virtual patient parameter values and generate the VCT. Throughout, we compare and contrast these methods to provide a broader view of the generation of virtual patients, and to aid the decision-making process for those looking to leverage virtual clinical trials in their research.

cancer biology↗

bioRxiv2020

Mathematical Model of Muscle Wasting in Cancer Cachexia

Cancer cachexia is a debilitating condition characterized by an extreme loss of skeletal muscle mass which negatively impacts patients quality of life, reduces their ability to sustain anticancer therapies, and increases the risk of mortality. Recent discoveries have identified the myostatin/activin-ActRIIB pathway as critical to muscle wasting by inducing satellite cell quiescence and increasing muscle-specific ubiquitin ligases responsible for atrophy. Remarkably, pharmacological blockade of the ActRIIB pathway has shown to reverse muscle wasting and prolong the survival time of tumor-bearing animals. To explore the implications of this signaling pathway and potential therapeutic targets in cachexia, we construct a novel mathematical model of muscle tissue subjected to tumor-derived cachexic factors. The model formulation tracks the intercellular interactions between cancer, satellite cell, and muscle cell populations. The model is parameterized by fitting to colon-26 mouse model data, and analysis provides insight into tissue growth in healthy, cancerous, and post-treatment conditions. Model predictions suggest that cachexia fundamentally alters muscle tissue health, as measured by the stem cell ratio, and this is only partially recovered by anti-cachexia treatment. Our mathematical findings suggest that the activation and proliferation of satellite cells, after blocking the myostatin/activin B pathway, is required to partially recover cancer-induced muscle loss.

cancer biology↗

bioRxiv2020

Mathematically Modeling Inflammation as a Promoter of Tumour Growth

Inflammation is now known to play a significant role in tumour growth and progression. It is also difficult to adequately quantify systemic inflammation and the resulting localized effects in cancer. Here we use experimental data to infer the possible contributions of inflammation in a mouse model of cancer. The model is validated by predicting tumour growth under anti-inflammatory treatments, and combination cancer therapies are explored. We then extend the model to consider simultaneous tumour implants at two distinct sites, which experimentally was shown to result in one large and one small tumour. We use this model to examine the role inflammation may play in the growth rate separation. Finally, we use this predictive two-tumour model to explore implications of inflammation on metastases, surgical removal of the primary, and adjuvant anti-inflammatory treatments. This work suggests that improved tumour control can be obtained by targeting both the cancer and host, through anti-inflammatory treatments, including reduced metastatic burden post-surgical removal of primary tumours.

cancer biology↗