Biology subjects

Giles, M.

Publications and source records attributed to Giles, M..

4 recordsLinked to original sources

bioRxiv2025

Chemotherapy dose scheduling via Q-learning in a Markov tumor model

We describe a Q-learning approach to optimized chemotherapy dose scheduling in a stochastic finite-cell Markov process that models tumor cell natural selection dynamics. The three competing subpopulations comprising our virtual tumor are a chemo-sensitive population (S), and two chemo-resistant populations, R1 and R2, each resistant to one of two drugs, C1 and C2. The two drugs are toggled off or on which constitute the actions (selection pressure) imposed on our state-variables (S, R1, R2), measured as proportions in our finite state-space of N cancer cells (S + R1 + R2 = N). After the converged chemo-dosing policies are obtained, corresponding to a given reward structure, we focus on three important aspects of chemotherapy dose scheduling. First, we identify the most likely evolutionary paths of the tumor cell populations in response to the optimized (converged) policies. Second, we quantify the robustness in the ability to reach our target of balanced co-existence in light of incomplete information in both the initial cell-populations as well as the state-variables at each step. Third, we evaluate the efficacy of simplified policies which exploit the symmetries uncovered from an examination of the full policy. Our reward structure is designed to delay the onset of chemo-resistance in the tumor by rewarding a well-balanced mix of co-existing states, while punishing unbalanced subpopulations to avoid extinction.

systems biology↗

bioRxiv2018

Using GPUs to accelerate computational diffusion MRI: From microstructure estimation to tractography and connectomes

The great potential of computational diffusion MRI (dMRI) relies on indirect inference of tissue microstructure and brain connections, since modelling and tractography frameworks map diffusion measurements to neuroanatomical features. This mapping however can be computationally highly expensive, particularly given the trend of increasing dataset sizes and the complexity in biophysical modelling. Limitations on computing resources can restrict data exploration and methodology development. A step forward is to take advantage of the computational power offered by recent parallel computing architectures, especially Graphics Processing Units (GPUs). GPUs are massive parallel processors that offer trillions of floating point operations per second, and have made possible the solution of computationally-intensive scientific problems that were intractable before. However, they are not inherently suited for all problems. Here, we present two different frameworks for accelerating dMRI computations using GPUs that cover the most typical dMRI applications: a framework for performing biophysical modelling and microstructure estimation, and a second framework for performing tractography and long-range connectivity estimation. The former provides a front-end and automatically generates a GPU executable file from a user-specified biophysical model, allowing accelerated non-linear model fitting in both deterministic and stochastic ways (Bayesian inference). The latter performs probabilistic tractography, it can generate whole-brain connectomes and supports new functionality for imposing anatomical constraints, such as inherent consideration of surface meshes (GIFTI files) along with volumetric images. We validate the frameworks against well-established CPU-based implementations and we show that despite the very different challenges for parallelising these problems, GPU-based designs can offer accelerations of more than two orders of magnitude in both cases.

neuroscience↗

bioRxiv2018

Seasonal changes in diet and toxicity in the Climbing Mantella frog (Mantella laevigata)

Poison frogs acquire chemical defenses from the environment for protection against potential predators. These defensive chemicals are lipophilic alkaloid toxins that are sequestered by poison frogs from dietary arthropods and stored in skin glands. Despite decades of research focusing on identifying poison frog toxins, we know relatively little about how environmental variation and subsequent arthropod availability impacts toxicity in poison frogs. We investigated how seasonal environmental variation influences poison frog toxin profiles through changes in the diet of the Climbing Mantella (Mantella laevigata). We collected M. laevigata females on the Nosy Mangabe island reserve in Madagascar during the wet and dry seasons and tested the hypothesis that seasonal differences in rainfall is associated with changes in the diet and skin toxin profiles of M. laevigata. The arthropod diet of each frog was characterized into five groups (i.e. ants, termites, mites, insect larvae, or other) using visual identification and cytochrome oxidase 1 DNA barcoding. We found that frog diet differed between the wet and dry seasons, where frogs had a more diverse diet in the wet season and consumed a higher percentage of ants in the dry season. To determine if seasonality was associated with variation in frog defensive chemical composition, we used gas chromatography / mass spectrometry to quantify toxins from individual skin samples. Although the assortment of identified toxins was similar across seasons, we detected significant differences in the abundance of certain alkaloids, which we hypothesize reflects seasonal variation in the diet of M. laevigata. We suggest that these variations could originate from seasonal changes in either arthropod leaf litter composition or changes in frog behavioral patterns. Although additional studies are needed to understand the consequences of long-term environmental shifts, this work suggests that toxin profiles are relatively robust against short-term environmental perturbations.

ecology↗

bioRxiv2016

Engineering Genetically-Encoded Mineralization and Magnetism via Directed Evolution

Genetically encoding the synthesis of functional nanomaterials such as magnetic nanoparticles enables sensitive and non-invasive biological sensing and control. Via directed evolution of the natural iron-sequestering ferritin protein, we discovered key mutations that lead to significantly enhanced cellular magnetism, resulting in increased physical attraction of ferritin-expressing cells to magnets and increased contrast for cellular magnetic resonance imaging (MRI). The magnetic mutants further demonstrate increased iron biomineralization measured by a novel fluorescent genetic sensor for intracellular free iron. In addition, we engineered Escherichia coli cells with multiple genomic knockouts to increase cellular accumulation of various metals. Lastly to explore further protein candidates for biomagnetism, we characterized members of the DUF892 family using the iron sensor and magnetic columns, confirming their intracellular iron sequestration that results in increased cellular magnetization.

synthetic biology↗