Biology subjects

Christodoulou, M. D.

Publications and source records attributed to Christodoulou, M. D..

8 recordsLinked to original sources

bioRxiv2026

ABC_GP_IPM:A Python package for applying GP and ABC to Integral Projection Models, with a Soay sheep case study

The integration of Gaussian Process (GP) models with Approximate Bayesian Com-putation (ABC) has been explored as a flexible framework for constructing Integral Projection Models (IPMs), enabling non-parametric modelling of demographic rela-tionships and the incorporation of population-level information without explicit like-lihoods. However, the practical implementation of this framework - particularly the selection of ABC summary statistics and the execution of ABC-PMC samplers - re-mains non-trivial and can limit its broader adoption. To address this gap, we introduce ABC_GP_IPM, a Python package that provides a streamlined and user-friendly interface for constructing GP- and ABC-based IPMs. We demonstrate the utility and performance of the package through a real-world case study of Soay sheep (Ovis aries), illustrating how the software simplifies complex modelling workflows and data management in IPMs.

evolutionary biology↗

bioRxiv2025

Improving Joint Estimation of Vital Rates in IPMs via Gaussian Processes and ABC

O_LIDeveloping population dynamic models that can flexibly adapt to different species remains a challenge due to the context-dependent nature of demographic studies. This work introduces ABC GP IPM, a novel Integral Projection Model (IPM) framework, aimed at mitigating key limitations associated with traditional IPMs, including (i) reliance on predefined vital rate models with restrictive assumptions, (ii) omission of potential vital rate interactions, and (iii) limited flexibility in incorporating domain- or user-specific knowledge. The purpose of this work is to enhance the adaptability and accuracy of IPMs across various ecological and evolutionary studies. C_LIO_LIOur methodology integrates Gaussian Process (GP) models with Approximate Bayesian Computation (ABC). GP models introduce a non-parametric structure that accommodates a broader range of demographic patterns, reducing sensitivity to specific model choices. The incorporation of ABC methods allows IPMs to integrate additional population-level information, enabling potential interactions among vital rates to be reflected in model outcomes without requiring additional datasets. The information can be user-modified, facilitating the tailoring of IPMs to specific requirements or domain-specific knowledge. Recognising the inherent challenge of selecting appropriate summary statistics in ABC applications, we further propose a systematic procedure tailored to navigate this complexity effectively when applying ABC GP to IPMs. C_LIO_LIThe method demonstrated strong flexibility and adaptability across different modeling scenarios. In simulation studies, ABC GP IPM showed notable improvements over traditional GLM-based IPMs across multiple population metrics commonly used in biological research. When applied to real-world datasets, the method captured complex demographic patterns more effectively, supporting its potential as a practical and adaptable alternative to existing IPM frameworks. C_LIO_LIThe ABC GP IPM framework provides greater flexibility in model specification, accommodating potential non-linearities with fewer assumptions. It also facilitates the incorporation of user expertise and domain-specific knowledge, enhancing its applicability to context-dependent demographic studies. While offering a more adaptable modeling approach, the method remains accessible to users without specialized expertise in statistical modeling. Importantly, ABC GP IPM delivers these improved outcomes using identical datasets as standard IPMs, facilitating easier adoption for studies that have applied traditional IPMs. C_LI

evolutionary biology↗

bioRxiv2025

ABC for high-dimensional modular models via MCMC samples

Many complex systems are modelled using modular models, where individual sub-models are estimated separately and then combined. While this simplifies inference, it fails to account for interactions between components. A natural solution is to estimate all components jointly, but this is often impractical due to intractable likelihoods. Approximate Bayesian Computation (ABC) provides a likelihood-free alternative, but its standard implementations are computationally inefficient, particularly when applied to high-dimensional modular models, or when sub-models involve costly machine learning methods, like Gaussian Process (GP) models. The ABC-Population Monte Carlo (ABC-PMC) framework improves on vanilla ABC by using sequential Monte Carlo sampling with adaptive tolerances and proposal kernels, yielding much higher acceptance rates and more efficient exploration of parameter space. Existing ABC-PMC algorithms are not, however, especially efficient in the high-dimensional parameter setting typical of modular models. We introduce a novel modification of the ABC-PMC method that leverages model modularity. Our approach refines the prior distribution and perturbation kernel by using precomputed Markov Chain Monte Carlo (MCMC) samples from individual sub-models, making parameter updates more efficient. Additionally, we employ an adaptive summary statistic weighting strategy that dynamically adjusts the contribution of different statistics, reducing the influence of less informative statistics. These modifications greatly reduce overall computational cost. In our case studies, the runtime for 10,000 simulation attempts drops from over 20 days to under 1 minute, following a one-off preprocessing step that consists of standard MCMC sampling for each sub-model (typically 3-10 hours, depending on model complexity). We apply our method to an ecological case study using an Integral Projection Model (IPM) for Cryptantha flava, where survival, growth, and reproduction processes are modelled using GP models. The results of the simulated and the real case studies demonstrate greatly improved computational efficiency while preserving inference quality. While the case study focuses on ecology, the method is applicable to a broad range of modular models where capturing interactions among sub-models is essential.

ecology↗

bioRxiv2023

Allometry of cell types in planarians by single cell transcriptomics

Allometry explores the relationship between an organisms body size and its various components, offering insights into ecology, physiology, metabolism, and disease. The cell is the basic unit of biological systems, and yet, the study of cell type allometry remains relatively unexplored. Single-cell RNA sequencing (scRNA-seq) provides a promising tool for investigating cell type allometry. Planarians, capable of growing and degrowing following allometric scaling rules, serve as an excellent model for such studies. We used scRNA-seq to examine cell type allometry in asexual planarians of different sizes, revealing that they consist of the same basic cell types but in varying proportions. Notably, the gut basal cells are the most responsive to changes in size, suggesting a role in energy storage. We capture the gene regulatory programs of distinct cell types in response to size. This research sheds light on the molecular and cellular aspects of cell type allometry in planarians and underscores the utility of scRNA-seq in such investigations.

systems biology↗

bioRxiv2021

Comparative plastomics of Amaryllidaceae: Inverted repeat expansion and the degradation of the ndh genes in Strumaria truncata Jacq.

Amaryllidaceae is a widespread and distinctive plant family contributing both food and ornamental plants. Here we present an initial survey of plastomes across the family and report on both structural rearrangements and gene losses. Most plastomes in the family are of similar gene arrangement and content however some taxa have shown gains in plastome length while in several taxa there is evidence of gene loss. Strumaria truncata shows a substantial loss of ndh family genes while three other taxa show loss of cemA, which has been reported only rarely. Our sparse sampling of the family has detected sufficient variation to suggest further sampling across the family could be a rich source of new information on plastome variation and evolution.

plant biology↗

bioRxiv2020

Apples before the fall: does shape stability coincide with maturity?

Fruit shape is the result of the interaction between genetic, epigenetic, environmental factors, and stochastic processes. As a core biological descriptor both for taxonomy and horticulture, the point at which shape stability is reached becomes paramount in apple cultivar identification, and authentication in commerce. Twelve apple cultivars were sampled at regular intervals from anthesis to harvest over two growing seasons. Linear and geometric morphometrics were analyzed to establish if and when shape stabilized and whether fruit asymmetry influenced this. Shape stability was detected in seven cultivars, four asymmetric and three symmetric. The remaining five did not stabilize. Shape stability, as defined here, is cultivar-dependent, and when it occurs, it is late in the growing season. Geometric morphometrics detected stability more readily than linear, especially in symmetric cultivars. Key shape features are important in apple marketing, giving the distinctness and apparent uniformity between cultivars expected at point of sale.

plant biology↗

bioRxiv2020

When do apples stop growing, and why does it matter?

Background Apples in the commercial food chain are harvested up to two weeks before maturity. We explore apple fruit development through the growing season to establish the point at which the features differentiating those cultivars become evident. This is relevant both for the understanding of the growing process and to ensure that any identification and classification tools can be used both on ripened-on-tree and stored fruit. Current literature presents some contradictory findings on apple development, we explored the size development of 12 apple cultivars in the Brogdale National Fruit Collection, UK over two growing seasons.Methods Fruit were sampled at regular time points throughout the growing season and four morphometrics (maximum length, maximum diameter, weight, and centroid size) were collected. These were regressed against growing degree days in order to appropriately describe the growth pattern observed.Results All four morphometrics were adequately described using log-log linear regressions, with adjusted R2 estimates ranging from 78.3% (maximum length) to 86.7% (weight). For all four morphometrics, a 10% increase in growing degree days was associated with a 1% increase in the morphometric measurement.Discussion Our findings refine previous work presenting rapid early growth followed by a plateau in later stages of development and are in disagreement with published expo-linear models. We established that apples harvested for commercial storage purposes, two weeks prior to maturity, showed only a modest decrease in size, demonstrating that size morphometric approaches are appropriate for classification of apples, both ripened-on-tree and stored.Competing Interest StatementThe authors have declared no competing interest.View Full Text

plant biology↗

bioRxiv2018

Can you make morphometrics work when you know the right answer? Pick and mix approaches for apple identification.

Morphological classification of living things has challenged science for several centuries and has led to a wide range of objective morphometric approaches in data gathering and analysis. In this paper we explore those methods using apple cultivars, a model biological system in which discrete groups are pre-defined but in which there is a high level of overall morphological similarity. The effectiveness of morphometric techniques in discovering the groups is evaluated using statistical learning tools. No one technique proved optimal in classification on every occasion, linear morphometric techniques slightly out-performing geometric (72.6% accuracy on test set versus 66.7%). The combined use of these techniques with post-hoc knowledge of their individual successes with particular cultivars achieves a notably higher classification accuracy (77.8%). From this we conclude that even with pre-determined discrete categories, a range of approaches is needed where those categories are intrinsically similar to each other, and we raise the question of whether in studies where potentially continuous natural variation is being categorised the level of match between categories is routinely set too high.

plant biology↗