Biology subjects

Plank, M. J.

Publications and source records attributed to Plank, M. J..

13 recordsLinked to original sources

bioRxiv2025

On the imbalance between production and exploitation of marine fish assemblages: a case study from the Celtic Seas

Sustainable management of marine ecosystems has to take into account the conservation of many species that are not themselves targets of fishing, in addition to those that are commercially exploited. Dynamic size-spectrum models suggest that fishing which leads species to have similar ratios of yield to production (i.e. similar exploitation ratios) is not sufficient to protect those that are rare: these species need to experience lower exploitation ratios. Here, the status of the demersal fish assemblage in the Celtic Seas is examined from the perspective of yield and production, using survey data collected over the period 2012 to 2016, and incorporating both common and rare species. The results give no evidence that rarer species have lower exploitation ratios than common ones. This suggests that current management methods are not not operating in a way that conserves the fish assemblage as a whole.

ecology↗

bioRxiv2025

Inference and prediction for random walkmodels in biology

Parameter inference is a critical step in the process of interpreting biological data using mathematical models. Inference provides a means of deriving quantitative, mechanistic insights from sparse, noisy data. While methods for parameter inference, parameter identifiability, and model prediction are well-developed for deterministic continuum models, working with biological applications often requires stochastic modelling approaches to capture inherent variability and randomness that can be prominent in biological measurements and data. Random walk models are especially useful for capturing spatiotemporal processes, such as ecological population dynamics, molecular transport phenomena, and collective behaviour associated with multicellular phenomena. This review focuses on parameter inference, identifiability analysis, and model prediction for a suite of biologically-inspired, stochastic agent-based models relevant to animal dispersal and populations of biological cells. With a particular emphasis on model prediction, we highlight roles for numerical optimisation and automatic differentiation. Open-source Julia code is provided to support scientific reproducibility. We encourage readers to use this code directly or adapt it to suit their interests and applications.

systems biology↗

bioRxiv2023

Network inference from temporal phosphoproteomics informed by protein-protein interactions

Network inference from time-course data holds the promise to overcome challenges associated with other methods for deciphering cell signaling networks. Integration of protein-protein interactions in this process is frequently employed to limit wiring options. In this study, a graph approach for the analysis of data of high temporal resolution is introduced and applied to a 5 s resolution phosphoproteomics dataset. Steiner trees informed by protein-protein interactions are constructed on individual time slices, which are then stitched together into a temporal signaling network. Systematic benchmarking against existing knowledge indicates that the approach enriches signaling-relevant edges. The workflow is compatible with future extensions for reliably extracting extended signaling paths.

bioinformatics↗

bioRxiv2021

A stochastic mathematical model of 4D tumour spheroids with real-time fluorescent cell cycle labelling

In vitro tumour spheroid experiments have been used to study avascular tumour growth and drug design for the last 50 years. Unlike simpler two-dimensional cell cultures, tumour spheroids exhibit heterogeneity within the growing population of cells that is thought to be related to spatial and temporal differences in nutrient availability. The recent development of real-time fluorescent cell cycle imaging allows us to identify the position and cell cycle status of individual cells within the growing population, giving rise to the notion of a four-dimensional (4D) tumour spheroid. In this work we develop the first stochastic individual-based model (IBM) of a 4D tumour spheroid and show that IBM simulation data qualitatively and quantitatively compare very well with experimental data from a suite of 4D tumour spheroid experiments performed with a primary human melanoma cell line. The IBM provides quantitative information about nutrient availability within the spheroid, which is important because it is very difficult to measure these data in standard tumour spheroid experiments. Software required to implement the IBM is available on GitHub, https://github.com/ProfMJSimpson/4DFUCCI.

biophysics↗

bioRxiv2021

pSiteExplorer: Visualization of the mass spectrometric data underlying phospho-site quantification

Mass spectrometry based phospho-proteomics is a widely used approach to assess protein phosphorylation. Intensities of phospho-peptide ions are obtained by integrating the MS signal over their chromatographic peaks. How individual peptide measurements mapping to the same phospho-site are combined for the quantification of the given site is, however, in most cases hidden from researchers conducting, reviewing, and reading these studies. I here describe pSiteExplorer, an R script that visualizes the peak intensities associated with phospho-sites in MaxQuant output tables. Barplots of MS intensities originating from phospho-peptides with distinct amino acid sequences due to missed cleavages, different numbers of phosphates and from all off-line chromatographic fractions and charge states are displayed. This tool will help gaining a deeper insight into phospho-site quantifications by contrasting individual and summed phospho-peptide intensities with the site-level values derived by MaxQuant. This will support the validation of quantification results, for example, for the selection of candidates for follow-up studies.

bioinformatics↗

bioRxiv2021

Fishing for biodiversity

Fisheries are damaging, and seemingly incompatible with the conservation of marine ecosystems. Yet fish are an important source of food, and support the lives of many people in coastal communities. This paper considers an idea that a moderate intensity of fishing, appropriately scaled across species, could help in maintaining biodiversity, rather than reducing it. The scaling comes from an intuition that rates of fishing mortality of species should be kept in line with production rates of the species, a notion known as balanced harvesting. This places species conservation and exploitation on an equal footing in a single equation, showing quantitatively the relative levels of fishing mortality that species of different abundance can support. Using a dynamic model of a marine ecosystem, we give numerical evidence showing for the first time that fishing, if scaled in this way, can protect rarer species, while allowing some exploitation of species with greater production. This works because fishing mortality rates, when scaled by production, are density-dependent. Such fishing, operating adaptively to follow species production rates over time, contains a feedback that would help to protect species from overfishing in the presence of uncertainty about how marine ecosystems work.

ecology↗

bioRxiv2020

Population dynamics with spatial structure and an Allee effect

Allee effects describe populations in which long-term survival is only possible if the population density is above some threshold level. A simple mathematical model of an Allee effect is one where initial densities below the threshold lead to population extinction, whereas initial densities above the threshold eventually asymptote to some positive carrying capacity density. Mean field models of population dynamics neglect spatial structure that can arise through short-range interactions, such as short-range competition and dispersal. The influence of such non mean-field effects has not been studied in the presence of an Allee effect. To address this we develop an individual-based model (IBM) that incorporates both short-range interactions and an Allee effect. To explore the role of spatial structure we derive a mathematically tractable continuum approximation of the IBM in terms of the dynamics of spatial moments. In the limit of long-range interactions where the mean-field approximation holds, our modelling framework accurately recovers the mean-field Allee threshold. We show that the Allee threshold is sensitive to spatial structure that mean-field models neglect. For example, we show that there are cases where the mean-field model predicts extinction but the population actually survives and vice versa. Through simulations we show that our new spatial moment dynamics model accurately captures the modified Allee threshold in the presence of spatial structure.

biophysics↗

bioRxiv2020

Cooperative and non-cooperative behaviour in the exploitation of a common renewable resource with environmental stochasticity

Classical fisheries biology aims to optimise fisheries-level outcomes, such as yield or profit, by controlling the fishing effort. This can be adjusted to allow for the effects of environmental stochasticity, or noise, in the population dynamics. However, when multiple fishing entities, which could represent countries, commercial organisations, or individual vessels, can autonomously determine their own fishing effort, the the optimal action for one fishing entity depends on the actions of others. Coupled with noise in the population dynamics, and with decisions about fishing effort made repeatedly, this becomes an iterated stochastic game. We tackle this problem using the tools of stochastic optimisation, first for the monopolists problem and then for the duopolists problem. In each case, we derive optimal policies that specify the best level of fishing effort for a given stock biomass. Under these optimal policies, we can calculate the equilibrium stock biomass, the expected long-term return from fishing and the probability of stock collapse. We also show that there is a threshold stock biomass below which it is optimal to stop fishing until the stock recovers. We then develop an agent-based model to test the effectiveness of simple strategies for responding to deviations by an opponent from a cooperative fishing level. Our results show that the economic value of the fishery to a monopolist, or to a consortium of fishing agents, is robust to a certain level of noise. However, without the means of making agreements about fishing effort, even low levels of noise make sustained cooperation between autonomous fishing agents difficult.

ecology↗

bioRxiv2019

Small-scale spatial structure affects predator-prey dynamics and coexistence

Small-scale spatial variability can affect community dynamics in many ecological and biological processes, such as predator-prey dynamics and immune responses. Spatial variability includes short-range neighbour-dependent interactions and small-scale spatial structure, such as clustering where individuals aggregate together, and segregation where individuals are spaced apart from one another. Yet, a large class of mathematical models aimed at representing these processes ignores these factors by making a classical mean-field approximation, where interactions between individuals are assumed to occur in proportion to their average density. Such mean-field approximations amount to ignoring spatial structure. In this work, we consider an individual based model of a two-species community that is composed of consumers and resources. The model describes migration, predation, competition and dispersal of offspring, and explicitly gives rise to varying degrees of spatial structure. We compare simulation results from the individual based model with the solution of a classical mean-field approximation, and this comparison provides insight into how spatial structure can drive the system away from mean-field dynamics. Our analysis reveals that mechanisms leading to intraspecific clustering and interspecific segregation, such as short-range predation and short-range dispersal, tend to increase the size of the resource species relative to the mean-field prediction. We show that under certain parameter regimes these mechanisms lead to the extinction of consumers whereas the classical mean-field model predicts the coexistence of both species.

ecology↗

bioRxiv2019

Kia kaua te reo e rite ki te moa, ka ngaro: Do not let the language suffer the same fate as the moa

More than a third of the worlds languages are currently classified as endangered and more than half are expected to go extinct by 2100. Strategies aimed at revitalising endangered languages have been implemented in numerous countries, with varying degrees of success. Here, we develop a new model regarding language transmission by dividing the population into defined proficiency categories and dynamically quantifying transition rates between categories. The model can predict changes in proficiency levels over time and, ultimately, whether a given endangered language is on a long-term trajectory towards extinction or recovery. We calibrate the model using data from Wales and show that the model predicts that the Welsh language will thrive in the long term. We then apply the model to te reo M[a]ori, the Indigenous language of New Zealand, as a case study. Initial conditions for this model are estimated using New Zealand census data. We modify the model to describe a country, such as New Zealand, where the endangered language is associated with a particular subpopulation representing the Indigenous People. We conclude that, with current learning rates, te reo M[a]ori is on a pathway towards extinction, but identify strategies that could help restore it to an upward trajectory.

ecology↗

bioRxiv2019

Small-scale spatial structure influences large-scale invasion rates

Local interactions among individual members of a population can generate intricate small-scale spatial structure, which can strongly influence population dynamics. The two-way interplay between local interactions and population dynamics is well understood in the relatively simple case where the population occupies a fixed domain with a uniform average density. However, the situation where the average population density is spatially varying is less well understood. This situation includes ecologically important scenarios such as species invasions, range shifts, and moving population fronts. Here, we investigate the dynamics of the spatial stochastic logistic model in a scenario where an initially confined population subsequently invades new, previously unoccupied territory. This simple model combines density-independent proliferation with dispersal, and density-dependent mortality via competition with other members of the population. We show that, depending on the spatial scales of dispersal and competition, either a clustered or a regular spatial structure develops over time within the invading population. In the short-range dispersal case, the invasion speed is significantly lower than standard predictions of the mean-field model. We conclude that mean-field models, even when they account for non-local processes such as dispersal and competition, can give misleading predictions for the speed of a moving invasion front.

cell biology↗

bioRxiv2019

Identifying density-dependent interactions in collective cell behaviour

Scratch assays are routinely used to study collective cell behaviour in vitro. Typical experimental protocols do not vary the initial density of cells, and typical mathematical modelling approaches describe cell motility and proliferation based on assumptions of linear diffusion and logistic growth. Jin et al. (2016) find that the behaviour of cells in scratch assays is density-dependent, and show that standard modelling approaches cannot simultaneously describe data initiated across a range of initial densities. To address this limitation, we calibrate an individual based model to scratch assay data across a large range of initial densities. Our model allows proliferation, motility, and a direction bias to depend on interactions between neighbouring cells. By considering a hierarchy of models where we systematically and sequentially remove interactions, we perform model selection analysis to identify the minimum interactions required for the model to simultaneously describe data across all initial densities. The calibrated model is able to match the experimental data across all densities using a single parameter distribution, and captures details about the spatial structure of cells. Our results provide strong evidence to suggest that motility is density-dependent in these experiments. On the other hand, we do not see the effect of crowding on proliferation in these experiments. These results are significant as they are precisely the opposite of the assumptions in standard continuum models, such as the Fisher-Kolmogorov equation and its generalisations.

cell biology↗

bioRxiv2017

Inferring parameters for a lattice-free model of cell migration and proliferation using experimental data

Collective cell spreading takes place in spatially continuous environments, yet it is often modelled using discrete lattice-based approaches. Here, we use data from a series of cell proliferation assays, with a prostate cancer cell line, to calibrate a spatially continuous individual based model (IBM) of collective cell migration and proliferation. The IBM explicitly accounts for crowding effects by modifying the rate of movement, direction of movement, and the rate of proliferation by accounting for pair-wise interactions. Taking a Bayesian approach we estimate the free parameters in the IBM using rejection sampling on three separate, independent experimental data sets. Since the posterior distributions for each experiment are similar, we perform simulations with parameters sampled from a new posterior distribution generated by combining the three data sets. To explore the predictive power of the calibrated IBM, we forecast the evolution of a fourth experimental data set. Overall, we show how to calibrate a lattice-free IBM to experimental data, and our work highlights the importance of interactions between individuals. Despite great care taken to distribute cells as uniformly as possible experimentally, we find evidence of significant spatial clustering over short distances, suggesting that standard mean-field models could be inappropriate.

cell biology↗