Biology subjects

Jorgensen, A. C. S.

Publications and source records attributed to Jorgensen, A. C. S..

5 recordsLinked to original sources

bioRxiv2024

Forecasting the adoption and spread of a community-based marine management initiative using agent-based models

While many successful initiatives for conserving nature exist, efforts to take them to scale have been inadequate. Moreover, conservation science currently lacks a systematic methodology for determining if or when interventions will reach effective scales and how programmatic decisions will affect the scaling process. This paper presents a modelling framework that aims to address both issues by operationalizing Diffusion of Innovations theory and local knowledge using agent-based modelling and Bayesian inference. By applying our framework to existing data on the spatiotemporal adoption of a community-based marine management initiative in Fiji, we demonstrate that our approach can identify the mechanisms that govern the observed adoption patterns. In this case, the relative advantage of the intervention, village social networks, and perceived knowledge stand out as important drivers of adoption. Using the identified causal processes, our approach can forecast business-as-usual and counterfactual future scenarios and hence inform conservation policy. Finally, we highlight the importance of spatiotemporal data for making detailed scaling predictions. We structure the paper as a step-by-step guide, highlighting our modelling decisions and possible limitations. Thus, besides presenting a case study, this paper serves as a template for practitioners and researchers to better model the scaling process of other conservation interventions.

ecology↗

bioRxiv2023

Inference of the core gene regulatory network underlying seam cell development in Caenorhabditis elegans

Gene regulatory networks are fundamental in cellular decision-making, yet even in well-studied systems, their topologies are often poorly characterised. The nematode Caenorhabditis elegans contains a population of stem-like cells, known as seam cells. While seam cells are essential to generate the majority of the animal epidermis as well as specific neurons, the architecture of the underlying gene network has not been elucidated. Here, we combine experiments, mathematical modelling and statistical inference to uncover the architecture of the seam cell gene network focusing on three core transcription factors (TFs), the GATA factors ELT-1, EGL-18 and the Engrailed homolog CEH-16. We use single-molecule FISH (smFISH) to quantify TF mRNA abundance in single seam cells in both wild type and mutant backgrounds. We then predict potential TF interactions and their quantitative strengths using a combination of Modular Response Analysis, ordinary differential equations and a Bayesian model discovery approach. Taken together, our findings suggest new relationships between core TFs in seam cells and highlight an approach that can be used to infer quantitative networks from smFISH data.

systems biology↗

bioRxiv2023

Bayesian model discovery for reverse-engineering biochemical networks from data

The reverse engineering of gene regulatory networks based on gene expression data is a challenging inference task. A related problem in computational systems biology lies in identifying signalling networks that perform particular functions, such as adaptation. Indeed, for many research questions, there is an ongoing search for efficient inference algorithms that can identify the simplest model among a larger set of related models. To this end, in this paper, we introduce SLIO_SCPLOWNC_SCPLOWG, a Bayesian sparse likelihood-free inference method using Gibbs sampling. We demonstrate that SLIO_SCPLOWNC_SCPLOWG can reverse engineer stochastic gene regulatory networks from single-cell data with high accuracy, outperforming state-of-the-art correlation-based methods. Furthermore, we show that SLIO_SCPLOWNC_SCPLOWG can successfully identify signalling networks that execute adaptation. Sparse hierarchical Bayesian inference thus provides a versatile tool for model discovery in systems biology and beyond.

systems biology↗

bioRxiv2023

Modelling capture efficiency of single cell RNA-sequencing data improves inference of transcriptome-wide burst kinetics

Gene expression is characterised by stochastic bursts of transcription that occur at brief and random periods of promoter activity. The kinetics of gene expression burstiness differs across the genome and is dependent on the promoter sequence, among other factors. Single-cell RNA sequencing (scRNA-seq) has made it possible to quantify the cell-to-cell variability in transcription at a global genome-wide level. However, scRNA-seq data is prone to technical variability, including low and variable capture efficiency of transcripts from individual cells. Here, we propose a novel mathematical theory for the observed variability in scRNA-seq data. Our method captures burst kinetics and variability in both cell size and capture efficiency, which allows us to propose several likelihood-based and simulation-based methods for the inference of burst kinetics from scRNA-seq data. Using both synthetic and real data, we show that the simulation-based methods provide an accurate, robust and flexible tool for inferring burst kinetics from scRNA-seq data. In particular, in supervised manner, a simulation-based inference method based on neural networks proves to be accurate and useful in application to both allele and non-allele specific scRNA-seq data.

bioinformatics↗

bioRxiv2021

Efficient inference for agent-based models of real-world phenomena

The modelling of many real-world problems relies on computationally heavy simulations. Since statistical inference rests on repeated simulations to sample the parameter space, the high computational expense of these simulations can become a stumbling block. In this paper, we compare two ways to mitigate this issue based on machine learning methods. One approach is to construct lightweight surrogate models to substitute the simulations used in inference. Alternatively, one might altogether circumnavigate the need for Bayesian sampling schemes and directly estimate the posterior distribution. We focus on stochastic simulations that track autonomous agents and present two case studies of real-world applications: tumour growths and the spread of infectious diseases. We demonstrate that good accuracy in inference can be achieved with a relatively small number of simulations, making our machine learning approaches orders of magnitude faster than classical simulation-based methods that rely on sampling the parameter space. However, we find that while some methods generally produce more robust results than others, no algorithm offers a one-size-fits-all solution when attempting to infer model parameters from observations. Instead, one must choose the inference technique with the specific real-world application in mind. The stochastic nature of the considered real-world phenomena poses an additional challenge that can become insurmountable for some approaches. Overall, we find machine learning approaches that create direct inference machines to be promising for real-world applications. We present our findings as general guidelines for modelling practitioners. Author summaryComputer simulations play a vital role in modern science as they are commonly used to compare theory with observations. One can thus infer the properties of a observed system by comparing the data to the predicted behaviour in different scenarios. Each of these scenarios corresponds to a simulation with slightly different settings. However, since real-world problems are highly complex, the simulations often require extensive computational resources, making direct comparisons with data challenging, if not insurmountable. It is, therefore, necessary to resort to inference methods that mitigate this issue, but it is not clear-cut what path to choose for any specific research problem. In this paper, we provide general guidelines for how to make this choice. We do so by studying examples from oncology and epidemiology and by taking advantage of developments in machine learning. More specifically, we focus on simulations that track the behaviour of autonomous agents, such as single cells or individuals. We show that the best way forward is problem-dependent and highlight the methods that yield the most robust results across the different case studies. We demonstrate that these methods are highly promising and produce reliable results in a small fraction of the time required by classic approaches that rely on comparisons between data and individual simulations. Rather than relying on a single inference technique, we recommend employing several methods and selecting the most reliable based on predetermined criteria.

bioinformatics↗