Petri nets in modelling glucose regulating processes in the liver
- URL: http://arxiv.org/abs/2405.11009v1
- Date: Fri, 17 May 2024 13:15:01 GMT
- Title: Petri nets in modelling glucose regulating processes in the liver
- Authors: Kamila Barylska, Anna GogoliĆska,
- Abstract summary: We present a Petri net model of glycolysis and glucose synthesis in the liver.
Our analysis shows that the model captures the interactions between different enzymes and substances.
The model constitutes the first element of our long-time goal to create the whole body model of the glucose regulation in a healthy human and a person with diabetes.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Diabetes is a chronic condition, considered one of the civilization diseases, that is characterized by sustained high blood sugar levels. There is no doubt that more and more people is going to suffer from diabetes, hence it is crucial to understand better its biological foundations. The essential processes related to the control of glucose levels in the blood are: glycolysis (process of breaking down of glucose) and glucose synthesis, both taking place in the liver. The glycolysis occurs during feeding and it is stimulated by insulin. On the other hand, the glucose synthesis arises during fasting and it is stimulated by glucagon. In the paper we present a Petri net model of glycolysis and glucose synthesis in the liver. The model is created based on medical literature. Standard Petri nets techniques are used to analyse the properties of the model: traps, reachability graphs, tokens dynamics, deadlocks analysis. The results are described in the paper. Our analysis shows that the model captures the interactions between different enzymes and substances, which is consistent with the biological processes occurring during fasting and feeding. The model constitutes the first element of our long-time goal to create the whole body model of the glucose regulation in a healthy human and a person with diabetes.
Related papers
- NutritionVerse-Direct: Exploring Deep Neural Networks for Multitask Nutrition Prediction from Food Images [63.314702537010355]
Self-reporting methods are often inaccurate and suffer from substantial bias.
Recent work has explored using computer vision prediction systems to predict nutritional information from food images.
This paper aims to enhance the efficacy of dietary intake estimation by leveraging various neural network architectures.
arXiv Detail & Related papers (2024-05-13T14:56:55Z) - Tertiary Lymphoid Structures Generation through Graph-based Diffusion [54.37503714313661]
In this work, we leverage state-of-the-art graph-based diffusion models to generate biologically meaningful cell-graphs.
We show that the adopted graph diffusion model is able to accurately learn the distribution of cells in terms of their tertiary lymphoid structures (TLS) content.
arXiv Detail & Related papers (2023-10-10T14:37:17Z) - Investigating Speed Deviation Patterns During Glucose Episodes: A
Quantile Regression Approach [2.3072218701168166]
Complication of glucose control in diabetes includes hypoglycemic and hyperglycemic episodes, which may impair cognitive and psychomotor functions needed for safe driving.
This paper was to determine patterns of diabetes speed behavior during acute glucose to drivers with diabetes who were euglycemic or control drivers without diabetes in a naturalistic driving environment.
arXiv Detail & Related papers (2023-10-03T18:27:34Z) - Learning Absorption Rates in Glucose-Insulin Dynamics from Meal
Covariates [28.39179475412449]
A meal's macronutritional content has nuanced effects on the absorption profile, which is difficult to model mechanistically.
We use a neural network to predict an individual's glucose absorption rate.
arXiv Detail & Related papers (2023-04-27T16:03:41Z) - Modeling glycemia in humans by means of Grammatical Evolution [4.26706629463264]
One of the main problems that arises in the (semi) automatic control of diabetes, is to get a model explaining how glycemia varies with insulin, food intakes and other factors.
This paper proposes the application of evolutionary computation techniques to obtain customized models of patients.
arXiv Detail & Related papers (2023-04-27T14:33:52Z) - Patterns Detection in Glucose Time Series by Domain Transformations and
Deep Learning [0.0]
We describe our research with the aim of predicting the future behavior of blood glucose levels, so that hypoglycemic events may be anticipated.
We have tested our proposed method using real data from 4 different diabetes patients with promising results.
arXiv Detail & Related papers (2023-03-30T09:08:31Z) - SUPR: A Sparse Unified Part-Based Human Representation [61.693373050670644]
We show that existing models of the head and hands fail to capture the full range of motion for these parts.
Previous body part models are trained using 3D scans that are isolated to the individual parts.
We propose a new learning scheme that jointly trains a full-body model and specific part models.
arXiv Detail & Related papers (2022-10-25T09:32:34Z) - Learning Graph Models for Retrosynthesis Prediction [90.15523831087269]
Retrosynthesis prediction is a fundamental problem in organic synthesis.
This paper introduces a graph-based approach that capitalizes on the idea that the graph topology of precursor molecules is largely unaltered during a chemical reaction.
Our model achieves a top-1 accuracy of $53.7%$, outperforming previous template-free and semi-template-based methods.
arXiv Detail & Related papers (2020-06-12T09:40:42Z) - Machine learning for the diagnosis of early stage diabetes using
temporal glucose profiles [0.20072624123275526]
Diabetes is a chronic disease that has a long latent period that complicates detection of the disease at an early stage.
We propose to use machine learning to detect the subtle change in the temporal pattern of glucose concentration.
Multi-layered perceptrons, convolutional neural networks, and recurrent neural networks all identified the degree of insulin resistance with high accuracy above $85%$.
arXiv Detail & Related papers (2020-05-18T13:31:12Z) - Learning Generative Models of Tissue Organization with Supervised GANs [46.569795520982325]
A key step in understanding the spatial organization of cells and tissues is the ability to construct generative models that accurately reflect that organization.
In this paper, we focus on building generative models of electron microscope (EM) images in which the positions of cell membranes and mitochondria have been densely annotated.
We propose a two-stage procedure that produces realistic images using Generative Adversarial Networks (or GANs) in a supervised way.
arXiv Detail & Related papers (2020-03-31T22:22:58Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.