Related papers: Glucagon and insulin production in pancreatic cells modeled using Petri nets and Boolean networks

Glucagon and insulin production in pancreatic cells modeled using Petri nets and Boolean networks

URL: http://arxiv.org/abs/2504.21578v1
Date: Wed, 30 Apr 2025 12:36:02 GMT
Title: Glucagon and insulin production in pancreatic cells modeled using Petri nets and Boolean networks
Authors: Kamila Barylska, Frank Delaplace, Anna Gogolińska, Ewa Pańkowska,
Abstract summary: Diabetes is a civilization chronic disease characterized by a constant elevated concentration of glucose in the blood.<n>To better understand those processes we set ourselves a goal to create a Petri net model of the glucose regulation in the whole body.<n>In this paper we introduce Petri nets models of insulin secretion in beta cell of the pancreas, and glucagon in the pancreas alpha cells.
Score: 0.0
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Diabetes is a civilization chronic disease characterized by a constant elevated concentration of glucose in the blood. Many processes are involved in the glucose regulation, and their interactions are very complex. To better understand those processes we set ourselves a goal to create a Petri net model of the glucose regulation in the whole body. So far we have managed to create a model of glycolysis and synthesis of glucose in the liver, and the general overview models of the glucose regulation in a healthy and diabetic person. In this paper we introduce Petri nets models of insulin secretion in beta cell of the pancreas, and glucagon in the pancreas alpha cells. Those two hormones have mutually opposite effects: insulin preventing hyperglycemia, and glucagon preventing hypoglycemia. Understanding the mechanisms of insulin and glucagon secretion constitutes the basis for understanding diabetes. We also present a model in which both processes occur together, depending on the blood glucose level. The dynamics of each model is analysed. Additionally, we transform the overall insulin and glucagon secretion system to a Boolean network, following standard transformation rules.

Related papers

GlucoLens: Explainable Postprandial Blood Glucose Prediction from Diet and Physical Activity [6.292642131180376]
Postprandial hyperglycemia, marked by the blood glucose level exceeding the normal range after meals, is a critical indicator of progression toward type 2 diabetes.<n>We propose GlucoLens, an explainable machine learning approach to predict PAUC and hyperglycemia from diet, activity, and recent glucose patterns.
arXiv Detail & Related papers (2025-03-05T22:10:14Z)
Blood Glucose Level Prediction in Type 1 Diabetes Using Machine Learning [0.0]
People with Type 1 Diabetes need synthetic insulin every day.<n>In diabetes management, continuous glucose monitoring is an important feature that provides near real-time blood glucose data.<n>In this research work, we used machine learning tools, deep neural networks, deep reinforcement learning, and voting and stacking regressors to predict blood glucose levels.
arXiv Detail & Related papers (2025-01-30T21:00:38Z)
From Glucose Patterns to Health Outcomes: A Generalizable Foundation Model for Continuous Glucose Monitor Data Analysis [47.23780364438969]
We present GluFormer, a generative foundation model for CGM data that learns nuanced glycemic patterns and translates them into predictive representations of metabolic health.<n>GluFormer generalizes to 19 external cohorts spanning different ethnicities and ages, 5 countries, 8 CGM devices, and diverse pathophysiological states.<n>In a longitudinal study of 580 adults with CGM data and 12-year follow-up, GluFormer identifies individuals at elevated risk of developing diabetes more effectively than blood HbA1C%.
arXiv Detail & Related papers (2024-08-20T13:19:06Z)
GlycanML: A Multi-Task and Multi-Structure Benchmark for Glycan Machine Learning [35.818061926699336]
GlycanML benchmark consists of diverse types of tasks including glycan taxonomy prediction, glycan immunogenicity prediction, glycosylation type prediction, and protein-glycan interaction prediction. By concurrently performing eight glycan taxonomy prediction tasks, we introduce the GlycanML-MTL testbed for multi-task learning (MTL) algorithms. Experimental results show the superiority of modeling glycans with multi-relational GNNs, and suitable MTL methods can further boost model performance.
arXiv Detail & Related papers (2024-05-25T12:35:31Z)
Petri nets in modelling glucose regulating processes in the liver [0.0]
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.
arXiv Detail & Related papers (2024-05-17T13:15:01Z)
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)
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)
Growing Isotropic Neural Cellular Automata [63.91346650159648]
We argue that the original Growing NCA model has an important limitation: anisotropy of the learned update rule. We demonstrate that cell systems can be trained to grow accurate asymmetrical patterns through either of two methods.
arXiv Detail & Related papers (2022-05-03T11:34:22Z)
Towards self-organized control: Using neural cellular automata to robustly control a cart-pole agent [62.997667081978825]
We use neural cellular automata to control a cart-pole agent. We trained the model using deep-Q learning, where the states of the output cells were used as the Q-value estimates to be optimized.
arXiv Detail & Related papers (2021-06-29T10:49:42Z)
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)
Constrained Multi-shape Evolution for Overlapping Cytoplasm Segmentation [49.992392231966015]
We present a novel and effective shape prior-based approach, called constrained multi-shape evolution. It segments all overlapping cytoplasms in the clump simultaneously by jointly evolving each cytoplasm's shape guided by the modeled shape priors. In the shape evolution, we compensate intensity deficiency for the segmentation by introducing not only the modeled local shape priors but also global shape priors.
arXiv Detail & Related papers (2020-04-08T09:08:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.