A cell pattern generation model based on an extended artificial regulatory network.

Biosystems

Universidad de Guadalajara, Zapopan, Jal, Mexico.

Published: January 2009

Cell pattern generation has a fundamental role in both artificial and natural development. This paper presents results from a model in which a genetic algorithm (GA) was used to evolve an artificial regulatory network (ARN) to produce predefined 2D cell patterns through the selective activation and inhibition of genes. The ARN used in this work is an extension of a model previously used to create simple geometrical patterns. The GA worked by evolving the gene regulatory network that was used to control cell reproduction, which took place in a testbed based on cellular automata (CA). After the final chromosomes were produced, a single cell in the middle of the CA lattice was allowed to replicate controlled by the ARN found by the GA, until the desired cell pattern was formed. The model was applied to the problem of generating a French flag pattern.

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.biosystems.2008.05.015DOI Listing

Publication Analysis

Top Keywords

cell pattern
12
regulatory network
12
pattern generation
8
artificial regulatory
8
cell
6
model
4
generation model
4
model based
4
based extended
4
extended artificial
4

Similar Publications

A common digestive system cancer with a dismal prognosis and a high death rate globally is breast cancer (BRCA). BRCA recurrence, metastasis, and medication resistance are all significantly impacted by cancer stem cells (CSCs). However, the relationship between CSCs and the tumor microenvironment in BRCA individuals remains unknown, and this information is critically needed.

View Article and Find Full Text PDF

The Stockholm Early Detection of Cancer Study (STEADY-CAN) cohort was established to investigate strategies for early cancer detection in a population-based context within Stockholm County, the capital region of Sweden. Utilising real-world data to explore cancer-related healthcare patterns and outcomes, the cohort links extensive clinical and laboratory data from both inpatient and outpatient care in the region. The dataset includes demographic information, detailed diagnostic codes, laboratory results, prescribed medications, and healthcare utilisation data.

View Article and Find Full Text PDF

Powder X-ray diffraction (PXRD) is a prevalent technique in materials characterization. While the analysis of PXRD often requires extensive human manual intervention, and most automated method only achieved at coarse-grained level. The more difficult and important task of fine-grained crystal structure prediction from PXRD remains unaddressed.

View Article and Find Full Text PDF

Identification of fatty acid anabolism patterns to predict prognosis and immunotherapy response in gastric cancer.

Discov Oncol

January 2025

Department of Clinical Laboratory, Laboratory Medicine Center, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China.

Gastric cancer (GC), one of the most common and heterogeneous malignancies, is the second leading cause of cancer death worldwide and is closely related to dietary habits. Fatty acid is one of the main nutrients of human beings, which is closely related to diabetes, hypertension and other diseases. However, the correlation between fatty acid metabolism and the development and progression of GC remains largely unknown.

View Article and Find Full Text PDF

Berberine (BBR) has been proved to inhibit the malignant progression of non-small cell lung cancer (NSCLC), but the underlying molecular mechanism still needs to be further revealed. NSCLC cells (A549 and H1299) were treated with BBR. CCK8 assay, colony formation assay, flow cytometry, TUNEL staining and transwell assay were used to examine cell proliferation, apoptosis and invasion.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!