Biology subjects

Laomettachit, T.

Publications and source records attributed to Laomettachit, T..

4 recordsLinked to original sources

bioRxiv2026

Stochastic Boolean Model of Death Signaling in MCF7:5C Predicts Cell Death Inducers and Inhibitors

Disrupted cell death signaling contributes significantly to the inappropriate survival of cancer cells, enabling them to evade the apoptotic processes that normally eliminate damaged or abnormal cells. To assess the impact of disrupted cell death signaling on cancer cell survival, we developed a continuous-time stochastic Boolean model of apoptosis signaling in MCF7:5C human breast cancer cells that are resistant to estrogen deprivation and undergo apoptosis in response to estrogen. The model was calibrated to replicate the dynamics of key apoptosis regulators in MCF7:5C cells before and after 17{beta}-estradiol (E2) treatment. Subsequently, the calibrated model was used to predict both the single and double perturbations that inhibit cell death in estradiol-treated MCF7:5C cells, and additional single interventions capable of reinducing apoptosis. For example, strains with single-gene deletion of PERK, eIF2, CHOP, or ATF4 enabled E2-treated MCF7:5C cells to evade apoptosis. However, additional inhibition of SRC, PI3K, ESR1, CEBPB, or MAPK8 restored apoptotic responses in these resistant cells. We identified 30 resistant cell strains with double mutations and 506 drug-target combinations capable of inducing four distinct modes of cell death: direct CASP7 activation, extrinsic apoptosis, intrinsic apoptosis, and combined intrinsic/extrinsic apoptosis. Overall, our model successfully explains the drug response behavior of MCF7:5C cells, predicts mechanisms of drug resistance, and suggests treatment strategies to overcome this resistance.

cancer biology↗

bioRxiv2024

Normal and Aberrant Cell Cycles Characterized by a Continuous-Time Stochastic Boolean Model of Cell Cycle Regulation in Budding Yeast

The cell cycle of budding yeast is governed by an intricate protein regulatory network whose dysregulation can lead to lethal mistakes or aberrant cell division cycles. In this work, we model this network in a Boolean framework for stochastic simulations. Our model is sufficiently detailed to account for the phenotypes of 41 mutant yeast strains (85% of the experimentally characterized strains that we simulated) and also to simulate an endoreplicating strain (multiple rounds of DNA synthesis without mitosis) and a strain that exhibits Cdc14 endocycles (periodic transitions between metaphase and anaphase). Because our model successfully replicates the observed properties of both wild-type yeast cells and many mutant strains, it provides a reasonable, validated starting point for more comprehensive stochastic-Boolean models of cell cycle controls. Such models may provide a better understanding of cell cycle anomalies in budding yeast and ultimately in mammalian cells.

systems biology↗

bioRxiv2020

PhotoModPlus: A webserver for photosynthetic protein prediction from a genome neighborhood feature

Identification of photosynthetic proteins and their functions is essential for understanding and improving photosynthetic efficiency. We present here a new webserver called PhotoModPlus as a platform to predict photosynthetic proteins via genome neighborhood networks (GNN) and a machine learning method. GNN facilitates users to visualize the overview of the conserved neighboring genes from multiple photosynthetic prokaryotic genomes and provides functional guidance to the query input. We also integrated a newly developed machine learning model for predicting photosynthesis-specific functions based on 24 prokaryotic photosynthesis-related GO terms, namely PhotoModGO, into the webserver. The new model was developed using a multi-label classification approach and genome neighborhood features. The performance of the new model was up to 0.872 of F1 measure, which was better than the sequence-based approaches evaluated by nested five-fold cross-validation. Finally, we demonstrated the applications of the webserver and the new model in the identification of novel photosynthetic proteins. The server was user-friendly designed and compatible with all devices and available at http://bicep.kmutt.ac.th/photomod or http://bicep2.kmutt.ac.th/photomod.

bioinformatics↗

bioRxiv2020

Photosynthetic protein classification using genome neighborhood-based machine learning feature

Identification of novel photosynthetic proteins is important for understanding and improving photosynthetic efficiency. Synergistically, genomic context such as genome neighborhood can provide additional useful information to identify the photosynthetic proteins. We, therefore, expected that applying the computational approach, particularly machine learning (ML) with the genome neighborhood-based feature should facilitate the photosynthetic function assignment. Our results revealed a functional relationship between photosynthetic genes and their genomic neighbors, indicating the possibility to assign functions from their genome neighborhood profile. Therefore, we created a new method for extracting the patterns based on genome neighborhood network (GNN) and applied for the photosynthetic protein classification using ML algorithms. Random forest (RF) classifier using genome neighborhood-based features achieved the highest accuracy up to 94% in the classification of photosynthetic proteins and also showed better performance (Mathews correlation coefficient = 0.852) than other available tools including the sequence similarity search (0.497) and ML-based method (0.512). Furthermore, we demonstrated the ability of our model to identify novel photosynthetic proteins comparing to the other methods. Our classifier is available at http://bicep.kmutt.ac.th/photomod_standalone, https://bit.ly/2S0I2Ox and DockerHub: https://hub.docker.com/r/asangphukieo/photomod

bioinformatics↗