Publications
2024
24) Sutthibutpong, T.*, Posansee, K., Liangruksa, M., Termsaithong, T., Piyayotai, S., Phitsuwan, P., Saparpakorn, P., Hannongbua, S., Laomettachit, T.* (2024). Combining deep learning and structural modeling to identify potential acetylcholinesterase inhibitors from Hericium erinaceus. ACS Omega, In press, https://doi.org/10.1021/acsomega.3c10459
23) Srithanyarat, T., Taoma, K., Sutthibutpong, T., Ruengjitchatchawalya, M., Liangruksa, M.*, Laomettachit, T.* (2024). Interpreting drug synergy in breast cancer with deep learning using target-protein inhibition profiles. BioData Mining, 17(8), 1–17. https://doi.org/10.1186/s13040-024-00359-z
22) Taoma, K., Ruengjitchatchawalya, M., Liangruksa, M.*, Laomettachit, T.* (2024). Boolean modeling of breast cancer signaling pathways uncovers mechanisms of drug synergy. PLOS ONE 19(2): e0298788. https://doi.org/10.1371/journal.pone.0298788
21) Soommat, P., Raethong, N., Ruengsang, R., Thananusak, R., Laomettachit, T., Laoteng, K., Saithong, T.*, Vongsangnak, W.* (2024). Light-exposed metabolic responses of Cordyceps militaris through transcriptome-integrated genome-scale modeling. Biology, 13(139), 1–13. https://doi.org/10.3390/biology13030139
2023
20) Posansee, K., Liangruksa, M., Termsaithong, T., Saparpakorn, P., Hannongbua, S., Laomettachit, T.*, Sutthibutpong, T.* (2023). Combined deep learning and molecular modeling techniques on the virtual screening of new mTOR inhibitors from the Thai mushroom database. ACS Omega, 8(41), 38373–38385.
2022
19) Laomettachit, T.*, Kraikivski, P., Tyson, J. J.* (2022). A continuous-time stochastic Boolean model provides a quantitative description of the budding yeast cell cycle. Scientific Reports, 12(1), 20302.
18) Anuntakarun, S., Lertampaiporn, S., Laomettachit, T., Wattanapornprom, W., Ruengjitchatchawalya, M.* (2022). mSRFR: a machine learning model using microalgal signature features for ncRNA classification. BioData Mining, 15(1), 1-11.
17) In-On, A., Thananusak, R., Ruengjitchatchawalya, M., Vongsangnak, W., Laomettachit, T.* (2022). Construction of light-responsive gene regulatory network for growth, development and secondary metabolite production in Cordyceps militaris. Biology, 11(1), 71.
2021
16) Kaewlin, N., Liangruksa, M., Laomettachit, T.* (2021). Development of a genetically integrated PBPK model for predicting uric acid homeostasis in humans. Thai Journal of Mathematics, 19(3), 971-980.
15) Liangruksa, M.*, Laomettachit, T., Siriwong, C. (2021). Enhancing gas sensing properties of novel palladium-decorated zinc oxide surface: a first-principles study. Materials Research Express, 8(4), 045004.
14) Sangphukieo, A., Laomettachit, T., Ruengjitchatchawalya, M.* (2021). PhotoModPlus: A web server for photosynthetic protein prediction from genome neighborhood features. PLOSE ONE, 16(3): e0248682.
13) Laomettachit, T.*, Liangruksa, M., Termsaithong, T., Tangthanawatsakul, A., Duangphakdee, O. (2021). A Model of infection in honeybee colonies with social immunity. PLOS ONE, 16(2), e0247294.
2020
12) Sukpol, W., Laomettachit, T., Tangthanawatsakul, A.* (2020). A mathematical model of stochastic phase transitions in breast cancer development. Solid State Technology, 63(3), 873-880.
11) Sangphukieo, A., Laomettachit, T., Ruengjitchatchawalya, M.* (2020). Photosynthetic protein classification using genome neighborhood-based machine learning feature. Scientific Reports, 10:7108. https://doi.org/10.1038/s41598-020-64053-w
2019
10) Tyson, JJ.*, Laomettachit, T., Kraikivski P. (2019). Modeling the dynamic behavior of biochemical regulatory networks. Journal of Theoretical Biology, 462: 514–527. link
2017
9) Laomettachit T, Puri IK, Liangruksa M.* (2017) A Two-step Model of TiO2 nanoparticle toxicity in human liver tissue. Toxicology and Applied Pharmacology, 334: 47–54. link
8) Liangruksa, M.*, Laomettachit, T., Wongwises, S. (2017) Theoretical study of DNA's deformation and instability subjected to mechanical stress. International Journal of Mechanical Sciences, 130:324-330. link
2016
7) Laomettachit, T.*, Termsaithong T, Sae-Tang A, Duangphakdee O (2016). Stop-signaling reduces split decisions without impairing accuracy in the honeybee nest-site selection process. Journal of Insect Behavior, 29: 557-577. link
6) Laomettachit, T., Chen, KC., Baumann, WT., Tyson, JJ.* (2016). A model of yeast cell-cycle regulation based on a standard component modeling strategy for protein regulatory networks. PLOS ONE, 11:5, e0153738. link
2015
5) Kraikivski, P., Chen, KC., Laomettachit, T., Murali, TM., Tyson, JJ.* (2015). From START to FINISH: Computational analysis of cell cycle control in budding yeast. npj Systems Biology and Applications. 1:15016. link
4) Sangphukieo, A., Nawae, W., Laomettachit, T., Supasitthimethee, U., Ruengjitchatchawalya, M.* (2015). Computational design of hypothetical new peptides based on a cyclotide scaffold as HIV gp120 inhibitor. PLOS ONE, 10:10, e0139562. link
3) Laomettachit T*, Termsaithong T, Sae-Tang A, Duangphakdee O (2015). Decision-making in honeybee swarms based on quality and distance information of candidate nest sites. Journal of Theoretical Biology, 364: 21-30. link
2014
2) Oguz, C., Palmisano, A., Laomettachit, T., Watson, LT., Baumann, WT., Tyson, JJ.* (2014). A Stochastic model correctly predicts changes in budding yeast cell cycle dynamics upon periodic expression of CLN2. PLOS ONE, 9(5): e96726. link
2013
1) Oguz, C., Laomettachit, T., Chen, KC., Watson, LT., Baumann, WT., Tyson, JJ.* (2013). Optimization and model reduction in the high dimensional parameter space of a budding yeast cell cycle model. BMC systems biology, 7: 53. link
