Systems BioMedicine (SymBio) Lab

Welcome to the SymBio lab

Research

Systems biomedicine

We use integrative approaches (e.g., math modeling, machine learning, structural modeling) to understand the complexity of diseases and their corresponding interventions.

Network biology

We construct and analyze biological networks (e.g., protein/gene regulatory networks), and seek insight into complex biological systems.

Physiological modeling

We use physiologically based pharmacokinetic (PBPK) modeling to evaluate the distribution and disposition of substances in organisms.

People

Teeraphan Laomettachit

I am an associate professor of systems biology at the Bioinformatics & Systems Biology Program , School of Bioresources and Technology , King Mongkut's University of Technology Thonburi (KMUTT) , Thailand.
I'm also affiliated with the Theoretical and Computational Physics (TCP) Group, Center of Excellence in Theoretical and Computational Science (TaCS-CoE), KMUTT.

email: teeraphan.lao[at]kmutt.ac.th

Monrudee Liangruksa

Kittisak Taoma

I am a PhD student in the SymBio lab. Most of my interests involve the use of machine learning with omics data (whenever it's available !) to answer the research problem in the area of personalized medicine.

Email: kittisaktaoma18@gmail.com

Piyanut Ratphibun

I am a PhD student affiliated with the SymBio Lab. My research journey approach on the way to "Medical Digital Twins". I hope to see it realized within my lifetime. I welcome advice from everyone.

Sujiraporn Pakchuen

I joined the SymBio lab in 2021 as a MS student. My research interests center around a network-based and data mining approaches to study gene-gene interaction for a better understanding of mechanisms driving cancer initiation. With my hybrid background (wet and dry labs), I’m currently studying the behavior of proteins with multiple phosphorylation sites essential for cell cycle regulation as a postdoc at UMass Chan Medical School.

Comkrit Lomloy

Kunyanat Athornworakul

Charoensub Ramwan

Lab news

11 Dec 2024
Lab lunch

6 Dec 2024
Comkrit successfully defended his thesis entitled 'Classifying Drug Combination Actions in Lung Cancer Cells through Gene-Protein Interaction Network Propagation'.

22 Nov 2024
Piyanut successfully defended her dissertation proposal entitled 'Integrating Transcriptomic Data and Dynamic Modeling for Early Breast Cancer Risk Assessment and Personalized Intervention'.

18 Oct 2024
In a publication published in npj Systems Biology and Applications, we developed a stochastic Boolean model to simulate the cell cycle of budding yeast, capturing the phenotypes of 83% of experimentally characterized mutant strains. This model provides a valuable starting point for further development of stochastic-Boolean models to better understand cell cycle aberrations in both yeast and mammalian cells.

14 Sep 2024
We developed an ODE-based dynamic model to accurately predict Spirulina's biomass and phycocyanin yields. The model successfully captured the effects of ammonium concentration on algal growth, C-phycocyanin production, enzyme regulation, metabolite levels, and related gene expression changes. The research article has been published in Algal Research (link to publication).

16 Aug 2024
SymBio lab members learned how to use an HPC server for their research.

1 Aug 2024
SymBio lab members attending ANSCSE 2024. The 27th International Annual Symposium on Computational Science and Engineering at Chulalongkorn University.

20 June 2024
Congratulations to Piyanut Ratphibun for receiving the Petchra Pra Jom Klao Doctoral Scholarship, a full PhD scholarship from KMUTT.

11 June 2024
We utilize an artificial intelligence framework to predict the intestinal effective permeability of two herbal compounds from Centella Asiatica. We then apply PBPK modeling to evaluate the biodistribution of these herbal compounds after oral administration in a rat model, aligning with established in vivo studies. The research article has been published in Journal of Biopharmaceutical Statistics (link to publication).

26 Mar 2024
We combine deep learning and structural modeling to identify possible acetylcholinesterase (AChE) inhibitors from Hericium erinaceus (lion’s mane mushroom). The research article has been published in ACS Omega (link to publication).

20 Mar 2024
Congratulations to Thanyawee Srithanyarat on her MSc graduation with a publication in Biodata Mining (link to publication).

6 Mar 2024
Lab journal club on circadian rhythms and medicine

23 Feb 2024
Our lab has published two publications:
1. 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
2. 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

Publications

2024

29) Taoma K., Tyson JJ., Laomettachit, T.*, Kraikivski, P.* (2024). Stochastic Boolean model of normal and aberrant cell cycles in budding yeast. npj Systems Biology and Applications, 10: 121, https://doi.org/10.1038/s41540-024-00452-3

28) Aowtrakool, N., Sopitthummakhun, A., Laomettachit, T., Ruengjitchatchawalya, M.* (2024). A dynamic model for predicting biomass and phycocyanin yields in Arthrospira (Spirulina) platensis: A guidance for effective batch cultivation. Algal Research, 83: 103709, https://doi.org/10.1016/j.algal.2024.103709

27) Chaiprasert, A., Han, P., Laomettachit, T., & Ruengjitchatchawalya, M.* (2024). Network analysis retrieving bioactive compounds from Spirulina (Arthrospira platensis) and their targets related to systemic lupus erythematosus. PLOS ONE, 19(8): e0309303. https://doi.org/10.1371/journal.pone.0309303

26) Sukpol, W., Laomettachit, T., & Tangthanawatsakul, A.* (2024). A cancer sub-population competition model reveals optimal levels of immune response that minimize tumor size. Journal of Computational Biology, https://doi.org/10.1089/cmb.2024.0618

25) Pumkathin, S., Hanlumyuang, Y., Wattanathana, W., Laomettachit, T., & Liangruksa, M.* (2024). Investigating pharmacokinetic profiles of Centella asiatica using machine learning and PBPK modelling. Journal of Biopharmaceutical Statistics, 1–16. https://doi.org/10.1080/10543406.2024.2358797

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, 9(14), 16311–16321, 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