Complex Cellular Phenotype Analysis Group

We are a computational biology research group with members coming from different scientific disciplines, including chemistry, cell biology, computer science, and bioinformatics.

The goal of our research is to understand the modes of action (MoAs) of xenobiotics, and predict their human toxicity and/or efficacy. We develop and use novel phenotypic and molecular profiling methods to elucidate the MoAs of xenobiotics, and build computational models that can predict in vivo effects based on these MoAs.

Our current research is focused on three major areas:

News & Announcements
  • (6 July 2020) Our papers on HIPPTox Liver Assay and optimum concentration response curve metric have been published in Archives of Toxicology. Congrats to James and Sreetama!
  • (18 Sept 2019) Our paper titled "Utility of in vitro bioactivity as a lower bound estimate of in vivo adverse effect levels and in risk-based prioritization" has been accepted by the Toxicological Sciences! The work is a collaboration between A*STAR, EPA, Health Canada, ECHA, and other agencies around the world. We contributed data generated from HIPPTox to the study.
  • (5 Sept 2019) Our paper titled "PI3K catalytic subunits α and β modulate cell death and IL-6 secretion induced by talc particles in human lung carcinoma cells" has been accepted by the ATS Red Journal! Congrats to Nic, Yin Yeng, and Joey.

Our research

Toxicodynamics of xenobiotics

Many xenobiotics have unknown and/or non-specific intracellular targets. To study the toxicodynamics of these chemicals, unbiased approaches that do not require prior information about the targets or mechanisms of the chemicals are required. Our goal is to elucidate the MoAs of xenobiotics in major target cell types using advanced phenotypic, signaling, and genomic profiling methods.

Phenotypic responses: Toxic xenobiotics often impair cellular functions and lead to changes in cellular phenotypes, such as reorganization of subcellular structures, up/down-regulation of biomolecules, or other phenotypes. Therefore, quantitative readouts based on changes in cellular phenotypes (Bougen-Zhukov et al., 2017) may be used as surrogate markers for predicting the toxicity of these chemicals. We have developed the first high-throughput and predictive in vitro nephrotoxicity assay (Loo et al., 2017a; Su et al., 2016). Our results suggest that a DNA damage response is commonly induced by different PTC toxicants that have diverse chemical structures and injury mechanisms.

Signaling responses: Besides cell injury, toxic xenobiotics may also induce signaling or inflammatory responses in their targeted cell types. We have developed a rapid, signaling-based cytotoxicity assay that may be used to predict cellular sensitivity to a cytotoxic agent, or identify co-treatments that may sensitize or desensitize cells to the agent (Loo et al., 2017b). We have also developed a predictive nephrotoxicity assay based on the RNA expression levels of two pro-inflammatory cytokines, namely interleukin (IL)-6 and -8 (Kandasamy et at., 2015; Su et al., 2014), and tested it on both primary human PTCs and induced pluripotent stem cells (iPSC)-derived PTC-like cells. These results suggest that inflammation is a general response of PTCs to PTC-toxic compounds.

Transcriptomic responses: The expressions of genes involved in key toxicity responses may be up- or down-regulated in response to toxic xenobiotics. Recent advances in transcriptomics technologies have enabled us to quantify these changes at the genome-wide level. In collaboration with Dr. Hoon from MEL, we are developing high-throughput transcriptomic methods to study concentration-dependent changes in key toxicity pathways.

Toxicity Mode-of-Action Discovery (ToxMAD) Platform: Together with four other research institutes in A*STAR, we are using various new molecular and phenotypic profiling technologies developed in A*STAR to elucidate the protein targets and MoAs of xenobiotics with high human exposure or safety concerns. Our focus is to study chemical analogs with related structures but differential cellular effects, and develop fit-for-purpose assays that will be used by regulatory agencies and industrial research laboratories to assess chemical safety.

Pulmonary effects of xenobiotics

Human lungs are exposed to inhaled or blood-borne soluble xenobiotics that may originate from the environment, food, consumer products, and/or pharmaceuticals. We are broadly interested in the understanding the biological targets and pathways affected by these chemicals in the lung cells.

In vitro toxicity models: We have recently developed a high-throughput and predictive in vitro pulmonary toxicity assay (Lee et al., 2018). We found that the resulting assay based on two phenotypic features of a human bronchial epithelial cell line, BEAS-2B, can accurately classify 33 reference chemicals with human pulmonotoxicity information (88.8% balance accuracy, 84.6% sensitivity, and 93.0% specificity). In comparison, the predictivity of a standard cell-viability assay on the same set of chemicals is much lower (77.1% balanced accuracy, 84.6% sensitivity, and 69.5% specificity). We also used the assay to evaluate 17 additional test chemicals with unknown/unclear human pulmonotoxicity, and experimentally confirmed that many of the pulmonotoxic reference and predicted-positive test chemicals induce DNA strand breaks and/or activation of the DNA-damage response (DDR) pathway.

Xenobiotic metabolism: In the lungs, bronchial and alveolar epithelial cells are major sites of xenobiotic metabolism, and thus are susceptible to the toxicity induced by xenobiotics that interfere with this process. In collaboration with Dr. Hao Fan from BII, we are studying the mechanisms of xenobiotics that can inhibit Cytochrome P450 family 1 member A1 (CYP1A1), a main extra-hepatic phase I metabolism enzyme highly expressed in the lungs and placenta. We have developed molecular docking models that can be used to predict potential CYP1A1 inhibitors.

Phenotypic profiling and computational biology

To extract biological information from the large amount of collected data, new and better methods and tools for image and data analysis are required. Most of our projects are based on the HIPPTox Platform and the cellXpress software developed by us. Our group also develops new methodologies for concentration response modeling, artificial intelligence, and assay automation.

High-throughput In-vitro Phenotypic Profiling (HIPPTox): Phenotypic profiling is a computational procedure to construct quantitative and compact representations of cellular phenotypes based on the cellular images collected in high-content imaging (HCI) experiments (Bougen-Zhukov et al., 2017). We have developed several computational methods for phenotypic profiling, which include the Drug-Profiling ("D-profiling") algorithm (Loo et al., 2007) and the Protein-localization Profiling ("P-profiling") algorithm (Loo et al., 2014). We have used the phenotypic profiles constructed using these methods to classify the effects of small molecules (Loo et al., 2007, 2009), compare spatial and functional divergence of proteins (Loo et al., 2014), or predict toxicity effects of xenobiotic compounds (Su et al., 2016). The HIPPTox Platform implements many of these methods, and can be used to detect the in vitro bioactivity of chemicals and build predictive in vitro toxicity assays. The core of the platform is a user-friendly and high-performance phenotypic profiling software called "cellXpress" (; Laksameethanasan et al., 2013), which can handle terabytes of image data and quantify millions of individual cells under different experimental conditions. We have applied HIPPTox to build predictive lung (Lee et al., 2018), kidney (Su et al., 2016), and liver toxicity assays.

Concentration response modeling: A concentration response curve (CRC) is commonly used to model the relationship between the concentration and effect of a perturbagen. However, for automated perturbagen classification based on quantitative phenotypic features from HCI, it is unclear if commonly used CRC metrics, such as the "half-maximal effective concentration" (EC50) that reports perturbagen potency, are still optimal. We have performed a systematic study on the performances of different CRC metrics in classifying four HCI datasets that consist of phenotypic features from different cell and feature types. Our results suggest that efficacy metrics, especially at higher concentration values, are more likely to provide the most useful information for perturbagen classification. Therefore, HCI experiments should include measurements at high perturbagen concentrations, and efficacy metrics should always be analyzed when building supervised classifiers based on phenotypic features.


23. (2020) Virtual screening of potentially endocrine-disrupting chemicals against nuclear receptors and its application to identify PPARγ-bound fatty acids Chaitanya K. Jaladanki, Yang He, Li Na Zhao, Sebastian Maurer-Stroh, Lit-Hsin Loo, Haiwei Song, and Hao Fan. Archives of Toxicology, in press [Link].
22. (2020) Optimum concentration–response curve metrics for supervised selection of discriminative cellular phenotypic endpoints for chemical hazard assessment James Miller, and Lit-Hsin Loo. Archives of Toxicology, 94:2951-2964 [Link].
21. (2020) Predicting direct hepatocyte toxicity in humans by combining high-throughput imaging of HepaRG cells and machine learning-based phenotypic profiling Faezah Hussain, Sreetama Basu, Javen Jun Hao Heng, Lit-Hsin Loo, and Daniele Zink. Archives of Toxicology, 94:2749-2767 [Link].
20. (2020) A case study with triazole fungicides to explore practical application of next generation hazard assessment methods for human health. Leo Van Der Ven, Emiel Rorije, Corinne Sprong, Daniele Zink, Remco Derr, Giel Hendriks, Lit-Hsin Loo, and Mirjam Luijten. Chemical Research in Toxicology, 33(3):834–848 [Link].
19. (2020) Utility of in vitro bioactivity as a lower bound estimate of in vivo adverse effect levels and in risk-based prioritization. Katie Paul Friedman, Matthew Gagne, Lit-Hsin Loo, Panagiotis Karamertzanis, Tatiana Netzeva, Tomasz Sobanski, Jill Franzosa, Ann Richard, Ryan Lougee, Andrea Gissi, Jia-Ying Joey Lee, Michelle Angrish, Jean-Lou Dorne, Stiven Foster, Kathleen Raffaele, Tina Bahadori, Maureen Gwinn, Jason Lambert, Maurice Whelan, Mike Rasenberg, Tara Barton-Maclaren, Russell S Thomas. Toxicological Sciences, 173(1):202-225 [Link].
18. (2019) PI3K catalytic subunits α and β modulate cell death and IL-6 secretion induced by talc particles in human lung carcinoma cells. Nicola Michelle Bougen-Zhukov, Yin Yeng Lee, Jia-Ying Joey Lee, Pyng Lee, and Lit-Hsin Loo. American Journal of Respiratory Cell and Molecular Biology, [Link].
17. (2018) Emerging technologies for food and drug safety. William Slikker Jr., Thalita Antonyde Souza Lima, Davide Archella, Jarbas Barbosa de Silva Junior, Tara Barton-Maclaren, Li Bo, Danitza Buvinich, Qasim Chaudhry, Peiying Chuang, Hubert Deluyker, Gary Domselaar, Meiruze Freitas, Barry Hardy, Hans-Georg Eichler, Marta Hugas, Kenneth Lee, Chia-Ding Liao, Lit-Hsin Loo, Haruhiro Okuda, Orish Ebere Orisakwe, Anil Patri, Carl Sactitono, Leming Shi, Primal Silva, Frank Sistare, Shraddha Thakkar, Weida Tong, Mary Lou Valdez, Maurice Whelan, and Anna Zhao-Wong. Regulatory Toxicology and Pharmacology, 98:115-128. [Link]
16. (2018) Building predictive in vitro pulmonary toxicity assays using high-throughput imaging and artificial intelligence. Jia-Ying Joey Lee, James Alastair Miller, Sreetama Basu, Ting-Zhen Vanessa Kee, and Lit-Hsin Loo. Archives of Toxicology, 92(6):2055-2075. [Link]
15. (2017) High-throughput prediction of nephrotoxicity in humans. Lit-Hsin Loo, Daniele Zink. Alternatives to Laboratory Animals, 45:241-252. [PDF]
14. (2017) Early spatiotemporal-specific changes in intermediate signals are predictive of cytotoxic sensitivity to TNFa and co-treatments. Lit-Hsin Loo, Nicola Bougen-Zhukov, Wei-Ling Cecilia Tan. Scientific Reports, 7:43541. [Link]
13. (2017) Large-scale image-based screening and profiling of cellular phenotypes. Nicola Bougen-Zhukov, Sheng Yang Loh, Hwee Kuan Lee, Lit-Hsin Loo. Cytometry Part A, 91A:115-125. [Link | PDF]
12. (2016) High-throughput imaging-based nephrotoxicity prediction for xenobiotics with diverse chemical structures. Ran Su, Sijing Xiong, Daniele Zink, and Lit-Hsin Loo. Archives of Toxicology, 90:2793-2808. [Link]
11. (2015) Prediction of drug-induced nephrotoxicity and injury mechanisms with human induced pluripotent stem cell-derived cells and machine learning methods. Karthikeyan Kandasamy, Jacqueline Chuah, Ran Su, Peng Huang, Kim Guan Eng, Sijing Xiong, Yao Li, Chun Siang Chia, Lit-Hsin Loo, and Daniele Zink. Scientific Reports, 5:12337. [Link]
10. (2014) Supervised prediction of drug-induced nephrotoxicity based on interleukin-6 and -8 expression levels. Ran Su, Yao Li, Daniele Zink, and Lit-Hsin Loo. BMC Bioinformatics, 15(Suppl 16):S16. [Link]
9. (2014) Quantitative protein localization signatures reveal an association between spatial and functional divergences of proteins. Lit-Hsin Loo, Danai Laksameethanasan, Yi-Ling Tung. PLOS Computational Biology, 10(3):e1003504. [Link]
8. (2013) cellXpress: a fast and user-friendly software platform for profiling cellular phenotypes. Danai Laksameethanasan, Rui Zhen Tan, Geraldine Wei-Ling Toh and Lit-Hsin Loo. BMC Bioinformatics14(Suppl 16):S4. [Link]
7. (2009) Heterogeneity in the physiological states and pharmacological responses of differentiating 3T3-L1 preadipocytes. Lit-Hsin Loo, Hai-Jui Lin, Dinesh K. Singh, Kathleen M. Lyons, Steven J. Altschuler and Lani F. Wu. Journal of Cell Biology, Vol. 187, No. 3, 375-384. [Link | JCB In-Focus article]
6. (2009) An approach for extensibly profiling the molecular states of cellular subpopulations. Lit-Hsin Loo, Hai-Jui Lin, Robert J. Steininger III, Yanqin Wang, Lani F. Wu and Steven J. Altschuler. Nature Methods, Vol. 6, 759-765. [Link]
5. (2007) Image-based multivariate profiling of drug responses from single cells. Lit-Hsin Loo, Lani F. Wu and Steven J. Altschuler. Nature Methods, Vol. 4, 445-453. [Link]
4. (2007) New criteria for selecting differentially expressed genes. Lit-Hsin Loo, Samuel Roberts, Leonid Hrebien, and Moshe Kam. IEEE Engineering in Medicine and Biology Magazine, Vol. 26, 17-26.
3. (2002) Classification of polypeptide spectra from rat liver samples. Lit-Hsin Loo, John Quinn, James Armitage, Hayley Cordingley, Samuel Roberts, Peter J Bugelski, Leonid Hrebien, and Moshe Kam. Proceedings of the IEEE 28th Annual Northeast Bioengineering Conference, 139-140.
2. (2002) Classification of pharmaceutical toxicity by feature analysis. John Quinn, Lit-Hsin Loo, James Armitage, Hayley Cordingley, Samuel Roberts, Peter J Bugelski, Moshe Kam, and Leonid Hrebien. Proceedings of the IEEE 28th Annual Northeast Bioengineering Conference, 211-212.
1. (2002) Cooperative Multi-agent Constellation Formation Under Sensing and Communication Constraints. Lit-Hsin Loo, Erwei Lin, Moshe Kam, and Pramod Varshney. Cooperative Control and Optimization, Vol. 66, Chapter 8, 143-169.

Current Team Members

LOO Lit HsinSenior Principal Investigator
James A. MILLERPostdoctoral Fellow
Su Su HTWEPostdoctoral Fellow
Zhong GuoruiPostdoctoral Fellow
LEE Jia Ying JoeySenior Research Officer
Kong Jia Wen CarmenResearch Officer
Fu Shufeng OscarResearch Officer

Loo Lit Hsin

LOO Lit Hsin, Ph.D.

Senior Principal Investigator
Complex Cellular Phenotype Analysis Group
Bioinformatics Institute, A*STAR

Adjunct Assistant Professor
Department of Pharmacology
Yong Loo Lin School of Medicine
National University of Singapore
Email: lhloo at bii dot a-star dot edu dot sg

Lit-Hsin Loo is leading a small interdisciplinary team of scientists at BII developing in vitro and computational models for predicting the toxicity and/or targets of chemical compounds with diverse or unknown structures. His team has developed novel image-based phenotypic profiling methods and tools that led to the first high-throughput and predictive in vitro platform for nephrotoxicity prediction. Before joining BII, Dr. Loo was a postdoctoral fellow in the Bauer Center for Genomics Research at Harvard University (2005), and then in the Department of Pharmacology at the UTSW Medical Center, USA (2005-2010).

  • (2016) Lush Prize 2016, Science Award
  • (2010) University of Texas Southwestern Medical Center Postdoctoral Award
  • (2009) Alfred Gilman Postdoctoral Award
  • (2005) Drexel Doctoral Award in Mathematical Sciences and Engineering


James A. MILLER, Ph.D.

Postdoctoral Research Fellow
Email: jamesam at bii dot a-star dot edu dot sg

James is an alumnus of Brasenose College, Oxford, graduating in 2009 from the Department of Chemistry. His Masters project was a theoretical study of phase spaces for confined matter inside carbon nanotubes. In 2015, he defended his doctoral thesis in computational nuclear physics at the University of Sheffield, and went on to postdoctoral work in the British National Nuclear Laboratory developing predictive algorithms for the treatment of highly active fission products. Now investigating biological as opposed to radiological toxins, James' work in the group focuses on image-based machine learning and cheminformatics. Through automated identification and classification of the microscopic changes in cells exposed to different stimuli, the intention is to develop novel methods of organ-specific toxicity prediction.

Su Su

Su Su HTWE, Ph.D.

Postdoctoral Research Fellow
Email: htwess at bii dot a-star dot edu dot sg

Su Su obtained her Ph.D. from the University of Nottingham, UK in 2017, after she graduated in M.Sc. (Immunology and Allergy) from the same school. Her research interest mainly focuses on the development of animal alternative models and assays for studying disease pathology. During her master and Ph.D., she has developed a number of physiologically relevant models for pulmonary inflammation and fibrosis. She has expertise in complex cell culture systems, including organ-on-a-chip and molecular techniques. In this group, she continues her research interest focusing on the development and validation of high-throughput in vitro organ-specific assays for drug screening and chemicals testing.


LEE Jia Ying Joey, MSc.

Senior Research Officer
Email: leejy at bii dot a-star dot edu dot sg

Joey graduated with a MSc degree in the field of Bioorganometallic Chemistry from the Department of Chemistry and Biological Chemistry of Nanyang Technological University (NTU), Singapore. In her Masters project, she investigated the interaction of triosmium carbonyl clusters with different of amino acids and oligopeptides with regards to its selectivity toward different residues. Her current work in the group focuses on the development and validation of high-throughput image-based experiments for organ-specific toxicity prediction.

Loo Lab Alumni

Postdoctoral Fellows:
  1. Sreetama Basu (2017-19)
  2. Nicola Bougen (2013-17) - now at Otago University, New Zealand
  3. Ran Su (2013-16) - now Associate Professor at Tianjin University, China
  4. Danai Laskameethanasan (2010-13)

Research Officers/Software Engineers:
  1. Paul Cain (2017-19)
  2. Janice Goh Jia Ni, A*STAR NSS Scholar (2017-18) - now at UCSF
  3. Michelle Su Yu Fah (2014-17)
  4. Mark NEO Wei Xuan (2014-16)
  5. Cecilia TAN Wei Ling (2010-13)

  1. Madison Chapel (University of Manitoba, 2019)
  2. Thaddea Chua Yun Fang (University of Toronto, 2017)
  3. Au Hui Her (University of Sheffield, 2016)
  4. Ang Yoong Kwei (University of Melbourne, 2016)
  5. Vanessa KEE Ting Zhen (Monash University, 2015)
  6. LEE Yin Yeng (Nanyang Technological University, 2014)
  7. Yi-Ling Irene TUNG (University of Michigan, 2013)
  8. TAN Wey Chyn (Multimedia University, 2011)

Life in the Loo Lab

Feb 2018. Our annual lab dinner. We had a fabulous 2017.

Jan 2018. Our very own Joey was getting married!

Sept 2017. We participated in the dragon boat contest.

Aug 2017. Joey, James, and Lit-Hsin attended the WC10 at Seattle.

Aug 2017. The Loo Lab Band, Seattle.

Careers @ the Loo Lab

We are always interested in postdoctoral-fellow candidates with backgrounds relevant to our work. Every year, we also take in one to two undergraduate interns to provide them with hand-on experiences in scientific research projects. Some of our interns also contributed to our published work. If you are interested to join our group, please send your CV to Dr. Lit-Hsin Loo (loolh at bii dot a-star dot edu dot sg). Specific openings in the lab are listed below:

  1. Postdoctoral Fellow in Computational Toxicology (1 Sept 2019)
  2. Internship in cell biology
  3. Internship in computational analysis of microscopy images

Postdoctoral Fellow in Bioinformatics and Computational Biology


A postdoctoral research fellow position in the areas of bioinformatics and computational biology is available in the Loo lab at the Bioinformatics Institute (BII), A*STAR, Singapore. The group is interested in studying chemical-induced toxicity using quantitative data modeling and cellular imaging methods. The group has recently developed the first high-throughput cellular imaging and computational platform for predicting chemical-induced kidney toxicity.

The successful candidate will be part of an interdisciplinary team that develops novel in vitro and computational models for predicting the toxicity of drugs, food ingredients, industrial chemicals, and environmental pollutants. He/she will develop new computational methods to analyze and model cellular responses based on chemical structures, cellular phenotypes, and gene and protein expression profiles. The candidate will have the opportunity to work in a highly stimulating environment, and participate in the global effort to develop next-generation and animal-free technologies for chemical safety assessment.


  • A strong quantitative background, with a Ph.D. in either biomedical engineering, computational biology, chemical engineering, chemistry, toxicology, pharmacology, computer science, or other related fields
  • At least three years of experience working on bioinformatics or computational biology related research problems
  • Strong knowledge in machine learning, data mining, regression analysis, statistics, and programming (R and/or Python) are required
  • Candidates with previous experience in analyzing gene expression data and biological pathways are especially encouraged to apply
  • Strong problem-solving skills, fluent in both spoken and written English, and able to work independently

Application Procedures:

Applicants should send

  • A one-page cover letter briefly describing prior research experience and accomplishments,
  • A one-page statement of research interests,
  • CV,
  • Contact information of two references, and
  • Reprints of one to two key papers
to Dr. Loo Lit Hsin (loolh at bii dot a-star dot edu dot sg). Only shortlisted candidates will be notified.

Internship in Cell Biology


An internship position in the area of cell biology is available in the Loo lab at the Bioinformatics Institute (BII) in Singapore. The successful candidate will study the responses of lung epithelial cells to drugs and other chemical compounds. He/she will have the opportunity to work in a highly interdisciplinary and stimulating environment, and engage in innovative cell biology research. The preferred duration of internship is three months, and the starting date is flexible.


Candidates must have completed university course work in the areas of biological and/or chemical sciences. Previous experience in performing cell culture work and/or molecular biology experiments is desired.

Application Procedures:

Applicants should contact Dr. Lit-Hsin Loo (loolh at bii dot a-star dot edu dot sg) for more information.

Internship in Computational Software Development


An internship position in the area of computational software development is available in the Loo lab at the Bioinformatics Institute (BII) in Singapore. The successful candidate will participate in the software development of image processing and machine learning tools. He/she will have the opportunity to work in a highly interdisciplinary and stimulating environment, and learn how computer science can help biologists to make biological discovery. The preferred duration of internship is three months, and the starting date is flexible.


Candidates must have strong knowledge in C++ programming under the Linux environment, and basic knowledge of machine learning and web programming. Previous experience in bioinformatics or molecular and cell biology is desired, but not required. Candidates with interdisciplinary training in computer science and biology are especially encouraged to apply.

Application Procedures:

Applicants should contact Dr. Lit-Hsin Loo (loolh at bii dot a-star dot edu dot sg) for more information.

Contact us

The Loo Lab is located at:

Bioinformatics Institute
30 Biopolis Street, #07-01 Matrix,
Singapore 138671

Email: loolh at bii dot a-star dot edu dot sg
Tel: (65) 6478 8298
Fax: (65) 6478 9048

[Direction to BII]

About BII

The Bioinformatics Institute (BII) was set up by the Agency for Science and Technology Research (A*STAR) in July 2001; it was re-launched with a strong scientific program in the autumn months of 2007. Located in the Biopolis, BII is conceived as the computational biology research and postgraduate training institute as well as a national resource centre in bioinformatics within the Biomedical Research Council (BMRC) of A*STAR.

The BII focuses on theoretical approaches aimed at understanding biomolecular mechanisms that underlie biological phenomena, the development of computational methods to support this discovery process, and experimental verification of predicted molecular and cellular functions of genes and proteins with biochemical methods. Together with the BMRC, A*STAR research institutes and multinational R&D organizations in the Biopolis, the BII is situated in a conducive environment for exchange of scientific knowledge and friendly interaction that will prompt greater collaborations, and position the Biopolis as a notable biomedical R&D hub in Asia and in the world.

© 2011-19, Complex Cellular Phenotype Analysis Group, A*STAR.