Translational Mechanobiology Lab


We share the VISION of EDUCATING ambitious young scientists and engineers to make impacts beyond individual efforts through team projects and collaborative learning in academia and industry.


Research Mission

Understand and control microenvironment impact on chronic liver disease progression. We TRANSLATE technologies and knowledges into solutions for drug development, diagnostics and therapeutics.

Research Goals

Quantitative analysis of the dynamic process of liver regeneration and chronic liver diseases.

Investigating the contraction and propagation mechanism of secretory lumen such as bile canaliculi.

Developing novel and useful biomaterials, cell sources, and analytics for long-term maintenance of highly functional epithelial cells in culture.

Developing robust, scalable, low cost and predictive in vitro drug and toxin testing platforms.

shared values

Shared Values

Respect : every TMBL member is important and yet consciously sensitive to other members and the collective impacts. Decisions incorporate inputs from all the stakeholders for fairness and transparency.

Professionalism: every TMBL member strives to attain ever higher quality and standard of her/his own work through mutual empowerment, critique and support to each other.

No-walls culture: solutions to real-life problems can never be confined within artificially-created boundaries (organizational, disciplinary, cultural, inter-personal, or mental inertia).


Featured Book


Cover Image of Book

Sheetz, M., and Yu, H. (2018) The Cell as a Machine, Cambridge University Press, ISBN: 9781107052734.

This unique introductory text has been written by a Lasker Award winning cell biologist; and his PhD student who turned into a biomedical engineer who innovates multiple technologies into award winning companies. It is based on the content and to serve as the text for a course, "Cell as a Machine”, offered by leading universities in the USA and Asia. The course started in 2003 as W3150/4150 in Columbia University, and then taught as course 2.799 in Massachusetts Institute of Technology, and MB5101 in National University of Singapore since 2009, which was extended in different years to Penn, Georgia Tech, UIUC, UC Berkeley/San Diego/Irvine/Davis/Merced, City College of New York, Minnesota, Wash U St Louis, Wisconsin, Boston U and Texas A&M in the US, Hong Kong UST and Zhejiang University in Asia.

The text explains cell functions using the engineering principles of robust devices. Adopting a process-based approach to understanding cell and tissue biology, it describes the molecular and mechanical features that enable the cell to be robust in operating its various components, and explores the ways in which molecular modules respond to environmental signals to execute complex functions. The design and operation of a variety of complex functions are covered, including engineering lipid bilayers to provide fluid boundaries and mechanical controls, adjusting cell shape and forces with dynamic filament networks, and DNA packaging for information retrieval and propagation. Numerous problems, case studies and application examples help readers connect theory with practice, and solutions for instructors and videos of lectures accompany the book online. Assuming only basic mathematical knowledge, this is an invaluable resource for graduate and senior undergraduate students taking single-semester courses in cell mechanics, biophysics, mechanobiology, and cell/tissue biology from a fresh perspective.

Uniquely links together the biology, biophysics, and engineering principles underlying cell functions
Avoids complex mathematical treatments, making it accessible to students with only a basic mathematical background
Additional information about many of the functions described in the book can be found online at



Featured Recent Publications



Sun, M., Wong, J.Y., Nugraha, B., Ananthanarayanan, A., Liu, Z., Lee, F., Gupta, K., Fong, L.S.E., Huang, X., and Yu, H. (2019) Cleavable cellulosic sponge for functional hepatic cell culture and retrieval. Biomaterials, May 2019; 201: 16-32. DOI: 10.1016/j.biomaterials.2019.01.046

Interconnected macroporous hydrogel is hydrophilic; it exhibits soft tissue-like mechanical property and aqueous-stable macroporosity for 3D spheroid culture. There is an unmet need to develop cleavable macroporous hydrogel, for the ease of retrieving functional spheroids for further in vitro and in vivo applications. We have developed and comprehensively characterized a hydroxypropyl-cellulose-disulfide sponge by systematically identifying strategies and synthesis schemes to confer cleavability to the sponge under cell-friendly conditions. It preserved the essential advantages of the macroporous hydrogel to support 3D spheroid formation and maintenance of sensitive hepatocytes while allowing rapid cleavage and retrieval of functional spheroids. By culturing HepaRG as spheroids in the cleavable sponge, we have accelerated HepaRG differentiation to 9 days compared to 28 days in 2D culture. Cytochrome P450 basal activity reached significantly higher level, while albumin secretion and fluorescein diacetate staining indicated the same at day 5. The purity of albumin+ hepatocytes reached 92.9% versus 7.1% of CK19+ cholangiocytes at day 9, a much stronger preference for hepatocytes than the 60% albumin+ hepatocytes purity in 2D culture. HepaRG differentiated hepatocytes were retrieved by cleaving the sponge with 10 mM tris-(2-carboxyethyl)-phosphine (TCEP) within 30 min preserving viability, plateability and positive albumin staining of the hepatocyte spheroids. This cleavable macroporous hydrogel sponge will support the rapid development of various 3D spheroid- or organoid-based applications in basic research and drug testing.



Tasnim, F., Xing, J., Huang, X., Mo, S., Wei, X., Tan, M.-H. and Yu, H. (2019) Generation of Mature Kupffer Cells from Human Induced Pluripotent Stem Cells. Biomaterials, February 2019; 192: 337-391. DOI: 10.1016/j.biomaterials.2018.11.016

Liver macrophages, Kupffer cells (KCs), play a critical role in drug-induced liver injury (DILI) and liver diseases including cholestasis, liver fibrosis and viral hepatitis. Application of KCs in in vitro models of DILI and liver diseases is hindered due to limited source of human KCs. In vivo, KCs originate from MYB-independent macrophage progenitors, which differentiate into liver-specific macrophages in response to hepatic cues in the liver. Here, we recapitulated KCs ontogeny by differentiation of MYB-independent iPSCs to macrophage-precursors and exposing them to hepatic cues to generate iPSC-derived KCs (iKCs). iKCs expressed macrophage markers (CD11/CD14/CD68/CD163/CD32) at 0.3-5 folds of primary adult human KCs (pKCs) and KC-specific CLEC-4F, ID1 and ID3. iKCs phagocytosed and secreted IL-6 and TNFα upon stimulation at levels similar to pKCs but different from non-liver macrophages. Hepatocyte-iKCs co-culture model was more sensitive in detecting hepatotoxicity induced by inflammation-associated drugs, Acetaminophen and Trovafloxacin, and Chlorpromazine-induced cholestasis when compared to hepatocytes alone. Overall, iKCs were mature, liver-specific and functional. Furthermore, donor-matched iKCs and iPSC-hepatocyte co-culture exhibited minimal non-specific background response compared to donor-mismatched counterpart. iKCs offer a mature renewable human cell source for liver-specific macrophages, useful in developing in vitro model to study DILI and liver diseases such as cholestasis.



Yu, Y., Wang, J., Ng, C.W., Ma, Y., Mo, S., Fong, L.S.E., Xing, J., Song, Z., Xie, Y., Si, K., Wee, A., Welsch, R.E., So, P.T.C., and Yu, H. (2018) Deep learning enhances automated scoring of liver fibrosis stages. Scientific Reports, 30 October 2018; 8: 16016. DOI: 10.1038/s41598-018-34300-2

Current liver fibrosis scoring by computer-assisted image analytics is not fully automated as it requires manual preprocessing (segmentation and feature extraction) typically based on domain knowledge in liver pathology. Deep learning-based algorithms can potentially classify these images without the need for preprocessing through learning from a large dataset of images. We investigated the performance of classification models built using a deep learning-based algorithm pre-trained using multiple sources of images to score liver fibrosis and compared them against conventional non-deep learning-based algorithms - artificial neural networks (ANN), multinomial logistic regression (MLR), support vector machines (SVM) and random forests (RF). Automated feature classification and fibrosis scoring were achieved by using a transfer learning-based deep learning network, AlexNet-Convolutional Neural Networks (CNN), with balanced area under receiver operating characteristic (AUROC) values of up to 0.85–0.95 versus ANN (AUROC of up to 0.87–1.00), MLR (AUROC of up to 0.73–1.00), SVM (AUROC of up to 0.69–0.99) and RF (AUROC of up to 0.94–0.99). Results indicate that a deep learning-based algorithm with transfer learning enables the construction of a fully automated and accurate prediction model for scoring liver fibrosis stages that is comparable to other conventional non-deep learning-based algorithms that are not fully automated.