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Nguyen Lab research

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About Dr Lan Nguyen

Dr Lan Nguyen studied Applied Mathematics and Computing at undergraduate in Christchurch, New Zealand before completing a PhD in Computational Systems Biology at Lincoln University in early 2010. During his PhD, he received mixed training in mathematical modelling, cell biology and systems biology under the mentorship of Professor Don Kulasiri. From 2010 to 2013, he undertook postdoctoral training at Systems Biology Ireland (SBI), one of Europe-leading systems biology institutes, mentored by systems biology pioneers Professors Boris Kholodenko (mathematical/computational modelling) and Walter Kolch (signal transduction, cancer signalling). He became a junior group leader at SBI in 2014. In late 2015, following a competitive worldwide search by Monash University, he was recruited to the Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute to establish and head the Integrated Network Modelling Laboratory. Over the last decade, through exploiting systems-based approaches that synergise advanced techniques from computational and experimental sciences, his studies have established new systems-level insights into cell signalling and cell-fate determination. His highly interdisciplinary research approach has also shed new lights on mechanisms of anti-cancer drug resistance and identified new strategies to treat cancer.

Our research

Research vision

Decades of experimental work using traditional biochemical approaches have been highly successful in characterising the function of single genes and proteins, and mapping them into functional pathways. However, it has become abundantly clear that cell signalling pathways do not act in isolation but interlink and cooperate to direct how cells respond to intra/extra-cellular cues, enabled by a multitude of complex crosstalk and feedback mechanisms. This realization together with the advent of -omics technologies that provide the ability to routinely measure thousands of genes and proteins, have necessitated a shift from studying single molecules to the study of interconnected networks. To fully understand cell signalling and behaviour, we thus need to move beyond studying individual pathways in isolation and undertake systematic and quantitative analysis of integrated networks that link functionally related pathways. This is, however, challenging to achieve via the historical and pathway-centric approach; instead, computational network modelling in integration with experimental analysis represents an innovative and powerful strategy to characterise biochemical networks, understand their dynamic behaviours and fully exploit their therapeutic potential.

Figure 1. Primary research themes currently undertaken in the Nguyen laboratory.

Driven by this ‘systems biology’ paradigm, the primary interest of the Nguyen Lab is to develop and exploit innovative systems-based approaches that integrate predictive computational modelling, bioinformatic techniques and wet-lab experiments to address fundamental questions in cell biology and cancer fields (Figure 1). A central feature of our work is the highly interdisciplinary and iterative nature of the approaches (Figure 2), which involve data generation, model building and training, model analysis and prediction, and experimental validation. The Nguyen lab thus nourishes a strongly cross-disciplinary research environment consisting of mixed expertise and resources.

Figure 2. An integrative modelling-experimental approach central to our studies.

Current projects

  1. Tackling adaptive resistance to anti-cancer drugs using systems modelling approach
  2. Friend or foe: Deciphering the conflicting functions of YAP in cancer
  3. Mathematical modelling of the PI3K-Akt-mTOR network
  4. Developing novel analytical and visualisation techniques for model analysis
  5. Network-level characterisation of TCR signalling in T cells

Visit Dr Nguyen's Monash research profile to see a full listing of current projects.

1. Tackling adaptive resistance to anti-cancer drugs using systems modelling approach

Targeted therapy has revolutionised cancer treatment, yet development of resistance to these therapies remains a primary reason for treatment failure and patient relapse. To date, studies have been largely focused on elucidating the genetic mechanisms of drug resistance. However, it has become clear that tumour cells also rely on non-genetic and highly adaptive mechanisms involving rapid and dynamic rewiring of signalling pathways in order to bypass the drug blockade. A key reason underlying this phenomenon is the complexity and highly interconnected nature of cell signalling pathways, featuring abundant crosstalk and feedback regulation that have evolved to provide homeostasis and robustness to normal cells, but are hijacked by transformed cells to overcome anti-cancer drug agents. Overcoming adaptive resistance requires an ability to integrate multiple cancer-relevant signalling pathways in unified frameworks, and describe their dynamics quantitatively at a system level.

Over the past five years, my laboratory has successfully exploited this integrative approach to gain new important systems-level insights into oncogenic signalling networks, placing our research at the forefront of the field. Exploiting this competitive position, this research theme aims to undertake a systems-level characterisation of drug-induced network remodelling; and based on this understanding, to devise new effective combination therapies for cancer. This will be achieved via an integrated approach that synergises predictive modelling and cutting-edge lab-based experiment (Figure 3).

Figure 3. A workflow of our systems-based approach to drug combination discovery.

1a. Overcoming network rewiring-mediated resistance in aggressive breast cancer

Triple-negative breast cancer (TNBC) is highly aggressive but patients have no targeted treatment options beside toxic chemotherapies. Like ants who are very good at seeking alternative ways to travel when their path is blocked, TNBC cells manage to find escape paths to bypass drug treatment. Combining computer simulations and lab work, this project aims to predict these escape paths and identify ways to block them by combining multiple drugs, which will lead to new treatments for this disease. Specifically, we are building predictive computational models of specific signalling networks in TNBC and will apply our recently developed Synergistic Drug Combination Discovery (SynDISCO) framework (PLOS Comp Biol, 2018) where the co-inhibition effects of all possible pair-wise combinations of network nodes are systematically evaluated, and compared, based on well-established quantitative drug synergy metrics. This provides a rational approach for ranking and prioritising drug combinations, thereby greatly enhancing the efficiency of therapeutic strategy development. Prioritized drug combinations will then be tested in appropriate in vitro and in vivo models. This work is being undertaken in collaboration with Professor Roger Daly (Monash University, Melbourne), Associate Professor Alex Swarbrick (Garvan Institute of Medical Research, Sydney) and clinician oncologist Professor Sherene Loi (Peter McCallum Cancer Centre).

1b. Unravelling mechanisms of resistance to PI3K inhibitors through integrative modelling of signalling network

The PI3K/Akt pathway plays a vital role in orchestrating multiple cellular responses such as metabolism, proliferation and survival. This pathway is highly activated in primary human cancers including breast cancer. PIK3CA, the gene coding for catalytic subunit p110α of PI3K, is the most commonly mutated oncogene in breast cancer. This implies that selective inhibition of PI3Kα may have robust antitumor efficacy in PIK3CA-mutant cancers, however, clinical responses to PI3Kα specific inhibitors as single-agent therapy have been disappointing due to intrinsic and acquired resistance. In collaboration with Dr Antonella Papa (Monash University), we are constructing novel multi-pathway computational models of PI3K signalling in combination with experimental validation to investigate network-level mechanisms of resistance to PI3Kα inhibition, based on which to identify drug combinations that can overcome this resistance.

2. Friend or foe: Deciphering the conflicting functions of YAP in cancer

The Hippo/YAP signalling pathway is dysregulated in ~80% of breast cancer cases, making it a potential therapeutic target. However, ongoing efforts aimed at targeting this pathway have yielded limited success. This is due to the fact that YAP, the pathway’s downstream effector and most feasible drug target, is found to display either pro-growth or pro-death function depending on specific contexts. Currently, it is not understood when and how YAP exhibits which function, and this lack of understanding hampers translation of YAP-directed therapy into clinics. Our data and others have indicated that the specific function of YAP is driven by its ability to form functionally distinct complexes with various transcriptional factors, and such formations are tightly controlled by phosphorylation events. This project utilises integrative systems biology approaches combining predictive computational modelling with experiments to decipher the dual functions of YAP in breast cancer, and exploit that understanding to develop effective ways to target YAP.

Figure 4. (A) A simplified diagram depicting the YAP regulatory network.
(B) A schematic depicting the dual role of YAP in cancer regulation.
Cancer cells tipped towards survival when YAP binds TEAD.

3. Mathematical modelling of the PI3K-Akt-mTOR network

3a. Identifying improved treatments targeting the PI3K pathway

The PI3K-Akt-mTOR signalling network plays a pivotal role in the regulation of cell growth and proliferation, and is highly complex with multiple feedback loops, pathway crosstalk, upstream regulators and downstream functions. Its frequent aberrations in cancers makes this network an important therapeutic target, and indeed many targeted drugs have been developed directed at its components. However, the clinical success in inhibiting PI3K, Akt, mTORC1 and/or mTORC2 has been disappointing due to the emergence of drug resistance. This project will employ an integrative systems approach to gain systems-level understanding of the network dynamics in cancer cells before and after drug treatment. This knowledge together with model simulations will help design new therapeutic strategies that exploit network vulnerabilities and are capable of overcoming resistance. Predictions will then be tested experimentally in the wet lab.

3b. Elucidating the functional role of DEPTOR in cancer and exploiting it for therapeutic purposes

DEPTOR was discovered as a unique endogenous inhibitor of both mTOR Complex 1 and 2. Intriguingly, DEPTOR expression is highly variable across various cancer types, and the functional role of DEPTOR in many cancer remains conflicting and poorly understood. We have recently constructed the first mathematical model of the mTOR/DEPTOR network and demonstrated a critical role of DEPTOR in regulating the network’s complex dynamic behaviour (Figure 5). This project continues to characterise the functional role of DEPTOR in cancer, with an aim to design effective therapeutic strategy targeting this protein.

Figure 5. (A) Simplified diagram depicting the interactions and feedback loops within the DEPTOR-mTOR network.
Reaction scheme of the DEPTOR-mTOR mathematical model.
(C) 3D simulation showing the effect of chaning model state variables on network dynamical behaviours,
demonstrating a critical role of DEPTOR in modulating network dynamics.

4. Developing novel analytical and visualisation techniques for model analysis

Biochemical networks are dynamic and multi-dimensional systems, consisting of tens or hundreds of molecular components. Elucidating the network dynamics in health and disease is crucial to better understand the disease mechanisms and derive effective therapeutic strategies. However, current approaches to analyse and visualise systems dynamics can often provide only low-dimensional projections of the network dynamics, which often does not present the multi-dimensional picture of the system behaviour. To address this issue, we have developed an innovative visualisation framework for high-dimensional network behaviour named “Dynamics Visualisation based on Parallel Coordinates” (DYVIPAC) which exploits the advantages provided by parallel coordinates graphs. DYVIPAC has proven to be very powerful in elucidating the governing conditions underlying specific network behaviours that are hidden in the high-dimensional space. This project aims to extend DYVIPAC by providing a GUI to this framework, in order to provide better and user-friendly ways to analyse and visualise complex network behaviours.

Figure 6. (A-B) Schematic illustrating DYVIPAC-based multi-dimensional analysis of dynamic behaviours and
visualisation using Parallel Coordinates (PC) plots (see Nguyen et al. (2015) for details).
PC plot showing the oscillations-inducing sets returned from a 6D analysis applied to the DEPTOR-mTOR model above.

5. Network-level characterisation of TCR signalling in T cells

A properly functioning immune system is critical for an organism’s health. Immune tolerance is a critical feature of the immune system that allows immune cells to mount effective responses against exogenous pathogens such as viruses and bacteria, while preventing attack to self-tissues. Activation-induced cell death (AICD) in T lymphocytes, in which repeated stimulations of the T-cell receptor (TCR) lead to activation and then apoptosis of T cells, is a major mechanism for T cell homeostasis and helps to maintain peripheral immune tolerance. A defect in AICD may lead to development of autoimmune diseases. Despite its importance, the regulatory mechanisms that underlie AICD remain poorly understood particularly at a systems level. In this project, we develop a mechanistic, multi-pathway, and dynamic model of the TCR signalling network and perform computational analyses to characterize the salient systems-level properties of AICD.

Figure 7. Schematic of a mathematical network model integrating multiple signalling
pathways downstream of the T cell receptor (Shin et al. 2019).


Kinetic, dynamic pathway modelling and simulation – MATLAB, Mathematica
Ordinary Differential Equation (ODE) modelling
Agent-based modelling
Sophisticated model calibration and parameter estimation
Model-based perturbation and sensitivity analysis
Systems dynamical analysis and visualisation

Western blot
Cell viability, apoptosis assays

Disease models

Breast cancer
Cancer cell lines
Patient derived xenografts
Orthotopic xenograft models


We collaborate with many scientists and research organisations around the world. Some of our more significant national and international collaborators are listed below. Click on the map to see the details for each of these collaborators (dive into specific publications and outputs by clicking on the dots).

Network and cancer biology:

Alex von Kriegsheim (Uni Edinburgh, UK – hydroxylation mediated signalling, cell migration, proteomics)
Roger Daly (Monash BDI – cancer signalling, phosphoproteomics and kinomics)
Sima Lev (Weizmann Institute of Science, Israel – TNBC, EGFR/PYK2)
Alex Swarbrick (Garvan Institute, Sydney - PDXs)
Helen Abud (Monash BDI – breast cancer organoids)
Tony, Ng (Kings College London, UK - ERBB network modelling, breast cancer)

Antonella Papa (Monash BDI – PI3K signalling, breast cancer)
James Burchfield (Charles Perkins, Sydney – AKT signalling)
David James (Charles Perkins, Sydney – AKT signalling)

Michael Lazarou (Monash BDI – mitophagy modelling)
Christen Mirth (Monash – insulin signalling in development)
Mirana Ramialison (Monash ARMI, heart development modelling)
Alex Cheong (Aston University, UK - hypoxia/HIF modelling)

Tianhai Tian (Monash - mathematical modelling)
Kwang-Hyun Cho (KAIST, Korea – mathematical modelling)

Research translation:

Medical oncologists: Prof Gary Richardson (Cabrini Health) and Prof Sherene Loi (Peter MacCallum Cancer Centre, Melbourne)

Student research projects

The Nguyen Lab offers a variety of Honours, Masters and PhD projects for students interested in joining our group. There are also a number of short term research opportunities available.

Please visit Supervisor Connect to explore the projects currently available in our Lab.