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Book: Computational modeling of signaling networks

Nguyen, Lan K.

(Methods in molecular biology ; 2634 ; Springer protocols)

2023

Author's details	edited by Lan K. Nguyen
Series title	Methods in molecular biology ; 2634 Springer protocols
Collection
Keywords	Cellular signal transduction ; Cell interaction
Subject code	571.74
Language	English
Size	xi, 386 Seiten, Illustrationen, 26 cm
Publisher	Humana Press
Publishing place	New York, NY
Publishing country	United States
Document type	Book
HBZ-ID	HT030006764
ISBN	978-1-0716-3007-5 ; 9781071630082 ; 1-0716-3007-5 ; 1071630083
Database	Catalogue ZB MED Medicine, Health

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Zs A 7042 -2634-	not available for loan	Lesesaal EG

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Article ; Online: Meta-Dynamic Network Modelling for Biochemical Networks.

Hart, Anthony / Nguyen, Lan K

Methods in molecular biology (Clifton, N.J.)

2023 Volume 2634, Page(s) 167–189

Abstract: ODE modelling requires accurate knowledge of parameter and state variable values to deliver accurate and robust predictions. Parameters and state variables, however, are rarely static and immutable entities, especially in a biological context. This ... ...

Abstract	ODE modelling requires accurate knowledge of parameter and state variable values to deliver accurate and robust predictions. Parameters and state variables, however, are rarely static and immutable entities, especially in a biological context. This observation undermines the predictions made by ODE models that rely on specific parameter and state variable values and limits the contexts in which their predictions remain accurate and useful. Meta-dynamic network (MDN) modelling is a technique that can be synergistically integrated into an ODE modelling pipeline to assist in overcoming these limitations. The core mechanic of MDN modelling is the generation of a large number of model instances, each with a unique set of parameters and/or state variable values, followed by the simulation of each to determine how parameter and state variable variation affects protein dynamics. This process reveals the range of possible protein dynamics for a given network topology. Since MDN modelling is integrated with traditional ODE modelling, it can also be used to investigate the underlying causal mechanics. This technique is particularly suited to the investigation of network behaviors in systems that are highly heterogenous or systems wherein the network properties can change over time. MDN is a collection of principles rather than a strict protocol, so in this chapter, we have introduced the core principles using an example, the Hippo-ERK crosstalk signalling network.
MeSH term(s)	Computer Simulation ; Signal Transduction ; Hippo Signaling Pathway ; Models, Biological
Language	English
Publishing date	2023-04-19
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	1940-6029
ISSN (online)	1940-6029
DOI	10.1007/978-1-0716-3008-2_8
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Article ; Online: SynDISCO: A Mechanistic Modeling-Based Framework for Predictive Prioritization of Synergistic Drug Combinations Targeting Cell Signalling Networks.

Shin, Sung-Young / Nguyen, Lan K

Methods in molecular biology (Clifton, N.J.)

2023 Volume 2634, Page(s) 357–381

Abstract: The widespread development of resistance to cancer monotherapies has prompted the need to identify combinatorial treatment approaches that circumvent drug resistance and achieve more durable clinical benefit. However, given the vast space of possible ... ...

Abstract	The widespread development of resistance to cancer monotherapies has prompted the need to identify combinatorial treatment approaches that circumvent drug resistance and achieve more durable clinical benefit. However, given the vast space of possible combinations of existing drugs, the inaccessibility of drug screens to candidate targets with no available drugs, and the significant heterogeneity of cancers, exhaustive experimental testing of combination treatments remains highly impractical. There is thus an urgent need to develop computational approaches that complement experimental efforts and aid the identification and prioritization of effective drug combinations. Here, we provide a practical guide to SynDISCO, a computational framework that leverages mechanistic ODE modeling to predict and prioritize synergistic combination treatments directed at signaling networks. We demonstrate the key steps of SynDISCO and its application to the EGFR-MET signaling network in triple negative breast cancer as an illustrative example. SynDISCO is, however, a network- and cancer-independent framework, and given a suitable ODE model of the network of interest, it could be leveraged to discover cancer-specific combination treatments.
MeSH term(s)	Humans ; Drug Synergism ; Drug Combinations ; Signal Transduction ; Neoplasms/drug therapy ; Computational Biology
Chemical Substances	Drug Combinations
Language	English
Publishing date	2023-04-19
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	1940-6029
ISSN (online)	1940-6029
DOI	10.1007/978-1-0716-3008-2_17
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Article ; Online: Multi-Dimensional Analysis of Biochemical Network Dynamics Using pyDYVIPAC.

Lan, Yunduo / Nguyen, Lan K

Methods in molecular biology (Clifton, N.J.)

2023 Volume 2634, Page(s) 33–58

Abstract: Biochemical networks are dynamic, nonlinear, and high-dimensional systems. Realistic kinetic models of biochemical networks often comprise a multitude of kinetic parameters and state variables. Depending on the specific parameter values, a network can ... ...

Abstract	Biochemical networks are dynamic, nonlinear, and high-dimensional systems. Realistic kinetic models of biochemical networks often comprise a multitude of kinetic parameters and state variables. Depending on the specific parameter values, a network can display one of a variety of possible dynamic behaviors such as monostable fixed point, damped oscillation, sustained oscillation, and/or bistability. Understanding how a network behaves under a particular parametric condition, and how its behavior changes as the model parameters are varied within the multidimensional parameter space are imperative for gaining a holistic understanding of the network dynamics. Such knowledge helps elucidate the parameter-to-dynamics mapping, uncover how cells make decisions under various pathophysiological contexts, and inform the design of biological circuits with desired behavior, where the latter is critical to the field of synthetic biology. In this chapter, we will present a practical guide to the multidimensional exploration, analysis, and visualization of network dynamics using pyDYVIPAC, which is a tool ideally suited to these purposes implemented in Python. The utility of pyDYVIPAC will be demonstrated using specific examples of biochemical networks with differing structures and dynamic properties via the interactive Jupyter Notebook environment.
MeSH term(s)	Kinetics ; Models, Biological
Language	English
Publishing date	2023-04-19
Publishing country	United States
Document type	Journal Article
ISSN	1940-6029
ISSN (online)	1940-6029
DOI	10.1007/978-1-0716-3008-2_2
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Article ; Online: Dynamics of ubiquitin-mediated signalling: insights from mathematical modelling and experimental studies.

Nguyen, Lan K

Briefings in bioinformatics

2016 Volume 17, Issue 3, Page(s) 479–493

Abstract: Post-translational modification of cellular proteins by ubiquitin is a pivotal regulatory event that controls not only protein degradation, but also a variety of non-proteolytic functions. Ubiquitination is involved in a broad array of physiological ... ...

Abstract	Post-translational modification of cellular proteins by ubiquitin is a pivotal regulatory event that controls not only protein degradation, but also a variety of non-proteolytic functions. Ubiquitination is involved in a broad array of physiological processes, and its dysregulation has been associated with many human diseases, including neuronal disorders and cancers. Ubiquitin-mediated signalling has thus come to the forefront of biomedical research. It is increasingly apparent that ubiquitination is a highly complex and dynamic process, evidenced by a myriad of ways of ubiquitin chain formation, tightly regulatory mechanisms involving E3 ligases and deubiquitinating enzymes and extensive crosstalk with other post-translational modifications. To unravel the complexity of ubiquitination and understand the dynamic properties of ubiquitin-mediated signalling are challenging, but critical topics in ubiquitin research, which will undoubtedly benefit our effort in developing strategies that could target ubiquitin signalling for therapeutics. Computational modelling and model-based approaches are emerging as promising tools that help tackle the complexity and provide useful frameworks for quantitative and dynamical analysis of ubiquitin signalling. In this article, I will discuss recent advances in our understanding of the dynamic behaviour of ubiquitination from both theoretical and experimental studies, and aspects of ubiquitin signalling that may have major dynamical consequences. It is expected the discussed issues will be of relevant interest to both the ubiquitin and systems biology fields.
Language	English
Publishing date	2016-05
Publishing country	England
Document type	Journal Article
ZDB-ID	2068142-2
ISSN	1477-4054 ; 1467-5463
ISSN (online)	1477-4054
ISSN	1467-5463
DOI	10.1093/bib/bbv052
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Article ; Online: Network rewiring, adaptive resistance and combating strategies in breast cancer.

Cremers, Constance Gaya / Nguyen, Lan K

Cancer drug resistance (Alhambra, Calif.)

2019 Volume 2, Issue 4, Page(s) 1106–1126

Abstract: Resistance to targeted anti-cancer drugs is a complex phenomenon and a major challenge in cancer treatment. It is becoming increasingly evident that a form of acquired drug resistance known as "adaptive resistance" is a common cause of treatment failure ... ...

Abstract	Resistance to targeted anti-cancer drugs is a complex phenomenon and a major challenge in cancer treatment. It is becoming increasingly evident that a form of acquired drug resistance known as "adaptive resistance" is a common cause of treatment failure and patient relapse in many cancers. Unlike classical resistance mechanisms that are acquired via genomic alterations, adaptive resistance is instead driven by non-genomic changes involving rapid and dynamic rewiring of signalling and/or transcriptional networks following therapy, enabled by complex pathway crosstalk and feedback regulation. Such network rewiring allows tumour cells to adapt to the drug treatment, circumvent the initial drug challenge and continue to survive in the presence of the drug. Despite its great clinical importance, adaptive resistance remains largely under-studied and poorly defined. This review is focused on recent findings which provide new insights into the mechanisms underlying adaptive resistance in breast cancer, highlighting how breast tumour cells rewire intracellular signalling pathways to overcome the stress of initial targeted therapy. In particular, we investigate adaptive resistance to targeted inhibition of two major oncogenic signalling axes frequently dysregulated in breast cancer, the PI3K-AKT-mTOR and RAS-MAPK signalling pathways; and discuss potential combination treatment strategies that overcome such resistance. In addition, we highlight application of quantitative and computational modelling as a novel integrative and powerful approach to gain network-level understanding of network rewiring, and rationally identify and prioritise effective drug combinations.
Language	English
Publishing date	2019-12-19
Publishing country	United States
Document type	Journal Article ; Review
ISSN	2578-532X
ISSN (online)	2578-532X
DOI	10.20517/cdr.2019.60
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Article ; Online: Integrative modeling and analysis of signaling crosstalk reveal molecular switches coordinating Yes-associated protein transcriptional activities.

Ghomlaghi, Milad / Theocharous, Mandy / Hoang, Nhan / Shin, Sung-Young / von Kriegsheim, Alex / O' Neill, Eric / Zhang, Tao / Nguyen, Lan K

iScience

2024 Volume 27, Issue 3, Page(s) 109031

Abstract: The transcriptional co-activator YAP forms complexes with distinct transcription factors, controlling cell fate decisions, such as proliferation and apoptosis. However, the mechanisms underlying its context-dependent function are poorly defined. This ... ...

Abstract	The transcriptional co-activator YAP forms complexes with distinct transcription factors, controlling cell fate decisions, such as proliferation and apoptosis. However, the mechanisms underlying its context-dependent function are poorly defined. This study explores the interplay between the TGF-β and Hippo pathways and their influence on YAP's association with specific transcription factors. By integrating iterative mathematical modeling with experimental validation, we uncover molecular switches, predominantly controlled by RASSF1A and ITCH, which dictate the formation of YAP-SMAD (proliferative) and YAP-p73 (apoptotic) complexes. Our results show that RASSF1A enhances the formation of apoptotic complexes, whereas ITCH promotes the formation of proliferative complexes. Notably, higher levels of ITCH transform YAP-SMAD activity from a transient to a sustained state, impacting cellular behaviors. Extending these findings to various breast cancer cell lines highlights the role of cellular context in YAP regulation. Our study provides new insights into the mechanisms of YAP transcriptional activities and their therapeutic implications.
Language	English
Publishing date	2024-01-26
Publishing country	United States
Document type	Journal Article
ISSN	2589-0042
ISSN (online)	2589-0042
DOI	10.1016/j.isci.2024.109031
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Article: Integrative modeling uncovers p21-driven drug resistance and prioritizes therapies for PIK3CA-mutant breast cancer.

Yip, Hon Yan Kelvin / Shin, Sung-Young / Chee, Annabel / Ang, Ching-Seng / Rossello, Fernando J / Wong, Lee Hwa / Nguyen, Lan K / Papa, Antonella

NPJ precision oncology

2024 Volume 8, Issue 1, Page(s) 20

Abstract: Utility of PI3Kα inhibitors like BYL719 is limited by the acquisition of genetic and non-genetic mechanisms of resistance which cause disease recurrence. Several combination therapies based on PI3K inhibition have been proposed, but a way to ... ...

Abstract	Utility of PI3Kα inhibitors like BYL719 is limited by the acquisition of genetic and non-genetic mechanisms of resistance which cause disease recurrence. Several combination therapies based on PI3K inhibition have been proposed, but a way to systematically prioritize them for breast cancer treatment is still missing. By integrating published and in-house studies, we have developed in silico models that quantitatively capture dynamics of PI3K signaling at the network-level under a BYL719-sensitive versus BYL719 resistant-cell state. Computational predictions show that signal rewiring to alternative components of the PI3K pathway promote resistance to BYL719 and identify PDK1 as the most effective co-target with PI3Kα rescuing sensitivity of resistant cells to BYL719. To explore whether PI3K pathway-independent mechanisms further contribute to BYL719 resistance, we performed phosphoproteomics and found that selection of high levels of the cell cycle regulator p21 unexpectedly promoted drug resistance in T47D cells. Functionally, high p21 levels favored repair of BYL719-induced DNA damage and bypass of the associated cellular senescence. Importantly, targeted inhibition of the check-point inhibitor CHK1 with MK-8776 effectively caused death of p21-high T47D cells, thus establishing a new vulnerability of BYL719-resistant breast cancer cells. Together, our integrated studies uncover hidden molecular mediators causing resistance to PI3Kα inhibition and provide a framework to prioritize combination therapies for PI3K-mutant breast cancer.
Language	English
Publishing date	2024-01-26
Publishing country	England
Document type	Journal Article
ISSN	2397-768X
ISSN	2397-768X
DOI	10.1038/s41698-024-00496-y
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Article ; Online: Dynamic modelling of the mTOR signalling network reveals complex emergent behaviours conferred by DEPTOR.

Varusai, Thawfeek M / Nguyen, Lan K

Scientific reports

2018 Volume 8, Issue 1, Page(s) 643

Abstract: The mechanistic Target of Rapamycin (mTOR) signalling network is an evolutionarily conserved network that controls key cellular processes, including cell growth and metabolism. Consisting of the major kinase complexes mTOR Complex 1 and 2 (mTORC1/2), the ...

Abstract	The mechanistic Target of Rapamycin (mTOR) signalling network is an evolutionarily conserved network that controls key cellular processes, including cell growth and metabolism. Consisting of the major kinase complexes mTOR Complex 1 and 2 (mTORC1/2), the mTOR network harbours complex interactions and feedback loops. The DEP domain-containing mTOR-interacting protein (DEPTOR) was recently identified as an endogenous inhibitor of both mTORC1 and 2 through direct interactions, and is in turn degraded by mTORC1/2, adding an extra layer of complexity to the mTOR network. Yet, the dynamic properties of the DEPTOR-mTOR network and the roles of DEPTOR in coordinating mTORC1/2 activation dynamics have not been characterised. Using computational modelling, systems analysis and dynamic simulations we show that DEPTOR confers remarkably rich and complex dynamic behaviours to mTOR signalling, including abrupt, bistable switches, oscillations and co-existing bistable/oscillatory responses. Transitions between these distinct modes of behaviour are enabled by modulating DEPTOR expression alone. We characterise the governing conditions for the observed dynamics by elucidating the network in its vast multi-dimensional parameter space, and develop strategies to identify core network design motifs underlying these dynamics. Our findings provide new systems-level insights into the complexity of mTOR signalling contributed by DEPTOR.
MeSH term(s)	Computer Simulation ; Humans ; Intracellular Signaling Peptides and Proteins/chemistry ; Intracellular Signaling Peptides and Proteins/metabolism ; Mechanistic Target of Rapamycin Complex 1/metabolism ; Mechanistic Target of Rapamycin Complex 2/metabolism ; Molecular Dynamics Simulation ; Proteolysis ; Signal Transduction ; Systems Analysis
Chemical Substances	Intracellular Signaling Peptides and Proteins ; DEPTOR protein, human (EC 2.7.1.1) ; Mechanistic Target of Rapamycin Complex 1 (EC 2.7.11.1) ; Mechanistic Target of Rapamycin Complex 2 (EC 2.7.11.1)
Language	English
Publishing date	2018-01-12
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-017-18400-z
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Article ; Online: Relationship Between Dimensionality and Convergence of Optimization Algorithms: A Comparison Between Data-Driven Normalization and Scaling Factor-Based Methods Using PEPSSBI.

Degasperi, Andrea / Nguyen, Lan K / Fey, Dirk / Kholodenko, Boris N

Methods in molecular biology (Clifton, N.J.)

2021 Volume 2385, Page(s) 91–115

Abstract: Ordinary differential equation models are used to represent intracellular signaling pathways in silico, aiding and guiding biological experiments to elucidate intracellular regulation. To construct such quantitative and predictive models of intracellular ...

Abstract	Ordinary differential equation models are used to represent intracellular signaling pathways in silico, aiding and guiding biological experiments to elucidate intracellular regulation. To construct such quantitative and predictive models of intracellular interactions, unknown parameters need to be estimated. Most of biological data are expressed in relative or arbitrary units, raising the question of how to compare model simulations with data. It has recently been shown that for models with large number of unknown parameters, fitting algorithms using a data-driven normalization of the simulations (DNS) performs best in terms of the convergence time and parameter identifiability. DNS approach compares model simulations and corresponding data both normalized by the same normalization procedure, without requiring additional parameters to be estimated, as necessary for widely used scaling factor-based methods. However, currently there is no parameter estimation software that directly supports DNS. In this chapter, we show how to apply DNS to dynamic models of systems and synthetic biology using PEPSSBI (Parameter Estimation Pipeline for Systems and Synthetic Biology). PEPSSBI is the first software that supports DNS, through algorithmically supported data normalization and objective function construction. PEPSSBI also supports model import using SBML and repeated parameter estimation runs executed in parallel either on a personal computer or a multi-CPU cluster.
MeSH term(s)	Algorithms ; Computer Simulation ; Models, Biological ; Signal Transduction ; Software ; Systems Biology
Language	English
Publishing date	2021-12-09
Publishing country	United States
Document type	Comparative Study ; Journal Article ; Research Support, Non-U.S. Gov't
ISSN	1940-6029
ISSN (online)	1940-6029
DOI	10.1007/978-1-0716-1767-0_5
Database	MEDical Literature Analysis and Retrieval System OnLINE

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