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Rukhlenko OS, Imoto H, Tambde A, McGillycuddy A, Junk P, Tuliakova A, Kolch W, Kholodenko BN. Cell State Transition Models Stratify Breast Cancer Cell Phenotypes and Reveal New Therapeutic Targets. Cancers (Basel) 2024; 16:2354. [PMID: 39001416 PMCID: PMC11240448 DOI: 10.3390/cancers16132354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2024] [Revised: 06/17/2024] [Accepted: 06/23/2024] [Indexed: 07/16/2024] Open
Abstract
Understanding signaling patterns of transformation and controlling cell phenotypes is a challenge of current biology. Here we applied a cell State Transition Assessment and Regulation (cSTAR) approach to a perturbation dataset of single cell phosphoproteomic patterns of multiple breast cancer (BC) and normal breast tissue-derived cell lines. Following a separation of luminal, basal, and normal cell states, we identified signaling nodes within core control networks, delineated causal connections, and determined the primary drivers underlying oncogenic transformation and transitions across distinct BC subtypes. Whereas cell lines within the same BC subtype have different mutational and expression profiles, the architecture of the core network was similar for all luminal BC cells, and mTOR was a main oncogenic driver. In contrast, core networks of basal BC were heterogeneous and segregated into roughly four major subclasses with distinct oncogenic and BC subtype drivers. Likewise, normal breast tissue cells were separated into two different subclasses. Based on the data and quantified network topologies, we derived mechanistic cSTAR models that serve as digital cell twins and allow the deliberate control of cell movements within a Waddington landscape across different cell states. These cSTAR models suggested strategies of normalizing phosphorylation networks of BC cell lines using small molecule inhibitors.
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Affiliation(s)
- Oleksii S Rukhlenko
- Systems Biology Ireland, School of Medicine, University College Dublin, D04 V1W8 Dublin, Ireland
| | - Hiroaki Imoto
- Systems Biology Ireland, School of Medicine, University College Dublin, D04 V1W8 Dublin, Ireland
| | - Ayush Tambde
- Systems Biology Ireland, School of Medicine, University College Dublin, D04 V1W8 Dublin, Ireland
- Stratford College, D06 T9V3 Dublin, Ireland
| | - Amy McGillycuddy
- Systems Biology Ireland, School of Medicine, University College Dublin, D04 V1W8 Dublin, Ireland
- School of Biological, Health and Sports Sciences, Technological University, D07 H6K8 Dublin, Ireland
| | - Philipp Junk
- Systems Biology Ireland, School of Medicine, University College Dublin, D04 V1W8 Dublin, Ireland
| | - Anna Tuliakova
- Systems Biology Ireland, School of Medicine, University College Dublin, D04 V1W8 Dublin, Ireland
| | - Walter Kolch
- Systems Biology Ireland, School of Medicine, University College Dublin, D04 V1W8 Dublin, Ireland
- Conway Institute of Biomolecular and Biomedical Research, University College Dublin, D04 V1W8 Dublin, Ireland
| | - Boris N Kholodenko
- Systems Biology Ireland, School of Medicine, University College Dublin, D04 V1W8 Dublin, Ireland
- Conway Institute of Biomolecular and Biomedical Research, University College Dublin, D04 V1W8 Dublin, Ireland
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT 06520, USA
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2
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Arakane K, Imoto H, Ormersbach F, Okada M. Extending BioMASS to construct mathematical models from external knowledge. BIOINFORMATICS ADVANCES 2024; 4:vbae042. [PMID: 38606187 PMCID: PMC11007111 DOI: 10.1093/bioadv/vbae042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 02/13/2024] [Accepted: 04/03/2024] [Indexed: 04/13/2024]
Abstract
Motivation Mechanistic modeling based on ordinary differential equations has led to numerous findings in systems biology by integrating prior knowledge and experimental data. However, the manual curation of knowledge necessary when constructing models poses a bottleneck. As the speed of knowledge accumulation continues to grow, there is a demand for a scalable means of constructing executable models. Results We previously introduced BioMASS-an open-source, Python-based framework-to construct, simulate, and analyze mechanistic models of signaling networks. With one of its features, Text2Model, BioMASS allows users to define models in a natural language-like format, thereby facilitating the construction of large-scale models. We demonstrate that Text2Model can serve as a tool for integrating external knowledge for mathematical modeling by generating Text2Model files from a pathway database or through the use of a large language model, and simulating its dynamics through BioMASS. Our findings reveal the tool's capabilities to encourage exploration from prior knowledge and pave the way for a fully data-driven approach to constructing mathematical models. Availability and implementation The code and documentation for BioMASS are available at https://github.com/biomass-dev/biomass and https://biomass-core.readthedocs.io, respectively. The code used in this article are available at https://github.com/okadalabipr/text2model-from-knowledge.
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Affiliation(s)
- Kiwamu Arakane
- Institute for Protein Research, Osaka University, Suita, Osaka 565-0871, Japan
| | - Hiroaki Imoto
- Institute for Protein Research, Osaka University, Suita, Osaka 565-0871, Japan
| | | | - Mariko Okada
- Institute for Protein Research, Osaka University, Suita, Osaka 565-0871, Japan
- Premium Research Institute for Human Metaverse Medicine (WPI-PRIMe), Osaka 565-0871, Japan
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3
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Knöchel J, Kloft C, Huisinga W. Index analysis: An approach to understand signal transduction with application to the EGFR signalling pathway. PLoS Comput Biol 2024; 20:e1011777. [PMID: 38315738 PMCID: PMC10868873 DOI: 10.1371/journal.pcbi.1011777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 02/15/2024] [Accepted: 12/21/2023] [Indexed: 02/07/2024] Open
Abstract
In systems biology and pharmacology, large-scale kinetic models are used to study the dynamic response of a system to a specific input or stimulus. While in many applications, a deeper understanding of the input-response behaviour is highly desirable, it is often hindered by the large number of molecular species and the complexity of the interactions. An approach that identifies key molecular species for a given input-response relationship and characterises dynamic properties of states is therefore highly desirable. We introduce the concept of index analysis; it is based on different time- and state-dependent quantities (indices) to identify important dynamic characteristics of molecular species. All indices are defined for a specific pair of input and response variables as well as for a specific magnitude of the input. In application to a large-scale kinetic model of the EGFR signalling cascade, we identified different phases of signal transduction, the peculiar role of Phosphatase3 during signal activation and Ras recycling during signal onset. In addition, we discuss the challenges and pitfalls of interpreting the relevance of molecular species based on knock-out simulation studies, and provide an alternative view on conflicting results on the importance of parallel EGFR downstream pathways. Beyond the applications in model interpretation, index analysis is envisioned to be a valuable tool in model reduction.
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Affiliation(s)
- Jane Knöchel
- Institute of Mathematics, Universität Potsdam, Potsdam, Germany
- Graduate Research Training Program PharMetrX: Pharmacometrics & Computational Disease Modeling, Freie Universität Berlin and Universität Potsdam, Berlin/Potsdam, Germany
| | - Charlotte Kloft
- Department of Clinical Pharmacy and Biochemistry, Institute of Pharmacy, Freie Universität Berlin, Berlin, Germany
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4
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Ram A, Murphy D, DeCuzzi N, Patankar M, Hu J, Pargett M, Albeck JG. A guide to ERK dynamics, part 1: mechanisms and models. Biochem J 2023; 480:1887-1907. [PMID: 38038974 PMCID: PMC10754288 DOI: 10.1042/bcj20230276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Revised: 11/02/2023] [Accepted: 11/06/2023] [Indexed: 12/02/2023]
Abstract
Extracellular signal-regulated kinase (ERK) has long been studied as a key driver of both essential cellular processes and disease. A persistent question has been how this single pathway is able to direct multiple cell behaviors, including growth, proliferation, and death. Modern biosensor studies have revealed that the temporal pattern of ERK activity is highly variable and heterogeneous, and critically, that these dynamic differences modulate cell fate. This two-part review discusses the current understanding of dynamic activity in the ERK pathway, how it regulates cellular decisions, and how these cell fates lead to tissue regulation and pathology. In part 1, we cover the optogenetic and live-cell imaging technologies that first revealed the dynamic nature of ERK, as well as current challenges in biosensor data analysis. We also discuss advances in mathematical models for the mechanisms of ERK dynamics, including receptor-level regulation, negative feedback, cooperativity, and paracrine signaling. While hurdles still remain, it is clear that higher temporal and spatial resolution provide mechanistic insights into pathway circuitry. Exciting new algorithms and advanced computational tools enable quantitative measurements of single-cell ERK activation, which in turn inform better models of pathway behavior. However, the fact that current models still cannot fully recapitulate the diversity of ERK responses calls for a deeper understanding of network structure and signal transduction in general.
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Affiliation(s)
- Abhineet Ram
- Department of Molecular and Cellular Biology, University of California, Davis, U.S.A
| | - Devan Murphy
- Department of Molecular and Cellular Biology, University of California, Davis, U.S.A
| | - Nicholaus DeCuzzi
- Department of Molecular and Cellular Biology, University of California, Davis, U.S.A
| | - Madhura Patankar
- Department of Molecular and Cellular Biology, University of California, Davis, U.S.A
| | - Jason Hu
- Department of Molecular and Cellular Biology, University of California, Davis, U.S.A
| | - Michael Pargett
- Department of Molecular and Cellular Biology, University of California, Davis, U.S.A
| | - John G. Albeck
- Department of Molecular and Cellular Biology, University of California, Davis, U.S.A
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5
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Yang H, Lin H, Sun X. Multiscale modeling of drug resistance in glioblastoma with gene mutations and angiogenesis. Comput Struct Biotechnol J 2023; 21:5285-5295. [PMID: 37941656 PMCID: PMC10628546 DOI: 10.1016/j.csbj.2023.10.037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 10/17/2023] [Accepted: 10/17/2023] [Indexed: 11/10/2023] Open
Abstract
Drug resistance is a prominent impediment to the efficacy of targeted therapies across various cancer types, including glioblastoma (GBM). However, comprehending the intricate intracellular and extracellular mechanisms underlying drug resistance remains elusive. Empirical investigations have elucidated that genetic aberrations, such as gene mutations, along with microenvironmental adaptation, notably angiogenesis, act as pivotal drivers of tumor progression and drug resistance. Nonetheless, mathematical models frequently compartmentalize these factors in isolation. In this study, we present a multiscale agent-based model of GBM, encompassing cellular dynamics, intricate signaling pathways, gene mutations, angiogenesis, and therapeutic interventions. This integrative framework facilitates an exploration of the interplay between genetic mutations and the vascular microenvironment in shaping the dynamic evolution of tumors during treatment with tyrosine kinase inhibitor. Our simulations unveil that mutations influencing the migration and proliferation of tumor cells expedite the emergence of phenotype heterogeneity, thereby exacerbating tumor invasion under both treated and untreated conditions. Moreover, angiogenesis proximate to the tumor fosters a protumoral milieu, augmenting mutation-induced drug resistance by increasing the survival rate of tumor cells. Collectively, our findings underscore the dual roles of intrinsic genetic mutations and extrinsic microenvironmental adaptations in steering tumor growth and drug resistance. Finally, we substantiate our model predictions concerning the impact of gene mutations and angiogenesis on the responsiveness of targeted therapies by integrating single-cell RNA-seq, spatial transcriptomics, bulk RNA-seq, and clinical data from GBM patients. The multidimensional approach enhances our understanding of the complexities governing drug resistance in glioma and offers insights into potential therapeutic strategies.
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Affiliation(s)
- Heng Yang
- Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou 510080, China
- School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, China
| | - Haofeng Lin
- Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou 510080, China
- School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, China
| | - Xiaoqiang Sun
- School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, China
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6
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Madsen RR, Toker A. PI3K signaling through a biochemical systems lens. J Biol Chem 2023; 299:105224. [PMID: 37673340 PMCID: PMC10570132 DOI: 10.1016/j.jbc.2023.105224] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 08/25/2023] [Accepted: 08/28/2023] [Indexed: 09/08/2023] Open
Abstract
Following 3 decades of extensive research into PI3K signaling, it is now evidently clear that the underlying network does not equate to a simple ON/OFF switch. This is best illustrated by the multifaceted nature of the many diseases associated with aberrant PI3K signaling, including common cancers, metabolic disease, and rare developmental disorders. However, we are still far from a complete understanding of the fundamental control principles that govern the numerous phenotypic outputs that are elicited by activation of this well-characterized biochemical signaling network, downstream of an equally diverse set of extrinsic inputs. At its core, this is a question on the role of PI3K signaling in cellular information processing and decision making. Here, we review the determinants of accurate encoding and decoding of growth factor signals and discuss outstanding questions in the PI3K signal relay network. We emphasize the importance of quantitative biochemistry, in close integration with advances in single-cell time-resolved signaling measurements and mathematical modeling.
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Affiliation(s)
- Ralitsa R Madsen
- MRC-Protein Phosphorylation and Ubiquitylation Unit, School of Life Sciences, University of Dundee, Dundee, Scotland, United Kingdom.
| | - Alex Toker
- Department of Pathology and Cancer Center, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA.
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7
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Leblanc JA, Sugiyama MG, Antonescu CN, Brown AI. Quantitative modeling of EGF receptor ligand discrimination via internalization proofreading. Phys Biol 2023; 20:056008. [PMID: 37557183 DOI: 10.1088/1478-3975/aceecd] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Accepted: 08/09/2023] [Indexed: 08/11/2023]
Abstract
The epidermal growth factor receptor (EGFR) is a central regulator of cell physiology that is stimulated by multiple distinct ligands. Although ligands bind to EGFR while the receptor is exposed on the plasma membrane, EGFR incorporation into endosomes following receptor internalization is an important aspect of EGFR signaling, with EGFR internalization behavior dependent upon the type of ligand bound. We develop quantitative modeling for EGFR recruitment to and internalization from clathrin domains, focusing on how internalization competes with ligand unbinding from EGFR. We develop two model versions: a kinetic model with EGFR behavior described as transitions between discrete states and a spatial model with EGFR diffusion to circular clathrin domains. We find that a combination of spatial and kinetic proofreading leads to enhanced EGFR internalization ratios in comparison to unbinding differences between ligand types. Various stages of the EGFR internalization process, including recruitment to and internalization from clathrin domains, modulate the internalization differences between receptors bound to different ligands. Our results indicate that following ligand binding, EGFR may encounter multiple clathrin domains before successful recruitment and internalization. The quantitative modeling we have developed describes competition between EGFR internalization and ligand unbinding and the resulting proofreading.
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Affiliation(s)
- Jaleesa A Leblanc
- Department of Physics, Toronto Metropolitan University, Toronto, Ontario, Canada
| | - Michael G Sugiyama
- Department of Chemistry and Biology, Toronto Metropolitan University, Toronto, Ontario, Canada
| | - Costin N Antonescu
- Department of Chemistry and Biology, Toronto Metropolitan University, Toronto, Ontario, Canada
| | - Aidan I Brown
- Department of Physics, Toronto Metropolitan University, Toronto, Ontario, Canada
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8
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Egert J, Kreutz C. Realistic simulation of time-course measurements in systems biology. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:10570-10589. [PMID: 37322949 DOI: 10.3934/mbe.2023467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
In systems biology, the analysis of complex nonlinear systems faces many methodological challenges. For the evaluation and comparison of the performances of novel and competing computational methods, one major bottleneck is the availability of realistic test problems. We present an approach for performing realistic simulation studies for analyses of time course data as they are typically measured in systems biology. Since the design of experiments in practice depends on the process of interest, our approach considers the size and the dynamics of the mathematical model which is intended to be used for the simulation study. To this end, we used 19 published systems biology models with experimental data and evaluated the relationship between model features (e.g., the size and the dynamics) and features of the measurements such as the number and type of observed quantities, the number and the selection of measurement times, and the magnitude of measurement errors. Based on these typical relationships, our novel approach enables suggestions of realistic simulation study designs in the systems biology context and the realistic generation of simulated data for any dynamic model. The approach is demonstrated on three models in detail and its performance is validated on nine models by comparing ODE integration, parameter optimization, and parameter identifiability. The presented approach enables more realistic and less biased benchmark studies and thereby constitutes an important tool for the development of novel methods for dynamic modeling.
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Affiliation(s)
- Janine Egert
- Institute of Medical Biometry and Statistics (IMBI), Faculty of Medicine and Medical Center - University of Freiburg, Stefan-Meier-Str. 26, 79104 Freiburg, Germany
- Centre for Integrative Biological Signalling Studies CIBSS, University of Freiburg, 79104 Freiburg, Germany
| | - Clemens Kreutz
- Institute of Medical Biometry and Statistics (IMBI), Faculty of Medicine and Medical Center - University of Freiburg, Stefan-Meier-Str. 26, 79104 Freiburg, Germany
- Centre for Integrative Biological Signalling Studies CIBSS, University of Freiburg, 79104 Freiburg, Germany
- Center for Data Analysis and Modelling (FDM), University of Freiburg, 79104 Freiburg, Germany
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9
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Fröhlich F, Gerosa L, Muhlich J, Sorger PK. Mechanistic model of MAPK signaling reveals how allostery and rewiring contribute to drug resistance. Mol Syst Biol 2023; 19:e10988. [PMID: 36700386 PMCID: PMC9912026 DOI: 10.15252/msb.202210988] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 11/29/2022] [Accepted: 12/15/2022] [Indexed: 01/27/2023] Open
Abstract
BRAF is prototypical of oncogenes that can be targeted therapeutically and the treatment of BRAFV600E melanomas with RAF and MEK inhibitors results in rapid tumor regression. However, drug-induced rewiring generates a drug adapted state thought to be involved in acquired resistance and disease recurrence. In this article, we study mechanisms of adaptive rewiring in BRAFV600E melanoma cells using an energy-based implementation of ordinary differential equation (ODE) modeling in combination with proteomic, transcriptomic and imaging data. We develop a method for causal tracing of ODE models and identify two parallel MAPK reaction channels that are differentially sensitive to RAF and MEK inhibitors due to differences in protein oligomerization and drug binding. We describe how these channels, and timescale separation between immediate-early signaling and transcriptional feedback, create a state in which the RAS-regulated MAPK channel can be activated by growth factors under conditions in which the BRAFV600E -driven channel is fully inhibited. Further development of the approaches in this article is expected to yield a unified model of adaptive drug resistance in melanoma.
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Affiliation(s)
- Fabian Fröhlich
- Laboratory of Systems Pharmacology, Department of Systems BiologyHarvard Medical SchoolBostonMAUSA
| | - Luca Gerosa
- Laboratory of Systems Pharmacology, Department of Systems BiologyHarvard Medical SchoolBostonMAUSA,Present address:
Genentech, Inc.South San FranciscoCAUSA
| | - Jeremy Muhlich
- Laboratory of Systems Pharmacology, Department of Systems BiologyHarvard Medical SchoolBostonMAUSA
| | - Peter K Sorger
- Laboratory of Systems Pharmacology, Department of Systems BiologyHarvard Medical SchoolBostonMAUSA
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10
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Wysocka EM, Page M, Snowden J, Simpson TI. Comparison of rule- and ordinary differential equation-based dynamic model of DARPP-32 signalling network. PeerJ 2022; 10:e14516. [PMID: 36540795 PMCID: PMC9760030 DOI: 10.7717/peerj.14516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 11/14/2022] [Indexed: 12/23/2022] Open
Abstract
Dynamic modelling has considerably improved our understanding of complex molecular mechanisms. Ordinary differential equations (ODEs) are the most detailed and popular approach to modelling the dynamics of molecular systems. However, their application in signalling networks, characterised by multi-state molecular complexes, can be prohibitive. Contemporary modelling methods, such as rule- based (RB) modelling, have addressed these issues. The advantages of RB modelling over ODEs have been presented and discussed in numerous reviews. In this study, we conduct a direct comparison of the time courses of a molecular system founded on the same reaction network but encoded in the two frameworks. To make such a comparison, a set of reactions that underlie an ODE model was manually encoded in the Kappa language, one of the RB implementations. A comparison of the models was performed at the level of model specification and dynamics, acquired through model simulations. In line with previous reports, we confirm that the Kappa model recapitulates the general dynamics of its ODE counterpart with minor differences. These occur when molecules have multiple sites binding the same interactor. Furthermore, activation of these molecules in the RB model is slower than in the ODE one. As reported for other molecular systems, we find that, also for the DARPP-32 reaction network, the RB representation offers a more expressive and flexible syntax that facilitates access to fine details of the model, easing model reuse. In parallel with these analyses, we report a refactored model of the DARPP-32 interaction network that can serve as a canvas for the development of more complex dynamic models to study this important molecular system.
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Affiliation(s)
- Emilia M. Wysocka
- School of Informatics, University of Edinburgh, Edinburgh, United Kingdom
| | | | | | - T. Ian Simpson
- School of Informatics, University of Edinburgh, Edinburgh, United Kingdom
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11
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Kariya Y, Honma M, Tokuda K, Konagaya A, Suzuki H. Utility of constraints reflecting system stability on analyses for biological models. PLoS Comput Biol 2022; 18:e1010441. [PMID: 36084151 PMCID: PMC9491612 DOI: 10.1371/journal.pcbi.1010441] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Revised: 09/21/2022] [Accepted: 07/26/2022] [Indexed: 12/03/2022] Open
Abstract
Simulating complex biological models consisting of multiple ordinary differential equations can aid in the prediction of the pharmacological/biological responses; however, they are often hampered by the availability of reliable kinetic parameters. In the present study, we aimed to discover the properties of behaviors without determining an optimal combination of kinetic parameter values (parameter set). The key idea was to collect as many parameter sets as possible. Given that many systems are biologically stable and resilient (BSR), we focused on the dynamics around the steady state and formulated objective functions for BSR by partial linear approximation of the focused region. Using the objective functions and modified global cluster Newton method, we developed an algorithm for a thorough exploration of the allowable parameter space for biological systems (TEAPS). We first applied TEAPS to the NF-κB signaling model. This system shows a damped oscillation after stimulation and seems to fit the BSR constraint. By applying TEAPS, we found several directions in parameter space which stringently determines the BSR property. In such directions, the experimentally fitted parameter values were included in the range of the obtained parameter sets. The arachidonic acid metabolic pathway model was used as a model related to pharmacological responses. The pharmacological effects of nonsteroidal anti-inflammatory drugs were simulated using the parameter sets obtained by TEAPS. The structural properties of the system were partly extracted by analyzing the distribution of the obtained parameter sets. In addition, the simulations showed inter-drug differences in prostacyclin to thromboxane A2 ratio such that aspirin treatment tends to increase the ratio, while rofecoxib treatment tends to decrease it. These trends are comparable to the clinical observations. These results on real biological models suggest that the parameter sets satisfying the BSR condition can help in finding biologically plausible parameter sets and understanding the properties of biological systems. We propose a new method to analyze the properties of biological dynamic models, which we named TEAPS (Thorough Exploration of Allowable Parameter Space). TEAPS can thoroughly determine combinations of parameter values for ordinary differential equations with which an initial state in a certain range converges to a particular fixed point. This stable and resilient behavior is a characteristic shared with many biological systems, including metabolic systems and intracellular signaling systems. Therefore, this thorough search outlined the possible parameter space as biological systems for target models, which helps to understand the system constraints when the target systems behave dynamically. The obtained parameter space can be used as an initial space for parameter tuning. For models that include a large number of parameters, the parameter space to be searched in the parameter tuning process is too large; therefore, narrowing down the space by TEAPS potentially contributes to the analysis of the dynamics of complicated biological models. Thus, our approach can partly overcome the current problem in parameter tuning and can advance the computational dynamic analyses of biological systems.
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Affiliation(s)
- Yoshiaki Kariya
- Department of Pharmacy, The University of Tokyo Hospital, Faculty of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
| | - Masashi Honma
- Department of Pharmacy, The University of Tokyo Hospital, Faculty of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
- * E-mail:
| | - Keita Tokuda
- Department of Computer Science, University of Tsukuba, Tsukuba, Ibaraki, Japan
| | - Akihiko Konagaya
- Molecular Robotics Research Institute, Limited, Kyowa Create Dai-ichi, Minato-ku, Tokyo, Japan
| | - Hiroshi Suzuki
- Department of Pharmacy, The University of Tokyo Hospital, Faculty of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
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12
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Rukhlenko OS, Halasz M, Rauch N, Zhernovkov V, Prince T, Wynne K, Maher S, Kashdan E, MacLeod K, Carragher NO, Kolch W, Kholodenko BN. Control of cell state transitions. Nature 2022; 609:975-985. [PMID: 36104561 PMCID: PMC9644236 DOI: 10.1038/s41586-022-05194-y] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Accepted: 08/04/2022] [Indexed: 11/09/2022]
Abstract
Understanding cell state transitions and purposefully controlling them is a longstanding challenge in biology. Here we present cell state transition assessment and regulation (cSTAR), an approach for mapping cell states, modelling transitions between them and predicting targeted interventions to convert cell fate decisions. cSTAR uses omics data as input, classifies cell states, and develops a workflow that transforms the input data into mechanistic models that identify a core signalling network, which controls cell fate transitions by influencing whole-cell networks. By integrating signalling and phenotypic data, cSTAR models how cells manoeuvre in Waddington's landscape1 and make decisions about which cell fate to adopt. Notably, cSTAR devises interventions to control the movement of cells in Waddington's landscape. Testing cSTAR in a cellular model of differentiation and proliferation shows a high correlation between quantitative predictions and experimental data. Applying cSTAR to different types of perturbation and omics datasets, including single-cell data, demonstrates its flexibility and scalability and provides new biological insights. The ability of cSTAR to identify targeted perturbations that interconvert cell fates will enable designer approaches for manipulating cellular development pathways and mechanistically underpinned therapeutic interventions.
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Affiliation(s)
- Oleksii S Rukhlenko
- Systems Biology Ireland, School of Medicine, University College Dublin, Dublin, Ireland
| | - Melinda Halasz
- Systems Biology Ireland, School of Medicine, University College Dublin, Dublin, Ireland
- Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin, Ireland
| | - Nora Rauch
- Systems Biology Ireland, School of Medicine, University College Dublin, Dublin, Ireland
| | - Vadim Zhernovkov
- Systems Biology Ireland, School of Medicine, University College Dublin, Dublin, Ireland
| | - Thomas Prince
- Systems Biology Ireland, School of Medicine, University College Dublin, Dublin, Ireland
| | - Kieran Wynne
- Systems Biology Ireland, School of Medicine, University College Dublin, Dublin, Ireland
| | - Stephanie Maher
- Systems Biology Ireland, School of Medicine, University College Dublin, Dublin, Ireland
| | - Eugene Kashdan
- Systems Biology Ireland, School of Medicine, University College Dublin, Dublin, Ireland
| | - Kenneth MacLeod
- Cancer Research UK Edinburgh Centre, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
| | - Neil O Carragher
- Cancer Research UK Edinburgh Centre, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
| | - Walter Kolch
- Systems Biology Ireland, School of Medicine, University College Dublin, Dublin, Ireland
- Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin, Ireland
| | - Boris N Kholodenko
- Systems Biology Ireland, School of Medicine, University College Dublin, Dublin, Ireland.
- Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin, Ireland.
- Department of Pharmacology, Yale University School of Medicine, New Haven, USA.
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13
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Stochastic model of ERK-mediated progesterone receptor translocation, clustering and transcriptional activity. Sci Rep 2022; 12:11791. [PMID: 35821038 PMCID: PMC9276744 DOI: 10.1038/s41598-022-13821-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Accepted: 05/27/2022] [Indexed: 11/26/2022] Open
Abstract
Progesterone receptor (PR) transcriptional activity is a key factor in the differentiation of the uterine endometrium. By consequence, progestin has been identified as an important treatment modality for endometrial cancer. PR transcriptional activity is controlled by extracellular-signal-regulated kinase (ERK) mediated phosphorylation, downstream of growth factor receptors such as EGFR. However, phosphorylation of PR also targets it for ubiquitination and destruction in the proteasome. Quantitative studies of these opposing roles are much needed toward validation of potential new progestin-based therapeutics. In this work, we propose a spatial stochastic model to study the effects of the opposing roles for PR phosphorylation on the levels of active transcription factor. Our numerical simulations confirm earlier in vitro experiments in endometrial cancer cell lines, identifying clustering as a mechanism that amplifies the ability of progesterone receptors to influence gene transcription. We additionally show the usefulness of a statistical method we developed to quantify and control variations in stochastic simulations in general biochemical systems, assisting modelers in defining minimal but meaningful numbers of simulations while guaranteeing outputs remain within a pre-defined confidence level.
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14
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Erdem C, Mutsuddy A, Bensman EM, Dodd WB, Saint-Antoine MM, Bouhaddou M, Blake RC, Gross SM, Heiser LM, Feltus FA, Birtwistle MR. A scalable, open-source implementation of a large-scale mechanistic model for single cell proliferation and death signaling. Nat Commun 2022; 13:3555. [PMID: 35729113 PMCID: PMC9213456 DOI: 10.1038/s41467-022-31138-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Accepted: 06/07/2022] [Indexed: 02/01/2023] Open
Abstract
Mechanistic models of how single cells respond to different perturbations can help integrate disparate big data sets or predict response to varied drug combinations. However, the construction and simulation of such models have proved challenging. Here, we developed a python-based model creation and simulation pipeline that converts a few structured text files into an SBML standard and is high-performance- and cloud-computing ready. We applied this pipeline to our large-scale, mechanistic pan-cancer signaling model (named SPARCED) and demonstrate it by adding an IFNγ pathway submodel. We then investigated whether a putative crosstalk mechanism could be consistent with experimental observations from the LINCS MCF10A Data Cube that IFNγ acts as an anti-proliferative factor. The analyses suggested this observation can be explained by IFNγ-induced SOCS1 sequestering activated EGF receptors. This work forms a foundational recipe for increased mechanistic model-based data integration on a single-cell level, an important building block for clinically-predictive mechanistic models.
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Affiliation(s)
- Cemal Erdem
- Department of Chemical & Biomolecular Engineering, Clemson University, Clemson, SC, USA.
| | - Arnab Mutsuddy
- Department of Chemical & Biomolecular Engineering, Clemson University, Clemson, SC, USA
| | - Ethan M Bensman
- Computer Science, School of Computing, Clemson University, Clemson, SC, USA
| | - William B Dodd
- Department of Chemical & Biomolecular Engineering, Clemson University, Clemson, SC, USA
| | - Michael M Saint-Antoine
- Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA
| | - Mehdi Bouhaddou
- Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, CA, USA
| | - Robert C Blake
- Center for Applied Scientific Computing, Lawrence Livermore National Laboratory, Livermore, CA, USA
| | - Sean M Gross
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
| | - Laura M Heiser
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
| | - F Alex Feltus
- Department of Genetics and Biochemistry, Clemson University, Clemson, SC, USA
- Biomedical Data Science and Informatics Program, Clemson University, Clemson, SC, USA
- Center for Human Genetics, Clemson University, Clemson, SC, USA
| | - Marc R Birtwistle
- Department of Chemical & Biomolecular Engineering, Clemson University, Clemson, SC, USA.
- Department of Bioengineering, Clemson University, Clemson, SC, USA.
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15
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Verma A, Manchel A, Melunis J, Hengstler JG, Vadigepalli R. From Seeing to Simulating: A Survey of Imaging Techniques and Spatially-Resolved Data for Developing Multiscale Computational Models of Liver Regeneration. FRONTIERS IN SYSTEMS BIOLOGY 2022; 2:917191. [PMID: 37575468 PMCID: PMC10421626 DOI: 10.3389/fsysb.2022.917191] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
Liver regeneration, which leads to the re-establishment of organ mass, follows a specifically organized set of biological processes acting on various time and length scales. Computational models of liver regeneration largely focused on incorporating molecular and signaling detail have been developed by multiple research groups in the recent years. These modeling efforts have supported a synthesis of disparate experimental results at the molecular scale. Incorporation of tissue and organ scale data using noninvasive imaging methods can extend these computational models towards a comprehensive accounting of multiscale dynamics of liver regeneration. For instance, microscopy-based imaging methods provide detailed histological information at the tissue and cellular scales. Noninvasive imaging methods such as ultrasound, computed tomography and magnetic resonance imaging provide morphological and physiological features including volumetric measures over time. In this review, we discuss multiple imaging modalities capable of informing computational models of liver regeneration at the organ-, tissue- and cellular level. Additionally, we discuss available software and algorithms, which aid in the analysis and integration of imaging data into computational models. Such models can be generated or tuned for an individual patient with liver disease. Progress towards integrated multiscale models of liver regeneration can aid in prognostic tool development for treating liver disease.
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Affiliation(s)
- Aalap Verma
- Daniel Baugh Institute for Functional Genomics and Computational Biology, Department of Pathology, Anatomy, and Cell Biology, Thomas Jefferson University, Philadelphia, PA, United States
| | - Alexandra Manchel
- Daniel Baugh Institute for Functional Genomics and Computational Biology, Department of Pathology, Anatomy, and Cell Biology, Thomas Jefferson University, Philadelphia, PA, United States
| | - Justin Melunis
- Daniel Baugh Institute for Functional Genomics and Computational Biology, Department of Pathology, Anatomy, and Cell Biology, Thomas Jefferson University, Philadelphia, PA, United States
| | - Jan G. Hengstler
- IfADo-Leibniz Research Centre for Working Environment and Human Factors, Technical University Dortmund, Dortmund, Germany
| | - Rajanikanth Vadigepalli
- Daniel Baugh Institute for Functional Genomics and Computational Biology, Department of Pathology, Anatomy, and Cell Biology, Thomas Jefferson University, Philadelphia, PA, United States
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16
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Shahidi N, Pan M, Tran K, Crampin EJ, Nickerson DP. A semantics, energy-based approach to automate biomodel composition. PLoS One 2022; 17:e0269497. [PMID: 35657966 PMCID: PMC9165793 DOI: 10.1371/journal.pone.0269497] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Accepted: 05/20/2022] [Indexed: 11/19/2022] Open
Abstract
Hierarchical modelling is essential to achieving complex, large-scale models. However, not all modelling schemes support hierarchical composition, and correctly mapping points of connection between models requires comprehensive knowledge of each model's components and assumptions. To address these challenges in integrating biosimulation models, we propose an approach to automatically and confidently compose biosimulation models. The approach uses bond graphs to combine aspects of physical and thermodynamics-based modelling with biological semantics. We improved on existing approaches by using semantic annotations to automate the recognition of common components. The approach is illustrated by coupling a model of the Ras-MAPK cascade to a model of the upstream activation of EGFR. Through this methodology, we aim to assist researchers and modellers in readily having access to more comprehensive biological systems models.
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Affiliation(s)
- Niloofar Shahidi
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - Michael Pan
- Systems Biology Laboratory, School of Mathematics and Statistics, and Department of Biomedical Engineering, University of Melbourne, Melbourne, Victoria, Australia
- ARC Centre of Excellence in Convergent Bio-Nano Science and Technology, Faculty of Engineering and Information Technology, University of Melbourne, Melbourne, Victoria, Australia
- School of Mathematics and Statistics, Faculty of Science, University of Melbourne, Victoria, Australia
| | - Kenneth Tran
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - Edmund J. Crampin
- Systems Biology Laboratory, School of Mathematics and Statistics, and Department of Biomedical Engineering, University of Melbourne, Melbourne, Victoria, Australia
- ARC Centre of Excellence in Convergent Bio-Nano Science and Technology, Faculty of Engineering and Information Technology, University of Melbourne, Melbourne, Victoria, Australia
- School of Mathematics and Statistics, Faculty of Science, University of Melbourne, Victoria, Australia
- School of Medicine, University of Melbourne, Melbourne, Victoria, Australia
| | - David P. Nickerson
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
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17
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OncoboxPD: human 51 672 molecular pathways database with tools for activity calculating and visualization. Comput Struct Biotechnol J 2022; 20:2280-2291. [PMID: 35615022 PMCID: PMC9120235 DOI: 10.1016/j.csbj.2022.05.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 05/05/2022] [Accepted: 05/05/2022] [Indexed: 12/29/2022] Open
Abstract
OncoboxPD (Oncobox pathway databank) available at https://open.oncobox.com is the collection of 51 672 uniformly processed human molecular pathways. Superposition of all pathways formed interactome graph of protein–protein interactions and metabolic reactions containing 361 654 interactions and 64 095 molecular participants. Pathways are uniformly classified by biological processes, and each pathway node is algorithmically functionally annotated by specific activator/repressor role. This enables online calculation of statistically supported pathway activation levels (PALs) with the built-in bioinformatic tool using custom RNA/protein expression profiles. Each pathway can be visualized as static or dynamic graph, where vertices are molecules participating in a pathway and edges are interactions or reactions between them. Differentially expressed nodes in a pathway can be visualized in two-color mode with user-defined color scale. For every comparison, OncoboxPD also generates a graph summarizing top up- and downregulated pathways.
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18
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Valls PO, Esposito A. Signalling dynamics, cell decisions, and homeostatic control in health and disease. Curr Opin Cell Biol 2022; 75:102066. [PMID: 35245783 PMCID: PMC9097822 DOI: 10.1016/j.ceb.2022.01.011] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Revised: 01/27/2022] [Accepted: 01/28/2022] [Indexed: 11/13/2022]
Abstract
Cell signalling engenders cells with the capability to receive and process information from the intracellular and extracellular environments, trigger and execute biological responses, and communicate with each other. Ultimately, cell signalling is responsible for maintaining homeostasis at the cellular, tissue and systemic level. For this reason, cell signalling is a topic of intense research efforts aimed to elucidate how cells coordinate transitions between states in developing and adult organisms in physiological and pathological conditions. Here, we review current knowledge of how cell signalling operates at multiple spatial and temporal scales, focusing on how single-cell analytical techniques reveal mechanisms underpinning cell-to-cell variability, signalling plasticity, and collective cellular responses.
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Affiliation(s)
- Pablo Oriol Valls
- MRC Cancer Unit, University of Cambridge, Cambridge, CB2 0XZ, United Kingdom
| | - Alessandro Esposito
- MRC Cancer Unit, University of Cambridge, Cambridge, CB2 0XZ, United Kingdom; Centre for Genome Engineering and Maintenance, College of Health, Medicine and Life Sciences, Brunel University London, Uxbridge, UB8 3PH, United Kingdom.
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19
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Sadhukhan S, Mishra PK. A multi-layered hybrid model for cancer cell invasion. Med Biol Eng Comput 2022; 60:1075-1098. [DOI: 10.1007/s11517-022-02514-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Accepted: 01/17/2022] [Indexed: 12/01/2022]
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20
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A synthetic gene circuit for imaging-free detection of signaling pulses. Cell Syst 2022; 13:131-142.e13. [PMID: 34739875 PMCID: PMC8857027 DOI: 10.1016/j.cels.2021.10.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 07/20/2021] [Accepted: 10/14/2021] [Indexed: 12/24/2022]
Abstract
Cells employ intracellular signaling pathways to sense and respond to changes in their external environment. In recent years, live-cell biosensors have revealed complex pulsatile dynamics in many pathways, but studies of these signaling dynamics are limited by the necessity of live-cell imaging at high spatiotemporal resolution. Here, we describe an approach to infer pulsatile signaling dynamics from a single measurement in fixed cells using a pulse-detecting gene circuit. We computationally screened for circuits with the capability to selectively detect signaling pulses, revealing an incoherent feedforward topology that robustly performs this computation. We implemented the motif experimentally for the Erk signaling pathway using a single engineered transcription factor and fluorescent protein reporter. Our "recorder of Erk activity dynamics" (READer) responds sensitively to spontaneous and stimulus-driven Erk pulses. READer circuits open the door to permanently labeling transient, dynamic cell populations to elucidate the mechanistic underpinnings and biological consequences of signaling dynamics.
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21
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Origin of diverse phosphorylation patterns in the ERBB system. Biophys J 2022; 121:470-480. [PMID: 34958777 PMCID: PMC8822607 DOI: 10.1016/j.bpj.2021.12.031] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Revised: 11/03/2021] [Accepted: 12/22/2021] [Indexed: 02/03/2023] Open
Abstract
Intercellular signals induce various cellular responses, including growth, proliferation, and differentiation, via the dynamic processes of signal transduction pathways. For cell fate decisions, ligand-binding induces the phosphorylation of ERBB receptors, which in turn activate downstream molecules. The ERBB family includes four subtypes, which diverged through two gene duplications from a common ancestor. Differences in the expression patterns of the subtypes have been reported between different organs in the human body. However, how these different expression properties influence the diverse phosphorylation levels of ERBB proteins is not well understood. Here we study the origin of the phosphorylation responses by experimental and mathematical analyses. The experimental measurements clarified that the phosphorylation levels heavily depend on the ERBB expression profiles. We developed a mathematical model consisting of the four subtypes as monomers, homodimers, and heterodimers and estimated the rate constants governing the phosphorylation responses from the experimental data. To understand the origin of the diversity, we analyzed the effects of the expression levels and reaction rates of the ERBB subtypes on the diversity. The difference in phosphorylation rates between ERBB subtypes showed a much greater contribution to the diversity than did the dimerization rates. This result implies that divergent evolution in phosphorylation reactions rather than in dimerization reactions after whole genome duplications was essential for increasing the diversity of the phosphorylation responses.
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22
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Scalable reaction network modeling with automatic validation of consistency in Event-B. Sci Rep 2022; 12:1287. [PMID: 35079072 PMCID: PMC8789811 DOI: 10.1038/s41598-022-05308-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Accepted: 12/08/2021] [Indexed: 11/27/2022] Open
Abstract
Constructing a large biological model is a difficult, error-prone process. Small errors in writing a part of the model cascade to the system level and their sources are difficult to trace back. In this paper we extend a recent approach based on Event-B, a state-based formal method with refinement as its central ingredient, allowing us to validate for model consistency step-by-step in an automated way. We demonstrate this approach on a model of the heat shock response in eukaryotes and its scalability on a model of the \documentclass[12pt]{minimal}
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\begin{document}$$\mathsf {ErbB}$$\end{document}ErbB signaling pathway. All consistency properties of the model were proved automatically with computer support.
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23
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Stapor P, Schmiester L, Wierling C, Merkt S, Pathirana D, Lange BMH, Weindl D, Hasenauer J. Mini-batch optimization enables training of ODE models on large-scale datasets. Nat Commun 2022; 13:34. [PMID: 35013141 PMCID: PMC8748893 DOI: 10.1038/s41467-021-27374-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2019] [Accepted: 11/11/2021] [Indexed: 11/09/2022] Open
Abstract
Quantitative dynamic models are widely used to study cellular signal processing. A critical step in modelling is the estimation of unknown model parameters from experimental data. As model sizes and datasets are steadily growing, established parameter optimization approaches for mechanistic models become computationally extremely challenging. Mini-batch optimization methods, as employed in deep learning, have better scaling properties. In this work, we adapt, apply, and benchmark mini-batch optimization for ordinary differential equation (ODE) models, thereby establishing a direct link between dynamic modelling and machine learning. On our main application example, a large-scale model of cancer signaling, we benchmark mini-batch optimization against established methods, achieving better optimization results and reducing computation by more than an order of magnitude. We expect that our work will serve as a first step towards mini-batch optimization tailored to ODE models and enable modelling of even larger and more complex systems than what is currently possible.
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Affiliation(s)
- Paul Stapor
- Helmholtz Zentrum München - German Research Center for Environmental Health, Institute of Computational Biology, 85764, Neuherberg, Germany
- Technische Universität München, Center for Mathematics, Chair of Mathematical Modeling of Biological Systems, 85748, Garching, Germany
| | - Leonard Schmiester
- Helmholtz Zentrum München - German Research Center for Environmental Health, Institute of Computational Biology, 85764, Neuherberg, Germany
- Technische Universität München, Center for Mathematics, Chair of Mathematical Modeling of Biological Systems, 85748, Garching, Germany
| | | | - Simon Merkt
- Universität Bonn, Faculty of Mathematics and Natural Sciences, 53115, Bonn, Germany
| | - Dilan Pathirana
- Universität Bonn, Faculty of Mathematics and Natural Sciences, 53115, Bonn, Germany
| | | | - Daniel Weindl
- Helmholtz Zentrum München - German Research Center for Environmental Health, Institute of Computational Biology, 85764, Neuherberg, Germany
| | - Jan Hasenauer
- Helmholtz Zentrum München - German Research Center for Environmental Health, Institute of Computational Biology, 85764, Neuherberg, Germany.
- Technische Universität München, Center for Mathematics, Chair of Mathematical Modeling of Biological Systems, 85748, Garching, Germany.
- Universität Bonn, Faculty of Mathematics and Natural Sciences, 53115, Bonn, Germany.
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24
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Relationship Between Dimensionality and Convergence of Optimization Algorithms: A Comparison Between Data-Driven Normalization and Scaling Factor-Based Methods Using PEPSSBI. Methods Mol Biol 2022; 2385:91-115. [PMID: 34888717 PMCID: PMC9446379 DOI: 10.1007/978-1-0716-1767-0_5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
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.
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25
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Rocca A, Kholodenko BN. Can Systems Biology Advance Clinical Precision Oncology? Cancers (Basel) 2021; 13:6312. [PMID: 34944932 PMCID: PMC8699328 DOI: 10.3390/cancers13246312] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Accepted: 12/10/2021] [Indexed: 12/13/2022] Open
Abstract
Precision oncology is perceived as a way forward to treat individual cancer patients. However, knowing particular cancer mutations is not enough for optimal therapeutic treatment, because cancer genotype-phenotype relationships are nonlinear and dynamic. Systems biology studies the biological processes at the systems' level, using an array of techniques, ranging from statistical methods to network reconstruction and analysis, to mathematical modeling. Its goal is to reconstruct the complex and often counterintuitive dynamic behavior of biological systems and quantitatively predict their responses to environmental perturbations. In this paper, we review the impact of systems biology on precision oncology. We show examples of how the analysis of signal transduction networks allows to dissect resistance to targeted therapies and inform the choice of combinations of targeted drugs based on tumor molecular alterations. Patient-specific biomarkers based on dynamical models of signaling networks can have a greater prognostic value than conventional biomarkers. These examples support systems biology models as valuable tools to advance clinical and translational oncological research.
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Affiliation(s)
- Andrea Rocca
- Hygiene and Public Health, Local Health Unit of Romagna, 47121 Forlì, Italy
| | - Boris N. Kholodenko
- Systems Biology Ireland, School of Medicine, University College Dublin, Belfield, D04 V1W8 Dublin, Ireland
- Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Belfield, D04 V1W8 Dublin, Ireland
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT 06520, USA
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26
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Zangooei MH, Margolis R, Hoyt K. Multiscale computational modeling of cancer growth using features derived from microCT images. Sci Rep 2021; 11:18524. [PMID: 34535748 PMCID: PMC8448838 DOI: 10.1038/s41598-021-97966-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Accepted: 08/30/2021] [Indexed: 11/26/2022] Open
Abstract
Advances in medical imaging technologies now allow noninvasive image acquisition from individual patients at high spatiotemporal resolutions. A relatively new effort of predictive oncology is to develop a paradigm for forecasting the future status of an individual tumor given initial conditions and an appropriate mathematical model. The objective of this study was to introduce a comprehensive multiscale computational method to predict cancer and microvascular network growth patterns. A rectangular lattice-based model was designed so different evolutionary scenarios could be simulated and for predicting the impact of diffusible factors on tumor morphology and size. Further, the model allows prediction-based simulation of cell and microvascular behavior. Like a single cell, each agent is fully realized within the model and interactions are governed in part by machine learning methods. This multiscale computational model was developed and incorporated input information from in vivo microscale computed tomography (microCT) images acquired from breast cancer-bearing mice. It was found that as the difference between expansion of the cancer cell population and microvascular network increases, cells undergo proliferation and migration with a greater probability compared to other phenotypes. Overall, multiscale computational model agreed with both theoretical expectations and experimental findings (microCT images) not used during model training.
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Affiliation(s)
- M Hossein Zangooei
- Department of Bioengineering, University of Texas at Dallas, BSB 13.929, 800 W Campbell Rd, Richardson, TX, 75080, USA
| | - Ryan Margolis
- Department of Bioengineering, University of Texas at Dallas, BSB 13.929, 800 W Campbell Rd, Richardson, TX, 75080, USA
| | - Kenneth Hoyt
- Department of Bioengineering, University of Texas at Dallas, BSB 13.929, 800 W Campbell Rd, Richardson, TX, 75080, USA.
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27
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Kang Y, Wan L, Wang Q, Yin Y, Liu J, Liu L, Wu H, Zhang L, Zhang X, Xu S, Pang D. Long noncoding RNA SNHG1 promotes TERT expression by sponging miR-18b-5p in breast cancer. Cell Biosci 2021; 11:169. [PMID: 34465388 PMCID: PMC8407068 DOI: 10.1186/s13578-021-00675-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Accepted: 08/03/2021] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Long noncoding RNA (lncRNA) small nucleolar RNA host gene 1 (SNHG1) plays a positive role in the progression of human malignant tumors. However, the molecular mechanism of SNHG1 remains elusive in breast cancer. RESULTS LncRNA SNHG1 was upregulated and had a positive relationship with poor prognosis according to bioinformatics analysis in pan-cancer including breast cancer. Silencing SNHG1 inhibited tumorigenesis in breast cancer both in vitro and in vivo. Mechanistically, SNHG1 functioned as a competing endogenous RNA (ceRNA) to promote TERT expression by sponging miR-18b-5p in breast cancer. miR-18b-5p acted as a tumor repressor in breast cancer. Moreover, the combination of SNHG1 knockdown and TERT inhibitor administration showed a synergistic inhibitory effect on breast cancer growth in vivo. Finally, E2F1 as a transcription factor, binding to SNHG1 promoter and enhanced SNHG1 transcription in breast cancer. CONCLUSIONS Our results provide a comprehensive understanding of the oncogenic mechanism of lncRNA SNHG1 in breast cancer. Importantly, we identified a novel E2F1-SNHG1-miR-18b-5p-TERT axis, which may be a potential therapeutic target for breast cancer. Our results also provided a potential treatment for breast cancer when knockdown SNHG1 and TERT inhibitor administration simultaneously.
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Affiliation(s)
- Yujuan Kang
- grid.412651.50000 0004 1808 3502Department of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin, 150040 China
| | - Lin Wan
- grid.412651.50000 0004 1808 3502Department of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin, 150040 China
| | - Qin Wang
- grid.412651.50000 0004 1808 3502Department of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin, 150040 China
| | - Yanling Yin
- grid.412651.50000 0004 1808 3502Department of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin, 150040 China
| | - Jiena Liu
- grid.412651.50000 0004 1808 3502Department of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin, 150040 China
| | - Lei Liu
- grid.412651.50000 0004 1808 3502Department of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin, 150040 China
| | - Hao Wu
- grid.412651.50000 0004 1808 3502Department of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin, 150040 China
| | - Lei Zhang
- grid.412651.50000 0004 1808 3502Department of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin, 150040 China
| | - Xin Zhang
- grid.412651.50000 0004 1808 3502Department of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin, 150040 China
| | - Shouping Xu
- Department of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin, 150040, China.
| | - Da Pang
- Department of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin, 150040, China. .,Heilongjiang Academy of Medical Sciences, Harbin, China.
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28
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Using proteomic and transcriptomic data to assess activation of intracellular molecular pathways. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2021; 127:1-53. [PMID: 34340765 DOI: 10.1016/bs.apcsb.2021.02.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Analysis of molecular pathway activation is the recent instrument that helps to quantize activities of various intracellular signaling, structural, DNA synthesis and repair, and biochemical processes. This may have a deep impact in fundamental research, bioindustry, and medicine. Unlike gene ontology analyses and numerous qualitative methods that can establish whether a pathway is affected in principle, the quantitative approach has the advantage of exactly measuring the extent of a pathway up/downregulation. This results in emergence of a new generation of molecular biomarkers-pathway activation levels, which reflect concentration changes of all measurable pathway components. The input data can be the high-throughput proteomic or transcriptomic profiles, and the output numbers take both positive and negative values and positively reflect overall pathway activation. Due to their nature, the pathway activation levels are more robust biomarkers compared to the individual gene products/protein levels. Here, we review the current knowledge of the quantitative gene expression interrogation methods and their applications for the molecular pathway quantization. We consider enclosed bioinformatic algorithms and their applications for solving real-world problems. Besides a plethora of applications in basic life sciences, the quantitative pathway analysis can improve molecular design and clinical investigations in pharmaceutical industry, can help finding new active biotechnological components and can significantly contribute to the progressive evolution of personalized medicine. In addition to the theoretical principles and concepts, we also propose publicly available software for the use of large-scale protein/RNA expression data to assess the human pathway activation levels.
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LncRNA TINCR favors tumorigenesis via STAT3-TINCR-EGFR-feedback loop by recruiting DNMT1 and acting as a competing endogenous RNA in human breast cancer. Cell Death Dis 2021; 12:83. [PMID: 33446634 PMCID: PMC7809450 DOI: 10.1038/s41419-020-03188-0] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2020] [Revised: 10/20/2020] [Accepted: 10/23/2020] [Indexed: 12/18/2022]
Abstract
The long noncoding RNA (lncRNA) TINCR has recently been found to be associated with the progression of human malignancies, but the molecular mechanism of TINCR action remains elusive, particularly in breast cancer. The oncogenic role of TINCR was examined in vitro and in vivo in breast cancer. Next, the interaction between TINCR, DNMT1, and miR-503-5p methylation was explored. Moreover, the mechanism by which TINCR enhances EGFR expression and downstream signaling via an RNA–RNA interaction was comprehensively investigated. Furthermore, upstream transcriptional regulation of TINCR expression by STAT3 was examined by performing chromatin immunoprecipitation. Finally, feedback signaling in the STAT3–TINCR–EGFR downstream cascade was also investigated. TINCR is upregulated in human breast cancer tissues, and TINCR knockdown suppresses tumorigenesis in vitro and in vivo. Mechanistically, TINCR recruits DNMT1 to the miR-503-5p locus promoter, which increases the methylation and suppresses the transcriptional expression of miR-503-5p. Furthermore, TINCR also functions as a competing endogenous RNA to upregulate EGFR expression by sponging miR-503-5p. In addition, TINCR stimulates JAK2–STAT3 signaling downstream from EGFR, and STAT3 reciprocally enhances the transcriptional expression of TINCR. Our findings broaden the current understanding of the diverse manners in which TINCR functions in cancer biology. The newly identified STAT3–TINCR–EGFR-feedback loop could serve as a potential therapeutic target for human cancer.
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Holzheu P, Großeholz R, Kummer U. Impact of explicit area scaling on kinetic models involving multiple compartments. BMC Bioinformatics 2021; 22:21. [PMID: 33430767 PMCID: PMC7798250 DOI: 10.1186/s12859-020-03913-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2019] [Accepted: 11/30/2020] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND Computational modelling of cell biological processes is a frequently used technique to analyse the underlying mechanisms and to generally understand the behaviour of these processes in the context of a pathway, network or even the whole cell. The most common technique in this context is the usage of ordinary differential equations that describe the kinetics of the relevant processes in mechanistic detail. Here, it is usually assumed that the content of the cell is well-stirred and thus homogeneous - which is of course an over-simplification, but often worked in the past. However, many processes happen at membranes and thus not in 3D, but in 2D. The scaling of the rates of these processes poses a special problem, if volumes of compartments are changed. They will typically scale with an area, but not with the volume of the involved compartment. However, commonly, this is neglected when setting up models and/or volume scaling also sometimes automatically happens when using modelling software in the field. RESULTS Here, we investigate generic as well as specific, realistic cases to find out, how strong the impact of the wrong scaling is for the outcome of simulations. We show that the importance of correct area scaling depends on the architecture of the reaction site and its changes upon volume alterations and it is hard to foresee, if it has a significant impact or not just by looking at the original model set-up. Moreover, scaled rates might exhibit more or less control over the behaviour of the system and therefore, accordingly, incorrect scaling will have more or less influence. CONCLUSIONS Working with multi-compartment reactions requires a careful consideration of the correct scaling of the rates when changing the volumes of the involved compartments. The error following incorrect scaling - often done by scaling with the volume of the respective compartments can lead to significant aberrations of model behaviour.
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Affiliation(s)
- Pascal Holzheu
- Department of Modeling of Biological Processes, COS Heidelberg/Bioquant, Im Neuenheimer Feld 267, 69120, Heidelberg, Germany
| | - Ruth Großeholz
- Department of Modeling of Biological Processes, COS Heidelberg/Bioquant, Im Neuenheimer Feld 267, 69120, Heidelberg, Germany
| | - Ursula Kummer
- Department of Modeling of Biological Processes, COS Heidelberg/Bioquant, Im Neuenheimer Feld 267, 69120, Heidelberg, Germany.
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York HM, Coyle J, Arumugam S. To be more precise: the role of intracellular trafficking in development and pattern formation. Biochem Soc Trans 2020; 48:2051-2066. [PMID: 32915197 PMCID: PMC7609031 DOI: 10.1042/bst20200223] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Revised: 08/24/2020] [Accepted: 08/26/2020] [Indexed: 02/07/2023]
Abstract
Living cells interpret a variety of signals in different contexts to elucidate functional responses. While the understanding of signalling molecules, their respective receptors and response at the gene transcription level have been relatively well-explored, how exactly does a single cell interpret a plethora of time-varying signals? Furthermore, how their subsequent responses at the single cell level manifest in the larger context of a developing tissue is unknown. At the same time, the biophysics and chemistry of how receptors are trafficked through the complex dynamic transport network between the plasma membrane-endosome-lysosome-Golgi-endoplasmic reticulum are much more well-studied. How the intracellular organisation of the cell and inter-organellar contacts aid in orchestrating trafficking, as well as signal interpretation and modulation by the cells are beginning to be uncovered. In this review, we highlight the significant developments that have strived to integrate endosomal trafficking, signal interpretation in the context of developmental biology and relevant open questions with a few chosen examples. Furthermore, we will discuss the imaging technologies that have been developed in the recent past that have the potential to tremendously accelerate knowledge gain in this direction while shedding light on some of the many challenges.
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Affiliation(s)
- Harrison M. York
- Monash Biomedicine Discovery Institute, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC 3800, Australia
| | - Joanne Coyle
- Monash Biomedicine Discovery Institute, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC 3800, Australia
| | - Senthil Arumugam
- Monash Biomedicine Discovery Institute, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC 3800, Australia
- European Molecular Biological Laboratory Australia (EMBL Australia), Monash University, Melbourne, VIC 3800, Australia
- ARC Centre of Excellence in Advanced Molecular Imaging, Monash University, Melbourne, VIC 3800, Australia
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32
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Kiyatkin A, van Alderwerelt van Rosenburgh IK, Klein DE, Lemmon MA. Kinetics of receptor tyrosine kinase activation define ERK signaling dynamics. Sci Signal 2020; 13:13/645/eaaz5267. [PMID: 32817373 DOI: 10.1126/scisignal.aaz5267] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
In responses to activation of receptor tyrosine kinases (RTKs), crucial cell fate decisions depend on the duration and dynamics of ERK signaling. In PC12 cells, epidermal growth factor (EGF) induces transient ERK activation that leads to cell proliferation, whereas nerve growth factor (NGF) promotes sustained ERK activation and cell differentiation. These differences have typically been assumed to reflect distinct feedback mechanisms in the Raf-MEK-ERK signaling network, with the receptors themselves acting as simple upstream inputs. We failed to confirm the expected differences in feedback type when investigating transient versus sustained signaling downstream of the EGF receptor (EGFR) and NGF receptor (TrkA). Instead, we found that ERK signaling faithfully followed RTK dynamics when receptor signaling was modulated in different ways. EGFR activation kinetics, and consequently ERK signaling dynamics, were switched from transient to sustained when receptor internalization was inhibited with drugs or mutations, or when cells expressed a chimeric receptor likely to have impaired dimerization. In addition, EGFR and ERK signaling both became more sustained when substoichiometric levels of erlotinib were added to reduce duration of EGFR kinase activation. Our results argue that RTK activation kinetics play a crucial role in determining MAP kinase cascade signaling dynamics and cell fate decisions, and that signaling outcome can be modified by activating a given RTK in different ways.
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Affiliation(s)
- Anatoly Kiyatkin
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT 06520, USA.,Cancer Biology Institute, Yale University, West Haven, CT 06516, USA
| | - Iris K van Alderwerelt van Rosenburgh
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT 06520, USA.,Cancer Biology Institute, Yale University, West Haven, CT 06516, USA
| | - Daryl E Klein
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT 06520, USA.,Cancer Biology Institute, Yale University, West Haven, CT 06516, USA
| | - Mark A Lemmon
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT 06520, USA. .,Cancer Biology Institute, Yale University, West Haven, CT 06516, USA
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33
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Bolado-Carrancio A, Rukhlenko OS, Nikonova E, Tsyganov MA, Wheeler A, Garcia-Munoz A, Kolch W, von Kriegsheim A, Kholodenko BN. Periodic propagating waves coordinate RhoGTPase network dynamics at the leading and trailing edges during cell migration. eLife 2020; 9:58165. [PMID: 32705984 PMCID: PMC7380942 DOI: 10.7554/elife.58165] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Accepted: 07/02/2020] [Indexed: 12/27/2022] Open
Abstract
Migrating cells need to coordinate distinct leading and trailing edge dynamics but the underlying mechanisms are unclear. Here, we combine experiments and mathematical modeling to elaborate the minimal autonomous biochemical machinery necessary and sufficient for this dynamic coordination and cell movement. RhoA activates Rac1 via DIA and inhibits Rac1 via ROCK, while Rac1 inhibits RhoA through PAK. Our data suggest that in motile, polarized cells, RhoA–ROCK interactions prevail at the rear, whereas RhoA-DIA interactions dominate at the front where Rac1/Rho oscillations drive protrusions and retractions. At the rear, high RhoA and low Rac1 activities are maintained until a wave of oscillatory GTPase activities from the cell front reaches the rear, inducing transient GTPase oscillations and RhoA activity spikes. After the rear retracts, the initial GTPase pattern resumes. Our findings show how periodic, propagating GTPase waves coordinate distinct GTPase patterns at the leading and trailing edge dynamics in moving cells.
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Affiliation(s)
- Alfonso Bolado-Carrancio
- Edinburgh Cancer Research Centre, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, United Kingdom
| | - Oleksii S Rukhlenko
- Systems Biology Ireland, School of Medicine and Medical Science, University College Dublin, Belfield, Ireland
| | - Elena Nikonova
- Systems Biology Ireland, School of Medicine and Medical Science, University College Dublin, Belfield, Ireland
| | - Mikhail A Tsyganov
- Systems Biology Ireland, School of Medicine and Medical Science, University College Dublin, Belfield, Ireland.,Institute of Theoretical and Experimental Biophysics, Pushchino, Russian Federation
| | - Anne Wheeler
- Edinburgh Cancer Research Centre, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, United Kingdom
| | - Amaya Garcia-Munoz
- Systems Biology Ireland, School of Medicine and Medical Science, University College Dublin, Belfield, Ireland
| | - Walter Kolch
- Systems Biology Ireland, School of Medicine and Medical Science, University College Dublin, Belfield, Ireland.,Conway Institute of Biomolecular & Biomedical Research, University College Dublin, Belfield, Ireland
| | - Alex von Kriegsheim
- Edinburgh Cancer Research Centre, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, United Kingdom.,Systems Biology Ireland, School of Medicine and Medical Science, University College Dublin, Belfield, Ireland
| | - Boris N Kholodenko
- Systems Biology Ireland, School of Medicine and Medical Science, University College Dublin, Belfield, Ireland.,Conway Institute of Biomolecular & Biomedical Research, University College Dublin, Belfield, Ireland.,Department of Pharmacology, Yale University School of Medicine, New Haven, United States
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34
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Bravi B, Rubin KJ, Sollich P. Systematic model reduction captures the dynamics of extrinsic noise in biochemical subnetworks. J Chem Phys 2020; 153:025101. [DOI: 10.1063/5.0008304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Barbara Bravi
- Institute of Theoretical Physics, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
| | - Katy J. Rubin
- Department of Mathematics, King’s College London, Strand, London WC2R 2LS, United Kingdom
| | - Peter Sollich
- Department of Mathematics, King’s College London, Strand, London WC2R 2LS, United Kingdom
- Institute for Theoretical Physics, Georg-August-University Göttingen, Friedrich-Hund-Platz 1, 37077 Göttingen, Germany
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Baeza J, Lawton AJ, Fan J, Smallegan MJ, Lienert I, Gandhi T, Bernhardt OM, Reiter L, Denu JM. Revealing Dynamic Protein Acetylation across Subcellular Compartments. J Proteome Res 2020; 19:2404-2418. [PMID: 32290654 DOI: 10.1021/acs.jproteome.0c00088] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Protein acetylation is a widespread post-translational modification implicated in many cellular processes. Recent advances in mass spectrometry have enabled the cataloging of thousands of sites throughout the cell; however, identifying regulatory acetylation marks have proven to be a daunting task. Knowledge of the kinetics and stoichiometry of site-specific acetylation is an important factor to uncover function. Here, an improved method of quantifying acetylation stoichiometry was developed and validated, providing a detailed landscape of dynamic acetylation stoichiometry within cellular compartments. The dynamic nature of site-specific acetylation in response to serum stimulation was revealed. In two distinct human cell lines, growth factor stimulation led to site-specific, temporal acetylation changes, revealing diverse kinetic profiles that clustered into several groups. Overlap of dynamic acetylation sites among two different human cell lines suggested similar regulatory control points across major cellular pathways that include splicing, translation, and protein homeostasis. Rapid increases in acetylation on protein translational machinery suggest a positive regulatory role under progrowth conditions. Finally, higher median stoichiometry was observed in cellular compartments where active acetyltransferases are well described. Data sets can be accessed through ProteomExchange via the MassIVE repository (ProteomExchange: PXD014453; MassIVE: MSV000084029).
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Affiliation(s)
- Josue Baeza
- Biomolecular Chemistry Department, School of Medicine and Public Health, University of Wisconsin-Madison, 53706 Madison, Wisconsin, United States.,Wisconsin Institute for Discovery, University of Wisconsin-Madison, 53715 Madison, Wisconsin, United States
| | - Alexis J Lawton
- Biomolecular Chemistry Department, School of Medicine and Public Health, University of Wisconsin-Madison, 53706 Madison, Wisconsin, United States.,Wisconsin Institute for Discovery, University of Wisconsin-Madison, 53715 Madison, Wisconsin, United States
| | - Jing Fan
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, 53715 Madison, Wisconsin, United States.,Morgridge Institute for Research, University of Wisconsin-Madison, 53715 Madison, Wisconsin, United States
| | - Michael J Smallegan
- Biomolecular Chemistry Department, School of Medicine and Public Health, University of Wisconsin-Madison, 53706 Madison, Wisconsin, United States.,Wisconsin Institute for Discovery, University of Wisconsin-Madison, 53715 Madison, Wisconsin, United States
| | - Ian Lienert
- Biognosys AG, Wagistrasse 25, CH-8952 Schlieren, Switzerland
| | - Tejas Gandhi
- Biognosys AG, Wagistrasse 25, CH-8952 Schlieren, Switzerland
| | | | - Lukas Reiter
- Biognosys AG, Wagistrasse 25, CH-8952 Schlieren, Switzerland
| | - John M Denu
- Biomolecular Chemistry Department, School of Medicine and Public Health, University of Wisconsin-Madison, 53706 Madison, Wisconsin, United States.,Wisconsin Institute for Discovery, University of Wisconsin-Madison, 53715 Madison, Wisconsin, United States
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36
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Getz M, Rangamani P, Ghosh P. Regulating cellular cyclic adenosine monophosphate: "Sources," "sinks," and now, "tunable valves". WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2020; 12:e1490. [PMID: 32323924 DOI: 10.1002/wsbm.1490] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2019] [Revised: 01/31/2020] [Accepted: 03/31/2020] [Indexed: 12/16/2022]
Abstract
A number of hormones and growth factors stimulate target cells via the second messenger pathways, which in turn regulate cellular phenotypes. Cyclic adenosine monophosphate (cAMP) is a ubiquitous second messenger that facilitates numerous signal transduction pathways; its production in cells is tightly balanced by ligand-stimulated receptors that activate adenylate cyclases (ACs), that is, "source" and by phosphodiesterases (PDEs) that hydrolyze it, that is, "sinks." Because it regulates various cellular functions, including cell growth and differentiation, gene transcription and protein expression, the cAMP signaling pathway has been exploited for the treatment of numerous human diseases. Reduction in cAMP is achieved by blocking "sources"; however, elevation in cAMP is achieved by either stimulating "source" or blocking "sinks." Here we discuss an alternative paradigm for the regulation of cellular cAMP via GIV/Girdin, the prototypical member of a family of modulators of trimeric GTPases, Guanine nucleotide Exchange Modulators (GEMs). Cells upregulate or downregulate cellular levels of GIV-GEM, which modulates cellular cAMP via spatiotemporal mechanisms distinct from the two most often targeted classes of cAMP modulators, "sources" and "sinks." A network-based compartmental model for the paradigm of GEM-facilitated cAMP signaling has recently revealed that GEMs such as GIV serve much like a "tunable valve" that cells may employ to finetune cellular levels of cAMP. Because dysregulated signaling via GIV and other GEMs has been implicated in multiple disease states, GEMs constitute a hitherto untapped class of targets that could be exploited for modulating aberrant cAMP signaling in disease states. This article is categorized under: Models of Systems Properties and Processes > Mechanistic Models Biological Mechanisms > Cell Signaling.
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Affiliation(s)
- Michael Getz
- Chemical Engineering Graduate Program, University of California San Diego, La Jolla, California, USA
| | - Padmini Rangamani
- Department of Mechanical and Aerospace Engineering, University of California San Diego, La Jolla, California, USA
| | - Pradipta Ghosh
- Department of Medicine, University of California San Diego, La Jolla, California, USA
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, California, USA
- Moores Comprehensive Cancer Center, University of California San Diego, La Jolla, California, USA
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Salazar-Cavazos E, Nitta CF, Mitra ED, Wilson BS, Lidke KA, Hlavacek WS, Lidke DS. Multisite EGFR phosphorylation is regulated by adaptor protein abundances and dimer lifetimes. Mol Biol Cell 2020; 31:695-708. [PMID: 31913761 PMCID: PMC7202077 DOI: 10.1091/mbc.e19-09-0548] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022] Open
Abstract
Differential epidermal growth factor receptor (EGFR) phosphorylation is thought to couple receptor activation to distinct signaling pathways. However, the molecular mechanisms responsible for biased signaling are unresolved due to a lack of insight into the phosphorylation patterns of full-length EGFR. We extended a single-molecule pull-down technique previously used to study protein-protein interactions to allow for robust measurement of receptor phosphorylation. We found that EGFR is predominantly phosphorylated at multiple sites, yet phosphorylation at specific tyrosines is variable and only a subset of receptors share phosphorylation at the same site, even with saturating ligand concentrations. We found distinct populations of receptors as soon as 1 min after ligand stimulation, indicating early diversification of function. To understand this heterogeneity, we developed a mathematical model. The model predicted that variations in phosphorylation are dependent on the abundances of signaling partners, while phosphorylation levels are dependent on dimer lifetimes. The predictions were confirmed in studies of cell lines with different expression levels of signaling partners, and in experiments comparing low- and high-affinity ligands and oncogenic EGFR mutants. These results reveal how ligand-regulated receptor dimerization dynamics and adaptor protein concentrations play critical roles in EGFR signaling.
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Affiliation(s)
| | | | - Eshan D Mitra
- Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545
| | | | - Keith A Lidke
- Comprehensive Cancer Center, and.,Department of Physics and Astronomy, University of New Mexico, Albuquerque, NM 87131
| | - William S Hlavacek
- Comprehensive Cancer Center, and.,Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545
| | - Diane S Lidke
- Department of Pathology.,Comprehensive Cancer Center, and
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Borisov N, Sorokin M, Garazha A, Buzdin A. Quantitation of Molecular Pathway Activation Using RNA Sequencing Data. Methods Mol Biol 2020; 2063:189-206. [PMID: 31667772 DOI: 10.1007/978-1-0716-0138-9_15] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Intracellular molecular pathways (IMPs) control all major events in the living cell. IMPs are considered hotspots in biomedical sciences and thousands of IMPs have been discovered for humans and model organisms. Knowledge of IMPs activation is essential for understanding biological functions and differences between the biological objects at the molecular level. Here we describe the Oncobox system for accurate quantitative scoring activities of up to several thousand molecular pathways based on high throughput molecular data. Although initially designed for gene expression and mainly RNA sequencing data, Oncobox is now also applicable for quantitative proteomics, microRNA and transcription factor binding sites mapping data. The Oncobox system includes modules of gene expression data harmonization, aggregation and comparison and a recursive algorithm for automatic annotation of molecular pathways. The universal rationale of Oncobox enables scoring of signaling, metabolic, cytoskeleton, immunity, DNA repair, and other pathways in a multitude of biological objects. The Oncobox system can be helpful to all those working in the fields of genetics, biochemistry, interactomics, and big data analytics in molecular biomedicine.
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Affiliation(s)
- Nicolas Borisov
- Laboratory of Clinical Bioinformatics, I.M. Sechenov First Moscow State Medical University, Moscow, Russia
- Omicsway Corp., Walnut, CA, USA
| | - Maxim Sorokin
- Laboratory of Clinical Bioinformatics, I.M. Sechenov First Moscow State Medical University, Moscow, Russia
- Omicsway Corp., Walnut, CA, USA
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia
| | | | - Anton Buzdin
- Laboratory of Clinical Bioinformatics, I.M. Sechenov First Moscow State Medical University, Moscow, Russia.
- Omicsway Corp., Walnut, CA, USA.
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia.
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39
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Chen EP, Song RS, Chen X. Mathematical model of hypoxia and tumor signaling interplay reveals the importance of hypoxia and cell-to-cell variability in tumor growth inhibition. BMC Bioinformatics 2019; 20:507. [PMID: 31638911 PMCID: PMC6802183 DOI: 10.1186/s12859-019-3098-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2019] [Accepted: 09/13/2019] [Indexed: 01/10/2023] Open
Abstract
Background Human tumor is a complex tissue with multiple heterogeneous hypoxic regions and significant cell-to-cell variability. Due to the complexity of the disease, the explanation of why anticancer therapies fail cannot be attributed to intrinsic or acquired drug resistance alone. Furthermore, there are inconsistent reports of hypoxia-induced kinase activities in different cancer cell-lines, where increase, decreases, or no change has been observed. Thus, we asked, why are there widely contrasting results in kinase activity under hypoxia in different cancer cell-lines and how does hypoxia play a role in anti-cancer drug sensitivity? Results We took a modeling approach to address these questions by analyzing the model simulation to explain why hypoxia driven signals can have dissimilar impact on tumor growth and alter the efficacy of anti-cancer drugs. Repeated simulations with varying concentrations of biomolecules followed by decision tree analysis reveal that the highly differential effects among heterogeneous subpopulation of tumor cells could be governed by varying concentrations of just a few key biomolecules. These biomolecules include activated serine/threonine-specific protein kinases (pRAF), mitogen-activated protein kinase kinase (pMEK), protein kinase B (pAkt), or phosphoinositide-4,5-bisphosphate 3-kinase (pPI3K). Additionally, the ratio of activated extracellular signal-regulated kinases (pERK) or pAkt to its respective total was a key factor in determining the sensitivity of pERK or pAkt to hypoxia. Conclusion This work offers a mechanistic insight into how hypoxia can affect the efficacy of anti-cancer drug that targets tumor signaling and provides a framework to identify the types of tumor cells that are either sensitive or resistant to anti-cancer therapy.
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Affiliation(s)
- Emile P Chen
- Computational Sciences, GlaxoSmithKline, Collegeville, PA, 19426, USA.
| | - Roy S Song
- Computational Sciences, GlaxoSmithKline, Collegeville, PA, 19426, USA
| | - Xueer Chen
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, 15206-3701, USA
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40
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Cowan AE, Mendes P, Blinov ML. ModelBricks-modules for reproducible modeling improving model annotation and provenance. NPJ Syst Biol Appl 2019; 5:37. [PMID: 31602314 PMCID: PMC6783478 DOI: 10.1038/s41540-019-0114-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2019] [Accepted: 08/28/2019] [Indexed: 01/27/2023] Open
Abstract
Most computational models in biology are built and intended for "single-use"; the lack of appropriate annotation creates models where the assumptions are unknown, and model elements are not uniquely identified. Simply recreating a simulation result from a publication can be daunting; expanding models to new and more complex situations is a herculean task. As a result, new models are almost always created anew, repeating literature searches for kinetic parameters, initial conditions and modeling specifics. It is akin to building a brick house starting with a pile of clay. Here we discuss a concept for building annotated, reusable models, by starting with small well-annotated modules we call ModelBricks. Curated ModelBricks, accessible through an open database, could be used to construct new models that will inherit ModelBricks annotations and thus be easier to understand and reuse. Key features of ModelBricks include reliance on a commonly used standard language (SBML), rule-based specification describing species as a collection of uniquely identifiable molecules, association with model specific numerical parameters, and more common annotations. Physical bricks can vary substantively; likewise, to be useful the structure of ModelBricks must be highly flexible-it should encapsulate mechanisms from single reactions to multiple reactions in a complex process. Ultimately, a modeler would be able to construct large models by using multiple ModelBricks, preserving annotations and provenance of model elements, resulting in a highly annotated model. We envision the library of ModelBricks to rapidly grow from community contributions. Persistent citable references will incentivize model creators to contribute new ModelBricks.
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Affiliation(s)
- Ann E. Cowan
- Center for Cell Analysis and Modeling, UConn Health, Farmington, CT USA
- Department of Molecular Biology and Biophysics, UConn Health, Farmington, CT USA
| | - Pedro Mendes
- Center for Cell Analysis and Modeling, UConn Health, Farmington, CT USA
- Center for Quantitative Medicine, UConn Health, Farmington, CT USA
- Department of Cell Biology, UConn Health, Farmington, CT USA
| | - Michael L. Blinov
- Center for Cell Analysis and Modeling, UConn Health, Farmington, CT USA
- Department of Genetics and Genome Sciences, UConn Health, Farmington, CT USA
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Quantitative Analysis of the Rewiring of Signaling Pathways to Alter Cancer Cell Fate. J Med Biol Eng 2019. [DOI: 10.1007/s40846-019-00489-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Fischer CL, Bates AM, Lanzel EA, Guthmiller JM, Johnson GK, Singh NK, Kumar A, Vidva R, Abbasi T, Vali S, Xie XJ, Zeng E, Brogden KA. Computational Models Accurately Predict Multi-Cell Biomarker Profiles in Inflammation and Cancer. Sci Rep 2019; 9:10877. [PMID: 31350446 PMCID: PMC6659691 DOI: 10.1038/s41598-019-47381-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2019] [Accepted: 07/15/2019] [Indexed: 01/28/2023] Open
Abstract
Individual computational models of single myeloid, lymphoid, epithelial, and cancer cells were created and combined into multi-cell computational models and used to predict the collective chemokine, cytokine, and cellular biomarker profiles often seen in inflamed or cancerous tissues. Predicted chemokine and cytokine output profiles from multi-cell computational models of gingival epithelial keratinocytes (GE KER), dendritic cells (DC), and helper T lymphocytes (HTL) exposed to lipopolysaccharide (LPS) or synthetic triacylated lipopeptide (Pam3CSK4) as well as multi-cell computational models of multiple myeloma (MM) and DC were validated using the observed chemokine and cytokine responses from the same cell type combinations grown in laboratory multi-cell cultures with accuracy. Predicted and observed chemokine and cytokine responses of GE KER + DC + HTL exposed to LPS and Pam3CSK4 matched 75% (15/20, p = 0.02069) and 80% (16/20, P = 0.005909), respectively. Multi-cell computational models became ‘personalized’ when cell line-specific genomic data were included into simulations, again validated with the same cell lines grown in laboratory multi-cell cultures. Here, predicted and observed chemokine and cytokine responses of MM cells lines MM.1S and U266B1 matched 75% (3/4) and MM.1S and U266B1 inhibition of DC marker expression in co-culture matched 100% (6/6). Multi-cell computational models have the potential to identify approaches altering the predicted disease-associated output profiles, particularly as high throughput screening tools for anti-inflammatory or immuno-oncology treatments of inflamed multi-cellular tissues and the tumor microenvironment.
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Affiliation(s)
- Carol L Fischer
- Department of Biology, Waldorf University, Forest City, IA, 50436, USA
| | - Amber M Bates
- Department of Human Oncology, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | - Emily A Lanzel
- Department of Oral Pathology, Radiology and Medicine, College of Dentistry, University of Iowa, Iowa City, IA, 52242, USA
| | - Janet M Guthmiller
- College of Dentistry, University of Nebraska Medical Center, Lincoln, NE, 68583, USA
| | - Georgia K Johnson
- Department of Periodontics, College of Dentistry, University of Iowa, Iowa City, IA, 52242, USA
| | - Neeraj Kumar Singh
- Cellworks Group Inc., San Jose, CA, 95110, USA.,Cellworks Research India Pvt. Ltd (Wholly owned subsidiary of Cellworks Group Inc.), Bangalore, India
| | - Ansu Kumar
- Cellworks Group Inc., San Jose, CA, 95110, USA.,Cellworks Research India Pvt. Ltd (Wholly owned subsidiary of Cellworks Group Inc.), Bangalore, India
| | - Robinson Vidva
- Cellworks Group Inc., San Jose, CA, 95110, USA.,Cellworks Research India Pvt. Ltd (Wholly owned subsidiary of Cellworks Group Inc.), Bangalore, India
| | - Taher Abbasi
- Cellworks Group Inc., San Jose, CA, 95110, USA.,Cellworks Research India Pvt. Ltd (Wholly owned subsidiary of Cellworks Group Inc.), Bangalore, India
| | - Shireen Vali
- Cellworks Group Inc., San Jose, CA, 95110, USA.,Cellworks Research India Pvt. Ltd (Wholly owned subsidiary of Cellworks Group Inc.), Bangalore, India
| | - Xian Jin Xie
- Division of Biostatistics and Computational Biology, College of Dentistry, University of Iowa, Iowa City, IA, 52242, USA
| | - Erliang Zeng
- Division of Biostatistics and Computational Biology, College of Dentistry, University of Iowa, Iowa City, IA, 52242, USA
| | - Kim A Brogden
- Department of Periodontics, College of Dentistry, University of Iowa, Iowa City, IA, 52242, USA. .,Iowa Institute for Oral Health Research, College of Dentistry, University of Iowa, Iowa City, IA, 52242, USA.
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Shin SY, Kim MW, Cho KH, Nguyen LK. Coupled feedback regulation of nuclear factor of activated T-cells (NFAT) modulates activation-induced cell death of T cells. Sci Rep 2019; 9:10637. [PMID: 31337782 PMCID: PMC6650396 DOI: 10.1038/s41598-019-46592-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2018] [Accepted: 05/28/2019] [Indexed: 12/20/2022] Open
Abstract
A properly functioning immune system is vital for an organism’s wellbeing. 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 maintain peripheral immune tolerance. Defects in AICD can lead to development of autoimmune diseases. Despite its importance, the regulatory mechanisms that underlie AICD remain poorly understood, particularly at an integrative network level. Here, we develop a dynamic multi-pathway model of the integrated TCR signalling network and perform model-based analysis to characterize the network-level properties of AICD. Model simulation and analysis show that amplified activation of the transcriptional factor NFAT in response to repeated TCR stimulations, a phenomenon central to AICD, is tightly modulated by a coupled positive-negative feedback mechanism. NFAT amplification is predominantly enabled by a positive feedback self-regulated by NFAT, while opposed by a NFAT-induced negative feedback via Carabin. Furthermore, model analysis predicts an optimal therapeutic window for drugs that help minimize proliferation while maximize AICD of T cells. Overall, our study provides a comprehensive mathematical model of TCR signalling and model-based analysis offers new network-level insights into the regulation of activation-induced cell death in T cells.
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Affiliation(s)
- Sung-Young Shin
- Department of Biochemistry and Molecular Biology, School of Biomedical Sciences, Monash University, Clayton, Victoria, 3800, Australia.,Biomedicine Discovery Institute, Monash University, Clayton, Victoria, 3800, Australia
| | - Min-Wook Kim
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST) 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Kwang-Hyun Cho
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST) 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea. .,Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST) 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea.
| | - Lan K Nguyen
- Department of Biochemistry and Molecular Biology, School of Biomedical Sciences, Monash University, Clayton, Victoria, 3800, Australia. .,Biomedicine Discovery Institute, Monash University, Clayton, Victoria, 3800, Australia.
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Local Riemannian geometry of model manifolds and its implications for practical parameter identifiability. PLoS One 2019; 14:e0217837. [PMID: 31158252 PMCID: PMC6546239 DOI: 10.1371/journal.pone.0217837] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2018] [Accepted: 05/20/2019] [Indexed: 11/19/2022] Open
Abstract
When non-linear models are fitted to experimental data, parameter estimates can be poorly constrained albeit being identifiable in principle. This means that along certain paths in parameter space, the log-likelihood does not exceed a given statistical threshold but remains bounded. This situation, denoted as practical non-identifiability, can be detected by Monte Carlo sampling or by systematic scanning using the profile likelihood method. In contrast, any method based on a Taylor expansion of the log-likelihood around the optimum, e.g., parameter uncertainty estimation by the Fisher Information Matrix, reveals no information about the boundedness at all. In this work, we present a geometric approach, approximating the original log-likelihood by geodesic coordinates of the model manifold. The Christoffel Symbols in the geodesic equation are fixed to those obtained from second order model sensitivities at the optimum. Based on three exemplary non-linear models we show that the information about the log-likelihood bounds and flat parameter directions can already be contained in this local information. Whereas the unbounded case represented by the Fisher Information Matrix is embedded in the geometric framework as vanishing Christoffel Symbols, non-vanishing constant Christoffel Symbols prove to define prototype non-linear models featuring boundedness and flat parameter directions of the log-likelihood. Finally, we investigate if those models could allow to approximate and replace computationally expensive objective functions originating from non-linear models by a surrogate objective function in parameter estimation problems.
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Lam I, Pickering CM, Mac Gabhann F. Context-dependent regulation of receptor tyrosine kinases: Insights from systems biology approaches. WILEY INTERDISCIPLINARY REVIEWS. SYSTEMS BIOLOGY AND MEDICINE 2019; 11:e1437. [PMID: 30255986 PMCID: PMC6537588 DOI: 10.1002/wsbm.1437] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/04/2017] [Revised: 06/07/2018] [Accepted: 08/08/2018] [Indexed: 12/14/2022]
Abstract
Receptor tyrosine kinases (RTKs) are cell membrane proteins that provide cells with the ability to sense proteins in their environments. Many RTKs are essential to development and organ growth. Derangement of RTKs-by mutation or by overexpression-is central to several developmental and adult disorders including cancer, short stature, and vascular pathologies. The mechanism of action of RTKs is complex and is regulated by contextual components, including the existence of multiple competing ligands and receptors in many families, the intracellular location of the RTK, the dynamic and cell-specific coexpression of other RTKs, and the commonality of downstream signaling pathways. This means that both the state of the cell and the microenvironment outside the cell play a role, which makes sense given the pivotal location of RTKs as the nexus linking the extracellular milieu to intracellular signaling and modification of cell behavior. In this review, we describe these different contextual components through the lens of systems biology, in which both computational modeling and experimental "omics" approaches have been used to better understand RTK networks. The complexity of these networks is such that using these systems biology approaches is necessary to get a handle on the mechanisms of pathology and the design of therapeutics targeting RTKs. In particular, we describe in detail three concrete examples (involving ErbB3, VEGFR2, and AXL) that illustrate how systems approaches can reveal key mechanistic and therapeutic insights. This article is categorized under: Biological Mechanisms > Cell Signaling Models of Systems Properties and Processes > Mechanistic Models Translational, Genomic, and Systems Medicine > Therapeutic Methods.
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Affiliation(s)
- Inez Lam
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland
| | - Christina M Pickering
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland
| | - Feilim Mac Gabhann
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland
- Department of Materials Science and Engineering, Johns Hopkins University, Baltimore, Maryland
- Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, Maryland
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Complex Systems Biology Approach in Connecting PI3K-Akt and NF-κB Pathways in Prostate Cancer. Cells 2019; 8:cells8030201. [PMID: 30813597 PMCID: PMC6468646 DOI: 10.3390/cells8030201] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2019] [Revised: 02/05/2019] [Accepted: 02/24/2019] [Indexed: 12/24/2022] Open
Abstract
Phosphatidylinositol 3′-OH kinase (PI3K)-Akt and transcription factor NF-κB are important molecules involved in the regulation of cell proliferation, apoptosis, and oncogenesis. Both PI3K-Akt and Nuclear Factor-kappaB (NF-κB) are involved in the development and progression of prostate cancer, however, the crosstalk and molecules connecting these pathway remains unclear. A multilevel system representation of the PI3K-Akt and NF-κB pathways was constructed to determine which signaling components contribute to adaptive behavior and coordination. In silico experiments conducted using PI3K-Akt and NF-κB, mathematical models were modularized using biological functionality and were validated using a cell culture system. Our analysis demonstrates that a component representing the IκB kinase (IKK) complex can coordinate these two pathways. It is expected that interruption of this molecule could represent a potential therapeutic target for prostate cancer.
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Iancu B, Sanwal U, Gratie C, Petre I. Refinement-based modeling of the ErbB signaling pathway. Comput Biol Med 2019; 106:91-96. [PMID: 30708221 DOI: 10.1016/j.compbiomed.2019.01.016] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2018] [Revised: 01/18/2019] [Accepted: 01/19/2019] [Indexed: 11/26/2022]
Abstract
The construction of large scale biological models is a laborious task, which is often addressed by adopting iterative routines for model augmentation, adding certain details to an initial high level abstraction of the biological phenomenon of interest. Refitting a model at every step of its development is time consuming and computationally intensive. The concept of model refinement brings about an effective alternative by providing adequate parameter values that ensure the preservation of its quantitative fit at every refinement step. We demonstrate this approach by constructing the largest-ever refinement-based biomodel, consisting of 421 species and 928 reactions. We start from an already fit, relatively small literature model whose consistency we check formally. We then construct the final model through an algorithmic step-by-step refinement procedure that ensures the preservation of the model's fit.
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Affiliation(s)
- Bogdan Iancu
- Computational Biomodeling Laboratory, Turku Centre for Computer Science, Finland; Department of Computer Science, Åbo Akademi University, Finland
| | - Usman Sanwal
- Computational Biomodeling Laboratory, Turku Centre for Computer Science, Finland; Department of Computer Science, Åbo Akademi University, Finland
| | - Cristian Gratie
- Computational Biomodeling Laboratory, Turku Centre for Computer Science, Finland; Department of Computer Science, Åbo Akademi University, Finland
| | - Ion Petre
- Computational Biomodeling Laboratory, Turku Centre for Computer Science, Finland; Department of Mathematics and Statistics, University of Turku, Finland; National Institute for Research and Development in Biological Sciences, Romania.
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Erickson KE, Rukhlenko OS, Posner RG, Hlavacek WS, Kholodenko BN. New insights into RAS biology reinvigorate interest in mathematical modeling of RAS signaling. Semin Cancer Biol 2019; 54:162-173. [PMID: 29518522 PMCID: PMC6123307 DOI: 10.1016/j.semcancer.2018.02.008] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2017] [Revised: 02/13/2018] [Accepted: 02/22/2018] [Indexed: 01/04/2023]
Abstract
RAS is the most frequently mutated gene across human cancers, but developing inhibitors of mutant RAS has proven to be challenging. Given the difficulties of targeting RAS directly, drugs that impact the other components of pathways where mutant RAS operates may potentially be effective. However, the system-level features, including different localizations of RAS isoforms, competition between downstream effectors, and interlocking feedback and feed-forward loops, must be understood to fully grasp the opportunities and limitations of inhibiting specific targets. Mathematical modeling can help us discern the system-level impacts of these features in normal and cancer cells. New technologies enable the acquisition of experimental data that will facilitate development of realistic models of oncogenic RAS behavior. In light of the wealth of empirical data accumulated over decades of study and the advancement of experimental methods for gathering new data, modelers now have the opportunity to advance progress toward realization of targeted treatment for mutant RAS-driven cancers.
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Affiliation(s)
- Keesha E Erickson
- Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA
| | - Oleksii S Rukhlenko
- Systems Biology Ireland, University College Dublin, Belfield, Dublin 4, Ireland
| | - Richard G Posner
- Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, USA
| | - William S Hlavacek
- Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA; University of New Mexico Comprehensive Cancer Center, Albuquerque, NM, USA
| | - Boris N Kholodenko
- Systems Biology Ireland, University College Dublin, Belfield, Dublin 4, Ireland; Conway Institute of Biomolecular & Biomedical Research, University College Dublin, Ireland; School of Medicine and Medical Science, University College Dublin, Belfield, Dublin 4, Ireland.
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50
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LC3/GABARAPs drive ubiquitin-independent recruitment of Optineurin and NDP52 to amplify mitophagy. Nat Commun 2019; 10:408. [PMID: 30679426 PMCID: PMC6345886 DOI: 10.1038/s41467-019-08335-6] [Citation(s) in RCA: 155] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2018] [Accepted: 12/21/2018] [Indexed: 12/17/2022] Open
Abstract
Current models of selective autophagy dictate that autophagy receptors, including Optineurin and NDP52, link cargo to autophagosomal membranes. This is thought to occur via autophagy receptor binding to Atg8 homologs (LC3/GABARAPs) through an LC3 interacting region (LIR). The LIR motif within autophagy receptors is therefore widely recognised as being essential for selective sequestration of cargo. Here we show that the LIR motif within OPTN and NDP52 is dispensable for Atg8 recruitment and selectivity during PINK1/Parkin mitophagy. Instead, Atg8s play a critical role in mediating ubiquitin-independent recruitment of OPTN and NDP52 to growing phagophore membranes via the LIR motif. The additional recruitment of OPTN and NDP52 amplifies mitophagy through an Atg8-dependent positive feedback loop. Rather than functioning in selectivity, our discovery of a role for the LIR motif in mitophagy amplification points toward a general mechanism by which Atg8s can recruit autophagy factors to drive autophagosome growth and amplify selective autophagy.
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