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Hladyshau S, Guan K, Nivedita N, Errede B, Tsygankov D, Elston TC. Multiscale Modeling of Bistability in the Yeast Polarity Circuit. Cells 2024; 13:1358. [PMID: 39195248 DOI: 10.3390/cells13161358] [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: 07/03/2024] [Revised: 08/05/2024] [Accepted: 08/13/2024] [Indexed: 08/29/2024] Open
Abstract
Cell polarity refers to the asymmetric distribution of proteins and other molecules along a specified axis within a cell. Polarity establishment is the first step in many cellular processes. For example, directed growth or migration requires the formation of a cell front and back. In many cases, polarity occurs in the absence of spatial cues. That is, the cell undergoes symmetry breaking. Understanding the molecular mechanisms that allow cells to break symmetry and polarize requires computational models that span multiple spatial and temporal scales. Here, we apply a multiscale modeling approach to examine the polarity circuit of yeast. In addition to symmetry breaking, experiments revealed two key features of the yeast polarity circuit: bistability and rapid dismantling of the polarity site following a loss of signal. We used modeling based on ordinary differential equations (ODEs) to investigate mechanisms that generate these behaviors. Our analysis revealed that a model involving positive and negative feedback acting on different time scales captured both features. We then extend our ODE model into a coarse-grained reaction-diffusion equation (RDE) model to capture the spatial profiles of polarity factors. After establishing that the coarse-grained RDE model qualitatively captures key features of the polarity circuit, we expand it to more accurately capture the biochemical reactions involved in the system. We convert the expanded model to a particle-based model that resolves individual molecules and captures fluctuations that arise from the stochastic nature of biochemical reactions. Our models assume that negative regulation results from negative feedback. However, experimental observations do not rule out the possibility that negative regulation occurs through an incoherent feedforward loop. Therefore, we conclude by using our RDE model to suggest how negative feedback might be distinguished from incoherent feedforward regulation.
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Affiliation(s)
- Siarhei Hladyshau
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332, USA
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA
| | - Kaiyun Guan
- Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Nivedita Nivedita
- Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Beverly Errede
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Denis Tsygankov
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA
| | - Timothy C Elston
- Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Computational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
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Szischik CL, Reves Szemere J, Balderrama R, Sánchez de la Vega C, Ventura AC. Transient frequency preference responses in cell signaling systems. NPJ Syst Biol Appl 2024; 10:86. [PMID: 39128915 PMCID: PMC11317535 DOI: 10.1038/s41540-024-00413-w] [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: 12/22/2023] [Accepted: 07/29/2024] [Indexed: 08/13/2024] Open
Abstract
Ligand-receptor systems, covalent modification cycles, and transcriptional networks are the fundamental components of cell signaling and gene expression systems. While their behavior in reaching a steady-state regime under step-like stimulation is well understood, their response under repetitive stimulation, particularly at early time stages is poorly characterized. Yet, early-stage responses to external inputs are arguably as informative as late-stage ones. In simple systems, a periodic stimulation elicits an initial transient response, followed by periodic behavior. Transient responses are relevant when the stimulation has a limited time span, or when the stimulated component's timescale is slow as compared to the timescales of the downstream processes, in which case the latter processes may be capturing only those transients. In this study, we analyze the frequency response of simple motifs at different time stages. We use dose-conserved pulsatile input signals and consider different metrics versus frequency curves. We show that in ligand-receptor systems, there is a frequency preference response in some specific metrics during the transient stages, which is not present in the periodic regime. We suggest this is a general system-level mechanism that cells may use to filter input signals that have consequences for higher order circuits. In addition, we evaluate how the described behavior in isolated motifs is reflected in similar types of responses in cascades and pathways of which they are a part. Our studies suggest that transient frequency preferences are important dynamic features of cell signaling and gene expression systems, which have been overlooked.
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Affiliation(s)
- Candela L Szischik
- Universidad de Buenos Aires, Facultad de Ciencias Exactas y Naturales, Departamento de Física. Ciudad Universitaria, 1428, Buenos Aires, Argentina
- Instituto de Fisiología, Biología Molecular y Neurociencias (IFIBYNE UBA-CONICET), Consejo Nacional de Investigaciones Científicas y Técnicas of Argentina-Universidad de Buenos Aires, 1428, Buenos Aires, Argentina
| | - Juliana Reves Szemere
- Universidad de Buenos Aires, Facultad de Ciencias Exactas y Naturales, Departamento de Física. Ciudad Universitaria, 1428, Buenos Aires, Argentina
- Instituto de Fisiología, Biología Molecular y Neurociencias (IFIBYNE UBA-CONICET), Consejo Nacional de Investigaciones Científicas y Técnicas of Argentina-Universidad de Buenos Aires, 1428, Buenos Aires, Argentina
- Universidad Pedagógica Nacional and Universidad Nacional de La Pampa, Facultad de Ciencias Exactas y Naturales, Departamento de Física, Santa Rosa, Argentina
| | - Rocío Balderrama
- Universidad de Buenos Aires, Facultad de Ciencias Exactas y Naturales, Departamento de Matemática. Ciudad Universitaria, Buenos Aires, Argentina
- Instituto de Investigaciones Matemáticas Luis A. Santaló (IMAS - CONICET), Consejo Nacional de Investigaciones Científicas y Técnicas of Argentina, Buenos Aires, Argentina
| | - Constanza Sánchez de la Vega
- Universidad de Buenos Aires, Facultad de Ciencias Exactas y Naturales, Departamento de Matemática. Ciudad Universitaria, Buenos Aires, Argentina
- Instituto de Cálculo, FCEyN, CONICET-UBA, Buenos Aires, Argentina
| | - Alejandra C Ventura
- Universidad de Buenos Aires, Facultad de Ciencias Exactas y Naturales, Departamento de Física. Ciudad Universitaria, 1428, Buenos Aires, Argentina.
- Instituto de Fisiología, Biología Molecular y Neurociencias (IFIBYNE UBA-CONICET), Consejo Nacional de Investigaciones Científicas y Técnicas of Argentina-Universidad de Buenos Aires, 1428, Buenos Aires, Argentina.
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Tummler K, Klipp E. Data integration strategies for whole-cell modeling. FEMS Yeast Res 2024; 24:foae011. [PMID: 38544322 PMCID: PMC11042497 DOI: 10.1093/femsyr/foae011] [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: 12/02/2023] [Revised: 03/15/2024] [Accepted: 03/26/2024] [Indexed: 04/25/2024] Open
Abstract
Data makes the world go round-and high quality data is a prerequisite for precise models, especially for whole-cell models (WCM). Data for WCM must be reusable, contain information about the exact experimental background, and should-in its entirety-cover all relevant processes in the cell. Here, we review basic requirements to data for WCM and strategies how to combine them. As a species-specific resource, we introduce the Yeast Cell Model Data Base (YCMDB) to illustrate requirements and solutions. We discuss recent standards for data as well as for computational models including the modeling process as data to be reported. We outline strategies for constructions of WCM despite their inherent complexity.
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Affiliation(s)
- Katja Tummler
- Humboldt-Universität zu Berlin, Faculty of Life Sciences, Institute of Biology, Theoretical Biophysics,, Invalidenstr. 42, 10115 Berlin, Germany
| | - Edda Klipp
- Humboldt-Universität zu Berlin, Faculty of Life Sciences, Institute of Biology, Theoretical Biophysics,, Invalidenstr. 42, 10115 Berlin, Germany
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Molecular switch architecture determines response properties of signaling pathways. Proc Natl Acad Sci U S A 2021; 118:2013401118. [PMID: 33688042 DOI: 10.1073/pnas.2013401118] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
Many intracellular signaling pathways are composed of molecular switches, proteins that transition between two states-on and off Typically, signaling is initiated when an external stimulus activates its cognate receptor that, in turn, causes downstream switches to transition from off to on using one of the following mechanisms: activation, in which the transition rate from the off state to the on state increases; derepression, in which the transition rate from the on state to the off state decreases; and concerted, in which activation and derepression operate simultaneously. We use mathematical modeling to compare these signaling mechanisms in terms of their dose-response curves, response times, and abilities to process upstream fluctuations. Our analysis elucidates several operating principles for molecular switches. First, activation increases the sensitivity of the pathway, whereas derepression decreases sensitivity. Second, activation generates response times that decrease with signal strength, whereas derepression causes response times to increase with signal strength. These opposing features allow the concerted mechanism to not only show dose-response alignment, but also to decouple the response time from stimulus strength. However, these potentially beneficial properties come at the expense of increased susceptibility to upstream fluctuations. We demonstrate that these operating principles also hold when the models are extended to include additional features, such as receptor removal, kinetic proofreading, and cascades of switches. In total, we show how the architecture of molecular switches govern their response properties. We also discuss the biological implications of our findings.
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