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Xue G, Zhang X, Li W, Zhang L, Zhang Z, Zhou X, Zhang D, Zhang L, Li Z. A logic-incorporated gene regulatory network deciphers principles in cell fate decisions. eLife 2024; 12:RP88742. [PMID: 38652107 PMCID: PMC11037919 DOI: 10.7554/elife.88742] [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] [Indexed: 04/25/2024] Open
Abstract
Organisms utilize gene regulatory networks (GRN) to make fate decisions, but the regulatory mechanisms of transcription factors (TF) in GRNs are exceedingly intricate. A longstanding question in this field is how these tangled interactions synergistically contribute to decision-making procedures. To comprehensively understand the role of regulatory logic in cell fate decisions, we constructed a logic-incorporated GRN model and examined its behavior under two distinct driving forces (noise-driven and signal-driven). Under the noise-driven mode, we distilled the relationship among fate bias, regulatory logic, and noise profile. Under the signal-driven mode, we bridged regulatory logic and progression-accuracy trade-off, and uncovered distinctive trajectories of reprogramming influenced by logic motifs. In differentiation, we characterized a special logic-dependent priming stage by the solution landscape. Finally, we applied our findings to decipher three biological instances: hematopoiesis, embryogenesis, and trans-differentiation. Orthogonal to the classical analysis of expression profile, we harnessed noise patterns to construct the GRN corresponding to fate transition. Our work presents a generalizable framework for top-down fate-decision studies and a practical approach to the taxonomy of cell fate decisions.
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Affiliation(s)
- Gang Xue
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking UniversityBeijingChina
| | - Xiaoyi Zhang
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking UniversityBeijingChina
| | - Wanqi Li
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking UniversityBeijingChina
| | - Lu Zhang
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking UniversityBeijingChina
| | - Zongxu Zhang
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking UniversityBeijingChina
| | - Xiaolin Zhou
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking UniversityBeijingChina
| | - Di Zhang
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking UniversityBeijingChina
| | - Lei Zhang
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking UniversityBeijingChina
- Beijing International Center for Mathematical Research, Center for Machine Learning Research, Peking UniversityBeijingChina
| | - Zhiyuan Li
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking UniversityBeijingChina
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking UniversityBeijingChina
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2
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Capponi S, Wang S. AI in cellular engineering and reprogramming. Biophys J 2024:S0006-3495(24)00245-5. [PMID: 38576162 DOI: 10.1016/j.bpj.2024.04.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 03/19/2024] [Accepted: 04/01/2024] [Indexed: 04/06/2024] Open
Abstract
During the last decade, artificial intelligence (AI) has increasingly been applied in biophysics and related fields, including cellular engineering and reprogramming, offering novel approaches to understand, manipulate, and control cellular function. The potential of AI lies in its ability to analyze complex datasets and generate predictive models. AI algorithms can process large amounts of data from single-cell genomics and multiomic technologies, allowing researchers to gain mechanistic insights into the control of cell identity and function. By integrating and interpreting these complex datasets, AI can help identify key molecular events and regulatory pathways involved in cellular reprogramming. This knowledge can inform the design of precision engineering strategies, such as the development of new transcription factor and signaling molecule cocktails, to manipulate cell identity and drive authentic cell fate across lineage boundaries. Furthermore, when used in combination with computational methods, AI can accelerate and improve the analysis and understanding of the intricate relationships between genes, proteins, and cellular processes. In this review article, we explore the current state of AI applications in biophysics with a specific focus on cellular engineering and reprogramming. Then, we showcase a couple of recent applications where we combined machine learning with experimental and computational techniques. Finally, we briefly discuss the challenges and prospects of AI in cellular engineering and reprogramming, emphasizing the potential of these technologies to revolutionize our ability to engineer cells for a variety of applications, from disease modeling and drug discovery to regenerative medicine and biomanufacturing.
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Affiliation(s)
- Sara Capponi
- IBM Almaden Research Center, San Jose, California; Center for Cellular Construction, San Francisco, California.
| | - Shangying Wang
- Bay Area Institute of Science, Altos Labs, Redwood City, California.
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3
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Akbari A, Haiman ZB, Palsson BO. A data-driven approach for timescale decomposition of biochemical reaction networks. mSystems 2024; 9:e0100123. [PMID: 38259168 PMCID: PMC10946255 DOI: 10.1128/msystems.01001-23] [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: 09/20/2023] [Accepted: 12/05/2023] [Indexed: 01/24/2024] Open
Abstract
Understanding the dynamics of biological systems in evolving environments is a challenge due to their scale and complexity. Here, we present a computational framework for the timescale decomposition of biochemical reaction networks to distill essential patterns from their intricate dynamics. This approach identifies timescale hierarchies, concentration pools, and coherent structures from time-series data, providing a system-level description of reaction networks at physiologically important timescales. We apply this technique to kinetic models of hypothetical and biological pathways, validating it by reproducing analytically characterized or previously known concentration pools of these pathways. Moreover, by analyzing the timescale hierarchy of the glycolytic pathway, we elucidate the connections between the stoichiometric and dissipative structures of reaction networks and the temporal organization of coherent structures. Specifically, we show that glycolysis is a cofactor-driven pathway, the slowest dynamics of which are described by a balance between high-energy phosphate bond and redox trafficking. Overall, this approach provides more biologically interpretable characterizations of network dynamics than large-scale kinetic models, thus facilitating model reduction and personalized medicine applications. IMPORTANCE Complex interactions within interconnected biochemical reaction networks enable cellular responses to a wide range of unpredictable environmental perturbations. Understanding how biological functions arise from these intricate interactions has been a long-standing problem in biology. Here, we introduce a computational approach to dissect complex biological systems' dynamics in evolving environments. This approach characterizes the timescale hierarchies of complex reaction networks, offering a system-level understanding at physiologically relevant timescales. Analyzing various hypothetical and biological pathways, we show how stoichiometric properties shape the way energy is dissipated throughout reaction networks. Notably, we establish that glycolysis operates as a cofactor-driven pathway, where the slowest dynamics are governed by a balance between high-energy phosphate bonds and redox trafficking. This approach enhances our understanding of network dynamics and facilitates the development of reduced-order kinetic models with biologically interpretable components.
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Affiliation(s)
- Amir Akbari
- Department of Bioengineering, University of California San Diego, La Jolla, California, USA
| | - Zachary B. Haiman
- Department of Bioengineering, University of California San Diego, La Jolla, California, USA
| | - Bernhard O. Palsson
- Department of Bioengineering, University of California San Diego, La Jolla, California, USA
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark
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4
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Luo X, Liu Z, Xu R. Adult tissue-specific stem cell interaction: novel technologies and research advances. Front Cell Dev Biol 2023; 11:1220694. [PMID: 37808078 PMCID: PMC10551553 DOI: 10.3389/fcell.2023.1220694] [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: 05/11/2023] [Accepted: 09/11/2023] [Indexed: 10/10/2023] Open
Abstract
Adult tissue-specific stem cells play a dominant role in tissue homeostasis and regeneration. Various in vivo markers of adult tissue-specific stem cells have been increasingly reported by lineage tracing in genetic mouse models, indicating that marked cells differentiation is crucial during homeostasis and regeneration. How adult tissue-specific stem cells with indicated markers contact the adjacent lineage with indicated markers is of significance to be studied. Novel methods bring future findings. Recent advances in lineage tracing, synthetic receptor systems, proximity labeling, and transcriptomics have enabled easier and more accurate cell behavior visualization and qualitative and quantitative analysis of cell-cell interactions than ever before. These technological innovations have prompted researchers to re-evaluate previous experimental results, providing increasingly compelling experimental results for understanding the mechanisms of cell-cell interactions. This review aimed to describe the recent methodological advances of dual enzyme lineage tracing system, the synthetic receptor system, proximity labeling, single-cell RNA sequencing and spatial transcriptomics in the study of adult tissue-specific stem cells interactions. An enhanced understanding of the mechanisms of adult tissue-specific stem cells interaction is important for tissue regeneration and maintenance of homeostasis in organisms.
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Affiliation(s)
| | | | - Ruoshi Xu
- State Key Laboratory of Oral Diseases, National Center for Stomatology, National Clinical Research Center for Oral Diseases, Department of Cariology and Endodontics, West China Hospital of Stomatology, Sichuan University, Chengdu, China
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5
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Michael C, Pancaldi F, Britton S, Kim OV, Peshkova AD, Vo K, Xu Z, Litvinov RI, Weisel JW, Alber M. Combined computational modeling and experimental study of the biomechanical mechanisms of platelet-driven contraction of fibrin clots. Commun Biol 2023; 6:869. [PMID: 37620422 PMCID: PMC10449797 DOI: 10.1038/s42003-023-05240-z] [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: 06/14/2023] [Accepted: 08/10/2023] [Indexed: 08/26/2023] Open
Abstract
While blood clot formation has been relatively well studied, little is known about the mechanisms underlying the subsequent structural and mechanical clot remodeling called contraction or retraction. Impairment of the clot contraction process is associated with both life-threatening bleeding and thrombotic conditions, such as ischemic stroke, venous thromboembolism, and others. Recently, blood clot contraction was observed to be hindered in patients with COVID-19. A three-dimensional multiscale computational model is developed and used to quantify biomechanical mechanisms of the kinetics of clot contraction driven by platelet-fibrin pulling interactions. These results provide important biological insights into contraction of platelet filopodia, the mechanically active thin protrusions of the plasma membrane, described previously as performing mostly a sensory function. The biomechanical mechanisms and modeling approach described can potentially apply to studying other systems in which cells are embedded in a filamentous network and exert forces on the extracellular matrix modulated by the substrate stiffness.
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Affiliation(s)
- Christian Michael
- Department of Mathematics, University of California Riverside, Riverside, CA, 92521, USA
- Center for Quantitative Modeling in Biology, University of California Riverside, Riverside, CA, 92521, USA
- Department of Microbiology and Immunology, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Francesco Pancaldi
- Department of Mathematics, University of California Riverside, Riverside, CA, 92521, USA
- Center for Quantitative Modeling in Biology, University of California Riverside, Riverside, CA, 92521, USA
| | - Samuel Britton
- Department of Mathematics, University of California Riverside, Riverside, CA, 92521, USA
- Center for Quantitative Modeling in Biology, University of California Riverside, Riverside, CA, 92521, USA
| | - Oleg V Kim
- Department of Cell and Developmental Biology, University of Pennsylvania School of Medicine, Philadelphia, PA, 19104, USA
- Department of Biomedical Engineering and Mechanics, Center for Soft Matter and Biological Physics, Virginia Tech, Blacksburg, VA, 24061, USA
| | - Alina D Peshkova
- Department of Pharmacology, University of Pennsylvania School of Medicine, Philadelphia, PA, 19104, USA
| | - Khoi Vo
- Department of Mathematics, University of California Riverside, Riverside, CA, 92521, USA
- Center for Quantitative Modeling in Biology, University of California Riverside, Riverside, CA, 92521, USA
| | - Zhiliang Xu
- Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, IN, 46556, USA
| | - Rustem I Litvinov
- Department of Cell and Developmental Biology, University of Pennsylvania School of Medicine, Philadelphia, PA, 19104, USA
| | - John W Weisel
- Department of Cell and Developmental Biology, University of Pennsylvania School of Medicine, Philadelphia, PA, 19104, USA.
| | - Mark Alber
- Department of Mathematics, University of California Riverside, Riverside, CA, 92521, USA.
- Center for Quantitative Modeling in Biology, University of California Riverside, Riverside, CA, 92521, USA.
- Department of Bioengineering, University of California Riverside, Riverside, CA, 92521, USA.
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6
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Akbari A, Haiman ZB, Palsson BO. A data-driven approach for timescale decomposition of biochemical reaction networks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.21.554230. [PMID: 37662221 PMCID: PMC10473577 DOI: 10.1101/2023.08.21.554230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
Understanding the dynamics of biological systems in evolving environments is a challenge due to their scale and complexity. Here, we present a computational framework for timescale decomposition of biochemical reaction networks to distill essential patterns from their intricate dynamics. This approach identifies timescale hierarchies, concentration pools, and coherent structures from time-series data, providing a system-level description of reaction networks at physiologically important timescales. We apply this technique to kinetic models of hypothetical and biological pathways, validating it by reproducing analytically characterized or previously known concentration pools of these pathways. Moreover, by analyzing the timescale hierarchy of the glycolytic pathway, we elucidate the connections between the stoichiometric and dissipative structures of reaction networks and the temporal organization of coherent structures. Specifically, we show that glycolysis is a cofactor driven pathway, the slowest dynamics of which are described by a balance between high-energy phosphate bond and redox trafficking. Overall, this approach provides more biologically interpretable characterizations of network dynamics than large-scale kinetic models, thus facilitating model reduction and personalized medicine applications.
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7
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Danciu DP, Hooli J, Martin-Villalba A, Marciniak-Czochra A. Mathematics of neural stem cells: Linking data and processes. Cells Dev 2023; 174:203849. [PMID: 37179018 DOI: 10.1016/j.cdev.2023.203849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 04/29/2023] [Accepted: 05/05/2023] [Indexed: 05/15/2023]
Abstract
Adult stem cells are described as a discrete population of cells that stand at the top of a hierarchy of progressively differentiating cells. Through their unique ability to self-renew and differentiate, they regulate the number of end-differentiated cells that contribute to tissue physiology. The question of how discrete, continuous, or reversible the transitions through these hierarchies are and the precise parameters that determine the ultimate performance of stem cells in adulthood are the subject of intense research. In this review, we explain how mathematical modelling has improved the mechanistic understanding of stem cell dynamics in the adult brain. We also discuss how single-cell sequencing has influenced the understanding of cell states or cell types. Finally, we discuss how the combination of single-cell sequencing technologies and mathematical modelling provides a unique opportunity to answer some burning questions in the field of stem cell biology.
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Affiliation(s)
- Diana-Patricia Danciu
- Heidelberg University, Institute of Mathematics (IMA), Im Neuenheimer Feld 205, 69120 Heidelberg, Germany; Interdisciplinary Center for Scientific Computing (IWR), Im Neuenheimer Feld 205, 69120 Heidelberg, Germany
| | - Jooa Hooli
- Heidelberg University, Institute of Mathematics (IMA), Im Neuenheimer Feld 205, 69120 Heidelberg, Germany; Interdisciplinary Center for Scientific Computing (IWR), Im Neuenheimer Feld 205, 69120 Heidelberg, Germany; Heidelberg University, Faculty of Biosciences, Im Neuenheimer Feld 234, 69120 Heidelberg, Germany; German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
| | - Ana Martin-Villalba
- German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
| | - Anna Marciniak-Czochra
- Heidelberg University, Institute of Mathematics (IMA), Im Neuenheimer Feld 205, 69120 Heidelberg, Germany; Interdisciplinary Center for Scientific Computing (IWR), Im Neuenheimer Feld 205, 69120 Heidelberg, Germany.
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8
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Thalheim T, Aust G, Galle J. Organoid Cultures In Silico: Tools or Toys? BIOENGINEERING (BASEL, SWITZERLAND) 2022; 10:bioengineering10010050. [PMID: 36671623 PMCID: PMC9854934 DOI: 10.3390/bioengineering10010050] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 12/28/2022] [Accepted: 12/29/2022] [Indexed: 01/03/2023]
Abstract
The implementation of stem-cell-based organoid culture more than ten years ago started a development that created new avenues for diagnostic analyses and regenerative medicine. In parallel, computational modelling groups realized the potential of this culture system to support their theoretical approaches to study tissues in silico. These groups developed computational organoid models (COMs) that enabled testing consistency between cell biological data and developing theories of tissue self-organization. The models supported a mechanistic understanding of organoid growth and maturation and helped linking cell mechanics and tissue shape in general. What comes next? Can we use COMs as tools to complement the equipment of our biological and medical research? While these models already support experimental design, can they also quantitatively predict tissue behavior? Here, we review the current state of the art of COMs and discuss perspectives for their application.
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Affiliation(s)
- Torsten Thalheim
- Interdisciplinary Institute for Bioinformatics (IZBI), Leipzig University, Härtelstr. 16–18, 04107 Leipzig, Germany
- Correspondence:
| | - Gabriela Aust
- Department of Surgery, Research Laboratories, Leipzig University, Liebigstraße 20, 04103 Leipzig, Germany
| | - Joerg Galle
- Interdisciplinary Institute for Bioinformatics (IZBI), Leipzig University, Härtelstr. 16–18, 04107 Leipzig, Germany
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9
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Maric DM, Velikic G, Maric DL, Supic G, Vojvodic D, Petric V, Abazovic D. Stem Cell Homing in Intrathecal Applications and Inspirations for Improvement Paths. Int J Mol Sci 2022; 23:ijms23084290. [PMID: 35457107 PMCID: PMC9027729 DOI: 10.3390/ijms23084290] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 03/26/2022] [Accepted: 04/01/2022] [Indexed: 02/04/2023] Open
Abstract
A transplanted stem cell homing is a directed migration from the application site to the targeted tissue. Intrathecal application of stem cells is their direct delivery to cerebrospinal fluid, which defines the homing path from the point of injection to the brain. In the case of neurodegenerative diseases, this application method has the advantage of no blood–brain barrier restriction. However, the homing efficiency still needs improvement and homing mechanisms elucidation. Analysis of current research results on homing mechanisms in the light of intrathecal administration revealed a discrepancy between in vivo and in vitro results and a gap between preclinical and clinical research. Combining the existing research with novel insights from cutting-edge biochips, nano, and other technologies and computational models may bridge this gap faster.
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Affiliation(s)
- Dusan M. Maric
- Department for Research and Development, Clinic Orto MD-Parks Dr Dragi Hospital, 21000 Novi Sad, Serbia;
- Faculty of Dentistry Pancevo, University Business Academy, 26000 Pancevo, Serbia
- Vincula Biotech Group, 11000 Belgrade, Serbia;
| | - Gordana Velikic
- Department for Research and Development, Clinic Orto MD-Parks Dr Dragi Hospital, 21000 Novi Sad, Serbia;
- Vincula Biotech Group, 11000 Belgrade, Serbia;
- Correspondence: (G.V.); (D.L.M.)
| | - Dusica L. Maric
- Department of Anatomy, Faculty of Medicine, University of Novi Sad, 21000 Novi Sad, Serbia
- Correspondence: (G.V.); (D.L.M.)
| | - Gordana Supic
- Institute for Medical Research, Military Medical Academy, 11000 Belgrade, Serbia; (G.S.); (D.V.)
- Medical Faculty of Military Medical Academy, University of Defense, 11000 Belgrade, Serbia
| | - Danilo Vojvodic
- Institute for Medical Research, Military Medical Academy, 11000 Belgrade, Serbia; (G.S.); (D.V.)
- Medical Faculty of Military Medical Academy, University of Defense, 11000 Belgrade, Serbia
| | - Vedrana Petric
- Infectious Diseases Clinic, Clinical Center of Vojvodina, 21000 Novi Sad, Serbia;
- Department of Infectious Diseases, Faculty of Medicine, University of Novi Sad, 21000 Novi Sad, Serbia
| | - Dzihan Abazovic
- Vincula Biotech Group, 11000 Belgrade, Serbia;
- Department for Regenerative Medicine, Biocell Hospital, 11000 Belgrade, Serbia
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10
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Iberite F, Gruppioni E, Ricotti L. Skeletal muscle differentiation of human iPSCs meets bioengineering strategies: perspectives and challenges. NPJ Regen Med 2022; 7:23. [PMID: 35393412 PMCID: PMC8991236 DOI: 10.1038/s41536-022-00216-9] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Accepted: 03/01/2022] [Indexed: 12/31/2022] Open
Abstract
Although skeletal muscle repairs itself following small injuries, genetic diseases or severe damages may hamper its ability to do so. Induced pluripotent stem cells (iPSCs) can generate myogenic progenitors, but their use in combination with bioengineering strategies to modulate their phenotype has not been sufficiently investigated. This review highlights the potential of this combination aimed at pushing the boundaries of skeletal muscle tissue engineering. First, the overall organization and the key steps in the myogenic process occurring in vivo are described. Second, transgenic and non-transgenic approaches for the myogenic induction of human iPSCs are compared. Third, technologies to provide cells with biophysical stimuli, biomaterial cues, and biofabrication strategies are discussed in terms of recreating a biomimetic environment and thus helping to engineer a myogenic phenotype. The embryonic development process and the pro-myogenic role of the muscle-resident cell populations in co-cultures are also described, highlighting the possible clinical applications of iPSCs in the skeletal muscle tissue engineering field.
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Affiliation(s)
- Federica Iberite
- The BioRobotics Institute, Scuola Superiore Sant'Anna, 56127, Pisa (PI), Italy. .,Department of Excellence in Robotics & AI, Scuola Superiore Sant'Anna, 56127, Pisa (PI), Italy.
| | - Emanuele Gruppioni
- Centro Protesi INAIL, Istituto Nazionale per l'Assicurazione contro gli Infortuni sul Lavoro, 40054, Vigorso di Budrio (BO), Italy
| | - Leonardo Ricotti
- The BioRobotics Institute, Scuola Superiore Sant'Anna, 56127, Pisa (PI), Italy.,Department of Excellence in Robotics & AI, Scuola Superiore Sant'Anna, 56127, Pisa (PI), Italy
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Wu S, Zhou T, Tian T. A robust method for designing multistable systems by embedding bistable subsystems. NPJ Syst Biol Appl 2022; 8:10. [PMID: 35338169 PMCID: PMC8956579 DOI: 10.1038/s41540-022-00220-1] [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] [Accepted: 02/15/2022] [Indexed: 12/21/2022] Open
Abstract
Although multistability is an important dynamic property of a wide range of complex systems, it is still a challenge to develop mathematical models for realising high order multistability using realistic regulatory mechanisms. To address this issue, we propose a robust method to develop multistable mathematical models by embedding bistable models together. Using the GATA1-GATA2-PU.1 module in hematopoiesis as the test system, we first develop a tristable model based on two bistable models without any high cooperative coefficients, and then modify the tristable model based on experimentally determined mechanisms. The modified model successfully realises four stable steady states and accurately reflects a recent experimental observation showing four transcriptional states. In addition, we develop a stochastic model, and stochastic simulations successfully realise the experimental observations in single cells. These results suggest that the proposed method is a general approach to develop mathematical models for realising multistability and heterogeneity in complex systems.
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Affiliation(s)
- Siyuan Wu
- School of Mathematics, Monash University, Melbourne, VIC, Australia
| | - Tianshou Zhou
- School of Mathematics and Statistics, Sun Yet-Sen University, Guangzhou, China
| | - Tianhai Tian
- School of Mathematics, Monash University, Melbourne, VIC, Australia.
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