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Wadkin LE, Makarenko I, Parker NG, Shukurov A, Figueiredo FC, Lako M. Human Stem Cells for Ophthalmology: Recent Advances in Diagnostic Image Analysis and Computational Modelling. CURRENT STEM CELL REPORTS 2023; 9:57-66. [PMID: 38145008 PMCID: PMC10739444 DOI: 10.1007/s40778-023-00229-0] [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] [Accepted: 11/07/2023] [Indexed: 12/26/2023]
Abstract
Purpose of Review To explore the advances and future research directions in image analysis and computational modelling of human stem cells (hSCs) for ophthalmological applications. Recent Findings hSCs hold great potential in ocular regenerative medicine due to their application in cell-based therapies and in disease modelling and drug discovery using state-of-the-art 2D and 3D organoid models. However, a deeper characterisation of their complex, multi-scale properties is required to optimise their translation to clinical practice. Image analysis combined with computational modelling is a powerful tool to explore mechanisms of hSC behaviour and aid clinical diagnosis and therapy. Summary Many computational models draw on a variety of techniques, often blending continuum and discrete approaches, and have been used to describe cell differentiation and self-organisation. Machine learning tools are having a significant impact in model development and improving image classification processes for clinical diagnosis and treatment and will be the focus of much future research.
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Affiliation(s)
- L. E. Wadkin
- School of Mathematics, Statistics and Physics, Newcastle University, Newcastle upon Tyne, UK
| | - I. Makarenko
- School of Mathematics, Statistics and Physics, Newcastle University, Newcastle upon Tyne, UK
| | - N. G. Parker
- School of Mathematics, Statistics and Physics, Newcastle University, Newcastle upon Tyne, UK
| | - A. Shukurov
- School of Mathematics, Statistics and Physics, Newcastle University, Newcastle upon Tyne, UK
| | - F. C. Figueiredo
- Department of Ophthalmology, Royal Victoria Infirmary, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
- Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - M. Lako
- Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
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2
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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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3
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Shah R, Del Vecchio D. Reprogramming multistable monotone systems with application to cell fate control. IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2020; 7:2940-2951. [PMID: 33437845 PMCID: PMC7799369 DOI: 10.1109/tnse.2020.3008135] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Multistability is a key property of dynamical systems modeling cellular regulatory networks implicated in cell fate decisions, where, different stable steady states usually represent distinct cell phenotypes. Monotone network motifs are highly represented in these regulatory networks. In this paper, we leverage the properties of monotone dynamical systems to provide theoretical results that guide the selection of inputs that trigger a transition, i.e., reprogram the network, to a desired stable steady state. We first show that monotone dynamical systems with bounded trajectories admit a minimum and a maximum stable steady state. Then, we provide input choices that are guaranteed to reprogram the system to these extreme steady states. For intermediate states, we provide an input space that is guaranteed to contain an input that reprograms the system to the desired state. We then provide implementation guidelines for finite-time procedures that search this space for such an input, along with rules to prune parts of the space during search. We demonstrate these results on simulations of two recurrent regulatory network motifs: self-activation within mutual antagonism and self-activation within mutual cooperation. Our results depend uniquely on the structure of the network and are independent of specific parameter values.
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Affiliation(s)
- Rushina Shah
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Domitilla Del Vecchio
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
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4
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Wu S, Cui T, Zhang X, Tian T. A non-linear reverse-engineering method for inferring genetic regulatory networks. PeerJ 2020; 8:e9065. [PMID: 32391205 PMCID: PMC7195839 DOI: 10.7717/peerj.9065] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Accepted: 04/05/2020] [Indexed: 12/19/2022] Open
Abstract
Hematopoiesis is a highly complex developmental process that produces various types of blood cells. This process is regulated by different genetic networks that control the proliferation, differentiation, and maturation of hematopoietic stem cells (HSCs). Although substantial progress has been made for understanding hematopoiesis, the detailed regulatory mechanisms for the fate determination of HSCs are still unraveled. In this study, we propose a novel approach to infer the detailed regulatory mechanisms. This work is designed to develop a mathematical framework that is able to realize nonlinear gene expression dynamics accurately. In particular, we intended to investigate the effect of possible protein heterodimers and/or synergistic effect in genetic regulation. This approach includes the Extended Forward Search Algorithm to infer network structure (top-down approach) and a non-linear mathematical model to infer dynamical property (bottom-up approach). Based on the published experimental data, we study two regulatory networks of 11 genes for regulating the erythrocyte differentiation pathway and the neutrophil differentiation pathway. The proposed algorithm is first applied to predict the network topologies among 11 genes and 55 non-linear terms which may be for heterodimers and/or synergistic effect. Then, the unknown model parameters are estimated by fitting simulations to the expression data of two different differentiation pathways. In addition, the edge deletion test is conducted to remove possible insignificant regulations from the inferred networks. Furthermore, the robustness property of the mathematical model is employed as an additional criterion to choose better network reconstruction results. Our simulation results successfully realized experimental data for two different differentiation pathways, which suggests that the proposed approach is an effective method to infer the topological structure and dynamic property of genetic regulations.
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Affiliation(s)
- Siyuan Wu
- School of Mathematics, Monash University, Clayton, VIC, Australia
| | - Tiangang Cui
- School of Mathematics, Monash University, Clayton, VIC, Australia
| | - Xinan Zhang
- School of Mathematics and Statistics, Central China Normal University, Wuhan, PR China
| | - Tianhai Tian
- School of Mathematics, Monash University, Clayton, VIC, Australia
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5
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Intracellular Energy Variability Modulates Cellular Decision-Making Capacity. Sci Rep 2019; 9:20196. [PMID: 31882965 PMCID: PMC6934696 DOI: 10.1038/s41598-019-56587-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2019] [Accepted: 12/12/2019] [Indexed: 12/14/2022] Open
Abstract
Cells generate phenotypic diversity both during development and in response to stressful and changing environments, aiding survival. Functionally vital cell fate decisions from a range of phenotypic choices are made by regulatory networks, the dynamics of which rely on gene expression and hence depend on the cellular energy budget (and particularly ATP levels). However, despite pronounced cell-to-cell ATP differences observed across biological systems, the influence of energy availability on regulatory network dynamics is often overlooked as a cellular decision-making modulator, limiting our knowledge of how energy budgets affect cell behaviour. Here, we consider a mathematical model of a highly generalisable, ATP-dependent, decision-making regulatory network, and show that cell-to-cell ATP variability changes the sets of decisions a cell can make. Our model shows that increasing intracellular energy levels can increase the number of supported stable phenotypes, corresponding to increased decision-making capacity. Model cells with sub-threshold intracellular energy are limited to a singular phenotype, forcing the adoption of a specific cell fate. We suggest that energetic differences between cells may be an important consideration to help explain observed variability in cellular decision-making across biological systems.
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6
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Bokes P, King JR. Limit-cycle oscillatory coexpression of cross-inhibitory transcription factors: a model mechanism for lineage promiscuity. MATHEMATICAL MEDICINE AND BIOLOGY-A JOURNAL OF THE IMA 2019; 36:113-137. [PMID: 30869799 DOI: 10.1093/imammb/dqy003] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/30/2017] [Accepted: 01/29/2018] [Indexed: 12/12/2022]
Abstract
Lineage switches are genetic regulatory motifs that govern and maintain the commitment of a developing cell to a particular cell fate. A canonical example of a lineage switch is the pair of transcription factors PU.1 and GATA-1, of which the former is affiliated with the myeloid and the latter with the erythroid lineage within the haematopoietic system. On a molecular level, PU.1 and GATA-1 positively regulate themselves and antagonize each other via direct protein-protein interactions. Here we use mathematical modelling to identify a novel type of dynamic behaviour that can be supported by such a regulatory architecture. Guided by the specifics of the PU.1-GATA-1 interaction, we formulate, using the law of mass action, a system of differential equations for the key molecular concentrations. After a series of systematic approximations, the system is reduced to a simpler one, which is tractable to phase-plane and linearization methods. The reduced system formally resembles, and generalizes, a well-known model for competitive species from mathematical ecology. However, in addition to the qualitative regimes exhibited by a pair of competitive species (exclusivity, bistable exclusivity, stable-node coexpression) it also allows for oscillatory limit-cycle coexpression. A key outcome of the model is that, in the context of cell-fate choice, such oscillations could be harnessed by a differentiating cell to prime alternately for opposite outcomes; a bifurcation-theory approach is adopted to characterize this possibility.
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Affiliation(s)
- Pavol Bokes
- Department of Applied Mathematics and Statistics, Comenius University, Bratislava, Slovakia
| | - John R King
- School of Mathematical Sciences and SBRC Nottingham, University of Nottingham, Nottingham, United Kingdom
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7
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Al-Radhawi MA, Kumar NS, Sontag ED, Del Vecchio D. Stochastic multistationarity in a model of the hematopoietic stem cell differentiation network. PROCEEDINGS OF THE ... IEEE CONFERENCE ON DECISION & CONTROL. IEEE CONFERENCE ON DECISION & CONTROL 2019; 2018:1886-1892. [PMID: 32153314 DOI: 10.1109/cdc.2018.8619300] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
A central issue in the analysis of multi-stable systems is that of controlling the relative size of the basins of attraction of alternative states through suitable choices of system parameters. We are interested here mainly in the stochastic version of this problem, that of shaping the stationary probability distribution of a Markov chain so that various alternative modes become more likely than others. Although many of our results are more general, we were motivated by an important biological question, that of cell differentiation. In the mathematical modeling of cell differentiation, it is common to think of internal states of cells (quanfitied by activation levels of certain genes) as determining the different cell types. Specifically, we study here the "PU.1/GATA-1 circuit" which is involved in the control of the development of mature blood cells from hematopoietic stem cells (HSCs). All mature, specialized blood cells have been shown to be derived from multipotent HSCs. Our first contribution is to introduce a rigorous chemical reaction network model of the PU.1/GATA-1 circuit, which incorporates current biological knowledge. We then find that the resulting ODE model of these biomolecular reactions is incapable of exhibiting multistability, contradicting the fact that differentiation networks have, by definition, alternative stable steady states. When considering instead the stochastic version of this chemical network, we analytically construct the stationary distribution, and are able to show that this distribution is indeed capable of admitting a multiplicity of modes. Finally, we study how a judicious choice of system parameters serves to bias the probabilities towards different stationary states. We remark that certain changes in system parameters can be physically implemented by a biological feedback mechanism; tuning this feedback gives extra degrees of freedom that allow one to assign higher likelihood to some cell types over others.
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Affiliation(s)
- M Ali Al-Radhawi
- Department of Mechanical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139.,Department of Electrical and Computer Engineering and Department of Bioengineering, Northeastern University, 805 Columbus Ave, Boston, MA 02115, USA
| | - Nithin S Kumar
- Department of Mechanical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139
| | - Eduardo D Sontag
- Department of Electrical and Computer Engineering and Department of Bioengineering, Northeastern University, 805 Columbus Ave, Boston, MA 02115, USA
| | - Domitilla Del Vecchio
- Department of Mechanical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139
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8
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Abdallah HM, Del Vecchio D. Computational Analysis of Altering Cell Fate. Methods Mol Biol 2019; 1975:363-405. [PMID: 31062319 PMCID: PMC7227774 DOI: 10.1007/978-1-4939-9224-9_17] [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: 03/29/2024]
Abstract
The notion of reprogramming cell fate is a direct challenge to the traditional view in developmental biology that a cell's phenotypic identity is sealed after undergoing differentiation. Direct experimental evidence, beginning with the somatic cell nuclear transfer experiments of the twentieth century and culminating in the more recent breakthroughs in transdifferentiation and induced pluripotent stem cell (iPSC) reprogramming, have rewritten the rules for what is possible with cell fate transformation. Research is ongoing in the manipulation of cell fate for basic research in disease modeling, drug discovery, and clinical therapeutics. In many of these cell fate reprogramming experiments, there is often little known about the genetic and molecular changes accompanying the reprogramming process. However, gene regulatory networks (GRNs) can in some cases be implicated in the switching of phenotypes, providing a starting point for understanding the dynamic changes that accompany a given cell fate reprogramming process. In this chapter, we present a framework for computationally analyzing cell fate changes by mathematically modeling these GRNs. We provide a user guide with several tutorials of a set of techniques from dynamical systems theory that can be used to probe the intrinsic properties of GRNs as well as study their responses to external perturbations.
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Affiliation(s)
- Hussein M Abdallah
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
| | - Domitilla Del Vecchio
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
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9
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Olariu V, Peterson C. Kinetic models of hematopoietic differentiation. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2018; 11:e1424. [PMID: 29660842 PMCID: PMC6191385 DOI: 10.1002/wsbm.1424] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/27/2017] [Revised: 02/13/2018] [Accepted: 03/16/2018] [Indexed: 01/02/2023]
Abstract
As cell and molecular biology is becoming increasingly quantitative, there is an upsurge of interest in mechanistic modeling at different levels of resolution. Such models mostly concern kinetics and include gene and protein interactions as well as cell population dynamics. The final goal of these models is to provide experimental predictions, which is now taking on. However, even without matured predictions, kinetic models serve the purpose of compressing a plurality of experimental results into something that can empower the data interpretation, and importantly, suggesting new experiments by turning "knobs" in silico. Once formulated, kinetic models can be executed in terms of molecular rate equations for concentrations or by stochastic simulations when only a limited number of copies are involved. Developmental processes, in particular those of stem and progenitor cell commitments, are not only topical but also particularly suitable for kinetic modeling due to the finite number of key genes involved in cellular decisions. Stem and progenitor cell commitment processes have been subject to intense experimental studies over the last decade with some emphasis on embryonic and hematopoietic stem cells. Gene and protein interactions governing these processes can be modeled by binary Boolean rules or by continuous-valued models with interactions set by binding strengths. Conceptual insights along with tested predictions have emerged from such kinetic models. Here we review kinetic modeling efforts applied to stem cell developmental systems with focus on hematopoiesis. We highlight the future challenges including multi-scale models integrating cell dynamical and transcriptional models. This article is categorized under: Models of Systems Properties and Processes > Mechanistic Models Developmental Biology > Stem Cell Biology and Regeneration.
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Affiliation(s)
- Victor Olariu
- Department of Computational Biology, Lund University, Lund, Sweden
| | - Carsten Peterson
- Department of Computational Biology, Lund University, Lund, Sweden
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10
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Wei J, Hu X, Zou X, Tian T. Reverse-engineering of gene networks for regulating early blood development from single-cell measurements. BMC Med Genomics 2017; 10:72. [PMID: 29297370 PMCID: PMC5751697 DOI: 10.1186/s12920-017-0312-z] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Recent advances in omics technologies have raised great opportunities to study large-scale regulatory networks inside the cell. In addition, single-cell experiments have measured the gene and protein activities in a large number of cells under the same experimental conditions. However, a significant challenge in computational biology and bioinformatics is how to derive quantitative information from the single-cell observations and how to develop sophisticated mathematical models to describe the dynamic properties of regulatory networks using the derived quantitative information. METHODS This work designs an integrated approach to reverse-engineer gene networks for regulating early blood development based on singel-cell experimental observations. The wanderlust algorithm is initially used to develop the pseudo-trajectory for the activities of a number of genes. Since the gene expression data in the developed pseudo-trajectory show large fluctuations, we then use Gaussian process regression methods to smooth the gene express data in order to obtain pseudo-trajectories with much less fluctuations. The proposed integrated framework consists of both bioinformatics algorithms to reconstruct the regulatory network and mathematical models using differential equations to describe the dynamics of gene expression. RESULTS The developed approach is applied to study the network regulating early blood cell development. A graphic model is constructed for a regulatory network with forty genes and a dynamic model using differential equations is developed for a network of nine genes. Numerical results suggests that the proposed model is able to match experimental data very well. We also examine the networks with more regulatory relations and numerical results show that more regulations may exist. We test the possibility of auto-regulation but numerical simulations do not support the positive auto-regulation. In addition, robustness is used as an importantly additional criterion to select candidate networks. CONCLUSION The research results in this work shows that the developed approach is an efficient and effective method to reverse-engineer gene networks using single-cell experimental observations.
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Affiliation(s)
- Jiangyong Wei
- School of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan, 430073 China
| | - Xiaohua Hu
- School of Computer, Central China Normal University, Wuhan, 430079 China
| | - Xiufen Zou
- School of Mathematics and Statistics, Wuhan University, Wuhan, 430072 China
| | - Tianhai Tian
- School of Mathematical Sciences, Monash University, Melbourne, VIC 3800 Australia
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11
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Affiliation(s)
- Alexander A. Spector
- Department
of Biomedical Engineering and ‡Translational Tissue Engineering
Center, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, United States
- Institute for Nanobiotechnology (INBT) and ∥Department of Material Sciences & Engineering, Johns Hopkins University Whiting School of Engineering, Baltimore 21218, Maryland, United States
| | - Warren L. Grayson
- Department
of Biomedical Engineering and ‡Translational Tissue Engineering
Center, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, United States
- Institute for Nanobiotechnology (INBT) and ∥Department of Material Sciences & Engineering, Johns Hopkins University Whiting School of Engineering, Baltimore 21218, Maryland, United States
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12
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Wijgaerts A, Wittevrongel C, Thys C, Devos T, Peerlinck K, Tijssen MR, Van Geet C, Freson K. The transcription factor GATA1 regulates NBEAL2 expression through a long-distance enhancer. Haematologica 2017; 102:695-706. [PMID: 28082341 PMCID: PMC5395110 DOI: 10.3324/haematol.2016.152777] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2016] [Accepted: 01/10/2017] [Indexed: 01/19/2023] Open
Abstract
Gray platelet syndrome is named after the gray appearance of platelets due to the absence of α-granules. It is caused by recessive mutations in NBEAL2, resulting in macrothrombocytopenia and myelofibrosis. Though using the term gray platelets for GATA1 deficiency has been debated, a reduced number of α-granules has been described for macrothrombocytopenia due to GATA1 mutations. We compared platelet size and number of α-granules for two NBEAL2 and two GATA1-deficient patients and found reduced numbers of α-granules for all, with the defect being more pronounced for NBEAL2 deficiency. We further hypothesized that the granule defect for GATA1 is due to a defective control of NBEAL2 expression. Remarkably, platelets from two patients, and Gata1-deficient mice, expressed almost no NBEAL2. The differentiation of GATA1 patient-derived CD34+ stem cells to megakaryocytes showed defective proplatelet and α-granule formation with strongly reduced NBEAL2 protein and ribonucleic acid expression. Chromatin immunoprecipitation sequencing revealed 5 GATA binding sites in a regulatory region 31 kb upstream of NBEAL2 covered by a H3K4Me1 mark indicative of an enhancer locus. Luciferase reporter constructs containing this region confirmed its enhancer activity in K562 cells, and mutagenesis of the GATA1 binding sites resulted in significantly reduced enhancer activity. Moreover, DNA binding studies showed that GATA1 and GATA2 physically interact with this enhancer region. GATA1 depletion using small interfering ribonucleic acid in K562 cells also resulted in reduced NBEAL2 expression. In conclusion, we herein show a long-distance regulatory region with GATA1 binding sites as being a strong enhancer for NBEAL2 expression.
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Affiliation(s)
- Anouck Wijgaerts
- Department of Cardiovascular Sciences, Center for Molecular and Vascular Biology, KULeuven, Belgium
| | - Christine Wittevrongel
- Department of Cardiovascular Sciences, Center for Molecular and Vascular Biology, KULeuven, Belgium
| | - Chantal Thys
- Department of Cardiovascular Sciences, Center for Molecular and Vascular Biology, KULeuven, Belgium
| | - Timothy Devos
- Department of Haematology, University Hospitals Leuven, Belgium
| | - Kathelijne Peerlinck
- Department of Cardiovascular Sciences, Center for Molecular and Vascular Biology, KULeuven, Belgium
| | - Marloes R Tijssen
- NHS Blood and Transplant, Cambridge Biomedical Campus, UK.,Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, UK
| | - Chris Van Geet
- Department of Cardiovascular Sciences, Center for Molecular and Vascular Biology, KULeuven, Belgium.,Department of Pediatrics, University Hospitals Leuven, Belgium
| | - Kathleen Freson
- Department of Cardiovascular Sciences, Center for Molecular and Vascular Biology, KULeuven, Belgium
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13
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Zhang F, Wu M, Kwoh CK, Zheng J. Power-Law Modeling of Cancer Cell Fates Driven by Signaling Data to Reveal Drug Effects. PLoS One 2016; 11:e0165049. [PMID: 27764199 PMCID: PMC5072688 DOI: 10.1371/journal.pone.0165049] [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: 04/09/2016] [Accepted: 10/05/2016] [Indexed: 11/18/2022] Open
Abstract
Extracellular signals are captured and transmitted by signaling proteins inside a cell. An important type of cellular responses to the signals is the cell fate decision, e.g., apoptosis. However, the underlying mechanisms of cell fate regulation are still unclear, thus comprehensive and detailed kinetic models are not yet available. Alternatively, data-driven models are promising to bridge signaling data with the phenotypic measurements of cell fates. The traditional linear model for data-driven modeling of signaling pathways has its limitations because it assumes that the a cell fate is proportional to the activities of signaling proteins, which is unlikely in the complex biological systems. Therefore, we propose a power-law model to relate the activities of all the measured signaling proteins to the probabilities of cell fates. In our experiments, we compared our nonlinear power-law model with the linear model on three cancer datasets with phosphoproteomics and cell fate measurements, which demonstrated that the nonlinear model has superior performance on cell fates prediction. By in silico simulation of virtual protein knock-down, the proposed model is able to reveal drug effects which can complement traditional approaches such as binding affinity analysis. Moreover, our model is able to capture cell line specific information to distinguish one cell line from another in cell fate prediction. Our results show that the power-law data-driven model is able to perform better in cell fate prediction and provide more insights into the signaling pathways for cancer cell fates than the linear model.
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Affiliation(s)
- Fan Zhang
- Biomedical Informatics Lab, School of Computer Science and Engineering, Nanyang Technological University, 639798, Singapore, Singapore
| | - Min Wu
- Data Analytic Department, Institute for Infocomm Research, Agency for Science, Technology and Research, 138632, Singapore, Singapore
| | - Chee Keong Kwoh
- Biomedical Informatics Lab, School of Computer Science and Engineering, Nanyang Technological University, 639798, Singapore, Singapore
| | - Jie Zheng
- Biomedical Informatics Lab, School of Computer Science and Engineering, Nanyang Technological University, 639798, Singapore, Singapore
- Complexity Institute, Nanyang Technological University, 637723, Singapore, Singapore
- Genome Institute of Singapore, Agency for Science, Technology and Research, 138672, Singapore, Singapore
- * E-mail:
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14
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Mathematical Models for Immunology: Current State of the Art and Future Research Directions. Bull Math Biol 2016; 78:2091-2134. [PMID: 27714570 PMCID: PMC5069344 DOI: 10.1007/s11538-016-0214-9] [Citation(s) in RCA: 79] [Impact Index Per Article: 9.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2016] [Accepted: 09/26/2016] [Indexed: 01/01/2023]
Abstract
The advances in genetics and biochemistry that have taken place over the last 10 years led to significant advances in experimental and clinical immunology. In turn, this has led to the development of new mathematical models to investigate qualitatively and quantitatively various open questions in immunology. In this study we present a review of some research areas in mathematical immunology that evolved over the last 10 years. To this end, we take a step-by-step approach in discussing a range of models derived to study the dynamics of both the innate and immune responses at the molecular, cellular and tissue scales. To emphasise the use of mathematics in modelling in this area, we also review some of the mathematical tools used to investigate these models. Finally, we discuss some future trends in both experimental immunology and mathematical immunology for the upcoming years.
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15
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E2F and GATA switches turn off WD repeat domain 77 expression in differentiating cells. Biochem J 2016; 473:2331-43. [PMID: 27274086 DOI: 10.1042/bcj20160130] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2016] [Accepted: 06/06/2016] [Indexed: 02/01/2023]
Abstract
WDR77 (WD repeat domain 77) is expressed during earlier lung development when cells are rapidly proliferating, but is absent from adult lung. It is re-activated during lung tumorigenesis and is essential for lung cancer cell proliferation. Signalling pathways/molecules that control WDR77 gene expression are unknown. Promoter mapping, gel shift assay and ChIP revealed that the WDR77 promoter contains bona fide response elements for E2F and GATA transcriptional factors as demonstrated in prostate cancer, lung cancer and erythroid cells, as well as in mouse lung tissues. The WDR77 promoter is transactivated by E2F1, E2F3, GATA1 and GATA6, but suppressed by E2F6, GATA1 and GATA3 in prostate cancer PC3 cells. WDR77 expression is associated with E2F1, E2F3, GATA2 and GATA6 occupancy on the WDR77 gene, whereas, in contrast, E2F6, GATA1 and GATA3 occupancy is associated with the loss of WDR77 expression during erythroid maturation and lung development. More importantly, the loss of WDR77 expression that results from E2F and GATA switches is required for cellular differentiation of erythroid and lung epithelial cells. In contrast, lung cancer cells avoid post-mitotic differentiation by sustaining WDR77 expression. Altogether, the present study provides a novel molecular mechanism by which WDR77 is regulated during erythroid and lung development and lung tumorigenesis.
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Papatsenko D, Lemischka IR. Emerging Modeling Concepts and Solutions in Stem Cell Research. Curr Top Dev Biol 2016; 116:709-21. [PMID: 26970649 DOI: 10.1016/bs.ctdb.2015.11.040] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/04/2022]
Abstract
Modern stem cell research, as well as other fields of contemporary biology involves quantitative sciences in many ways. Identifying candidates for key differentiation or reprogramming factors, tracing global transcriptome changes, or finding drugs is now broadly involves bioinformatics and biostatistics. However, the next key step, understanding the underlying reasons and establishing causal links leading to differentiation or reprogramming requires qualitative and quantitative biological models describing complex biological systems. Currently, quantitative modeling is a challenging science, capable to deliver rather modest results or predictions. What model types are the most popular and what features of stem cell behavior they are capturing? What new insights do we expect from the computational modeling of stem cells in the foreseeable future? Current review attempts to approach these essential questions by considering published quantitative models and solutions emerging in the area of stem cell research.
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Affiliation(s)
- Dmitri Papatsenko
- Department of Regenerative and Developmental Biology, Icahn School of Medicine at Mount Sinai, New York, USA; Black Family Stem Cell Institute, Mount Sinai School of Medicine, New York, USA
| | - Ihor R Lemischka
- Department of Regenerative and Developmental Biology, Icahn School of Medicine at Mount Sinai, New York, USA; Black Family Stem Cell Institute, Mount Sinai School of Medicine, New York, USA; Department of Pharmacology and System Therapeutics, Mount Sinai School of Medicine, Systems Biology Center New York, New York, USA.
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Zhang F, Chen H, Zhao LN, Liu H, Przytycka TM, Zheng J. Generalized logical model based on network topology to capture the dynamical trends of cellular signaling pathways. BMC SYSTEMS BIOLOGY 2016; 10 Suppl 1:7. [PMID: 26818802 PMCID: PMC4895646 DOI: 10.1186/s12918-015-0249-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Background Cellular responses to extracellular perturbations require signaling pathways to capture and transmit the signals. However, the underlying molecular mechanisms of signal transduction are not yet fully understood, thus detailed and comprehensive models may not be available for all the signaling pathways. In particular, insufficient knowledge of parameters, which is a long-standing hindrance for quantitative kinetic modeling necessitates the use of parameter-free methods for modeling and simulation to capture dynamic properties of signaling pathways. Results We present a computational model that is able to simulate the graded responses to degradations, the sigmoidal biological relationships between signaling molecules and the effects of scheduled perturbations to the cells. The simulation results are validated using experimental data of protein phosphorylation, demonstrating that the proposed model is capable of capturing the main trend of protein activities during the process of signal transduction. Compared with existing simulators, our model has better performance on predicting the state transitions of signaling networks. Conclusion The proposed simulation tool provides a valuable resource for modeling cellular signaling pathways using a knowledge-based method.
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Affiliation(s)
- Fan Zhang
- Biomedical Informatics Graduate Lab, School of Computer Engineering, Nanyang Technological University, Singapore 639798, Singapore.
| | - Haoting Chen
- Biomedical Informatics Graduate Lab, School of Computer Engineering, Nanyang Technological University, Singapore 639798, Singapore. .,Department of Industrial Engineering and Operations Research, Columbia University, New York, NY 10027, USA.
| | - Li Na Zhao
- Bioinformatics Institute, Agency for Science, Technology and Research, Singapore 138671, Singapore.
| | - Hui Liu
- Biomedical Informatics Graduate Lab, School of Computer Engineering, Nanyang Technological University, Singapore 639798, Singapore. .,Lab of Information Management, Changzhou University, Changzhou, Jiangsu 213164, China.
| | - Teresa M Przytycka
- National Center for Biotechnology Information, NLM/NIH, Bethesda, MD 20894, USA.
| | - Jie Zheng
- Biomedical Informatics Graduate Lab, School of Computer Engineering, Nanyang Technological University, Singapore 639798, Singapore. .,Complexity Institute, Nanyang Technological University, Singapore 637723, Singapore. .,Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore 138672, Singapore.
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Bayesian Computation Methods for Inferring Regulatory Network Models Using Biomedical Data. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2016; 939:289-307. [DOI: 10.1007/978-981-10-1503-8_12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Enciso J, Mendoza L, Pelayo R. Normal vs. Malignant hematopoiesis: the complexity of acute leukemia through systems biology. Front Genet 2015; 6:290. [PMID: 26442108 PMCID: PMC4566035 DOI: 10.3389/fgene.2015.00290] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2015] [Accepted: 08/31/2015] [Indexed: 01/03/2023] Open
Affiliation(s)
- Jennifer Enciso
- Oncology Research Unit, Mexican Institute for Social Security Mexico City, Mexico ; Biochemistry Sciences Program, Universidad Nacional Autónoma de México Mexico City, Mexico
| | - Luis Mendoza
- Departamento de Biología Molecular y Biotecnología, Instituto de Investigaciones Biomédicas, Universidad Nacional Autónoma de México Mexico City, Mexico
| | - Rosana Pelayo
- Oncology Research Unit, Mexican Institute for Social Security Mexico City, Mexico
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Wu Q, Smith-Miles K, Tian T. Approximate Bayesian computation schemes for parameter inference of discrete stochastic models using simulated likelihood density. BMC Bioinformatics 2014; 15 Suppl 12:S3. [PMID: 25473744 PMCID: PMC4243104 DOI: 10.1186/1471-2105-15-s12-s3] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Mathematical modeling is an important tool in systems biology to study the dynamic property of complex biological systems. However, one of the major challenges in systems biology is how to infer unknown parameters in mathematical models based on the experimental data sets, in particular, when the data are sparse and the regulatory network is stochastic. RESULTS To address this issue, this work proposed a new algorithm to estimate parameters in stochastic models using simulated likelihood density in the framework of approximate Bayesian computation. Two stochastic models were used to demonstrate the efficiency and effectiveness of the proposed method. In addition, we designed another algorithm based on a novel objective function to measure the accuracy of stochastic simulations. CONCLUSIONS Simulation results suggest that the usage of simulated likelihood density improves the accuracy of estimates substantially. When the error is measured at each observation time point individually, the estimated parameters have better accuracy than those obtained by a published method in which the error is measured using simulations over the entire observation time period.
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Deng Z, Tian T. A continuous optimization approach for inferring parameters in mathematical models of regulatory networks. BMC Bioinformatics 2014; 15:256. [PMID: 25070047 PMCID: PMC4261783 DOI: 10.1186/1471-2105-15-256] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2013] [Accepted: 07/09/2014] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND The advances of systems biology have raised a large number of sophisticated mathematical models for describing the dynamic property of complex biological systems. One of the major steps in developing mathematical models is to estimate unknown parameters of the model based on experimentally measured quantities. However, experimental conditions limit the amount of data that is available for mathematical modelling. The number of unknown parameters in mathematical models may be larger than the number of observation data. The imbalance between the number of experimental data and number of unknown parameters makes reverse-engineering problems particularly challenging. RESULTS To address the issue of inadequate experimental data, we propose a continuous optimization approach for making reliable inference of model parameters. This approach first uses a spline interpolation to generate continuous functions of system dynamics as well as the first and second order derivatives of continuous functions. The expanded dataset is the basis to infer unknown model parameters using various continuous optimization criteria, including the error of simulation only, error of both simulation and the first derivative, or error of simulation as well as the first and second derivatives. We use three case studies to demonstrate the accuracy and reliability of the proposed new approach. Compared with the corresponding discrete criteria using experimental data at the measurement time points only, numerical results of the ERK kinase activation module show that the continuous absolute-error criteria using both function and high order derivatives generate estimates with better accuracy. This result is also supported by the second and third case studies for the G1/S transition network and the MAP kinase pathway, respectively. This suggests that the continuous absolute-error criteria lead to more accurate estimates than the corresponding discrete criteria. We also study the robustness property of these three models to examine the reliability of estimates. Simulation results show that the models with estimated parameters using continuous fitness functions have better robustness properties than those using the corresponding discrete fitness functions. CONCLUSIONS The inference studies and robustness analysis suggest that the proposed continuous optimization criteria are effective and robust for estimating unknown parameters in mathematical models.
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Affiliation(s)
| | - Tianhai Tian
- School of Mathematical Sciences, Monash University, Melbourne 3800, Victoria, Australia.
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