Clonal abundance patterns in hematopoiesis: Mathematical modeling and parameter estimation
Hematopoiesis has been studied via stem cell labeling using barcodes, viral integration sites (VISs), or in situ methods. Subsequent proliferation and differentiation preserve the tag identity, thus defining a clone of mature cells across multiple cell type or lineages. By tracking the population of...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-02-01
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| Series: | Frontiers in Systems Biology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fsysb.2023.893366/full |
| _version_ | 1811168358930841600 |
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| author | Yunbei Pan Yunbei Pan Maria R. D’Orsogna Maria R. D’Orsogna Min Tang Thomas Stiehl Tom Chou Tom Chou |
| author_facet | Yunbei Pan Yunbei Pan Maria R. D’Orsogna Maria R. D’Orsogna Min Tang Thomas Stiehl Tom Chou Tom Chou |
| author_sort | Yunbei Pan |
| collection | DOAJ |
| description | Hematopoiesis has been studied via stem cell labeling using barcodes, viral integration sites (VISs), or in situ methods. Subsequent proliferation and differentiation preserve the tag identity, thus defining a clone of mature cells across multiple cell type or lineages. By tracking the population of clones, measured within samples taken at discrete time points, we infer physiological parameters associated with a hybrid stochastic-deterministic mathematical model of hematopoiesis. We analyze clone population data from Koelle et al. (Koelle et al., 2017) and compare the states of clones (mean and variance of their abundances) and the state-space density of clones with the corresponding quantities predicted from our model. Comparing our model to the tagged granulocyte populations, we find parameters (stem cell carrying capacity, stem cell differentiation rates, and the proliferative potential of progenitor cells, and sample sizes) that provide reasonable fits in three out of four animals. Even though some observed features cannot be quantitatively reproduced by our model, our analyses provides insight into how model parameters influence the underlying mechanisms in hematopoiesis. We discuss additional mechanisms not incorporated in our model. |
| first_indexed | 2024-04-10T16:24:52Z |
| format | Article |
| id | doaj.art-a8265ca37027489a9de972e9bae799fe |
| institution | Directory Open Access Journal |
| issn | 2674-0702 |
| language | English |
| last_indexed | 2024-04-10T16:24:52Z |
| publishDate | 2023-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Systems Biology |
| spelling | doaj.art-a8265ca37027489a9de972e9bae799fe2023-02-09T07:05:08ZengFrontiers Media S.A.Frontiers in Systems Biology2674-07022023-02-01310.3389/fsysb.2023.893366893366Clonal abundance patterns in hematopoiesis: Mathematical modeling and parameter estimationYunbei Pan0Yunbei Pan1Maria R. D’Orsogna2Maria R. D’Orsogna3Min Tang4Thomas Stiehl5Tom Chou6Tom Chou7Department of Computational Medicine, UCLA, Los Angeles, CA, United StatesDepartment of Mathematics, California State University at Northridge, Los Angeles, CA, United StatesDepartment of Computational Medicine, UCLA, Los Angeles, CA, United StatesDepartment of Mathematics, California State University at Northridge, Los Angeles, CA, United StatesInstitute of Natural Sciences, Shanghai Jiaotong University, Shanghai, ChinaInstitute of Computational Biomedicine, RWTH Aachen University, Aachen, GermanyDepartment of Computational Medicine, UCLA, Los Angeles, CA, United StatesDepartment of Mathematics, UCLA, Los Angeles, CA, United StatesHematopoiesis has been studied via stem cell labeling using barcodes, viral integration sites (VISs), or in situ methods. Subsequent proliferation and differentiation preserve the tag identity, thus defining a clone of mature cells across multiple cell type or lineages. By tracking the population of clones, measured within samples taken at discrete time points, we infer physiological parameters associated with a hybrid stochastic-deterministic mathematical model of hematopoiesis. We analyze clone population data from Koelle et al. (Koelle et al., 2017) and compare the states of clones (mean and variance of their abundances) and the state-space density of clones with the corresponding quantities predicted from our model. Comparing our model to the tagged granulocyte populations, we find parameters (stem cell carrying capacity, stem cell differentiation rates, and the proliferative potential of progenitor cells, and sample sizes) that provide reasonable fits in three out of four animals. Even though some observed features cannot be quantitatively reproduced by our model, our analyses provides insight into how model parameters influence the underlying mechanisms in hematopoiesis. We discuss additional mechanisms not incorporated in our model.https://www.frontiersin.org/articles/10.3389/fsysb.2023.893366/fullstem cellshematopoiesisbarcodesclonal trackingdifferentiation |
| spellingShingle | Yunbei Pan Yunbei Pan Maria R. D’Orsogna Maria R. D’Orsogna Min Tang Thomas Stiehl Tom Chou Tom Chou Clonal abundance patterns in hematopoiesis: Mathematical modeling and parameter estimation Frontiers in Systems Biology stem cells hematopoiesis barcodes clonal tracking differentiation |
| title | Clonal abundance patterns in hematopoiesis: Mathematical modeling and parameter estimation |
| title_full | Clonal abundance patterns in hematopoiesis: Mathematical modeling and parameter estimation |
| title_fullStr | Clonal abundance patterns in hematopoiesis: Mathematical modeling and parameter estimation |
| title_full_unstemmed | Clonal abundance patterns in hematopoiesis: Mathematical modeling and parameter estimation |
| title_short | Clonal abundance patterns in hematopoiesis: Mathematical modeling and parameter estimation |
| title_sort | clonal abundance patterns in hematopoiesis mathematical modeling and parameter estimation |
| topic | stem cells hematopoiesis barcodes clonal tracking differentiation |
| url | https://www.frontiersin.org/articles/10.3389/fsysb.2023.893366/full |
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