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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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Andersson E, Sjö M, Kaji K, Olariu V. CELLoGeNe - An energy landscape framework for logical networks controlling cell decisions. iScience 2022; 25:104743. [PMID: 35942105 PMCID: PMC9356104 DOI: 10.1016/j.isci.2022.104743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 06/01/2022] [Accepted: 07/05/2022] [Indexed: 11/29/2022] Open
Abstract
Experimental and computational efforts are constantly made to elucidate mechanisms controlling cell fate decisions during development and reprogramming. One powerful computational method is to consider cell commitment and reprogramming as movements in an energy landscape. Here, we develop Computation of Energy Landscapes of Logical Gene Networks (CELLoGeNe), which maps Boolean implementation of gene regulatory networks (GRNs) into energy landscapes. CELLoGeNe removes inadvertent symmetries in the energy landscapes normally arising from standard Boolean operators. Furthermore, CELLoGeNe provides tools to visualize and stochastically analyze the shapes of multi-dimensional energy landscapes corresponding to epigenetic landscapes for development and reprogramming. We demonstrate CELLoGeNe on two GRNs governing different aspects of induced pluripotent stem cells, identifying experimentally validated attractors and revealing potential reprogramming roadblocks. CELLoGeNe is a general framework that can be applied to various biological systems offering a broad picture of intracellular dynamics otherwise inaccessible with existing methods. CELLoGeNe – Computation of Energy Landscapes of Logical Gene Networks Cell states as landscape attractors Maintenance and acquisition of cell pluripotency applications Single cell stochastic landscape navigation and visualization tool
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Discrete Logic Modeling of Cell Signaling Pathways. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2022; 2488:159-181. [PMID: 35347689 DOI: 10.1007/978-1-0716-2277-3_12] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Cell signaling pathways often crosstalk generating complex biological behaviors observed in different cellular contexts. Frequently, laboratory experiments focus on a few putative regulators, alone unable to predict the molecular mechanisms behind the observed phenotypes. Here, systems biology complements these approaches by giving a holistic picture to complex signaling crosstalk. In particular, Boolean network models are a meaningful tool to study large network behaviors and can cope with incomplete kinetic information. By introducing a model describing pathways involved in hematopoietic stem cell maintenance, we present a general approach on how to model cell signaling pathways with Boolean network models.
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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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5
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Raghav PK, Gangenahalli G. PU.1 Mimic Synthetic Peptides Selectively Bind with GATA-1 and Allow c-Jun PU.1 Binding to Enhance Myelopoiesis. Int J Nanomedicine 2021; 16:3833-3859. [PMID: 34113102 PMCID: PMC8187006 DOI: 10.2147/ijn.s303235] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Accepted: 04/20/2021] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Hematopoietic stem cells' commitment to myelopoiesis builds immunity to prevent infection. This process is controlled through transcription factor, especially Purine rich box 1 (PU.1) protein, which plays a central role in regulating myelopoiesis. The β3/β4 region of PU.1 accommodates a coactivator transcription factor, c-Jun, to activate myelopoiesis. However, an erythroid transcription factor, GATA-1, competes with c-Jun for the β3/β4 region, abolishing myelopoiesis and promoting erythropoiesis. This competitive regulation decides the hematopoietic stem cells' commitment towards either erythroid or myeloid lineage. METHODS Therefore, this study investigated the in vitro and in vivo effect of novel synthetic PU.1 β3/β4 mimic peptide analogs and peptide-loaded hydrophilic poly(D,L-lactide-co-glycolide) (PLGA) nanoparticles. RESULTS The designed peptides significantly increase the expression of corresponding myeloid markers, specifically CD33 in vitro. However, the in vivo delivery of peptide-loaded PLGA nanoparticles, which have sustained release effect of peptides, increases 10.8% of granulocytes as compared to control. CONCLUSION The observations showed that the fabricated nanoparticles protected the loaded peptides from the harsh intracellular environment for a longer duration without causing any toxicity. These findings highlight the possibility to use these peptides and peptide-loaded nanoparticles to increase hematopoietic stem cell commitment to myeloid cells in case of opportunistic infection.
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Affiliation(s)
- Pawan Kumar Raghav
- Division of Stem Cell and Gene Therapy Research, Institute of Nuclear Medicine and Allied Sciences (INMAS), Delhi, 110054, India
| | - Gurudutta Gangenahalli
- Division of Stem Cell and Gene Therapy Research, Institute of Nuclear Medicine and Allied Sciences (INMAS), Delhi, 110054, India
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Merlevede A, Legault EM, Drugge V, Barker RA, Drouin-Ouellet J, Olariu V. A quantitative model of cellular decision making in direct neuronal reprogramming. Sci Rep 2021; 11:1514. [PMID: 33452356 PMCID: PMC7810861 DOI: 10.1038/s41598-021-81089-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2020] [Accepted: 01/01/2021] [Indexed: 12/25/2022] Open
Abstract
The direct reprogramming of adult skin fibroblasts to neurons is thought to be controlled by a small set of interacting gene regulators. Here, we investigate how the interaction dynamics between these regulating factors coordinate cellular decision making in direct neuronal reprogramming. We put forward a quantitative model of the governing gene regulatory system, supported by measurements of mRNA expression. We found that nPTB needs to feed back into the direct neural conversion network most likely via PTB in order to accurately capture quantitative gene interaction dynamics and correctly predict the outcome of various overexpression and knockdown experiments. This was experimentally validated by nPTB knockdown leading to successful neural conversion. We also proposed a novel analytical technique to dissect system behaviour and reveal the influence of individual factors on resulting gene expression. Overall, we demonstrate that computational analysis is a powerful tool for understanding the mechanisms of direct (neuronal) reprogramming, paving the way for future models that can help improve cell conversion strategies.
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Affiliation(s)
- Adriaan Merlevede
- Computational Biology and Biological Physics, Department of Astronomy and Theoretical Physics, Lund University, 223 62, Lund, Sweden
| | - Emilie M Legault
- Faculté de Pharmacie, Université de Montréal, Montreal, QC, H3T 1J4, Canada
| | - Viktor Drugge
- Computational Biology and Biological Physics, Department of Astronomy and Theoretical Physics, Lund University, 223 62, Lund, Sweden
| | - Roger A Barker
- Cambridge Centre for Brain Repair, University of Cambridge, Forvie Site, Robinson Way, Cambridge, CB2 2PY, UK
| | | | - Victor Olariu
- Computational Biology and Biological Physics, Department of Astronomy and Theoretical Physics, Lund University, 223 62, Lund, Sweden.
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Abstract
PURPOSE OF REVIEW The hematopoietic compartment is tasked with the establishment and maintenance of the entire blood program in steady-state and in response to stress. Key to this process are hematopoietic stem cells (HSCs), which possess the unique ability to self-renew and differentiate to replenish blood cells throughout an organism's lifetime. Though tightly regulated, the hematopoietic system is vulnerable to both intrinsic and extrinsic factors that influence hematopoietic stem and progenitor cell (HSPC) fate. Here, we review recent advances in our understanding of hematopoietic regulation under stress conditions such as inflammation, aging, mitochondrial defects, and damage to DNA or endoplasmic reticulum. RECENT FINDINGS Recent studies have illustrated the vast mechanisms involved in regulating stress-induced hematopoiesis, including cytokine-mediated lineage bias, gene signature changes in aged HSCs associated with chronic inflammation, the impact of clonal hematopoiesis and stress tolerance, characterization of the HSPC response to endoplasmic reticulum stress and of several epigenetic regulators that influence HSPC response to cell cycle stress. SUMMARY Several key recent findings have deepened our understanding of stress hematopoiesis. These studies will advance our abilities to reduce the impact of stress in disease and aging through clinical interventions to treat stress-related outcomes.
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Olariu V, Yui MA, Krupinski P, Zhou W, Deichmann J, Andersson E, Rothenberg EV, Peterson C. Multi-scale Dynamical Modeling of T Cell Development from an Early Thymic Progenitor State to Lineage Commitment. Cell Rep 2021; 34:108622. [PMID: 33440162 DOI: 10.1016/j.celrep.2020.108622] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Revised: 04/24/2020] [Accepted: 12/18/2020] [Indexed: 01/13/2023] Open
Abstract
Intrathymic development of committed progenitor (pro)-T cells from multipotent hematopoietic precursors offers an opportunity to dissect the molecular circuitry establishing cell identity in response to environmental signals. This transition encompasses programmed shutoff of stem/progenitor genes, upregulation of T cell specification genes, proliferation, and ultimately commitment. To explain these features in light of reported cis-acting chromatin effects and experimental kinetic data, we develop a three-level dynamic model of commitment based upon regulation of the commitment-linked gene Bcl11b. The levels are (1) a core gene regulatory network (GRN) architecture from transcription factor (TF) perturbation data, (2) a stochastically controlled chromatin-state gate, and (3) a single-cell proliferation model validated by experimental clonal growth and commitment kinetic assays. Using RNA fluorescence in situ hybridization (FISH) measurements of genes encoding key TFs and measured bulk population dynamics, this single-cell model predicts state-switching kinetics validated by measured clonal proliferation and commitment times. The resulting multi-scale model provides a mechanistic framework for dissecting commitment dynamics.
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Affiliation(s)
- Victor Olariu
- Computational Biology and Biological Physics, Department of Astronomy and Theoretical Physics, Lund University, Lund, Sweden
| | - Mary A Yui
- Division of Biology and Biological Engineering, 156-29, California Institute of Technology, Pasadena, CA 91125, USA
| | - Pawel Krupinski
- Computational Biology and Biological Physics, Department of Astronomy and Theoretical Physics, Lund University, Lund, Sweden
| | - Wen Zhou
- Division of Biology and Biological Engineering, 156-29, California Institute of Technology, Pasadena, CA 91125, USA
| | - Julia Deichmann
- Computational Biology and Biological Physics, Department of Astronomy and Theoretical Physics, Lund University, Lund, Sweden
| | - Emil Andersson
- Computational Biology and Biological Physics, Department of Astronomy and Theoretical Physics, Lund University, Lund, Sweden
| | - Ellen V Rothenberg
- Division of Biology and Biological Engineering, 156-29, California Institute of Technology, Pasadena, CA 91125, USA.
| | - Carsten Peterson
- Computational Biology and Biological Physics, Department of Astronomy and Theoretical Physics, Lund University, Lund, Sweden.
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9
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Ikonomi N, Kühlwein SD, Schwab JD, Kestler HA. Awakening the HSC: Dynamic Modeling of HSC Maintenance Unravels Regulation of the TP53 Pathway and Quiescence. Front Physiol 2020; 11:848. [PMID: 32848827 PMCID: PMC7411231 DOI: 10.3389/fphys.2020.00848] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Accepted: 06/24/2020] [Indexed: 12/22/2022] Open
Abstract
Hematopoietic stem cells (HSCs) provide all types of blood cells during the entire life of the organism. HSCs are mainly quiescent and can eventually enter the cell cycle to differentiate. HSCs are maintained and tightly regulated in a particular environment. The stem cell niche regulates dormancy and awakening. Deregulations of this interplay can lead to hematopoietic failure and diseases. In this paper, we present a Boolean network model that recapitulates HSC regulation in virtue of external signals coming from the niche. This Boolean network integrates and summarizes the current knowledge of HSC regulation and is based on extensive literature research. Furthermore, dynamic simulations suggest a novel systemic regulation of TP53 in homeostasis. Thereby, our model indicates that TP53 activity is balanced depending on external stimulations, engaging a regulatory mechanism involving ROS regulators and RAS activated transcription factors. Finally, we investigated different mouse models and compared them to in silico knockout simulations. Here, the model could recapitulate in vivo observed behaviors and thus sustains our results.
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Affiliation(s)
- Nensi Ikonomi
- Institute of Medical Systems Biology, Ulm University, Ulm, Germany
| | - Silke D Kühlwein
- Institute of Medical Systems Biology, Ulm University, Ulm, Germany
| | - Julian D Schwab
- Institute of Medical Systems Biology, Ulm University, Ulm, Germany
| | - Hans A Kestler
- Institute of Medical Systems Biology, Ulm University, Ulm, Germany
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10
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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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11
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Eldred KC, Avelis C, Johnston RJ, Roberts E. Modeling binary and graded cone cell fate patterning in the mouse retina. PLoS Comput Biol 2020; 16:e1007691. [PMID: 32150546 PMCID: PMC7082072 DOI: 10.1371/journal.pcbi.1007691] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2019] [Revised: 03/19/2020] [Accepted: 01/27/2020] [Indexed: 12/20/2022] Open
Abstract
Nervous systems are incredibly diverse, with myriad neuronal subtypes defined by gene expression. How binary and graded fate characteristics are patterned across tissues is poorly understood. Expression of opsin photopigments in the cone photoreceptors of the mouse retina provides an excellent model to address this question. Individual cones express S-opsin only, M-opsin only, or both S-opsin and M-opsin. These cell populations are patterned along the dorsal-ventral axis, with greater M-opsin expression in the dorsal region and greater S-opsin expression in the ventral region. Thyroid hormone signaling plays a critical role in activating M-opsin and repressing S-opsin. Here, we developed an image analysis approach to identify individual cone cells and evaluate their opsin expression from immunofluorescence imaging tiles spanning roughly 6 mm along the D-V axis of the mouse retina. From analyzing the opsin expression of ~250,000 cells, we found that cones make a binary decision between S-opsin only and co-expression competent fates. Co-expression competent cells express graded levels of S- and M-opsins, depending nonlinearly on their position in the dorsal-ventral axis. M- and S-opsin expression display differential, inverse patterns. Using these single-cell data, we developed a quantitative, probabilistic model of cone cell decisions in the retinal tissue based on thyroid hormone signaling activity. The model recovers the probability distribution for cone fate patterning in the mouse retina and describes a minimal set of interactions that are necessary to reproduce the observed cell fates. Our study provides a paradigm describing how differential responses to regulatory inputs generate complex patterns of binary and graded cell fates. The development of a cell in a mammalian tissue is governed by a complex regulatory network that responds to many input signals to give the cell a distinct identity, a process referred to as cell-fate specification. Some of these cell fates have binary on-or-off gene expression patterns, while others have graded gene expression that changes across the tissue. Differentiation of the photoreceptor cells that sense light in the mouse retina provides a good example of this process. Here, we explore how complex patterns of cell fates are specified in the mouse retina by building a computational model based on analysis of a large number of photoreceptor cells from microscopy images of whole retinas. We use the data and the model to study what exactly it means for a cell to have a binary or graded cell fate and how these cell fates can be distinguished from each other. Our study shows how tens-of-thousands of individual photoreceptor cells can be patterned across a complex tissue by a regulatory network, creating a different outcome depending upon the received inputs.
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Affiliation(s)
- Kiara C. Eldred
- Department of Biology, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Cameron Avelis
- Department of Biophysics, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Robert J. Johnston
- Department of Biology, Johns Hopkins University, Baltimore, Maryland, United States of America
- * E-mail: (RJJ); (ER)
| | - Elijah Roberts
- Department of Biophysics, Johns Hopkins University, Baltimore, Maryland, United States of America
- * E-mail: (RJJ); (ER)
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12
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The recent advances in the mathematical modelling of human pluripotent stem cells. SN APPLIED SCIENCES 2020; 2:276. [PMID: 32803125 PMCID: PMC7391994 DOI: 10.1007/s42452-020-2070-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Accepted: 01/17/2020] [Indexed: 12/20/2022] Open
Abstract
Human pluripotent stem cells hold great promise for developments in regenerative medicine and drug design. The mathematical modelling of stem cells and their properties is necessary to understand and quantify key behaviours and develop non-invasive prognostic modelling tools to assist in the optimisation of laboratory experiments. Here, the recent advances in the mathematical modelling of hPSCs are discussed, including cell kinematics, cell proliferation and colony formation, and pluripotency and differentiation.
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13
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Rothenberg EV. Causal Gene Regulatory Network Modeling and Genomics: Second-Generation Challenges. J Comput Biol 2019; 26:703-718. [PMID: 31063008 DOI: 10.1089/cmb.2019.0098] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
Gene regulatory network modeling has played a major role in advancing the understanding of developmental systems, by crystallizing structures of relevant extant information, by formally posing hypothetical functional relationships between network elements, and by offering clear predictive tests to improve understanding of the mechanisms driving developmental progression. Both ordinary differential equation (ODE)-based and Boolean models have also been highly successful in explaining dynamics within subcircuits of more complex processes. In a very small number of cases, gene regulatory network models of much more global scope have been proposed that successfully predict the dynamics of the processes establishing most of an embryonic body plan. Can such successes be expanded to very different developmental systems, including post-embryonic mammalian systems? This perspective discusses several problems that must be solved in more quantitative and predictive theoretical terms, to make this possible. These problems include: the effects of cellular history on chromatin state and how these affect gene accessibility; the dose dependence of activities of many transcription factors (a problem for Boolean models); stochasticity of some transcriptional outputs (a problem for simple ODE models); response timing delays due to epigenetic remodeling requirements; functionally different kinds of repression; and the regulatory syntax that governs responses of genes with multiple enhancers.
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Affiliation(s)
- Ellen V Rothenberg
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California
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14
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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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