Systems biology of stem cells: three useful perspectives to help overcome the paradigm of linear pathways

Huang, Sui

doi:10.1098/rstb.2011.0008

Cited by 92 publications

(79 citation statements)

References 78 publications

(120 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Attractor states display robustness against stochastic fluctuations, such that a clonal population of cells appears as a bounded "cloud" of cells when the gene expression pattern of each cell is displayed as a point in a high-dimensional gene expression space (7). This robustness is the reason why cells can collectively be identified as a distinct phenotype, representing what we know as "cell type," despite the substantial cell-cell variability.…”

mentioning

confidence: 99%

“…In this theoretical framework, the distinct cell states or substates, such as multipotent states or terminal cell types in normal tissues or the stem-like (tumor-initiating) or metastatic state in cancer, are all attractor states: they are distinct "self-stabilizing" configurations of gene activities across the genome that arise because of constraints in the collective gene expression imposed by gene-gene regulatory interactions of the GRN (1,7). Attractor states display robustness against stochastic fluctuations, such that a clonal population of cells appears as a bounded "cloud" of cells when the gene expression pattern of each cell is displayed as a point in a high-dimensional gene expression space (7).…”

mentioning

confidence: 99%

See 1 more Smart Citation

Dynamics inside the cancer cell attractor reveal cell heterogeneity, limits of stability, and escape

Wennborg

Aurell

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

111

100

View full text Add to dashboard Cite

The observed intercellular heterogeneity within a clonal cell population can be mapped as dynamical states clustered around an attractor point in gene expression space, owing to a balance between homeostatic forces and stochastic fluctuations. These dynamics have led to the cancer cell attractor conceptual model, with implications for both carcinogenesis and new therapeutic concepts. Immortalized and malignant EBV-carrying B-cell lines were used to explore this model and characterize the detailed structure of cell attractors. Any subpopulation selected from a population of cells repopulated the whole original basin of attraction within days to weeks. Cells at the basin edges were unstable and prone to apoptosis. Cells continuously changed states within their own attractor, thus driving the repopulation, as shown by fluorescent dye tracing. Perturbations of key regulatory genes induced a jump to a nearby attractor. Using the Fokker-Planck equation, this cell population behavior could be described as two virtual, opposing influences on the cells: one attracting toward the center and the other promoting diffusion in state space (noise). Transcriptome analysis suggests that these forces result from high-dimensional dynamics of the gene regulatory network. We propose that they can be generalized to all cancer cell populations and represent intrinsic behaviors of tumors, offering a previously unidentified characteristic for studying cancer.cancer cell attractor | cell heterogeneity | edge cells | gene regulatory network | cell population dynamics

show abstract

mentioning

confidence: 99%

mentioning

confidence: 99%

Dynamics inside the cancer cell attractor reveal cell heterogeneity, limits of stability, and escape

Wennborg

Aurell

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

111

100

View full text Add to dashboard Cite

show abstract

“…While many possible combinations of individual gene expression states appear combinatorially possible, only a subset of these exists in reality. Furthermore, cells expressing similar levels of pluripotency genes may have reached that position from different starting points and be on different trajectories [79].…”

Section: Mechanisms That Underpin Pluripotencymentioning

confidence: 99%

“…This has led to the hypothesis that heterogeneity allows differentiation to proceed while retaining a pluripotent population [64]. This idea is pursued in relation to the transition of ES cells to EpiSCs by Osorno & Chambers. In this issue, Huang [79] discusses such heterogeneity using mathematical representations of cell identities in terms of the quantitative state of gene expression in a cell at a given time. While many possible combinations of individual gene expression states appear combinatorially possible, only a subset of these exists in reality.…”

Section: Mechanisms That Underpin Pluripotencymentioning

confidence: 99%

The evolving biology of cell reprogramming

Wilmut

Sullivan²,

Chambers³

2011

Phil. Trans. R. Soc. B

View full text Add to dashboard Cite

Modern stem cell biology has achieved a transformation that was thought by many to be every bit as unattainable as the ancient alchemists' dream of transforming base metals into gold. Exciting opportunities arise from the process known as ‘cellular reprogramming’ in which cells can be reliably changed from one tissue type to another. This is enabling novel approaches to more deeply investigate the fundamental basis of cell identity. In addition, new opportunities have also been created to study (perhaps even to treat) human genetic and degenerative diseases. Specific cell types that are affected in inherited disease can now be generated from easily accessible cells from the patient and compared with equivalent cells from healthy donors. The differences in cellular phenotype between the two may then be identified, and assays developed to establish therapies that prevent the development or progression of disease symptoms. Cellular reprogramming also has the potential to create new cells to replace those whose death or dysfunction causes disease symptoms. For patients suffering from inherited cases of degenerative diseases like Parkinson's disease or amyotrophic lateral sclerosis (also known as motor neuron disease), the future realization of such cell-based therapies would truly be worth its weight in gold. However, before this enormous potential can become a reality, several significant biological and technical challenges must be overcome. Furthermore, to maintain the credibility of the scientific community with the general public, it is important that hope-inspiring advances are not over-hyped. The papers in this issue of the Philosophical Transactions of the Royal Society B : Biological Sciences cover many areas relevant to this topic. In this Introduction , we provide an overall context in which to consider these individual papers.

show abstract

“…Together, these linked conditions increase the risk to develop metabolic syndrome and type-2 diabetes mellitus. To understand how such complex syndrome emerges, it is necessary to use an integrative, system-level and dynamic approach that takes into consideration: the non-linearity of the interactions, the strong effect of the environment, the constant crosstalk and feed forward or feed-back interactions among the genetic and nongenetic components involved, and the synchronic or concerted nature of various regulatory events and conditions involved [5][6][7][8][9]. Most studies have focused on the direct relationship between macrophages and obesity [10], meanwhile, important questions concerning the relationship between obesity, insulin, and CD4+ T cell type populations and plastic changes among them remain unaddressed.…”

Section: Introductionmentioning

confidence: 99%

The CD4+ T cell regulatory network mediates inflammatory responses during acute hyperinsulinemia: a simulation study

Martinez-Sanchez

Hiriart

Álvarez-Buylla

2016

Preprint

View full text Add to dashboard Cite

Background: Obesity is frequently linked to insulin resistance, high insulin levels, chronic inflammation, and alterations in the behaviour of CD4+ T cells. Despite the biomedical importance of this condition, the system-level mechanisms that alter CD4+ T cell differentiation and plasticity are not well understood. Results: We model how hyperinsulinemia alters the dynamics of the CD4+ T regulatory network, and this, in turn, modulates cell differentiation and plasticity. Different polarizing microenvironments are simulated under basal and high levels of insulin to assess impacts on cell-fate attainment and robustness in response to transient perturbations. In the presence of high levels of insulin Th1 and Th17 become more stable to transient perturbations, and their basin sizes are augmented, Tr1 cells become less stable or disappear, while TGFβ producing cells remain unaltered. Hence, the model provides a dynamic system-level framework and explanation to further understand the documented and apparently paradoxical role of TGFβ in both inflammation and regulation of immune responses, as well as the emergence of the adipose Treg phenotype. Furthermore, our simulations provide new predictions on the impact of the microenvironment in the coexistence of the different cell types, suggesting that in pro-Th1, pro-Th2 and pro-Th17 environments effector and regulatory cells can coexist, but that high levels of insulin severely diminish regulatory cells, especially in a pro-Th17 environment. Conclusions: This work provides a first step towards a system-level formal and dynamic framework to integrate further experimental data in the study of complex inflammatory diseases.

show abstract

Systems biology of stem cells: three useful perspectives to help overcome the paradigm of linear pathways

Cited by 92 publications

References 78 publications

Dynamics inside the cancer cell attractor reveal cell heterogeneity, limits of stability, and escape

Dynamics inside the cancer cell attractor reveal cell heterogeneity, limits of stability, and escape

The evolving biology of cell reprogramming

The CD4+ T cell regulatory network mediates inflammatory responses during acute hyperinsulinemia: a simulation study

Contact Info

Product

Resources

About