162 research outputs found
Theory of Robustness of Irreversible Differentiation in a Stem Cell System: Chaos hypothesis
Based on extensive study of a dynamical systems model of the development of a
cell society, a novel theory for stem cell differentiation and its regulation
is proposed as the ``chaos hypothesis''. Two fundamental features of stem cell
systems - stochastic differentiation of stem cells and the robustness of a
system due to regulation of this differentiation - are found to be general
properties of a system of interacting cells exhibiting chaotic intra-cellular
reaction dynamics and cell division, whose presence does not depend on the
detail of the model. It is found that stem cells differentiate into other cell
types stochastically due to a dynamical instability caused by cell-cell
interactions, in a manner described by the Isologous Diversification theory.
This developmental process is shown to be stable not only with respect to
molecular fluctuations but also with respect to removal of cells. With this
developmental process, the irreversible loss of multipotency accompanying the
change from a stem cell to a differentiated cell is shown to be characterized
by a decrease in the chemical diversity in the cell and of the complexity of
the cellular dynamics. The relationship between the division speed and this
loss of multipotency is also discussed. Using our model, some predictions that
can be tested experimentally are made for a stem cell system.Comment: 31 pages, 10 figures, submitted to Jour. Theor. Bio
Pluripotency, differentiation, and reprogramming: A gene expression dynamics model with epigenetic feedback regulation
Characterization of pluripotent states, in which cells can both self-renew
and differentiate, and the irreversible loss of pluripotency are important
research areas in developmental biology. In particular, an understanding of
these processes is essential to the reprogramming of cells for biomedical
applications, i.e., the experimental recovery of pluripotency in differentiated
cells. Based on recent advances in dynamical-systems theory for gene
expression, we propose a gene-regulatory-network model consisting of several
pluripotent and differentiation genes. Our results show that cellular-state
transition to differentiated cell types occurs as the number of cells
increases, beginning with the pluripotent state and oscillatory expression of
pluripotent genes. Cell-cell signaling mediates the differentiation process
with robustness to noise, while epigenetic modifications affecting gene
expression dynamics fix the cellular state. These modifications ensure the
cellular state to be protected against external perturbation, but they also
work as an epigenetic barrier to recovery of pluripotency. We show that
overexpression of several genes leads to the reprogramming of cells, consistent
with the methods for establishing induced pluripotent stem cells. Our model,
which involves the inter-relationship between gene expression dynamics and
epigenetic modifications, improves our basic understanding of cell
differentiation and reprogramming
Universal relationship in gene-expression changes for cells in steady-growth state
Cells adapt to different conditions by altering a vast number of components,
which is measurable using transcriptome analysis. Given that a cell undergoing
steady growth is constrained to sustain each of its internal components, the
abundance of all the components in the cell has to be roughly doubled during
each cell division event. From this steady-growth constraint, expression of all
genes is shown to change along a one-parameter curve in the state space in
response to the environmental stress. This leads to a global relationship that
governs the cellular state: By considering a relatively moderate change around
a steady state, logarithmic changes in expression are shown to be proportional
across all genes, upon alteration of stress strength, with the proportionality
coefficient given by the change in the growth rate of the cell. This theory is
confirmed by transcriptome analysis of Escherichia Coli in response to several
stresses.Comment: 7 pages (5 figures) + 2 Supplementary pages (figures
Chaotic expression dynamics implies pluripotency: when theory and experiment meet
<p>Abstract</p> <p>Background</p> <p>During normal development, cells undergo a unidirectional course of differentiation that progressively decreases the number of cell types they can potentially become. Pluripotent stem cells can differentiate into several types of cells, but terminally differentiated cells cannot differentiate any further. A fundamental problem in stem cell biology is the characterization of the difference in cellular states, e.g., gene expression profiles, between pluripotent stem cells and terminally differentiated cells.</p> <p>Presentation of the hypothesis</p> <p>To address the problem, we developed a dynamical systems model of cells with intracellular protein expression dynamics and interactions with each other. According to extensive simulations, cells with irregular (chaotic) oscillations in gene expression dynamics have the potential to differentiate into other cell types. During development, such complex oscillations are lost successively, leading to a loss of pluripotency. These simulation results, together with recent single-cell-level measurements in stem cells, led us to the following hypothesis regarding pluripotency: Chaotic oscillation in the expression of some genes leads to cell pluripotency and affords cellular state heterogeneity, which is supported by itinerancy over quasi-stable states. Differentiation stabilizes these states, leading to a loss of pluripotency.</p> <p>Testing the hypothesis</p> <p>To test the hypothesis, it is crucial to measure the time course of gene expression levels at the single-cell level by fluorescence microscopy and fluorescence-activated cell sorting (FACS) analysis. By analyzing the time series of single-cell-level expression data, one can distinguish whether the variation in protein expression level over time is due only to stochasticity in expression dynamics or originates from the chaotic dynamics inherent to cells, as our hypothesis predicts. By further analyzing the expression in differentiated cell types, one can examine whether the loss of pluripotency is accompanied by a loss of oscillation.</p> <p>Implications of the hypothesis</p> <p>Recovery of pluripotency from determined cells is a long-standing aspiration, from both scientific and clinical perspectives. Our hypothesis suggests a feasible route to recover the potential to differentiate, i.e., by increasing the variety of expressed genes to restore chaotic expression dynamics, as is consistent with the recent generation of induced pluripotent stem (iPS) cells.</p> <p>Reviewers</p> <p>This article was reviewed by David Krakauer, Jeroen van Zon (nominated by Rob de Boer), and Williams S. Hlavacek.</p
Evolutionary origin of power-laws in Biochemical Reaction Network; embedding abundance distribution into topology
The evolutionary origin of universal statistics in biochemical reaction
network is studied, to explain the power-law distribution of reaction links and
the power-law distributions of chemical abundances. Using cell models with
catalytic reaction network, we find evidence that the power-law distribution in
abundances of chemicals emerges by the selection of cells with higher growth
speeds. Through the further evolution, this inhomogeneity in chemical
abundances is shown to be embedded in the distribution of links, leading to the
power-law distribution. These findings provide novel insights into the nature
of network evolution in living cells.Comment: 11 pages, 3 figure
Time-programmable drug dosing allows the manipulation, suppression and reversal of antibiotic drug resistance in vitro
Multi-drug strategies have been attempted to prolong the efficacy of existing antibiotics, but with limited success. Here we show that the evolution of multi-drug-resistant Escherichia coli can be manipulated in vitro by administering pairs of antibiotics and switching between them in ON/OFF manner. Using a multiplexed cell culture system, we find that switching between certain combinations of antibiotics completely suppresses the development of resistance to one of the antibiotics. Using this data, we develop a simple deterministic model, which allows us to predict the fate of multi-drug evolution in this system. Furthermore, we are able to reverse established drug resistance based on the model prediction by modulating antibiotic selection stresses. Our results support the idea that the development of antibiotic resistance may be potentially controlled via continuous switching of drugs
Oscillatory Protein Expression Dynamics Endows Stem Cells with Robust Differentiation Potential
The lack of understanding of stem cell differentiation and proliferation is a fundamental problem in developmental biology. Although gene regulatory networks (GRNs) for stem cell differentiation have been partially identified, the nature of differentiation dynamics and their regulation leading to robust development remain unclear. Herein, using a dynamical system modeling cell approach, we performed simulations of the developmental process using all possible GRNs with a few genes, and screened GRNs that could generate cell type diversity through cell-cell interactions. We found that model stem cells that both proliferated and differentiated always exhibited oscillatory expression dynamics, and the differentiation frequency of such stem cells was regulated, resulting in a robust number distribution. Moreover, we uncovered the common regulatory motifs for stem cell differentiation, in which a combination of regulatory motifs that generated oscillatory expression dynamics and stabilized distinct cellular states played an essential role. These findings may explain the recently observed heterogeneity and dynamic equilibrium in cellular states of stem cells, and can be used to predict regulatory networks responsible for differentiation in stem cell systems
Experimental optimization of probe length to increase the sequence specificity of high-density oligonucleotide microarrays
<p>Abstract</p> <p>Background</p> <p>High-density oligonucleotide arrays are widely used for analysis of genome-wide expression and genetic variation. Affymetrix GeneChips – common high-density oligonucleotide arrays – contain perfect match (PM) and mismatch (MM) probes generated by changing a single nucleotide of the PMs, to estimate cross-hybridization. However, a fraction of MM probes exhibit larger signal intensities than PMs, when the difference in the amount of target specific hybridization between PM and MM probes is smaller than the variance in the amount of cross-hybridization. Thus, pairs of PM and MM probes with greater specificity for single nucleotide mismatches are desirable for accurate analysis.</p> <p>Results</p> <p>To investigate the specificity for single nucleotide mismatches, we designed a custom array with probes of different length (14- to 25-mer) tethered to the surface of the array and all possible single nucleotide mismatches, and hybridized artificially synthesized 25-mer oligodeoxyribonucleotides as targets in bulk solution to avoid the effects of cross-hybridization. The results indicated the finite availability of target molecules as the probe length increases. Due to this effect, the sequence specificity of the longer probes decreases, and this was also confirmed even under the usual background conditions for transcriptome analysis.</p> <p>Conclusion</p> <p>Our study suggests that the optimal probe length for specificity is 19–21-mer. This conclusion will assist in improvement of microarray design for both transcriptome analysis and mutation screening.</p
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