Masa Tsuchiya, Ph.D.

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Current Work:
Elucidation of the genomic mechanism that guides the cell-fate change is one of the fundamental issues of biology. We have elucidated Cell-Fate Genomic Mechanism based on Temporal Development of Genome Expression (high-throughput data) from Embryo to Cancer Development: When and How the Cell-Fate Change Occurs.

The following fundamental issues in biology in the recent papers (below) are addressed:
- Is there any underlying principle that self-regulates the time evolution of whole-genome expression?
- Can we identify a peculiar genome region that guides the super-critical genome and determines the cell-fate change?
- Can we delineate a universal mechanism to understand the processes of the cell-fate change, and to further comprehend when and how it occurs?

Our Essential Finding:
There exists a Center of the Cell Fate in the genome, determining the cell-fate change based on a potential Universal Genomic Mechanism.

Key Words: Genome Expression; Self-Organized Criticality (SOC); Center of Cell Fate; Genome Engine; Genome Attractor; Biological Statistical Mechanics; Transition Theory; Thermodynamically Open System

- Recent Publications (free download):
1. Masa Tsuchiya, Alessandro Giuliani and Kenichi Yoshikawa (2020). Cell-Fate Determination from Embryo to Cancer Development: Genomic Mechanism Elucidated: https://www.mdpi.com/1422-0067/21/13/4581/htm

2.Giovanna Zimatore, Masa Tsuchiya, Midori Hashimoto, Andrzej Kasperski, Alessandro Giuliani (2019). Self-Organization of Whole Gene Expression through Coordinated Chromatin Structural Transition: Validation of Self-Organized Critical Control of Genome Expression:
https://www.biorxiv.org/content/biorxiv/early/2019/11/25/852681.full.pdf
Phone: 81+90-5019-1420

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Alessandro Giuliani

Istituto Superiore di Sanità

Kenichi Yoshikawa

Doshisha University

Andrzej Kasperski

University of Zielona Gora

Jekaterina Erenpreisa

Università degli Studi "La Sapienza" di Roma

Franco Orsucci

University of Amsterdam

Lloyd Demetrius

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Papers by Masa Tsuchiya, Ph.D.

Synchronization between Attractors: Genomic Mechanism of Cell-Fate Change

by Masa Tsuchiya, Ph.D. and Alessandro Giuliani

International Journal of Molecular Sciences, 2023

Herein, we provide a brief overview of complex systems theory approaches to investigate the genom... more Herein, we provide a brief overview of complex systems theory approaches to investigate the genomic mechanism of cell-fate changes. Cell trajectories across the epigenetic landscape, whether in development, environmental responses, or disease progression, are controlled by extensively coordinated genome-wide gene expression changes. The elucidation of the mechanisms underlying these coherent expression changes is of fundamental importance in cell biology and for paving the road to new therapeutic approaches. In previous studies, we pointed at dynamic criticality as a plausible characteristic of genome-wide transition dynamics guiding cell fate. Whole-genome expression develops
an engine-like organization (genome engine) in order to establish an autonomous dynamical system, capable of both homeostasis and transition behaviors. A critical set of genes behaves as a critical
point (CP) that serves as the organizing center of cell-fate change. When the system is pushed away from homeostasis, the state change that occurs at the CP makes local perturbation spread over the
genome, demonstrating self-organized critical (SOC) control of genome expression. Oscillating-Mode genes (which normally keep genome expression on pace with microenvironment fluctuations), when
in the presence of an effective perturbative stimulus, drive the dynamics of synchronization, and thus guide the cell-fate transition.

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A Unified Genomic Mechanism of Cell-Fate Change

by Alessandro Giuliani and Masa Tsuchiya, Ph.D.

M. Kloc, J. Z. Kubiak (eds.), Nuclear, Chromosomal, and Genomic Architecture in Biology and Medicine, Results and Problems in Cell Differentiation 70, , 2022

The purpose of our studies is to elucidate the nature of massive control of the whole genome expr... more The purpose of our studies is to elucidate the nature of massive control of the whole genome expression with a particular emphasis on cell-fate change. The whole genome expression is coordinated by the emergence of a critical point (CP: a peculiar set of biphasic genes) with the genome acting as an integrated dynamical system. In response to stimuli, the genome expression self-organizes into local sub-, near-, and super-critical states, each exhibiting distinct collective behaviors with its center of mass acting as a local attractor, coexisting with the whole genome attractor (GA). The CP serves as the organizing center of cell-fate change, and its activation makes local perturbation to spread over the genome affecting GA. The activation of CP is in turn elicited by genes with elevated temporal variance (oscillating-mode genes), normally in charge to keep genome expression at pace with microenvironment fluctuations. When oscillation exceeds a given threshold, the CP synchronizes with the GA driving genome expression state transition. The expression synchronization wave invading the entire genome is fostered by the fusion-splitting dynamics of silencing pericentromere-associated heterochromatin domains and the consequent folding-unfolding transitions of transcribing euchromatin domains. The proposed mechanism is a unified step toward a time-evolutional transition theory of biological regulation.

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< Contributed Talk 4> Finding Biological Roles of Averaging Effect of Large Numbers: The Existence of Genome Vehicles Guiding Cell Fate Decision

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Time-development of the characteristic behaviors of SOC

A) MCF-7 cells and B) HL-60 cells. At each experimental time point, t... more A) MCF-7 cells and B) HL-60 cells. At each experimental time point, tj (A: 18 time points and B: 13 points; Methods), the (ensemble) average nrmsf value of the CP at t = tj, <nrmsf(CP(tj))> is evaluated at the sandpile-type critical point (top of the sandpile: right panels). In the top center panels, <nrmsf(CP(tj))>, is plotted against the natural log of tj (A: min and B: hr). Error bar represents the sensitivity of <nrmsf(CP(tj))> around the CP(tj), where the bar length corresponds to the change in nrmsf in the x-coordinate (fold-change in expression) from x(CP(tj))—d to x(CP(tj)) + d for a given d (A: d = 0.005; B: d = 0.01; due to much more mRNAs in MCF-7 cells). Temporal averages of <nrmsf(CP(tj))> are A) HRG = 0.094 and EGF = 0.081, and B) DMSO, atRA = 0.078, where an overbar represents temporal average. Note: An overbar for a temporal average is used when ensemble and temporal averages are needed to distinguish. A) MCF-7 cells: The temporal trends of <nrmsf(CP(tj))> are different for HRG and EGF. The onset of scaling divergence (left panels: second and third rows) occurs at around (black dashed line), and reflect the onset of a ‘genome avalanche’ (Methods). B) HL-60 cells: The trends of <nrmsf(CP(tj))> for the responses to both DMSO (black line) and atRA (blue) seem to be similar after 18h (i.e., global perturbation; see section VI). The scaling-divergent behaviors for both DMSO and atRA reveal the collapse of autonomous bistable switch (ABS [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0167912#pone.0167912.ref010" target="_blank">10</a>,<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0167912#pone.0167912.ref011" target="_blank">11</a>]) exhibited by the mass of groups in the scaling region (black solid cycles) for both DMSO and atRA. The onset of divergent behavior does not occur around the CP ( = 0.078), but rather is extended from the CP (see section III). The power law of scaling behavior in the form of 1- <nrmsf> = α<ε>-β is: A) α = 1.29 & β = 0.172 (p< 10−10) for HRG, and α = 1.26 & β = 0.157 (p<10−9) for EGF; B) α = 1.60 & β = 0.301 (p< 10−6) for DMSO, and α = 1.42 & β = 0.232 (p< 10−6) for atRA. Each dot (different time points are shown by colors) represents an average value of A) n = 742 mRNAs for MCF-7 cells, and B) n = 505 mRNAs for HL-60 cells.</p

The SOC control landscape as revealed by a sandpile-to-sandpile transition in mouse embryo development

The development of a sandpile transitional behavior between sequential cell states sugge... more

Genome-state change revealed through erasure of the initial-state criticality in overall expression (cell population level): The grouping of overall expression at t = tj (j ≠ 0) according to the fold change in expression from the initial overall expression (t</i...

This event points to the time when the genome-state change occurs. On the x<... more This event points to the time when the genome-state change occurs. On the x-axis, ln(<ε(t)/ε(0h)>) (t: min or hr) represents the natural log of the ensemble average (< >) of the fold change in expression, ε(t)/ε(t = 0h), and on the y-axis, ln(<ε(t)>) represents the natural log of the ensemble average of expression, <ε(t)>. A) MCF-7 cells: In HRG-stimulated cells (black), the divergent behavior in up-regulation is no longer observed, and the same shape is apparent after 3h. This suggests that the genome-state change occurs at 3h (red) through erasure of the initial-state up-regulation process (partial erasure). In contrast, in EGF-stimulated cells (blue), almost the same sandpile profile remains for up to 36h, which suggests that no genome-state change occurs (see the local perturbation in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0167912#pone.0167912.g014" target="_blank">Fig 14</a>). B) HL-60 cells: A CP is erased at 24h (red) and 48h (red) in DMSO- (black) and atRA-stimulated (blue) cells through the disappearance of divergent behaviors in both up- and down-regulation of the initial state (full erasure), respectively. Furthermore, these plots suggest that the epigenomic states in DMSO- and atRA-stimulated cells become the same after 48h. Note: The Pearson correlations of overall expression between different time points are near-unity (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0167912#pone.0167912.g001" target="_blank">Fig 1A</a>). Each dot represents the average value of A) n = 742 mRNAs for MCF-7 cells, and B) n = 505 mRNAs for HL-60 cells.</p

Bifurcation of CESs in DEAB of the expression

The bifurcations of CESs in DEAB of the expression for 15–20 min are examined with an in... more The bifurcations of CESs in DEAB of the expression for 15–20 min are examined with an incremental change in a segment, v < rmsf < v + r, as an extension of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0097411#pone-0097411-g004" target="_blank">Figure 4</a>, where the range r is set to 0.4 and v is a variable of rmsf. The bifurcation diagrams of the expression (v against the expression; first row) at t = 20 min, and the expression change (v against the change in the expression for 15–20 min; second row) are plotted for HRG (left panel) and EGF (right). The bifurcation diagram of the expression defines the expression level at ln(ε) = 2.075 (lower: low- and upper: high-expression) because of the existence of a valley, which separates the low and high CESs (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0097411#pone-0097411-g006" target="_blank">Figure 6</a>), whereas the bifurcation diagram of the expression change shows three activity levels of CES: ON (positive change in the expression), EQ (near zero) and OFF (negative change in the expression). The bifurcation diagrams clearly show distinct characteristic expression domains between HRG and EGF: static, transit and dynamic domains for rmsf <0.21, 0.21< rmsf <0.42, and 0.42< rmsf for HRG, and static and transit domains for rmsf <0.16 and 0.16< rmsf for EGF (see details in the main text).</p

A coherent expression state (CES), which contains approximately 1000 expression points, ... more

A Quantitative Evaluation of Symmetry Breaking In Nonlinear-Oscillatory System - Based on Flux Dynamics (Effective force) View Point

Panel A): First row- frequency distribution (bin size = 0.1) of mRNAs (n</i&... more Panel A): First row- frequency distribution (bin size = 0.1) of mRNAs (n = 440 mRNAs) at 10 min shows a unimodal to bimodal change around critical point (0.090 <nrmsf< 0.092). The x-axis represents the natural log of mRNA expression, ln(ε(10min)) for a specific range of nrmsf: unimodal (left panel: 0.105 <nrmsf< 0.109), flattened unimodal (middle panel: 0.090 <nrmsf< 0.092), and bimodal (right panel: 0.084 <nrmsf< 0.086). The y-axis represents frequency of expression. Second row- the corresponding probability density profile in the regulatory space—expression vs. expression change in log scale with probability density (color bars) confirms the unimodal to bimodal transition through flattened unimodality, where a black arrow points to the bifurcation of low-expression state (LES). Panel B): First row—the frequency distribution of barcode genes (n = 182 barcodes) shows a unimodal to bimodal change around a critical point (0.108 <nrmsf< 0.112) for unimodal (left), flattened (middle) and bimodal (right) distributions. Second row—this is confirmed by probability density profile in the regulatory space. Both mRNAs and barcode genes on chromosomes reveal the existence of self-similar power law (scaling) behavior around CP analogous to that of the whole mRNA expression (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0128565#pone.0128565.g001" target="_blank">Fig 1</a>), which is an essential characteristic of SOC.</p

Correlation of gene expression profiles

A) The same cell types: Left panels: a near-unity Pearson correlation... more A) The same cell types: Left panels: a near-unity Pearson correlation, r, in whole gene expression (N: total number of mRNAs) within the same cell type is shown for different types of molecular stimulation (first row: HRG- vs. EGF-stimulated MCF-7 cells; second row: DMSO- vs. atRA-stimulated HL-60 cells). Right panels: 313 (n) gene expressions, which have a common probe ID among four transcriptome microarray expression data sets (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0167912#sec005" target="_blank">Methods</a>) also show a near-unity Pearson correlation within the same cell type. B) Different cell types (n = 313 gene expressions): The near-unity Pearson correlation between independent samples of the same cell type breaks down when the gene expression profiles come from different (HL-60 and MCF-7) cell types. ε(t) represents the ensemble of expression at time point t (N: the whole set; n: an ensemble set) and ln(ε(t)) represents its natural logarithm, where the natural log of an individual expression value is taken. Plots show t = 90min for MCF-7-stimulated cells and t = 18h for HL-60-stimulated cells.</p

Unimodal to bimodal frequency distribution for DEAB of the expression

The profiles of the frequency distribution of the expression (ln(<... more The profiles of the frequency distribution of the expression (ln(ε(t))) from 15 min to 20 min change from unimodal to bimodal for A) high-variance expression (the root mean square fluctuation, rmsf >0.42) and B) low-variance expression (rmsf <0.42). First row: the HRG response for rmsf >0.42 shows a peak-shift of unimodal profiles from t = 15 min (blue histogram) to t = 20 min (red) with a change in the lower to higher value of the expression, while binomial frequency distributions between 15 min (blue polygonal line) and 20 min (red histogram) almost perfectly overlap each other for rmsf <0.42. Second row: the EGF response shows almost the perfect overlap of profiles for both unimodal (rmsf >0.42) and bimodal (rmsf <0.42) distributions, which suggests that there is no temporal averaging response, consistent with DEAB of the expression for the EGF response (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0097411#pone-0097411-g001" target="_blank">Figure 1A</a>). For all histograms in this report, the bin size is set to 0.05.</p

Genetic landscape of the characteristic expression domains

Each row (A: HRG and B: EGF) corresponds to frequency distributions of mRNA expression (... more Each row (A: HRG and B: EGF) corresponds to frequency distributions of mRNA expression (first) and genetic landscapes (second: side view). In the genetic landscape, a static domain with a valley is characterized by two CESs: A) HES1(EQ) and LES1(OFF) for rmsf <0.21; and B) HES(EQ) and LES(EQ) for rmsf <0.16, a transit domain is characterized by A) HES1(EQ) for 0.21< rmsf <0.42; and B) HES(EQ) for rmsf >0.16, and a dynamic domain is characterized by three CESs: A) LES2(ON), HES2(ON) and HES1(EQ) for rmsf >0.42, which is the result of superimposition of the genetic landscapes between 15 min and 20 min (right panel in the second row); a state shift occurs from LES2(ON) at 15 min to HES2(ON) at 20 min, consistent with the unimodal shift of the frequency distribution (A: first row). The red arrow points to the valley to separate the low and high CESs. The activity level of a coherent expression state (ON, EQ and OFF) refers to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0097411#pone-0097411-g005" target="_blank">Figure 5</a>.</p

Schematic illustration of genetic criticality in DEAB of the expression

The putative genetic energy potential (15 min: blue; 20 min: red) with a fixed change in... more

Genomic expression dynamics revealed through a flux analysis that includes crosstalk with the environment: A) Average values of interaction flux (colored arrows) for MCF-7 and HL-60 cells show that a sub-critical state acts as an internal ‘source’, where IN flux from the environment is distribute...

In contrast, a super-critical state acts as an internal ‘sink’ that receives IN fluxes f... more In contrast, a super-critical state acts as an internal ‘sink’ that receives IN fluxes from other critical states, and the same amount of expression flux is sent to the environment, due to the average flux balance (Methods). Furthermore, the formation of a dominant cyclic state flux is revealed between super- and sub-critical states through the environment. The average interaction flux is represented as i-j: interaction flux of the ith critical state with the jth critical state (i, j = 1: super- (Super; red), 2: near- (Near; blue), 3: sub-critical state (Sub; purple)), and a colored arrow for an internal i-j interaction points in the direction of interaction with the relative amount of flux (see details in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0167912#pone.0167912.t002" target="_blank">Table 2</a>; positive and negative values represent incoming and outgoing flux, respectively, at a critical state; outline and base colors are based on the ith critical and jth critical states, respectively). E represents the internal nucleus environment. B) An early flux dynamic event in the HRG response resulting from the HRG interaction flux dynamics (see C) are shown. Interaction flux dynamics i< = j (or i = >j; color based on j) represent the interaction flux from the jth critical state to the ith critical state or vice versa. The interaction fluxes (see HRG in C; the flux direction changes at y = 0) align to suppress the cyclic state flux at 10min (the first point in C), where the interaction flux shows 1< = E, 1 = >2, and 1 = >3 at the super-critical state, 2< = E, 2< = 1, 2 = >3 at the near-critical state, and 3 = >E, 3< = 1, 3< = 2 at the sub-critical state. They then align to enhance the cyclic state flux at 45 min (5th point in C). This change in the dynamic flux structure is due to the global perturbation at 15-20min (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0167912#pone.0167912.g014" target="_blank">Fig 14</a>; section VI). At each node, the net flux (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0167912#pone.0167912.g011" target="_blank">Fig 11</a>; 10min: first point; 45min: 5th point) is indicated as IN for net incoming flux (y >0), OUT for outgoing flux (y <0), or Balance (y~0). Note: The average flux balance at each node is maintained, but not at individual time points. C) Notably, for MCF-7 cell fates (HRG and EGF), near-synchronous interaction flux dynamics are seen at sub-critical states. The plots further show that the overall patterns are similar between the same cell types, which again supports cell-type-specific SOC control. Dashed lines represent interaction flux dynamics for i-j (color based on j).</p

Simulation of Flux Dynamics on Linearly Coupled Harmonic Oscillators

Barcode genes and temporal response of super-critical genes on chromosomes

A) Genes of critical phases are mapped into a human chromosome (UCSC hg19), corrected fo... more A) Genes of critical phases are mapped into a human chromosome (UCSC hg19), corrected for the multiplicity of probes (mRNA expression) such as multiple probes of a gene due to an mRNA variant; the x- and y-axes show the chromosome position and chromosome number including X and Y chromosomes, respectively. Based on the distinct physical properties of the critical states, genes on a chromosome are clustered to form barcode genes (yellow: super-critical (N = 1262); red: near-critical (N = 3072); blue: sub-critical (N = 2952); gray: the unknown chromosome region), where the boundary of a barcode is defined as when two neighboring genes belong to different critical states; for instance, a sequence of.. S-S-S-T-T-S.. has two barcode genes, S-S-S and T-T; D: dynamic domain: super-critical; T: transit domain: near-critical; S: static domain: sub-critical (the plot was provided by I. E. Motoike). B) Frequency distribution of the correlation length of barcode genes for critical phases (the colors are the same as those in the barcode) shows the size of barcode genes within the range from kbp to Mbp for all states. The correlation length is estimated as the base length from the start codon of the initial gene to the end codon of the last gene within a barcode; the x- and y-axes show the common logarithm of a base pair and the number of barcodes (bin size: one-tenth of a unit length). C) Pearson correlation, P(t0;tj) of super-critical genes on chromosomes, which shows temporally four most responsive chromosomes (chromosome 2: black, 14: blue, 16: red, 19: green) with the singular like responses at 15–30 min (see also <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0128565#pone.0128565.g004" target="_blank">Fig 4A</a>). The x-axis represents log10(tj[min]).</p

Synchronization of barcode genes on chromosomes with mRNA coherent expression dynamics

Panel A): super-critical state, Panel B): sub-critical state for barcode genes. The pane... more Panel A): super-critical state, Panel B): sub-critical state for barcode genes. The panels present the probability density functions of barcode genes on the regulatory space (first row: x: change in the natural logarithm of expression at 10–15 min; y: natural logarithm of expression at 10 min); second row: x: change at 15–20 min; y: at 15 min; third row: x: change at 15–20 min; y: at 20 min), showing a similar opposite oscillation between sub-critical and super-critical states in mRNA expression dynamics (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0128565#pone.0128565.g006" target="_blank">Fig 6A</a>). Panels C-E): mRNAs versus barcode genes. Panels F-H): a variety of barcode genes. Temporal Pearson correlations for sub-critical as well as near-critical barcode genes confirm the ON-OFF synchronization with mRNA expression dynamics, the same in terms of stochasticity and coherent oscillation (coherent stochastic oscillation: CSO; D: stochasticity; E: coherent oscillation), whereas super-critical barcodes reveal the opposite phase of CSO, showing similar temporal trend to critical states of mRNA (mRNAs (dark colors): subcritical: black; near-critical: blue; super-critical: red; barcodes: the corresponding lighter colors). Panels C & F: Pearson correlation, P(t0;tj); Panels D & G: P(t1-t0;tj+1-tj), and Panels E & H: P(tj;tj+1−tj). Temporal Pearson correlations (F-H) confirm that barcodes without single gene (multiple genes: dashed lines) clearly follow similar trends of temporal correlations of the whole barcodes of critical states (solid lines), and thus similar correlation response to the mRNA dynamics. Correlation trends of barcodes are shown to be clearly distinct from random-barcodes: one (green dotted line: random-barcode I) for average value of randomly selected barcodes (n = 200) out of the whole barcodes combining critical states with 100 repeats–forming Gaussian distribution, and another (brown dotted line: random-barcode II) for barcode genes (N = 3130) of randomly mixed critical states, where genes on chromosome are randomly selected, and for each selected barcode, the number of elements is assigned randomly from 1 to 4 neighboring genes.</p

Local and global perturbations in self-organizing genome-wide expression: The kinetic energy self-flux dynamics (y-axis) for the CM of a critical state (see section VI) exhibit clear energy-dissipative behavior

Notably, the results show the occurrence of global and local perturbations of self-organ... more Notably, the results show the occurrence of global and local perturbations of self-organization. Pulse-like global perturbations show a transition from IN to OUT net kinetic energy flux or vice versa (IN-OUT switching) in more than one critical state: at 15-20min, HRG; at 12-18h, DMSO; and at 2-4h (significant) and 12-18h, atRA. In contrast, local perturbation is observed in the EGF response for up to 36h: there is only a marked response in the super-critical state, and almost no response in the other states (i.e., the dynamics of the CM of critical states are localized around their average: <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0167912#pone.0167912.g011" target="_blank">Fig 11</a>). The results suggest that the global and local perturbations differentiate MCF-7 cell fates, whereas global perturbations drive the state change in HL-60 cells (see section I).</p

Schematic illustration of autonomous bistable switch (ABS) with genetic ‘energy profile’ in DEAB of the expression change

First row: the schematic illustration depicts the temporal development of ABS showing th... more

Synchronization between Attractors: Genomic Mechanism of Cell-Fate Change

by Masa Tsuchiya, Ph.D. and Alessandro Giuliani

International Journal of Molecular Sciences, 2023

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A Unified Genomic Mechanism of Cell-Fate Change

by Alessandro Giuliani and Masa Tsuchiya, Ph.D.

M. Kloc, J. Z. Kubiak (eds.), Nuclear, Chromosomal, and Genomic Architecture in Biology and Medicine, Results and Problems in Cell Differentiation 70, , 2022

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< Contributed Talk 4> Finding Biological Roles of Averaging Effect of Large Numbers: The Existence of Genome Vehicles Guiding Cell Fate Decision

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Time-development of the characteristic behaviors of SOC

The SOC control landscape as revealed by a sandpile-to-sandpile transition in mouse embryo development

The development of a sandpile transitional behavior between sequential cell states sugge... more

Bifurcation of CESs in DEAB of the expression

A coherent expression state (CES), which contains approximately 1000 expression points, ... more

A Quantitative Evaluation of Symmetry Breaking In Nonlinear-Oscillatory System - Based on Flux Dynamics (Effective force) View Point

Correlation of gene expression profiles

Unimodal to bimodal frequency distribution for DEAB of the expression

Genetic landscape of the characteristic expression domains

Schematic illustration of genetic criticality in DEAB of the expression

The putative genetic energy potential (15 min: blue; 20 min: red) with a fixed change in... more

Simulation of Flux Dynamics on Linearly Coupled Harmonic Oscillators

Barcode genes and temporal response of super-critical genes on chromosomes

Synchronization of barcode genes on chromosomes with mRNA coherent expression dynamics

Schematic illustration of autonomous bistable switch (ABS) with genetic ‘energy profile’ in DEAB of the expression change

First row: the schematic illustration depicts the temporal development of ABS showing th... more

A Quantitative Evaluation of Symmetry Breaking in Nonlinear-Oscillatory System (Version 5) Based on Flux Dynamics (Effective force) View Point

by Masa Tsuchiya, Ph.D. and Kenichi Yoshikawa

Presentation, 2018

The aim of this presentation is to provide further foundations on the flux dynamics method [1,2] ... more The aim of this presentation is to provide further foundations on the flux dynamics method [1,2] for the dynamics of genome expression, which exhibits interaction of coherent dynamics of distinct expression response domains (critical states) in terms of autonomous self-organization with a critical transition (Autonomous Self-Organized Criticality: ASOC).

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Masa Tsuchiya, Ph.D.

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