scholarly journals Relational state transition dynamics

2008 ◽  
Vol 76 (1) ◽  
pp. 130-144 ◽  
Author(s):  
Giuseppe Scollo ◽  
Giuditta Franco ◽  
Vincenzo Manca
Keyword(s):  
State Transition ◽  
Transition Dynamics ◽  
Relational State

State Transition Dynamics

2011 ◽  
pp. 32-55 ◽  
Author(s):  
Vincenzo Manca ◽  
Giuditta Franco ◽  
Giuseppe Scollo
Keyword(s):  
Dynamical Systems ◽  
State Transition ◽  
Molecular Computing ◽  
Discrete Models ◽  
Classical Dynamics ◽  
Transition Systems ◽  
Transition Dynamics ◽  
Models Of Computation ◽  
Simple Set ◽  
State Transition Systems

Classical dynamics concepts are analysed in the basic mathematical setting of state transition systems where time and space are both completely discrete and no structure is assumed on the state’s space. Interesting relationships between attractors and recurrence are identified and some features of chaos are expressed in simple, set theoretic terms. String dynamics is proposed as a unifying concept for dynamical systems arising from discrete models of computation, together with illustrative examples. The relevance of state transition systems and string dynamics is discussed from the perspective of molecular computing.

scholarly journals Morphological State Transition Dynamics in EGF-Induced Epithelial to Mesenchymal Transition

2019 ◽  
Vol 8 (7) ◽  
pp. 911 ◽  
Author(s):  
Vimalathithan Devaraj ◽  
Biplab Bose
Keyword(s):  
Epithelial To Mesenchymal Transition ◽  
State Transition ◽  
Egf Receptor ◽  
Cancer Cell Line ◽  
Experimental Conditions ◽  
Transition Dynamics ◽  
Mesenchymal Transition ◽  
State Process ◽  
Level Model ◽  
Phenotypic State

Epithelial to Mesenchymal Transition (EMT) is a multi-state process. Here, we investigated phenotypic state transition dynamics of Epidermal Growth Factor (EGF)-induced EMT in a breast cancer cell line MDA-MB-468. We have defined phenotypic states of these cells in terms of their morphologies and have shown that these cells have three distinct morphological states—cobble, spindle, and circular. The spindle and circular states are the migratory phenotypes. Using quantitative image analysis and mathematical modeling, we have deciphered state transition trajectories in different experimental conditions. This analysis shows that the phenotypic state transition during EGF-induced EMT in these cells is reversible, and depends upon the dose of EGF and level of phosphorylation of the EGF receptor (EGFR). The dominant reversible state transition trajectory in this system was cobble to circular to spindle to cobble. We have observed that there exists an ultrasensitive on/off switch involving phospho-EGFR that decides the transition of cells in and out of the circular state. In general, our observations can be explained by the conventional quasi-potential landscape model for phenotypic state transition. As an alternative to this model, we have proposed a simpler discretized energy-level model to explain the observed state transition dynamics.

scholarly journals A Multidimensional Array Representation of State-Transition Model Dynamics

2020 ◽  
Vol 40 (2) ◽  
pp. 242-248
Author(s):  
Eline M. Krijkamp ◽  
Fernando Alarid-Escudero ◽  
Eva A. Enns ◽  
Petros Pechlivanoglou ◽  
M.G. Myriam Hunink ◽  
...  
Keyword(s):  
State Transition ◽  
Disease Incidence ◽  
Health State ◽  
Health States ◽  
Transition Dynamics ◽  
Alternative Approach ◽  
Model Dynamics ◽  
Potential Applications ◽  
State Transition Model ◽  
Multidimensional Array

Cost-effectiveness analyses often rely on cohort state-transition models (cSTMs). The cohort trace is the primary outcome of cSTMs, which captures the proportion of the cohort in each health state over time (state occupancy). However, the cohort trace is an aggregated measure that does not capture information about the specific transitions among health states (transition dynamics). In practice, these transition dynamics are crucial in many applications, such as incorporating transition rewards or computing various epidemiological outcomes that could be used for model calibration and validation (e.g., disease incidence and lifetime risk). In this article, we propose an alternative approach to compute and store cSTMs outcomes that capture both state occupancy and transition dynamics. This approach produces a multidimensional array from which both the state occupancy and the transition dynamics can be recovered. We highlight the advantages of the multidimensional array over the traditional cohort trace and provide potential applications of the proposed approach with an example coded in R to facilitate the implementation of our method.

scholarly journals Power Law versus Exponential State Transition Dynamics: Application to Sleep-Wake Architecture

PLoS ONE ◽  
2010 ◽  
Vol 5 (12) ◽  
pp. e14204 ◽  
Author(s):  
Jesse Chu-Shore ◽  
M. Brandon Westover ◽  
Matt T. Bianchi
Keyword(s):  
Power Law ◽  
State Transition ◽  
Transition Dynamics

scholarly journals Inferring Cell-State Transition Dynamics from Lineage Trees and Endpoint Single-Cell Measurements

Cell Systems ◽  
2016 ◽  
Vol 3 (5) ◽  
pp. 419-433.e8 ◽  
Author(s):  
Sahand Hormoz ◽  
Zakary S. Singer ◽  
James M. Linton ◽  
Yaron E. Antebi ◽  
Boris I. Shraiman ◽  
...  
Keyword(s):  
Single Cell ◽  
State Transition ◽  
Transition Dynamics ◽  
Cell State ◽  
Lineage Trees

scholarly journals A Multidimensional Array Representation of State-Transition Model Dynamics

2019 ◽  
Author(s):  
Eline M. Krijkamp ◽  
Fernando Alarid-Escudero ◽  
Eva A. Enns ◽  
Petros Pechlivanoglou ◽  
M.G. Myriam Hunink ◽  
...  
Keyword(s):  
State Transition ◽  
Disease Incidence ◽  
Health State ◽  
Health States ◽  
Transition Dynamics ◽  
Model Dynamics ◽  
Potential Applications ◽  
State Transition Model ◽  
Multidimensional Matrix ◽  
Transition Models

ABSTRACTCost-effectiveness analyses often rely on cohort state-transition models (cSTMs). The cohort trace is the primary outcome of cSTMs, which captures the proportion of the cohort in each health state over time (state occupancy). However, the cohort trace is an aggregated measure that does not capture the information about the specific transitions among the health states (transition dynamics). In practice, these transition dynamics are crucial in many applications, such as incorporating transition rewards or computing various epidemiological outcomes that could be used for model calibration and validation (e.g. disease incidence and lifetime risk). In this manuscript we propose modifying the transitional cSTMs cohort trace computation to compute and store cSTMs dynamics that capture both state occupancy and transition dynamics. This approach produces a multidimensional matrix from which both the state occupancy and the transition dynamics can be recovered. We highlight the advantages and potential applications of this approach with an example coded in R to facilitate the implementation of our method.

scholarly journals Automatic definition of robust microbiome sub-states in longitudinal data

2018 ◽  
Author(s):  
Beatriz García-Jiménez ◽  
Mark D Wilkinson
Keyword(s):  
Microbial Community ◽  
Longitudinal Data ◽  
State Transition ◽  
Transition Dynamics ◽  
Stable Cluster ◽  
Broad Applicability ◽  
State Identification ◽  
Community Evolution ◽  
Definition Of ◽  
Domain Independent

The analysis of microbiome dynamics would allow us to elucidate patterns within microbial community evolution; however, microbiome state-transition dynamics have been scarcely studied. This is in part because a necessary first-step in such analyses has not been well-defined: how to deterministically describe a microbiome’s ”state”. Clustering in states have been widely studied, although no standard has been concluded yet. We propose a generic, domain-independent and automatic procedure to determine a reliable set of microbiome sub-states within a specific dataset, and with respect to the conditions of the study. The robustness of sub-state identification is established by the combination of diverse techniques for stable cluster verification. We reuse four distinct longitudinal microbiome datasets to demonstrate the broad applicability of our method, analysing results with different taxa subset allowing to adjust it depending on the application goal, and showing that the methodology provides a set of robust sub-states to examine in downstream studies about dynamics in microbiome.

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