Trapped liquid drop in a microchannel: Multiple stable states

2013 ◽  
Vol 87 (6) ◽  
Author(s):  
Zhengjia Wang ◽  
Cheng-Chung Chang ◽  
Siang-Jie Hong ◽  
Yu-Jane Sheng ◽  
Heng-Kwong Tsao
Keyword(s):  
Liquid Drop ◽  
Stable States ◽  
Multiple Stable States

scholarly journals Population dynamics of synthetic Terraformation motifs

2016 ◽  
Author(s):  
Ricard V. Solé ◽  
Raúl Montañez ◽  
Salvador Duran Nebreda ◽  
Daniel Rodriguez-Amor ◽  
Blai Vidiella ◽  
...  
Keyword(s):  
Habitat Degradation ◽  
Ecological Engineering ◽  
Ecological Interactions ◽  
Stable States ◽  
Population Extinction ◽  
Intensive Exploitation ◽  
Engineering Designs ◽  
Synthetic Organisms ◽  
Multiple Stable States ◽  
Catastrophic Shifts

Ecosystems are complex systems, currently experiencing several threats associated with global warming, intensive exploitation, and human-driven habitat degradation. Such threats are pushing ecosystems to the brink of collapse. Because of a general presence of multiple stable states, including states involving population extinction, and due to intrinsic nonlinearities associated with feedback loops, collapse can occur in a catastrophic manner. Such catastrophic shifts have been suggested to pervade many of the future transitions affecting ecosystems at many different scales. Many studies have tried to delineate potential warning signals predicting such ongoing shifts but little is known about how such transitions might be effectively prevented. It has been recently suggested that a potential path to prevent or modify the outcome of these transitions would involve designing synthetic organisms and synthetic ecological interactions that could push these endangered systems out of the critical boundaries. Four classes of such ecological engineering designs orTerraformation motifshave been defined in a qualitative way. Here we develop the simplest mathematical models associated with these motifs, defining the expected stability conditions and domains where the motifs shall properly work.

scholarly journals Multiple stable states of tree cover in a global land surface model due to a fire-vegetation feedback

2016 ◽  
Vol 43 (12) ◽  
pp. 6324-6331 ◽  
Author(s):  
G. Lasslop ◽  
V. Brovkin ◽  
C. H. Reick ◽  
S. Bathiany ◽  
S. Kloster
Keyword(s):  
Land Surface ◽  
Land Surface Model ◽  
Tree Cover ◽  
Surface Model ◽  
Stable States ◽  
Vegetation Feedback ◽  
Global Land ◽  
Multiple Stable States

scholarly journals How adaptive immunity constrains the composition and fate of large bacterial populations

2018 ◽  
Vol 115 (32) ◽  
pp. E7462-E7468 ◽  
Author(s):  
Madeleine Bonsma-Fisher ◽  
Dominique Soutière ◽  
Sidhartha Goyal
Keyword(s):  
Stochastic Dynamics ◽  
Phage Genome ◽  
Population Level ◽  
Abundance Distribution ◽  
Stable States ◽  
Bacterial Populations ◽  
Multiple Stable States ◽  
Time Invariant ◽  
Microbial Systems ◽  
Rank Abundance

Features of the CRISPR-Cas system, in which bacteria integrate small segments of phage genome (spacers) into their DNA to neutralize future attacks, suggest that its effect is not limited to individual bacteria but may control the fate and structure of whole populations. Emphasizing the population-level impact of the CRISPR-Cas system, recent experiments show that some bacteria regulate CRISPR-associated genes via the quorum sensing (QS) pathway. Here we present a model that shows that from the highly stochastic dynamics of individual spacers under QS control emerges a rank-abundance distribution of spacers that is time invariant, a surprising prediction that we test with dynamic spacer-tracking data from literature. This distribution depends on the state of the competing phage–bacteria population, which due to QS-based regulation may coexist in multiple stable states that vary significantly in their phage-to-bacterium ratio, a widely used ecological measure to characterize microbial systems.

scholarly journals Computing and Stability in Cortical Networks

2004 ◽  
Vol 16 (7) ◽  
pp. 1385-1412 ◽  
Author(s):  
Peter E. Latham ◽  
Sheila Nirenberg
Keyword(s):  
Cortical Neurons ◽  
Unstable State ◽  
Stable States ◽  
Attractor Networks ◽  
Firing Rates ◽  
The Face ◽  
Multiple Stable States ◽  
Background State ◽  
Do So

Cortical neurons are predominantly excitatory and highly interconnected. In spite of this, the cortex is remarkably stable: normal brains do not exhibit the kind of runaway excitation one might expect of such a system. How does the cortex maintain stability in the face of this massive excitatory feedback? More importantly, how does it do so during computations, which necessarily involve elevated firing rates? Here we address these questions in the context of attractor networks—networks that exhibit multiple stable states, or memories. We find that such networks can be stabilized at the relatively low firing rates observed in vivo if two conditions are met: (1) the background state, where all neurons are firing at low rates, is inhibition dominated, and (2) the fraction of neurons involved in a memory is above some threshold, so that there is sufficient coupling between the memory neurons and the background. This allows “dynamical stabilization” of the attractors, meaning feedback from the pool of background neurons stabilizes what would otherwise be an unstable state. We suggest that dynamical stabilization may be a strategy used for a broad range of computations, not just those involving attractors.

Elephants and Fire as Causes of Multiple Stable States in the Serengeti-Mara Woodlands

10.2307/5037 ◽  
1990 ◽  
Vol 59 (3) ◽  
pp. 1147 ◽  
Author(s):  
Holly T. Dublin ◽  
A.R.E. Sinclair ◽  
J. McGlade
Keyword(s):  
Stable States ◽  
Multiple Stable States

scholarly journals Modeling signaling-dependent pluripotent cell states with boolean logic can predict cell fate transitions

2017 ◽  
Author(s):  
Ayako Yachie-Kinoshita ◽  
Kento Onishi ◽  
Joel Ostblom ◽  
Eszter Posfai ◽  
Janet Rossant ◽  
...  
Keyword(s):  
Stem Cells ◽  
Cell Fate ◽  
Expression Patterns ◽  
Embryonic Stem ◽  
Boolean Logic ◽  
Connected Components ◽  
Dynamic Heterogeneity ◽  
Stable States ◽  
Input Signals ◽  
Multiple Stable States

Pluripotent stem cells (PSCs) exist in multiple stable states, each with specific cellular properties and molecular signatures. The process by which pluripotency is either maintained or destabilized to initiate specific developmental programs is poorly understood. We have developed a model to predict stabilized PSC gene regulatory network (GRN) states in response to combinations of input signals. While previous attempts to model PSC fate have been limited to static cell compositions, our approach enables simulations of dynamic heterogeneity by combining an Asynchronous Boolean Simulation (ABS) strategy with simulated single cell fate transitions using Strongly Connected Components (SCCs). This computational framework was applied to a reverse-engineered and curated core GRN for mouse embryonic stem cells (mESCs) to simulate responses to LIF, Wnt/β-catenin, FGF/ERK, BMP4, and Activin A/Nodal pathway activation. For these input signals, our simulations exhibit strong predictive power for gene expression patterns, cell population composition, and nodes controlling cell fate transitions. The model predictions extend into early PSC differentiation, demonstrating, for example, that a Cdx2-high/Oct4-low state can be efficiently and robustly generated from mESCs residing in a naïve and signal-receptive state sustained by combinations of signaling activators and inhibitors.One Sentence SummaryPredictive control of pluripotent stem cell fate transitions

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