scholarly journals Early warning signals in chemical reaction networks

2020 ◽  
Vol 56 (26) ◽  
pp. 3725-3728
Author(s):  
Oliver R. Maguire ◽  
Albert S. Y. Wong ◽  
Jan Harm Westerdiep ◽  
Wilhelm T. S. Huck
Keyword(s):  
Complex Systems ◽  
Chemical Reaction ◽  
Early Warning ◽  
Reaction Network ◽  
Chemical Reaction Networks ◽  
Chemical Reaction Network ◽  
Reaction Networks ◽  
Warning Signals ◽  
Early Warning Signals ◽  
Complex Chemical

Many natural and man-made complex systems display early warning signals when close to an abrupt shift in behaviour. Here we show that such early warning signals appear in a complex chemical reaction network.

Inference of Chemical Reaction Networks Using Hybrid S-system Models

2007 ◽  
Vol 2 (1) ◽  
Author(s):  
Dominic P Searson ◽  
Mark J Willis ◽  
Simon J Horne ◽  
Allen R Wright
Keyword(s):  
Chemical Reaction ◽  
Power Law ◽  
Network Inference ◽  
Batch Reactor ◽  
Reaction Network ◽  
Chemical Reaction Networks ◽  
Chemical Reaction Network ◽  
Reaction Networks ◽  
Fed Batch ◽  
System Equations

This article demonstrates, using simulations, the potential of the S-system formalism for the inference of unknown chemical reaction networks from simple experimental data, such as that typically obtained from laboratory scale reaction vessels. Virtually no prior knowledge of the products and reactants is assumed. S-systems are a power law formalism for the canonical approximate representation of dynamic non-linear systems. This formalism has the useful property that the structure of a network is dictated only by the values of the power law parameters. This means that network inference problems (e.g. inference of the topology of a chemical reaction network) can be recast as parameter estimation problems. The use of S-systems for network inference from data has been reported in a number of biological fields, including metabolic pathway analysis and the inference of gene regulatory networks. Here, the methodology is adapted for use as a hybrid modelling tool to facilitate the reverse engineering of chemical reaction networks using time series concentration data from fed-batch reactor experiments. The principle of the approach is demonstrated with noisy simulated data from fed-batch reactor experiments using a hypothetical reaction network comprising 5 chemical species involved in 4 parallel reactions. A co-evolutionary algorithm is employed to evolve the structure and the parameter values of the S-system equations concurrently. The S-system equations are then interpreted in order to construct a network diagram that accurately reflects the underlying chemical reaction network.

A Synthon Approach to Artificial Chemistry

2009 ◽  
Vol 15 (1) ◽  
pp. 89-103 ◽  
Author(s):  
Tom Lenaerts ◽  
Hugues Bersini
Keyword(s):  
Chemical Reaction ◽  
Reaction Network ◽  
Logical Structure ◽  
Chemical Reaction Networks ◽  
The Other ◽  
Reaction Networks ◽  
Complex Chemical ◽  
Topological Evolution ◽  
The One ◽  
Insight Into

A coevolutionary model is discussed that incorporates the logical structure of constitutional chemistry and its kinetics on the one hand and the topological evolution of the chemical reaction network on the other hand. The motivation for designing this model is twofold. First, experiments that are to provide insight into chemical problems should be expressed in a syntax that remains as close as possible to real chemistry. Second, the study of physical properties of the complex chemical reaction networks requires growing models that incorporate features realistic from a biochemical perspective. In this article the theory and algorithms underlying the coevolutionary model are explained, and two illustrative examples are provided. These examples show that one needs to be careful in making general claims concerning the structure of chemical reaction networks.

scholarly journals Detecting and distinguishing tipping points using spectral early warning signals

2020 ◽  
Vol 17 (170) ◽  
pp. 20200482
Author(s):  
T. M. Bury ◽  
C. T. Bauch ◽  
M. Anand
Keyword(s):  
Complex Systems ◽  
Early Warning ◽  
Dynamical Regime ◽  
Tipping Points ◽  
Warning Signals ◽  
Early Warning Signals ◽  
Density Dependent ◽  
Critical Transitions ◽  
Fold Bifurcation ◽  
Characteristic Signal

Theory and observation tell us that many complex systems exhibit tipping points—thresholds involving an abrupt and irreversible transition to a contrasting dynamical regime. Such events are commonly referred to as critical transitions. Current research seeks to develop early warning signals (EWS) of critical transitions that could help prevent undesirable events such as ecosystem collapse. However, conventional EWS do not indicate the type of transition, since they are based on the generic phenomena of critical slowing down. For instance, they may fail to distinguish the onset of oscillations (e.g. Hopf bifurcation) from a transition to a distant attractor (e.g. Fold bifurcation). Moreover, conventional EWS are less reliable in systems with density-dependent noise. Other EWS based on the power spectrum (spectral EWS) have been proposed, but they rely upon spectral reddening, which does not occur prior to critical transitions with an oscillatory component. Here, we use Ornstein–Uhlenbeck theory to derive analytic approximations for EWS prior to each type of local bifurcation, thereby creating new spectral EWS that provide greater sensitivity to transition proximity; higher robustness to density-dependent noise and bifurcation type; and clues to the type of approaching transition. We demonstrate the advantage of applying these spectral EWS in concert with conventional EWS using a population model, and show that they provide a characteristic signal prior to two different Hopf bifurcations in data from a predator–prey chemostat experiment. The ability to better infer and differentiate the nature of upcoming transitions in complex systems will help humanity manage critical transitions in the Anthropocene Era.

scholarly journals Early warning signals of recovery in complex systems

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Christopher F. Clements ◽  
Michael A. McCarthy ◽  
Julia L. Blanchard
Keyword(s):  
Complex Systems ◽  
Early Warning ◽  
Warning Signals ◽  
Early Warning Signals

A possible construction of a complex chemical reaction network

1977 ◽  
Vol 45 (3) ◽  
pp. 225-233 ◽  
Author(s):  
István Nemes ◽  
Tamás Vidóczy ◽  
László Botár ◽  
Dezsö Gál
Keyword(s):  
Chemical Reaction ◽  
Reaction Network ◽  
Chemical Reaction Network ◽  
Complex Chemical ◽  
Complex Chemical Reaction

From systems biology to systems chemistry: metabolomic procedures enable insight into complex chemical reaction networks in water

RSC Advances ◽  
2014 ◽  
Vol 4 (32) ◽  
pp. 16777 ◽  
Author(s):  
Michaël Méret ◽  
Daniel Kopetzki ◽  
Thomas Degenkolbe ◽  
Sabrina Kleessen ◽  
Zoran Nikoloski ◽  
...  
Keyword(s):  
Systems Biology ◽  
Chemical Reaction ◽  
Chemical Reaction Networks ◽  
Reaction Networks ◽  
Complex Chemical ◽  
Complex Chemical Reaction ◽  
Systems Chemistry ◽  
Insight Into

scholarly journals An algorithm for building and integrating dynamic systems based on reaction networks

2021 ◽  
Vol 8 (1) ◽  
pp. 49
Author(s):  
Petar Chernev
Keyword(s):  
Chemical Reaction ◽  
Dynamic Systems ◽  
Network Theory ◽  
Dynamic Models ◽  
Reaction Network ◽  
Chemical Reaction Network ◽  
Reaction Networks ◽  
Complex Interactions ◽  
Model Complex ◽  
Chemical Reaction Network Theory

In the present work we give an overview and implementation of an algorithm for building and integrating dynamic systems from reaction networks. Reaction networks have their roots in chemical reaction network theory, but their nature is general enough that they can be applied in many fields to model complex interactions. Our aim is to provide a simple to use program that allows for quick prototyping of dynamic models based on a system of reactions. After introducing the concept of a reaction and a reaction network in a general way, not necessarily connected to chemistry, we outlay the algorithm for building its associated system of ODEs. Finally, we give a few example usages where we examine a range of growth-decay models in the context of reaction networks.

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