scholarly journals Grip on complexity in chemical reaction networks

2017 ◽  
Vol 13 ◽  
pp. 1486-1497 ◽  
Author(s):  
Albert S Y Wong ◽  
Wilhelm T S Huck

A new discipline of “systems chemistry” is emerging, which aims to capture the complexity observed in natural systems within a synthetic chemical framework. Living systems rely on complex networks of chemical reactions to control the concentration of molecules in space and time. Despite the enormous complexity in biological networks, it is possible to identify network motifs that lead to functional outputs such as bistability or oscillations. To truly understand how living systems function, we need a complete understanding of how chemical reaction networks (CRNs) create function. We propose the development of a bottom-up approach to design and construct CRNs where we can follow the influence of single chemical entities on the properties of the network as a whole. Ultimately, this approach should allow us to not only understand such complex networks but also to guide and control their behavior.

RSC Advances ◽  
2014 ◽  
Vol 4 (32) ◽  
pp. 16777 ◽  
Author(s):  
Michaël Méret ◽  
Daniel Kopetzki ◽  
Thomas Degenkolbe ◽  
Sabrina Kleessen ◽  
Zoran Nikoloski ◽  
...  

1999 ◽  
Vol 10 (4) ◽  
pp. 464-471 ◽  
Author(s):  
Christian Siehs ◽  
Bernd Mayer ◽  
Christian Siehs

2018 ◽  
Vol 15 (145) ◽  
pp. 20180283 ◽  
Author(s):  
Niall Murphy ◽  
Rasmus Petersen ◽  
Andrew Phillips ◽  
Boyan Yordanov ◽  
Neil Dalchau

Methods from stochastic dynamical systems theory have been instrumental in understanding the behaviours of chemical reaction networks (CRNs) arising in natural systems. However, considerably less attention has been given to the inverse problem of synthesizing CRNs with a specified behaviour, which is important for the forward engineering of biological systems. Here, we present a method for generating discrete-state stochastic CRNs from functional specifications, which combines synthesis of reactions using satisfiability modulo theories and parameter optimization using Markov chain Monte Carlo. First, we identify candidate CRNs that have the possibility to produce correct computations for a given finite set of inputs. We then optimize the parameters of each CRN, using a combination of stochastic search techniques applied to the chemical master equation, to improve the probability of correct behaviour and rule out spurious solutions. In addition, we use techniques from continuous-time Markov chain theory to analyse the expected termination time for each CRN. We illustrate our approach by synthesizing CRNs for probabilistically computing majority, maximum and division, producing both known and previously unknown networks, including a novel CRN for probabilistically computing the maximum of two species. In future, synthesis techniques such as these could be used to automate the design of engineered biological circuits and chemical systems.


2009 ◽  
Vol 15 (5) ◽  
pp. 578-597
Author(s):  
Marcello Farina ◽  
Sergio Bittanti

2021 ◽  
Author(s):  
Samuel M. Blau ◽  
Hetal D Patel ◽  
Evan Walter Clark Spotte-Smith ◽  
Xiaowei Xie ◽  
Shyam Dwaraknath ◽  
...  

Modeling reactivity with chemical reaction networks could yield fundamental mechanistic understanding that would expedite the development of processes and technologies for energy storage, medicine, catalysis, and more. Thus far, reaction...


2020 ◽  
Vol 53 (2) ◽  
pp. 11497-11502
Author(s):  
Lőrinc Márton ◽  
Katalin M. Hangos ◽  
Gábor Szederkényi

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