A Simple Self-Maintaining Metabolic System: Robustness, Autocatalysis, Bistability
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It is shown here that a simple (M,R)-system consisting of three interlocking catalytic cycles, with every catalyst produced by the system itself, can both establish a non-trivial steady state and maintain this despite continuous loss of the catalysts by irreversible degradation.Abstract:
A living organism must not only organize itself from within; it must also maintain its organization in the face of changes in its environment and degradation of its components. We show here that a simple (M,R)-system consisting of three interlocking catalytic cycles, with every catalyst produced by the system itself, can both establish a non-trivial steady state and maintain this despite continuous loss of the catalysts by irreversible degradation. As long as at least one catalyst is present at a sufficient concentration in the initial state, the others can be produced and maintained. The system shows bistability, because if the amount of catalyst in the initial state is insufficient to reach the non-trivial steady state the system collapses to a trivial steady state in which all fluxes are zero. It is also robust, because if one catalyst is catastrophically lost when the system is in steady state it can recreate the same state. There are three elementary flux modes, but none of them is an enzyme-maintaining mode, the entire network being necessary to maintain the two catalysts.read more
Citations
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References
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Book
Autopoiesis and Cognition: The Realization of the Living
TL;DR: In this paper, Maturana et al. describe the organization of the living and discuss the role of the human brain in the brain's ability to learn and adapt to the environment.
Journal ArticleDOI
COPASI---a COmplex PAthway SImulator
Stefan Hoops,Sven Sahle,Ralph Gauges,Christine Lee,Jürgen Pahle,Natalia Simus,Mudita Singhal,Liang Xu,Pedro Mendes,Ursula Kummer +9 more
TL;DR: COPASI is presented, a platform-independent and user-friendly biochemical simulator that offers several unique features, and numerical issues with these features are discussed; in particular, the criteria to switch between stochastic and deterministic simulation methods, hybrid deterministic-stochastic methods, and the importance of random number generator numerical resolution in Stochastic simulation.