Attractor Metabolic Networks

Abstract

BackgroundThe experimental observations and numerical studies with dissipative metabolic networks have shown that cellular enzymatic activity self-organizes spontaneously leading to the emergence of a Systemic Metabolic Structure in the cell, characterized by a set of different enzymatic reactions always locked into active states (metabolic core) while the rest of the catalytic processes are only intermittently active. This global metabolic structure was verified for Escherichia coli, Helicobacter pylori and Saccharomyces cerevisiae, and it seems to be a common key feature to all cellular organisms. In concordance with these observations, the cell can be considered a complex metabolic network which mainly integrates a large ensemble of self-organized multienzymatic complexes interconnected by substrate fluxes and regulatory signals, where multiple autonomous oscillatory and quasi-stationary catalytic patterns simultaneously emerge. The network adjusts the internal metabolic activities to the external change by means of flux plasticity and structural plasticity.Methodology/Principal FindingsIn order to research the systemic mechanisms involved in the regulation of the cellular enzymatic activity we have studied different catalytic activities of a dissipative metabolic network under different external stimuli. The emergent biochemical data have been analysed using statistical mechanic tools, studying some macroscopic properties such as the global information and the energy of the system. We have also obtained an equivalent Hopfield network using a Boltzmann machine. Our main result shows that the dissipative metabolic network can behave as an attractor metabolic network.Conclusions/SignificanceWe have found that the systemic enzymatic activities are governed by attractors with capacity to store functional metabolic patterns which can be correctly recovered from specific input stimuli. The network attractors regulate the catalytic patterns, modify the efficiency in the connection between the multienzymatic complexes, and stably retain these modifications. Here for the first time, we have introduced the general concept of attractor metabolic network, in which this dynamic behavior is observed.

Long-term memory in the migration movements of enucleatedAmoeba proteus

“…For instance, the single cellamoeboid Physarumpolycephalum can find the minimum-length solution between two points in a maze 19 , forming an optimized network closely approaching the purposely designed Tokyo railway system 20 to resolve complex multiobjective foraging problems 21 , and to learn and recall past events 22 . These and other advances in the study of the cellular systemic metabolic properties 23,24 are bringing forth a new conceptual framework that defines the cell essentially as adynamic metabolic reactorin which most of the constituent biomolecules are synthesized and destroyed through cyclic biochemical reactions driven by enzymes.In turn, the enzymatic processes are integrated in functional networks that, on a global level, shape one of the most complex dynamic systems in nature 16 . As a consequence, millions of ions and molecules are continuously transformed with extreme precision through tightly regulated reactions, all of which are confined to a reduced space of microns.…”

Inferring metabolic phenotypes from the exometabolome through a thermodynamic variational principle

“…The thermodynamics of reaction networks, a subject that goes back to at least [30] and it has recently gained attention from a stochastic perspective [31,32] suggests that steady states should be characterized in terms of their entropy production. Since such principles should hold for cellular systems as well [16,[33][34][35], it is interesting to study what type of information can be obtained about a cellʼs metabolism from applying such ideas. In this work, we have studied the problem of inferring the metabolic phenotype from the level of metabolites in the extracellular space (i.e.…”

Evidences of conditioned behavior in Amoeba Proteus

“…However, different studies at the cellular level suggest some metabolic processes that could be involved. For instance, Hopfield-like attractor dynamics have been observed in self-organized enzymatic networks which have the capacity to store functional catalytic patterns that can be correctly recovered by specific input stimuli 8 . In addition, evidences of functional memory which can be embedded in multiple stable molecular marks during epigenetic processes have been reported 9 .…”