What makes biochemical networks tick?
In view of the increasing number of reported concentration oscillations in living cells, methods are needed that can identify the causes of these oscillations. These causes always derive from the influences that concentrations have on reaction rates. The influences reach over many molecular reaction steps and are defined by the detailed molecular topology of the network. So-called 'autoinfluence paths', which quantify the influence of one molecular species upon itself through a particular path through the network, can have positive or negative values. The former bring a tendency towards instability. In this molecular context a new graphical approach is presented that enables the classification of network topologies into oscillophoretic and nonoscillophoretic, i.e. into ones that can and ones that cannot induce concentration oscillations. The network topologies are formulated in terms of a set of uni-molecular and bi-molecular reactions, organized into branched cycles of directed reactions, and presented as graphs. Subgraphs of the network topologies are then classified as negative ones (which can) and positive ones (which cannot) give rise to oscillations. A subgraph is oscillophoretic (negative) when it contains more positive than negative autoinfluence paths. Whether the former generates oscillations depends on the values of the other subgraphs, which again depend on the kinetic parameters. An example shows how this can be established. By following the rules of our new approach, various oscillatory kinetic models can be constructed and analyzed, starting from the classified simplest topologies and then working towards desirable complications. Realistic biochemical examples are analyzed with the new method, illustrating two new main classes of oscillophore topologies.
Goldstein
Boris N
BN
Institute of Theoretical and Experimental Biophysics, Russian Academy of Sciences, Pushchino, Moscow Region, Russia.
Ermakov
Gennady
G
Centelles
Josep J
JJ
Westerhoff
Hans V
HV
Cascante
Marta
M
D000465:Algorithms; D000818:Animals; D002468:Cell Physiological Phenomena; D003198:Computer Simulation; D025461:Feedback, Physiological / Q000502:physiology*; D005786:Gene Expression Regulation / Q000502:physiology*; D006706:Homeostasis; D006801:Humans; D008954:Models, Biological; D009716:Numerical Analysis, Computer-Assisted; D015398:Signal Transduction / Q000502:physiology*