Power of stochastic kinetic models: From biological signaling and antibiotic activities to T cell activation and cancer initiation dynamics

Abstract

All chemical processes exhibit two main universal features. They are stochastic because chemical reactions might happen only after random successful collisions of reacting species, and they are dynamic because the amount of reactants and products changes with time. Since biological processes rely heavily

Thermodynamically consistent determination of free energies and rates in kinetic cycle models

“…Living cells perform a multitude of functions such as cell signaling, reaction catalysis, gene expression, DNA replication, or transmembrane transport ( 1 ). At the molecular level, these cellular processes can be represented as kinetic networks ( 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 ) that consist of distinct states of molecules and the reactions that interconvert between the states. In a kinetic network (or kinetic graph), each reaction is quantified by its forward and reverse rate, and so a set of master equations relates the change in the population of each state to fluxes between states via mass-action kinetics ( 14 ).…”

“…In a kinetic network (or kinetic graph), each reaction is quantified by its forward and reverse rate, and so a set of master equations relates the change in the population of each state to fluxes between states via mass-action kinetics ( 14 ). When parameterized correctly, these networks provide a powerful framework for analyzing nonequilibrium systems in a variety of external conditions; for example, macroscopic observables can be calculated from microscopic quantities in a bottom-up multiscale approach ( 6 , 7 , 9 , 13 ) and pathways within the network can be identified to gain insights into underlying molecular mechanisms ( 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 ).…”