In silico labeling reveals the time-dependent label half-life and transit-time in dynamical systems

Abstract

BackgroundMathematical models of dynamical systems facilitate the computation of characteristic properties that are not accessible experimentally. In cell biology, two main properties of interest are (1) the time-period a protein is accessible to other molecules in a certain state - its half-life - and (2) the time it spends when passing through a subsystem - its transit-time. We discuss two approaches to quantify the half-life, present the novel method of in silico labeling, and introduce the label half-life and label transit-time. The developed method has been motivated by laboratory tracer experiments. To investigate the kinetic properties and behavior of a substance of interest, we computationally label this species in order to track it throughout its life cycle. The corresponding mathematical model is extended by an additional set of reactions for the labeled species, avoiding any double-counting within closed circuits, correcting for the influences of upstream fluxes, and taking into account combinatorial multiplicity for complexes or reactions with several reactants or products. A profile likelihood approach is used to estimate confidence intervals on the label half-life and transit-time.ResultsApplication to the JAK-STAT signaling pathway in Epo-stimulated BaF3-EpoR cells enabled the calculation of the time-dependent label half-life and transit-time of STAT species. The results were robust against parameter uncertainties.ConclusionsOur approach renders possible the estimation of species and label half-lives and transit-times. It is applicable to large non-linear systems and an implementation is provided within the PottersWheel modeling framework (http://www.potterswheel.de).

Profile likelihood in systems biology

“…In the Systems Biology setting, the parameter profile likelihood has been proposed for the calculation of confidence intervals and in addition for the investigation of parameter identifiability [4,5]. It is increasingly applied in recent years [6][7][8][9][10][11][12].…”

Dissolved Gases and Ice Fracturing During the Freezing of a Multicellular Organism: Lessons from Tardigrades

“… 33 , 34 Previous research in anhydrobiosis has shown that during desiccation, the cell promotes glycolysis with subsequent amino acid anabolism. 40 This has to do with polyhydroxy compounds, mainly carbohydrates. 41 The survival strategy is the accumulation of high concentrations of small carbohydrates, such as trehalose, sucrose, maltose, or raffinose, before drying.…”

“… 39 Four types of cryptobiosis: anhydrobiosis (lack of water), anoxybiosis (lack of oxygen), cryobiosis (freezing), and osmobiosis (high solute concentration) is common for most. 39 , 40 Tardigrades are resistant to extreme environmental changes, including desiccation, freezing, and radiation. 35 , 40 , 44 , 61 In adverse environments (during cryptobiosis), they reduce their metabolic activity to undetectable levels 40 , 60 and become dehydrated while forming the tun state.…”

“… 39 , 40 Tardigrades are resistant to extreme environmental changes, including desiccation, freezing, and radiation. 35 , 40 , 44 , 61 In adverse environments (during cryptobiosis), they reduce their metabolic activity to undetectable levels 40 , 60 and become dehydrated while forming the tun state. Such state allows them to essentially stop all bioprocesses for periods that can be a substantial portion of their life span, until environmental conditions improve and the tardigrades return to their active state.…”

“…Such state allows them to essentially stop all bioprocesses for periods that can be a substantial portion of their life span, until environmental conditions improve and the tardigrades return to their active state. 40 …”