Dataset Information

The effect of feedback on pathway transient response.

ABSTRACT: The effect of variation of the rate of input of material on the transient behaviour of metabolic pathways is examined. This reveals the existence of three transient times which make up the overall pathway transient. Two of these have been described previously and represent the times required for the accumulation of the free intermediate pool and the pool of enzyme-bound intermediate. They are state functions and as such are independent of the way in which the steady state was reached. The third is attributable to the variation in the rate of input of material to the pathway. It is dependent on three further factors. These are (a) the time required for the initial enzyme to reach its own steady state, (b) substrate depletion and (c) feedback. The description of the transient is: (Formula: see text) where V0 represents the rate of input and Vss represents the steady-state flux. The transient time associated with the transition between steady-states is shown to be a simple function of the transients for the establishment of each steady state from rest and may be expressed as: tau = tau b-Va/Vb . tau a where Va and Vb refer to the fluxes in the two steady states and tau a and tau b represent the transient times for the establishment of each of the steady-states from rest. The total pathway transient may now be completely defined as: (formula: see text) where summation over all intermediates, I, is implied. The significance of this to the analysis of pathway behaviour is discussed with more general examples of pathway transient analysis.

SUBMITTER: Easterby JS

PROVIDER: S-EPMC1153110 | biostudies-other | 1986 Feb

REPOSITORIES: biostudies-other

ACCESS DATA

Similar Datasets

Project description:Background: Event related potential (ERP) components, such as P3, N2, and FRN, are potential metrics for assessing feedback response as a form of performance monitoring. Most research studies investigate these ERP components using clinical or research-grade electroencephalography (EEG) systems. Wearable EEGs, which are an affordable alternative, have the potential to assess feedback response using ERPs but have not been sufficiently evaluated. Feedback-related ERPs also have not been scientifically evaluated in interactive settings that are similar to daily computer use. In this study, a consumer-grade wearable EEG system was assessed for its feasibility to collect feedback-related ERPs through an interactive software module that provided an environment in which users were permitted to navigate freely within the program to make decisions. Methods: The recording hardware, which costs < $1,500 in total, incorporated the OpenBCI Cyton Board with Daisy chain, a consumer-grade EEG system that costs $949 USD. Seventeen participants interacted with an oddball paradigm and an interactive module designed to elicit feedback-related ERPs. The features of interests for the oddball paradigm were the P3 and N2 components. The features of interests for the interactive module were the P3, N2, and FRN components elicited in response to positive, neutral, and two types of negative feedback. The FRN was calculated by subtracting the positive feedback response from the negative feedback responses. Results: The P3 and N2 components of the oddball paradigm indicated statistically significant differences between infrequent targets and frequent targets which is in line with current literature. The P3 and N2 components elicited in the interactive module indicated statistically significant differences between positive, neutral, and negative feedback responses. There were no significant differences between the FRN types and significant interactions with channel group and FRN type. Conclusion: The OpenBCI Cyton, after some modifications, shows potential for eliciting and assessing P3, N2, and FRN components, which are important indicators for performance monitoring, in an interactive setting.

Dataset Information

The effect of feedback on pathway transient response.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets