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Mass action stoichiometric simulation models: incorporating kinetics and regulation into stoichiometric models.

ABSTRACT: The ability to characterize biological dynamics is important for understanding the integrated molecular processes that underlie normal and abnormal cellular states. The availability of metabolomic data, in addition to new developments in the formal description of dynamic states of networks, has enabled a new data integration approach for building large-scale kinetic networks. We show that dynamic network models can be constructed in a scalable manner using metabolomic data mapped onto stoichiometric models, resulting in mass action stoichiometric simulation (MASS) models. Enzymes and their various functional states are represented explicitly as compounds, or nodes in a stoichiometric network, within this formalism. Analyses and simulations of MASS models explicitly show that regulatory enzymes can control dynamic states of networks in part by binding numerous metabolites at multiple sites. Thus, network functional states are reflected in the fractional states of a regulatory enzyme, such as the fraction of the total enzyme concentration that is in a catalytically active versus inactive state. The feasible construction of MASS models represents a practical means to increase the size, scope, and predictive capabilities of dynamic network models in cell and molecular biology.

SUBMITTER: Jamshidi N

PROVIDER: S-EPMC2808481 | biostudies-literature | 2010 Jan

REPOSITORIES: biostudies-literature

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Mass action stoichiometric simulation models: incorporating kinetics and regulation into stoichiometric models.

Jamshidi Neema N Palsson Bernhard Ø BØ

Biophysical journal 20100101 2

The ability to characterize biological dynamics is important for understanding the integrated molecular processes that underlie normal and abnormal cellular states. The availability of metabolomic data, in addition to new developments in the formal description of dynamic states of networks, has enabled a new data integration approach for building large-scale kinetic networks. We show that dynamic network models can be constructed in a scalable manner using metabolomic data mapped onto stoichiome ...[more]

PMID: 20338839

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Project description:BackgroundDetermining the parameters of a mathematical model from quantitative measurements is the main bottleneck of modelling biological systems. Parameter values can be estimated from steady-state data or from dynamic data. The nature of suitable data for these two types of estimation is rather different. For instance, estimations of parameter values in pathway models, such as kinetic orders, rate constants, flux control coefficients or elasticities, from steady-state data are generally based on experiments that measure how a biochemical system responds to small perturbations around the steady state. In contrast, parameter estimation from dynamic data requires time series measurements for all dependent variables. Almost no literature has so far discussed the combined use of both steady-state and transient data for estimating parameter values of biochemical systems.ResultsIn this study we introduce a constrained optimization method for estimating parameter values of biochemical pathway models using steady-state information and transient measurements. The constraints are derived from the flux connectivity relationships of the system at the steady state. Two case studies demonstrate the estimation results with and without flux connectivity constraints. The unconstrained optimal estimates from dynamic data may fit the experiments well, but they do not necessarily maintain the connectivity relationships. As a consequence, individual fluxes may be misrepresented, which may cause problems in later extrapolations. By contrast, the constrained estimation accounting for flux connectivity information reduces this misrepresentation and thereby yields improved model parameters.ConclusionThe method combines transient metabolic profiles and steady-state information and leads to the formulation of an inverse parameter estimation task as a constrained optimization problem. Parameter estimation and model selection are simultaneously carried out on the constrained optimization problem and yield realistic model parameters that are more likely to hold up in extrapolations with the model.

Project description:Theoretical and empirical accounts suggest that impairments in self-other discrimination processes are likely to promote the expression of hallucinations. Studies using a variety of paradigms involving self-performed actions argue in favor of perspective taking confusion in hallucination-prone subjects. However, our understanding of such processes during adolescence is still at an early stage. The present study thus aims (1) to delineate the neural correlates sustaining mental simulation of actions involving self-performed actions (first-person perspective; 1PP) and other-performed actions (third-person perspective; 3PP) during adolescence (2) to identify atypical activation patterns during 1PP/3PP mental simulation of actions in hallucination-prone adolescents (3) to examine whether differential risk for schizophrenia (clinical vs. genetic) is also associated with differential impairments in the 1PP/3PP mental simulation of actions during adolescence. Twenty-two typically developing controls (Control group; 6 females), 12 hallucination-prone adolescents [auditory hallucination (AH) group; 7 females] and 13 adolescents with 22q11.2 Deletion Syndrome (22q11.2DS group; 4 females) were included in the study. During the fMRI task, subjects were presented with a cue (self-other priming cues) indicating to perform the task using either a first person perspective ("you"-1PP) or a third person perspective ("best friend"-3PP) and then they were asked to mentally simulate actions based on the type of cue. Hallucination-proneness was assessed using a self-report questionnaire [Cardiff Anomalous Perception Scale (CAPS)]. Our results indicated that atypical patterns of cerebral activation, particularly in the key areas of self-other distinction, were found in both groups at risk for auditory hallucinations (AHs and 22q11.2DS). More precisely, adolescents in the AH group presented decreased activations in the right middle occipital gyrus BA19, left cingulate gyrus BA31, and right precuneus BA31 for the 3PP > 1PP contrast. Adolescents in the 22q11.2DS group presented decreased activations in the right superior occipital gyrus BA19, left caudate tail and left precuneus BA7 for the 3PP > 1PP contrast. In comparison to the Control group, only the 22q11.2DS adolescents showed a decreased activation for other-related cues (prime other > prime self contrast) in areas of visual imagery, episodic memory and social cognition. This study characterizes the neural correlates of mental imagery for actions during adolescence, and suggests that a differential risk for hallucination-proneness (clinical vs. genetic) is associated to similar patterns of atypical activations in key areas sustaining self-other discrimination processes. These observations may provide relevant information for future research and prevention strategies with regards to hallucination-proneness during adolescence.

Dataset Information

Mass action stoichiometric simulation models: incorporating kinetics and regulation into stoichiometric models.

Publications

Mass action stoichiometric simulation models: incorporating kinetics and regulation into stoichiometric models.

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