Dataset Information

Predicting biological system objectives de novo from internal state measurements.

ABSTRACT:

Background

Optimization theory has been applied to complex biological systems to interrogate network properties and develop and refine metabolic engineering strategies. For example, methods are emerging to engineer cells to optimally produce byproducts of commercial value, such as bioethanol, as well as molecular compounds for disease therapy. Flux balance analysis (FBA) is an optimization framework that aids in this interrogation by generating predictions of optimal flux distributions in cellular networks. Critical features of FBA are the definition of a biologically relevant objective function (e.g., maximizing the rate of synthesis of biomass, a unit of measurement of cellular growth) and the subsequent application of linear programming (LP) to identify fluxes through a reaction network. Despite the success of FBA, a central remaining challenge is the definition of a network objective with biological meaning.

Results

We present a novel method called Biological Objective Solution Search (BOSS) for the inference of an objective function of a biological system from its underlying network stoichiometry as well as experimentally-measured state variables. Specifically, BOSS identifies a system objective by defining a putative stoichiometric "objective reaction," adding this reaction to the existing set of stoichiometric constraints arising from known interactions within a network, and maximizing the putative objective reaction via LP, all the while minimizing the difference between the resultant in silico flux distribution and available experimental (e.g., isotopomer) flux data. This new approach allows for discovery of objectives with previously unknown stoichiometry, thus extending the biological relevance from earlier methods. We verify our approach on the well-characterized central metabolic network of Saccharomyces cerevisiae.

Conclusion

We illustrate how BOSS offers insight into the functional organization of biochemical networks, facilitating the interrogation of cellular design principles and development of cellular engineering applications. Furthermore, we describe how growth is the best-fit objective function for the yeast metabolic network given experimentally-measured fluxes.

SUBMITTER: Gianchandani EP

PROVIDER: S-EPMC2258290 | biostudies-literature | 2008 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Predicting biological system objectives de novo from internal state measurements.

Gianchandani Erwin P EP Oberhardt Matthew A MA Burgard Anthony P AP Maranas Costas D CD Papin Jason A JA

BMC bioinformatics 20080124

<h4>Background</h4>Optimization theory has been applied to complex biological systems to interrogate network properties and develop and refine metabolic engineering strategies. For example, methods are emerging to engineer cells to optimally produce byproducts of commercial value, such as bioethanol, as well as molecular compounds for disease therapy. Flux balance analysis (FBA) is an optimization framework that aids in this interrogation by generating predictions of optimal flux distributions i ...[more]

PMID: 18218092

Similar Datasets

Project description:The possibility of using two different isotopomers, for the incorporation of isotopically labeled amino acids, was explored to enable longitudinal studies of de novo synthesis of two export liver proteins, albumin and fibrinogen. The agreement of the synthesis rates between the two different labels was evaluated along with the reproducibility of repeated experiments using different time intervals. Healthy volunteers were studied in a standardized fed state. Protocol A (n = 10) involved two measurements 48 hours apart. Protocol B (n = 6) involved three measurements at baseline and five hours and then seven days after the initial measurement. De novo synthesis of albumin and fibrinogen by the incorporation of D5-phenylalanine or D8-phenylalanine were measured using the flooding dose technique. Albumin and fibrinogen were isolated from plasma using standard techniques. Fractional and absolute synthesis rates were calculated. Repeated measurements employing the two isotoptomers showed good agreement for albumin fractional synthesis rate after 48 hours (p = 0.92) and after 7 days (p = 0.99), with a coefficient of variation of 5.9% when using the same isotopic label. For fibrinogen, the coefficient of variation for the fractional synthesis rate employing the same isotopic label was 16.6%. Repeated measurements after 48 hours and seven days showed less agreement although there was no statistical difference (P = 0.32 and P = 0.30 respectively). Repeated measurement after five hours showed a statistical significant difference for the fractional synthesis rate of fibrinogen (p = 0.008) but not for albumin (p = 0.12). Repeated measurements of albumin de novo synthesis more than 48 hours apart show acceptable agreement using either one or two different isotopic labels. For fibrinogen the larger intra-individual scatter necessitates larger study groups to detect changes in longitudinal studies. Repeated measurements within 48 hours need to be validated further.

Dataset Information

Predicting biological system objectives de novo from internal state measurements.

Background

Results

Conclusion

Publications

Predicting biological system objectives de novo from internal state measurements.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets