Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

The Staphylococcus Epidermidis RP62A Metabolic Network: Validation and Intervention Strategies

Version 1 : Received: 27 July 2022 / Approved: 2 August 2022 / Online: 2 August 2022 (09:33:09 CEST)

How to cite: Guil, F.; Sánchez-Cid, G.; García, J.M. The Staphylococcus Epidermidis RP62A Metabolic Network: Validation and Intervention Strategies. Preprints 2022, 2022080059 (doi: 10.20944/preprints202208.0059.v1). Guil, F.; Sánchez-Cid, G.; García, J.M. The Staphylococcus Epidermidis RP62A Metabolic Network: Validation and Intervention Strategies. Preprints 2022, 2022080059 (doi: 10.20944/preprints202208.0059.v1).

Abstract

Increasingly, systems biology is gaining relevance in basic and applied research. The combination of computational biology with wet lab produces a synergy that results in an exponential increase in knowledge of biological systems. The study of microorganisms such as Staphylococcus epidermidis RP62A enables the researcher to understand better its metabolic network, which allows the design of effective strategies to treat infections caused by this species or others. S. epidermidis is the second cause of infection in patients with joint implants, so treating its proliferation seems vital for public health. There are different approaches to the analysis of metabolic networks. Flux Balance Analysis (FBA) is one of the most widespread streams. It allows the study of large metabolic networks, their structural properties, the optimization of metabolic flux, and the search for intervention strategies to modify the state of the metabolic network. This work presents the validation of the Staphylococcus epidermidis RP62A metabolic network model elaborated by Díaz-Calvo et al.. Then, we elaborate further on the network analysis’s essential reactions, classifying them. Finally, we introduce some proposals to intervene in the network and design knock-outs.

Keywords

Staphylococcus epidermidis; metabolic network validation; minimal cut sets; knock-outs; systems biology

Subject

BIOLOGY, Other

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