Virtual-organism toy-model as a tool to develop bioinformatics approaches of Systems Biology for medical-target discovery

TL;DR

This paper introduces a simplified virtual organism model based on physiological rules to aid in developing bioinformatics tools for Systems Biology, aiming to improve understanding of human cell complexity with less data.

Contribution

It presents a novel toy-model of a virtual organism that combines topological and functional analysis to facilitate easier and more effective study of biological systems.

Findings

Combining topological and functional analysis improves virtual organism understanding.

The model requires less input data for meaningful insights.

Potential to enhance real-life biological research and target discovery.

Abstract

Systems Biology has emerged in the last years as a new holistic approach based on the global understanding of cells instead of only being focused on their individual parts (genes or proteins), to better understand the complexity of human cells. Since the Systems Biology still does not provide the most accurate answers to our questions due to the complexity of cells and the limited quality of available information to perform a good gene/protein map analysis, we have created simpler models to ensure easier analysis of the map that represents the human cell. Therefore, a virtual organism has been designed according to the main physiological rules for humans in order to replicate the human organism and its vital functions. This toy model was constructed by defining the topology of its genes/proteins and the biological functions associated to it. There are several examples of these toy models that emulate natural processes to perform analysis of the virtual life in order to design the best strategy to understand real life. The strategy applied in this study combines topological and functional analysis integrating the knowledge about the relative position of a node among the others in the map with the conclusions generated by mathematical models that reproduce functional data of the virtual organism. Our results demonstrate that the combination of both strategies allows better understanding of our virtual organism even with the lower input of information needed and therefore it can be a potential tool to better understand the real life.