The challenges of modeling mammalian biocomplexity

Area of Science:

• Genomics
• Systems Biology
• Pharmacogenomics

Background:

• Understanding human genetic factors' link to disease risk and drug response is crucial.
• Predicting biological outcomes from genomic data is challenging due to complex gene-environment interactions.
• Current methods struggle to integrate diverse biological information for holistic analysis.

Purpose of the Study:

• To highlight the need for novel approaches in systems biology.
• To address the challenges in measuring and modeling complex biological interactions.
• To propose a framework for understanding 'superorganism' metabolic processes.

Main Methods:

• Review of current challenges in genomic data analysis.
• Discussion of systems biology approaches for integrating multivariate data.
• Conceptualization of modeling metabolic compartments in symbiotic mammalian systems.

Main Results:

• Identified the nonlinear and conditional influence of environmental factors on disease risk.
• Highlighted the difficulty in analytical and bioinformatic modeling of complex datasets.
• Proposed viewing complex animals as 'superorganisms' with interactive metabolic processes.

Conclusions:

• Novel approaches are required to measure and model metabolic compartments.
• Integration of cell types and genomes connected by cometabolic processes is essential.
• Advanced systems biology is key to understanding gene-environment-drug interactions.