APPLICATION AND EFFECT OF ARTIFICIAL INTELLIGENCE FOR THE FUNCTIONING OF METABOLIC BIOENGINEERING IN SYSTEMS BIOLOGY

Abstract

Emerging synthetic biology capabilities have the potential to dramatically improve

our ability to engineer biological systems. However, a fundamental obstacle to

realizing this potential is the inability to accurately predict biological behavior

following modification of the corresponding genotype. This method systematically

leverages arbitrary amounts of new data to improve predictions, and does not assume

any particular interactions, but rather implicitly chooses the most predictive ones.

Metabolic bioengineering aims to maximize the production of bio-economically

important substances (compounds, enzymes, or other proteins) through the

optimization of the genetics, cellular processes and growth conditions of

microorganisms. To achieve this goal, a large system of experimental techniques,

compound libraries, computational methods and data resources, including multi-

omics data, are used. The recent advent of multi-omics systems biology approaches

significantly impacted the field by opening new avenues to perform dynamic and

large-scale analyses that deepen our knowledge on the manipulations. However,

given the great potential of transcriptomics, proteomics, and metabolomics,

integrating the data to gain a more comprehensive understanding is not a trivial task.

Key words: Artificial intelligence, systems biology, machine learning, metabolic

bioengineering.Abstract

Emerging synthetic biology capabilities have the potential to dramatically improve

our ability to engineer biological systems. However, a fundamental obstacle to

realizing this potential is the inability to accurately predict biological behavior

following modification of the corresponding genotype. This method systematically

leverages arbitrary amounts of new data to improve predictions, and does not assume

any particular interactions, but rather implicitly chooses the most predictive ones.

Metabolic bioengineering aims to maximize the production of bio-economically

important substances (compounds, enzymes, or other proteins) through the

optimization of the genetics, cellular processes and growth conditions of

microorganisms. To achieve this goal, a large system of experimental techniques,

compound libraries, computational methods and data resources, including multi-

omics data, are used. The recent advent of multi-omics systems biology approaches

significantly impacted the field by opening new avenues to perform dynamic and

large-scale analyses that deepen our knowledge on the manipulations. However,

given the great potential of transcriptomics, proteomics, and metabolomics,

integrating the data to gain a more comprehensive understanding is not a trivial task.

Key words: Artificial intelligence, systems biology, machine learning, metabolic

bioengineering.