All plant and animal kingdom organisms use highly connected biochemical networks to facilitate sustaining, proliferation, and growth functions. While the biochemical network details are well known, the understanding of the intense regulation principles is still limited. We chose to investigate the Hermetia illucens fly at the larval stage because this stage is a crucial period for the successful accumulation and allocation of resources for the subsequent organism’s developmental stages. We combined iterative wet lab experiments and innovative metabolic modeling design approaches to simulate and explain the H. illucens larval stage resource allocation processes and biotechnology potential. We performed time-based growth and high-value chemical compound accumulation wet lab chemical analysis experiments on larvae and the Gainesville diet composition. We built and validated the first H. illucens medium-size, stoichiometric metabolic model to predict the effects of diet-based alterations on fatty acid allocation potential. Using optimization methods such as flux balance and flux variability analysis on the novel insect metabolic model, we predicted that doubled essential amino acid consumption increased the growth rate by 32%, but pure glucose consumption had no positive impact on growth. In the case of doubled pure valine consumption, the model predicted a 2% higher growth rate. In this study, we describe a new framework for researching the impact of dietary alterations on the metabolism of multi-cellular organisms at different developmental stages for improved, sustainable, and directed high-value chemicals.

Genome-scale metabolic modeling is widely used to study the impact of metabolism on the phenotype of different organisms. While substrate modeling reflects the potential distribution of carbon and other chemical elements within the model, the additional use of omics data, e.g., transcriptome, has implications when researching the genotype–phenotype responses to environmental changes. Several algorithms for transcriptome analysis using genome-scale metabolic modeling have been proposed. Still, they are restricted to specific objectives and conditions and lack flexibility, have software compatibility issues, and require advanced user skills. We classified previously published algorithms, summarized transcriptome pre-processing, integration, and analysis methods, and implemented them in the newly developed transcriptome analysis tool IgemRNA, which (1) has a user-friendly graphical interface, (2) tackles compatibility issues by combining previous data input and pre-processing algorithms in MATLAB, and (3) introduces novel algorithms for the automatic comparison of different transcriptome datasets with or without Cobra Toolbox 3.0 optimization algorithms. We used publicly available transcriptome datasets from Saccharomyces cerevisiae BY4741 and H4-S47D strains for validation. We found that IgemRNA provides a means for transcriptome and environmental data validation on biochemical network topology since the biomass function varies for different phenotypes. Our tool can detect problematic reaction constraints.

Genome scale metabolic modelling is widely used technique to research metabolism impacts on organism properties. Additional omics data integration enables a more precise genotype-phenotype analysis for biotechnology, medicine and life sciences. Transcriptome data amounts rapidly increase each year. Many transcriptome analysis tools with integrated genome scale metabolic modelling are proposed. But these tools have own restrictions, compatibility issues and the necessity of previous experience and advanced user skills. We have analysed and classified published tools, summarized possible transcriptome pre-processing, and analysis methods and implemented them in the new transcriptome analysis tool IgemRNA. Tool novelty is the possibility of transcriptomics data pre-processing approach, analysis of transcriptome with or without genome scale metabolic models and different thresholding and gene mapping approach availability. In comparison with usual Gene set enrichment analysis methods, IgemRNA options provide additional transcriptome data validation, where minimal metabolic network connectivity and flux requirements are met. IgemRNA allows to process transcriptome datasets, compare data between different phenotypes, execute multiple analysis and data filtering functions. All this is done via graphical user interface. IgemRNA is compatible with Cobra Toolbox 3.0 and uses some of its functions for genome scale metabolic model optimization tasks. IgemRNA is open access software available at https://github.com/BigDataInSilicoBiologyGroup/IgemRNA.

All plant and animal kingdom organisms use highly connected biochemical networks to facilitate sustaining, proliferation and growth functions. While biochemical network details are well known, the understanding of intense regulation principles is still limited. We chose to investigate Hermetia illucens fly at the larval stage as it is crucial for successful resource accumulation and allocation for the consequential organism's developmental stages. We combined the iterative wet lab experiments and innovative metabolic modeling design approaches, to simulate and explain the H. illucens larval stage resource allocation processes and biotechnology potential. We performed time-based growth and high-value chemical compound accumulation wet lab chemical analysis experiments in larvae and Gainesville diet composition. To predict diet-based alterations on fatty acid allocation potential, we built and validated the first H. illucens medium-size stoichiometric metabolic model. Using optimization methods like Flux balance and Flux variability analysis on the novel insect metabolic model, it predicted that doubled essential amino acid consumption increased the growth rate by 32%, but pure glucose consumption had no positive impact on growth. In the case of doubled pure valine consumption, the model predicted a 2% higher growth rate. In this study, we describe a new framework to research the impact of dietary alterations on the metabolism of multi-cellular organisms at different developmental stages for improved, sustainable and directed high-value chemicals.