Studying hundreds of genes and proteins and their effects on living organisms is an important aspect in postgenomic biology. With the improvement of high-throughout techniques from microarrays and gene assembly to next-generation sequencing and massively parallel gene assembly and editing, computational tools are an integral part of laboratory-based biologists as large volumes of research data is generated on a daily basis.
With large amounts of accessible data, models can be a useful tool and platform to aggregate, identify gaps and contradictions in current research knowledge. When written in a formal language, models can be simulated and the results of such simulations can be compared to experimental data. Given the same input, the model is complete if the simulated results match experimental data within an acceptable tolerance. Otherwise, the differences between simulated results and experimental data can provide a direction pointing towards inaccurate or insufficient knowledge used for modelling; thereby, suggesting scopes for future studies. For example, whole cell metabolic models built from current data can represent the state of knowledge for a particular host, which forms an in silico parallel to the biological host. If the predicted intracellular metabolite concentrations are substantially different from experimental results given the same media conditions, this suggests a deficiency in state of knowledge; hence, providing directions for future studies.
We welcome Original Research articles, Case Studies, Tutorials, Reviews, Mini-reviews, and Perspective articles. Themes of interest include, but are not limited to:
• Development of models for biological systems at various scales from molecular level (such as enzymatic pathways) to organism level (such as circulatory system).
• Comparative studies and/or applications of existing and/or published models for metabolic engineering, synthetic biology and systems biology.
• Integration of models with omics (genomics, transcriptomics, proteomics, metabolomics, fluxomics) data.
• Rules / heuristics of model building and interpreting between various levels of model abstraction.
• Limitations of existing models.
• Parameter optimization and model selection for particular applications.
• Scale-up models of metabolite production from laboratory scale to fermentation scale.
• Development of computational tools for model development, simulation, and model analysis.
The Editors would like to thank
Dr. Wai Keat Yam, who has acted as the Research Topic coordinator. Dr Yam participated in the preparation of the Research Topic proposal.