Recon 3D enables a three-dimensional view of gene variation in human metabolism

Recon3D represents the most comprehensive human metabolic network model to date, accounting for 3,288 open reading frames (representing 17% of functionally annotated human genes), 13,543 metabolic reactions involving 4,140 unique metabolites, and 12,890 protein structures. These data provide a unique resource for investigating molecular mechanisms of human metabolism.

Machine Learning in Computational Biology to Accelerate High-Throughput Protein Expression

The Human Protein Atlas (HPA) enables the simultaneous characterization of thousands of proteins across various tissues to pinpoint their spatial location in the human body. This has been achieved through transcriptomics and high-throughput immunohistochemistry-based approaches, where over 40 000 unique human protein fragments have been expressed in E. coli. These datasets enable quantitative tracking of entire cellular proteomes and present new avenues for understanding molecular-level properties influencing expression and solubility.

Genetic Algorithm Based Optimization of a Peptidic Scaffold for Sequestration and Hydration of CO2

Biomimicry is a strategy that makes practical use of evolution to find efficient and sustainable ways to produce chemical compounds or engineer products. Exploring the natural machinery of enzymes for the production of desired compounds is a highly profitable investment, but the design of efficient biomimetic systems remains a considerable challenge.

Characterizing post-translational modifications in prokaryote metabolism using a multi-scale workflow

Understanding the complex interactions of protein posttranslational modifications (PTMs) represents a major challenge in metabolic engineering, synthetic biology, and the biomedical sciences. Here, we present a workflow that integrates multiplex automated genome editing (MAGE), genome-scale metabolic modeling, and atomistic molecular dynamics to study the effects of PTMs on metabolic enzymes and microbial fitness.

CHARACTERIZING STRAIN VARIATION IN ENGINEERED E. COLI USING A MULTI-OMICS BASED WORKFLOW

Understanding the complex interactions that occur between heterologous and native biochemical pathways represents a major challenge in metabolic engineering and synthetic biology. We present a workflow that integrates metabolomics, proteomics, and genome-scale models of Escherichia coli metabolism to study the effects of introducing a heterologous pathway into a microbial host.

Multi-omic data integration enables discovery of hidden biological regularities

We develop advanced data integration methods for multi-level analysis of genomic, transcriptomic, ribosomal profiling, proteomic and fluxomic data. Integrating omics data with models enabled the discovery of two novel regularities: condition invariant in vivo turnover rates of enzymes and the correlation of protein structural motifs and translational pausing.