Identifying Key In Silico Knockout for Enhancement of Limonene Yield Through Dynamic Metabolic Modelling.

Living cells display dynamic and complex behaviors. To understand their response and to infer novel insights not possible with traditional reductionist approaches, over the last few decades various computational modelling methodologies have been developed. In this chapter, we focus on modelling the dynamic metabolic response, using linear and nonlinear ordinary differential equations, of an engineered Escherichia coli MG1655 strain with plasmid pJBEI-6409 that produces limonene. We show the systems biology steps involved from collecting time-series data of living cells, to dynamic model creation and fitting the model with experimental responses using COPASI software.

Efficiency of Protein Renewal Is Limited by Feed Intake and Not by Protein Lifetime in Aging Caenorhabditis elegans.

Protein turnover maintains the proteome's functional integrity. Here, protein turnover efficiency over time in wild-type Caenorhabditis elegans was assessed using inverse [15N]-pulse labeling up to 7 days after the egg-laying phase at 20 °C. Isotopic analysis of some abundant proteins was executed favoring data quality over quantity for mathematical modeling. Surprisingly, isotopic enrichment over time reached an upper limit showing an apparent cessation of protein renewal well before death, with protein fractions inaccessible to turnover ranging from 14 to 83%. For life span modulation, worms were raised at different temperatures after egg laying. Mathematical modeling of isotopic enrichment points either to a slowdown of protein turnover or to an increasing protein fraction resistant to turnover with time. Most notably, the estimated time points of protein turnover cessation from our mathematical model were highly correlated with the observed median life span. Thrashing and pumping rates over time were linearly correlated with isotopic enrichment, therefore linking protein/tracer intake to protein turnover rate and protein life span. If confirmed, life span extension is possible by optimizing protein turnover rate through modulating protein intake in C. elegans and possibly other organisms. While proteome maintenance benefits from a high protein turnover rate, protein turnover is fundamentally energy-intensive, where oxidative stress contributes to damage that it is supposed to repair.

Metabolomics and modelling approaches for systems metabolic engineering.

Metabolic engineering involves the manipulation of microbes to produce desirable compounds through genetic engineering or synthetic biology approaches. Metabolomics involves the quantitation of intracellular and extracellular metabolites, where mass spectrometry and nuclear magnetic resonance based analytical instrumentation are often used. Here, the experimental designs, sample preparations, metabolite quenching and extraction are essential to the quantitative metabolomics workflow. The resultant metabolomics data can then be used with computational modelling approaches, such as kinetic and constraint-based modelling, to better understand underlying mechanisms and bottlenecks in the synthesis of desired compounds, thereby accelerating research through systems metabolic engineering. Constraint-based models, such as genome scale models, have been used successfully to enhance the yield of desired compounds from engineered microbes, however, unlike kinetic or dynamic models, constraint-based models do not incorporate regulatory effects. Nevertheless, the lack of time-series metabolomic data generation has hindered the usefulness of dynamic models till today. In this review, we show that improvements in automation, dynamic real-time analysis and high throughput workflows can drive the generation of more quality data for dynamic models through time-series metabolomics data generation. Spatial metabolomics also has the potential to be used as a complementary approach to conventional metabolomics, as it provides information on the localization of metabolites. However, more effort must be undertaken to identify metabolites from spatial metabolomics data derived through imaging mass spectrometry, where machine learning approaches could prove useful. On the other hand, single-cell metabolomics has also seen rapid growth, where understanding cell-cell heterogeneity can provide more insights into efficient metabolic engineering of microbes. Moving forward, with potential improvements in automation, dynamic real-time analysis, high throughput workflows, and spatial metabolomics, more data can be produced and studied using machine learning algorithms, in conjunction with dynamic models, to generate qualitative and quantitative predictions to advance metabolic engineering efforts.