May 22 2018 12:00 PM
May 22 2018 02:00 PM
Synthetic biology is an effective solution to produce valuable and novel products economically (less cost and more yields). In a specific host organism, designing heterologous pathway to produce valuable compounds needs knowing effective foreign reactions to activate. The question is which exogenous reactions are the most effective in that specific host? To know the most suitable foreign reactions to the host, one needs to check all possible routes from many different organisms. However, complex biological systems for different organisms make it a challenge for synthetic biologists. To reduce the complexity, computational methods based on different approaches suggest and rank heterologous pathways. One of the approaches to design heterologous pathways is based on thermodynamic data. Thermodynamic data can explain the transformation of a molecule in a biological system. It has several applications including synthetic biology.
To rationally design a productive heterologous biosynthesis system, it is essential to consider the suitability of foreign reactions for the specific endogenous metabolic infrastructure of a host. We developed a novel method called Metabolic Route Explorer (MRE) which for a given pair of starting and desired compounds in a given chassis organism ranks biosynthesis routes from the perspective of the integration of new reactions into the endogenous metabolic system. For each promising heterologous biosynthesis pathway, MRE suggests actual enzymes for foreign metabolic reactions and generates information on competing endogenous reactions for the consumption of metabolites. These unique, chassis-centered features distinguish MRE from existing pathway design tools and allow synthetic biologists to evaluate the design of their biosynthesis systems from a different angle. By using biosynthesis of a range of high-value natural products as a case study, we show that MRE is an effective tool to guide the design and optimization of heterologous biosynthesis pathways. The URL of MRE is http://www.cbrc.kaust.edu.sa/mre/.
Accurate and wide-ranging prediction of thermodynamic parameters for biochemical reactions can facilitate deeper insights into the workings and the design of metabolic systems. The available experimental data for thermodynamics information is limited to a small fraction of the already available databases. Due to the difficulties of experimental procedures and the small fraction of the experimental data available, there is a need of computational methods to estimate thermodynamics data. Here, we introduce a machine learning method with chemical fingerprint-based features for the prediction of the Gibbs free energy of biochemical reactions. By comparing our method with state-of-the-art linear regression-based methods for the standard Gibbs free energy prediction, we demonstrated that its prediction accuracy is most favorable. Our results show direct evidence that a number of 2D fingerprints collectively provide useful information about the Gibbs free energy of biochemical reactions.
Meshari Alazmi received the BS and MS degrees in computer science from University of Hail and University of Missouri, respectively. He is a PhD student in Computational Bioscience Research Center, and CEMSE Division supervised by Prof. Xin Gao. His main areas of research include bioinformatics, machine learning, and systems biology.