1: Introduction: Developing computational tools for chassis-centered biosynthetic pathway design is very important for a productive heterologous biosynthesis system by considering enormous foreign biosynthetic reactions. For many cases, a pathway to produce a target molecule consists of both native and heterologous reactions when utilizing a microbial organism as the host organism. Due to tens of thousands of biosynthetic reactions existed in nature, it’s not trivial to identify which could be served as heterologous ones to produce the target molecule in a specific organism. In the present work, we integrate more than ten thousand of E. coli non-native reactions and utilize a probability-based algorithm to search pathway. Moreover, we build a user friendly web server named EcoSynther. It is able to explore the precursors and heterologous reactions needed to produce a target molecule in Escherichia coli K12 MG1655, and then applies FBA(flux balance analysis) to calculate theoretical yields of each candidate pathway. Compared with other chassis-centered biosynthetic pathway design tools, EcoSynther has two unique features: (1) allow for automatic search without knowing a precursor in E. coli and (2) evaluate the candidate pathways under constraints from E. coli physiological states and growth conditions.
2: Query Submitted:
2.1 Substrate/Condition: Substrate and Condition allow users to specify which kind of environment including main carbon source and oxygen state that E. coli grows in. The alternative options for substrate are glucose, xylose or glycerol, and alternative options for condition are anaerobic or aerobic according to previous study.
2.2. Target / Intermetiate metabolite(s):
These two inputboxes allow users to input the target compound and intermediate metabolites(non-necessary to input). The intermediate metabolites help users to filter out the biosynthetic pathways which don’t involve the intermediates.
2.3. HeterRxn-steps: It must be an integer, which means how many heterologous reactions involved in candidate pathway at most. Of course, the computing time will be prolonged with its increasment,the maximum number of heterologous reactions is limited to 20.
2.4. Iterations: Because the principle of the pathway-searching algorithm is based on probability, users should enter the iteration-searching times. For example: 5,000 times or more. Of course, the computing time will be prolonged with its increasment,the maximum of iteration is limited to 30,000.(For instance, when searching astaxanthin, Iterations and HeterRxn-steps are usually set 30,000 and 9 respectively,it always takes about one minute).
It represents a percentage that minimum growth rate of mutant type E. coli accounts for maximum theoretical growth rate of WT type. Users could enter a decimal between 0 and 1, the larger entered number, the faster growth does the organism will get.
3: Search Results:
3.1: Set the numbers of pathways to show
3.2: Calculate the carbon fluxes and theoretical yields of the target molecule in selected pathway
 Orth, J., et al. A comprehensive genome-scale reconstruction of Escherichia coli metabolism—2011. In, Molecular Systems Biology. 2011. p. 535-544
 Feist, A.M., Zielinski, D.C. and Orth, J.D. Model-driven evaluation of the production potential for growth- coupled products of Escherichia coli. In, Metabolic Engineering. 2010. p. 173-186.
 Varma, A., Boesch, B.W. and Palsson, B.O. Stoichiometric interpretation of Escherichia coli glucose catabolism under various oxygenation rates. In, Applied and Environmental Microbiology. 1993. p. 2465-2473