Filling the knowledge gap in metabolism for analyzing biochemical reactions and designing synthetic pathways

Conference Dates

July 16-20, 2017


Computational methods are indispensable for comprehending and analyzing the existing knowledge of biology and biochemistry, and for deepening our understanding of cell metabolism toward discovering novel knowledge on metabolic functions. We review here the latest advancements and contributions of the methods we are developing in the last 15 years. We have developed two main methods, BNICE.ch and BridgIT, which have been used for exploiting the known and discovering novel biochemistry. BNICE.ch and BridgIT have been used for (i) the development of two databases of biochemical reactions, ATLAS I and II, (ii) the atom mapping of all known and novel reactions and pathways, and (iii) the gap-filling of metabolism in various organisms. Using BNICE.ch we reconstructed all the possible reactions, known and hypothetical, between all known metabolites in biological databases, and this study resulted in a network of more than 137’000 known and novel reactions, each connecting two or more biological compounds. The results were organized into the ATLAS I of Biochemistry (http://lcsb-databases.epfl.ch/pathways/atlas/). However, through understanding of metabolism lags behind explaining the chemodiversity observed in living organisms. In ATLAS II, we applied our methods on millions of chemical compounds and discovered the enzyme reaction rules that can identify reactive sites on chemical compounds, act on them and connect them back to known metabolic precursors. To better characterize the generated hypothetical reactions and to associate them to the known enzymatic reactions, we developed a complementary tool to BNICE.ch, we called BridgIT. BridgIT is capable of identifying candidate enzymes among different organisms with potential similarity in their catalytic properties and it maps biochemistry to the open reading frames, in order to guide the choice of genes for protein engineering and design of enzymes for the biotransformation of the hypothetical reactions. Furthermore, we developed “iAM.NICE” (in silico Atom Mapped Network Integrated Computational Explorer) that uses the BNICE.ch methodology to generate atom mappings for all the generated reactions and pathways (known and novel). iAM.NICE creates in silico labelled metabolites as substrates, and transfers the labels from all the atoms in substrates, to the products according to known reaction mechanisms defined in the generalised reactions rules. We will further discuss the challenges and opportunities for the development of these and similar methods and for their application in a broad class of biotechnology problems from synthetic biology to drug and nutrition design.

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