Today, many products we know, use and consume are produced by biological systems — from medicines to nutritional products to chemicals. There are a multitude of institutes and companies around the globe utilizing synthetic biology and genomics to address issues of titer, productivity and yield of commercial products which tend to be manufactured in conventional systems such as E. coli, yeast and mammalian cell lines e.g. CHO or HEK. These systems have limitations but are pursued because of the developed tools and the familiarity with regulatory agencies. At Synthetic Genomics Inc. we have extensive expertise in biodiscovery and cutting-edge genomics to develop improved or novel hosts with the goal of achieving a step change improvement in production costs and quality. These novel hosts will also open opportunities for innovative and sustainable solutions. Many products are currently sitting on the shelves of R&D labs because traditional hosts made producing those products technical unfeasible or cost prohibitive. We believe that every product, solution and innovation starts with data. We have sequenced and annotated thousands of genomes and metagenomes from diverse environments around the world. Our proprietary bioinformatics platform enables scientists with a set of analytical and design tools, to decipher complex genomic information, understand phenotypic and metabolic traits and translate them into actionable information. Understanding nature’s design rules, how living organisms have evolved, and how they function allows smooth transition from in silico design to DNA synthesis and assembly. Here I show several examples where we have developed novel production systems by engineering and optimizing both the cell specific productivity as well as the manufacturing process. We continue to leverage to the power of Mother Nature to revolutionize the current paradigm in bio-based production.

The rapid and large-scale pathogenesis of influenza virus requires constant monitoring, and frequent vaccine development to protect the world population not only from seasonal influenza but also from novel influenza A viruses that could trigger a pandemic. Seasonal Influenza is an acute viral infection and is estimated to cause 3 to 5 million cases of severe illness and around 250,000 to 500,000 deaths worldwide1. Among the three subtypes, type A is the only one known to cause pandemics. Previously developed models utilize a wide range of predictive algorithms to model the antigenic distance of influenza a viruses and achieved great success. However, these models only measure the contribution of chosen amino acids as individuals, which lacks the context that changes of amino acids in Hemagglutinin may have composite effects since they form a 3D structure in space. Besides reporting point mutations with their association of influenza epidemic, they also involve only a limited number of amino acid properties4. Understanding the combination effect of point mutations of influenza, A and expanding the number of amino acids in the analysis may better unveil the relationship between HA sequence and its antigenicity. In this study, we design a convoluted neural network (CNN) to model the patterns of HA protein sequence to analyze the patterns introduced by individual mutations and their associated and combination effect. Furthermore, we systematically analyze all available amino acid properties for the predictability of H3N2 antigenicity. Particle swam optimization algorithm is used to construct the structure of CNN. This approach produces good results with 10-fold cross validation of over 94% unbiased estimate, and blind prediction of 100% accuracy.