Forward engineering of biology is an attractive concept, with virtually limitless potential applications ranging from pharmaceutical manufacturing to sustainable production of chemicals and fu­els. But the complexity of biological systems makes them chal­lenging to engineer. That’s why it is important to start forward engineering projects with the correct mindset – and avoid the pitfalls of over-rationalizing or brute-forcing your way to get the desired phenotype.

We’ve talked about the fundamental principles and approaches to forward engineering biology in a recent blog post. Now let’s take a look at some common misconceptions about forward engineering.

Myth 1: Forward engineering is built on rational understanding and design

Biology is complex, and we have a limited understanding of how it works. Even in E. coli, 35% of genes have no understood func­tion. Nevertheless, there have been numerous examples of creat­ing novel functions where both known and undefined parts work together to produce desired outputs. Informed design ideas may have a higher success rate, but the sheer size of genomic sequence space provides a great advantage. Our recent white paper on forward engineering illustrates the advantage of diversity gen­eration approaches that combine rational and exploratory designs to identify more beneficial variants in a short amount of time.

Myth 2: You need to test everything you build

The tools available today allow you to design and build libraries of thousands of edits. At first glance, it might seem prudent to test everything you have built, but this can require significant effort. Fortunately, you do not need to exhaustively sample the entire population to identify a lot of beneficial diversity for subsequent rounds of optimization. Instead, you can strike a balance between the build and test capacities to make optimal use of resources and time. For this purpose, Inscripta has developed bioinformatics tools that analyze your library and rec­ommend the ideal screening sample size.

Myth 3: Combinatorial search space is too large to fully explore

After identifying a set of individually beneficial edits, the next phase of forward engineering is to recombine and test these edits together. A perceived challenge with evaluating combinatorial libraries is that they are too large to perform an exhaustive search. But it turns out that you can sample a vanishingly small fraction of the theoretical space and still obtain highly improved variants. This approach mimics evolutionary principles and can help achieve dramatic improvements in a short time.

Myth 4: Forward engineering requires tremendous resources, effort, and time

Historically, strain engineering has been slow, laborious, and expensive. Fortunately, new tools and strategies have produced dramatic improvements across every important aspect of the process, reducing team size (from dozens to a few), cycle times (from months to weeks or days), cost, and the overall rate of progress. This enables organizations to substantially de-risk the strain develop­ment process and pursue more opportunities in less time and with fewer resources than ever before.

To learn more about using forward engineering to accelerate your projects, visit our website.