Revision as of 02:53, 18 September 2015

Notebooks

Modeling an in silico alternative

Why Model?

Experimentation makes up the backbone of any scientific discipline, and synthetic biology is no exception. It is only through experimental analysis that we can understand, test, and refine our biological creations in a rigorous manner. One might be tempted to ask why computation and simulation need even make appearances on our stage. We shall attempt to say a few words in the defense of mathematical modeling.

To the question “Why model?”, Joshua Epstein, the 2008 recipient of the NIH Director’s Pioneer Award, offers a simple retort: “You are a modeler,” by which he means to say that we all are constantly running implicit models inside our head often without realizing it. When a PCR yields unexpected results, for example, we may call upon an internal model of DNA replication in order to assess the points at which reality may have diverged from expectation: perhaps the primers bound to the template DNA non-selectively or maybe our chosen annealing temperature was too high. Whenever scientists generate hypotheses based on some internal picture in their head, those scientists are practicing the age old tradition of modeling.

The question then becomes, “Why use mathematical models?” Our PCR example from above offers an answer to this question as well. In determining whether nonspecificity lay at the root of our faulty PCR, we may resort to a read-alignment program that can computationally predict potential primer binding sites. And it is standard practice to use an annealing-temperature calculator before designing a set of PCR cycles. The existence and widespread use of tools such as these illustrates the inadequacy of our minds in solving certain problems without aid as well as the consequent need for explicit quantitative models and computing machinery.

In the case of our project, we used computational tools to help model the molecular dynamics of two of our biosynthetic reactions. These were instances where human intuition alone could not reliably answer a question that interested us, namely, which enzyme (PAL or FDC) or substrate (CoA or Acetate) exerts greater control on the flux or amount of product in our specified pathway? It is this question that we shall explore below.

Constructing a Model for Styrene Synthesis

We wanted to create a plasmid containing our three genes of interest, PAL, UbiX, and FDC, but we didn’t know which order to place the genes in our plasmid. Since the genes placed closer to the beginning of the operon are transcribed more than those closer to the end, we would want to place our most influential enzymes toward the promoter, and our less important enzymes toward the end. There are 3! = 6 orderings of these three genes so we would potentially make 6 plasmids, test their styrene productivity levels, and submit the one which yielded the most styrene. The described process would have cost us weeks of our time as well as lab resources and money. Instead of creating all 6 plasmids and conducting the wet lab experiments, we instead turned to our mathematical model. In order to model our enzymatic pathway, we created a system of ordinary differential equations based on the Michaelis Menten enzyme model shown to the right. Numerically simulating the model in MATLAB gave us curves that represented the species concentration as a function of time over a specified period of time.