Biological systems contain complex metabolic pathways with many nonlinearities and synergies that make them difficult to predict from first principles. Protein synthesis is a canonical example of such a pathway. Here we show how cell-free protein synthesis may be improved through a series of iterated high-throughput experiments guided by a machine-learning algorithm implementing a form of evolutionary design of experiments (Evo-DoE). The algorithm predicts fruitful experiments from statistical models of the previous experimental results, combined with stochastic exploration of the experimental space. The desired experimental response, or evolutionary fitness, was defined as the yield of the target product, and new experimental conditions were discovered to have ∼ 350% greater yield than the standard. An analysis of the best experimental conditions discovered indicates that there are two distinct classes of kinetics, thus showing how our evolutionary design of experiments is capable of significant innovation, as well as gradual improvement.

Building variant ribosomes offers opportunities to reveal fundamental principles underlying ribosome biogenesis and to make ribosomes with altered properties. However, cell viability limits mutations that can be made to the ribosome. To address this limitation, the in vitro integrated synthesis, assembly and translation (iSAT) method for ribosome construction from the bottom up was recently developed. Unfortunately, iSAT is complex, costly, and laborious to researchers, partially due to the high cost of reaction buffer containing over 20 components. In this study, we develop iSAT in Escherichia coli BL21Rosetta2 cell lysates, a commonly used bacterial strain, with a cost-effective poly sugar and nucleotide monophosphate-based metabolic scheme. We achieved a 10-fold increase in protein yield over our base case with an evolutionary design of experiments approach, screening 490 reaction conditions to optimize the reaction buffer. The computationally guided, cell-free, high-throughput technology presented here augments the way we approach multicomponent synthetic biology projects and efforts to repurpose ribosomes.

BackgroundWe consider the problem of optimizing a liposomal drug formulation: a complex chemical system with many components (e.g., elements of a lipid library) that interact nonlinearly and synergistically in ways that cannot be predicted from first principles.Methodology/Principal FindingsThe optimization criterion in our experiments was the percent encapsulation of a target drug, Amphotericin B, detected experimentally via spectrophotometric assay. Optimization of such a complex system requires strategies that efficiently discover solutions in extremely large volumes of potential experimental space. We have designed and implemented a new strategy of evolutionary design of experiments (Evo-DoE), that efficiently explores high-dimensional spaces by coupling the power of computer and statistical modeling with experimentally measured responses in an iterative loop.ConclusionsWe demonstrate how iterative looping of modeling and experimentation can quickly produce new discoveries with significantly better experimental response, and how such looping can discover the chemical landscape underlying complex chemical systems.

Abstract. We study a new variant of the dissipative particle dynamics (DPD) model that includes the possibility of dynamically forming and breaking strong bonds. The emergent reaction kinetics may then interact with self-assembly processes. We observe that self-assembled amphiphilic aggregations such as micelles have a catalytic effect on chemical reaction networks, changing both equilibrium concentrations and reaction frequencies. These simulation results are in accordance with experimental results on the so-called "concentration effect".