Site dependent time optimization of protein synthesis with special regard to accuracy

Automated Design Framework for Synthetic Biology Exploiting Pareto Optimality

In this work we consider Pareto optimality for automated design in synthetic biology. We present a generalized framework based on a mixed-integer dynamic optimization formulation that, given design specifications, allows the computation of Pareto optimal sets of designs, that is, the set of best trade-offs for the metrics of interest. We show how this framework can be used for (i) forward design, that is, finding the Pareto optimal set of synthetic designs for implementation, and (ii) reverse design, that is, analyzing and inferring motifs and/or design principles of gene regulatory networks from the Pareto set of optimal circuits. Finally, we illustrate the capabilities and performance of this framework considering four case studies. In the first problem we consider the forward design of an oscillator. In the remaining problems, we illustrate how to apply the reverse design approach to find motifs for stripe formation, rapid adaption, and fold-change detection, respectively.

Receptor-Agonist Interactions in Service-Theoretic Perspective, Effects of Molecular Timing on the Shape of Dose-Response Curves

Service-theoretic concepts and methods, widely used in other fields (e.g., telecommunication and operations research), are useful also in a biochemical setting because the treatment of biocatalysts (enzymes, receptors) as servers and their ligands as customers, based on the established formal methods of service or queuing theory, may lead to insights and results unobtainable by conventional, mass-action-law-based theories. In this article, we apply the service-theoretic approach to receptor-agonist systems and show how by changing the stochastic time pattern of "operationally relevant" point events (e.g., instants of agonist arrival, instants of post-climax agonist departure) a great variety of dose-response curves may be generated, even in very simple reaction schemes, which, according to mass action kinetics, invariably lead to hyperbolic r(A) curves (r and A stand for response and agonist concentration, respectively). The molecular timing inherent to a hyperbolic response system is not optimal: for instance, at the agonist concentration A(50), half of the agonist molecules are rejected ("lost") because of unfortunate timing of the arrival events. The fraction of lost arrivers can be diminished considerably if the arrivals are better timed: "sub-Poisson" arrivals improve the timing and, thus, convert hyperbolic r(A) curves into "lifted" nonhyperbolic ones. Conversely, "super-Poisson" arrivals make the non-optimal timing in hyperbolic response systems even worse and, thus, convert hyperbolic r(A) curves into "depressed" nonhyperbolic ones. Furthermore, under special timing conditions, nonhyperbolic r(A) curves can be generated, which are partly lifted, partly depressed relative to the reference hyperbola, and which resemble in shape well-known nonhyperbolic forms of enzyme and receptor kinetics (negatively cooperative, positively cooperative, and sigmoidal kinetics). In addition unusual (undulatory and sawtooth-like) r(A) curves can be generated solely by changing the temporal pattern of arrival and service completion instants. Virtually any shape of dose-response curves may be obtained by allowing for probability distributions whose characteristic shape varies with their mean; we call such distributions "variomorphic" and apply them to the arrival process of agonist molecules.

Model-based inference of gene expression dynamics from sequence information

A dynamic model of prokaryotic gene expression is developed that makes considerable use of gene sequence information. The main contribution arises from the fact that the combined gene expression model allows us to access the impact of altering a nucleotide sequence on the dynamics of gene expression rates mechanistically. The high level of detail of the mathematical model is considered as an important step towards bringing together the tremendous amount of biological in-depth knowledge that has been accumulated at the molecular level, using a systems level analysis (in the sense of a bottom-up, inductive approach). This enables to the model to provide highly detailed insights into the various steps of the protein expression process and it allows us to access possible targets for model-based design. Taken as a whole, the mathematical gene expression model presented in this study provides a comprehensive framework for a thorough analysis of sequence-related effects on the stages of mRNA synthesis, mRNA degradation and ribosomal translation, as well as their nonlinear interconnectedness. Therefore, it may be useful in the rational design of recombinant bacterial protein synthesis systems, the modulation of enzyme activities in pathway design, in vitro protein biosynthesis, and RNA-based vaccination.