Abstract
AbstractSynthetic biologists use and combine diverse biological parts to build systems such as genetic circuits that perform desirable functions in, for example, biomedical or industrial applications. Computer-aided design methods have been developed to help choose appropriate network structures and biological parts for a given design objective. However, they almost always model the behavior of the network in an average cell, despite pervasive cell-to-cell variability. Here, we present a computational framework to guide the design of synthetic biological circuits while accounting for cell-to-cell variability explicitly. Our design method integrates a NonLinear Mixed-Effect (NLME) framework into an existing algorithm for design based on ordinary differential equation (ODE) models. The analysis of a recently developed transcriptional controller demonstrates first insights into design guidelines when trying to achieve reliable performance under cell-to-cell variability. We anticipate that our method not only facilitates the rational design of synthetic networks under cell-to-cell variability, but also enables novel applications by supporting design objectives that specify the desired behavior of cell populations.
Highlights
For the commonly applied models in the form of ordinary differential equations (ODEs), both design problems can be addressed by investigating the space of model parameters to assess circuit behaviors in relation to design objectives encoded by a reference for the desired behavior
Stochastic design frameworks that account for cell-to-cell variability due to intrinsic noise with low molecule copy numbers are beginning to emerge, but computational complexity currently limits them to small networks, steady-state, and homogeneous model parameters in a cell population [18]
We propose a general framework we call population design that aims to help biologists interested in synthetic circuit design to account for cell-to-cell variability via ODE-based NonLinear Mixed-Effect (NLME)
Summary
Synthetic biology aims at establishing novel functions in biological systems, or to re-engineer existing ones, in many areas such as new materials or cell-based therapies that are starting to see real-world applications [21]. The conceptual core of the field’s rational engineering approach to establish, for example, the corresponding synthetic gene circuits are a systematic design-build-test cycle and the use of predictive mathematical models throughout this cycle to design, analyze, and tune the circuits [14]. For the commonly applied models in the form of ordinary differential equations (ODEs), both design problems can be addressed by investigating the space of model parameters to assess (predicted) circuit behaviors in relation to design objectives encoded by a reference for the desired behavior. With sampling-based methods such as (approximate) Bayesian computation, this defines a ‘viable’ subspace of the parameter space where the behavior is consistent with the design objective (Fig. 1A,B) [2,10,17]
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