Computational Prediction of Synthetic Circuit Function Across Growth Conditions

Abstract

A challenge in the design and construction of synthetic genetic circuits is that they will operate within biological systems that have noisy and changing parameter regimes that are largely unmeasurable. The outcome is that these circuits do not operate within design specifications or have a narrow operational envelope in which they can function. This behavior is often observed as a lack of reproducibility in function from day to day or lab to lab. Moreover, this narrow range of operating conditions does not promote reproducible circuit function in deployments where environmental conditions for the chassis are changing, as environmental changes can affect the parameter space in which the circuit is operating. Here we describe a computational method for assessing the robustness of circuit function across broad parameter regions. Previously designed circuits are assessed by this computational method and then circuit performance is measured across multiple growth conditions in budding yeast. The computational predictions are correlated with experimental findings, suggesting that the approach has predictive value for assessing the robustness of a circuit design.

Robustness and reproducibility of simple and complex synthetic logic circuit designs using a DBTL loop

“…The experimental data were fitted to Equation 1 (for activations), and Equation 2 (for repressions) derived from Cello (16): where y iss is the steady-state output promoter activity of part i; y imin and y imax are the minimal and maximal output promoter activities (obtained from experimental results), respectively, for part i ; κ i and n i are the affinity and cooperativity of transcription factor binding (obtained with the fitting algorithm); and, finally, y i− 1 SS is the steady-state input promoter activity from the previous part’s output (calculated also using Equations 1 or 2). Using the Hill function parameter value estimations a resulting ODE model is then analyzed using the Runge-Kutta-Fehlberg (4,5) method (59) implemented in iBioSim (60) to obtain steady-state output predictions for each design under different input concentrations (shown in Appendix Figs. 14 and 15).…”

“…The performance of a circuit is evaluated as a circuit’s ability to express fluorescence (ON) or not (OFF) as intended by the circuit’s logic given the presence or absence of chemical inputs. Logic circuits designed to exhibit OR and NOR logic were chosen based on preliminary data analysis in which previously built OR and NOR circuits performed poorly ((4; 39) and Fig. 6 in (25)).…”

“…The most common approaches to achieving reproducibility in results seek to standardize protocols and analyses and tightly control experimental conditions. However, in the field of synthetic biology, reproducible function of synthetic genetic circuits may be closely tied to the robustness of the construct design (4). By incorporating measures of robustness into the design principles of synthetic biology, one may be able to generate constructs that have functions that are robust to changes in genetic components or in experimental conditions, causing increased reproducibility across laboratories.…”

Highly-automated, high-throughput replication of yeast-based logic circuit design assessments

“…However, they also show that the performance of the current set of designs is unreliable for many gates. In follow-on work, we have extended our closed loop of experimentation to cover the design phase as well as design analysis and evaluation; we discuss these issues in two forthcoming papers ( 15 , 16 ).…”

Robustness and reproducibility of simple and complex synthetic logic circuit designs using a DBTL loop

“…The most common approaches to achieving reproducibility in results seek to standardize protocols and analyses and tightly control experimental conditions. However, in the field of synthetic biology, the reproducible function of synthetic genetic circuits may be closely tied to the robustness of the construct design ( 4 ). By incorporating measures of robustness into the design principles of synthetic biology, one may be able to generate constructs that have functions that are robust to changes in genetic components or in experimental conditions, causing increased reproducibility across laboratories.…”