Search for: All records

Synthetic biology is a field at the intersection of biology and engineering. Inspired by engineering principles, researchers use defined parts to build functionally defined biological circuits. Genetic design automation allows scientists to design, model, and analyze their genetic circuits in silico before building them in the lab, saving time and resources in the process. Establishing synthetic biology’s future is dependent on genetic design automation, since the computational approach opens the field to a broad, interdisciplinary community. However, challenges with part libraries, standards, and software tools are currently stalling progress in the field. This review first covers re- cent advancements in genetic design automation, followed by an assessment of the challenges ahead, and a proposed automated genetic design workflow for the future. 

In synthetic biology, combinational circuits are used to program cells for various new applications like biosensors, drug delivery systems, and biofuels. Similar to asynchronous electronic circuits, some combinational genetic circuits may show unwanted switching variations (glitches) caused by multiple input changes. Depending on the biological circuit, glitches can cause irreversible effects and jeopardize the circuit’s functionality. This paper presents a stochastic analysis to predict glitch propensities for three implementations of a genetic circuit with known glitching behavior. The analysis uses STochastic Approximate Model-checker for INfinite-state Analysis (STAMINA), a tool for stochastic verification. The STAMINA results were validated by comparison to stochastic simulation in iBioSim resulting in further improvements of STAMINA. This paper demonstrates that stochastic verification can be utilized by genetic designers to evaluate design choices and input restrictions to achieve a desired reliability of operation. 

Stochastic model checking is a technique for analyzing systems that possess probabilistic characteristics. However, its scalability is limited as probabilistic models of real-world applications typically have very large or infinite state space. This paper presents a new infinite state CTMC model checker, STAMINA, with improved scalability. It uses a novel state space approximation method to reduce large and possibly infinite state CTMC models to finite state representations that are amenable to existing stochastic model checkers. It is integrated with a new property-guided state expansion approach that improves the analysis accuracy. Demonstration of the tool on several benchmark examples shows promising results in terms of analysis efficiency and accuracy compared with a state-of-the-art CTMC model checker that deploys a similar approximation method.