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Living organisms are differentiated by their genetic material-millions to billions of DNA bases encoding thousands of genes. These genes are translated into a vast array of proteins, many of which have functions that are still unknown. Previously, it was believed that simply knowing the genetic sequence of an organism would be the key to unlocking all understanding. However, as DNA sequencing technology has become affordable, it has become clear that living cells are governed by complex, multilayered networks of gene regulation that cannot be deduced from sequence alone. Synthetic biology as a field might best be characterized as a learn-by-building approach, in which scientists attempt to engineer molecular pathways that do not exist in nature. In doing so, they test the limits of both natural and engineered organisms.

The ability to rapidly design, build, and test prototypes is of key importance to every engineering discipline. DNA assembly often serves as a rate limiting step of the prototyping cycle for synthetic biology. Recently developed DNA assembly methods such as isothermal assembly and type IIS restriction enzyme systems take different approaches to accelerate DNA construction. We introduce a hybrid method, Golden Gate-Gibson (3G), that takes advantage of modular part libraries introduced by type IIS restriction enzyme systems and isothermal assembly’s ability to build large DNA constructs in single pot reactions. Our method is highly efficient and rapid, facilitating construction of entire multigene circuits in a single day. Additionally, 3G allows generation of variant libraries enabling efficient screening of different possible circuit constructions. We characterize the efficiency and accuracy of 3G assembly for various construct sizes, and demonstrate 3G by characterizing variants of an inducible cell-lysis circuit.

The ability to rapidly design, build, and test prototypes is of key importance to every engineering discipline. DNA assembly often serves as a rate limiting step of the prototyping cycle for synthetic biology. Recently developed DNA assembly methods such as isothermal assembly and type IIS restriction enzyme systems take different approaches to accelerate DNA construction. We introduce a hybrid method, Golden Gate-Gibson (3G), that takes advantage of modular part libraries introduced by type IIS restriction enzyme systems and isothermal assembly‘s ability to build large DNA constructs in single pot reactions. Our method is highly efficient and rapid, facilitating construction of entire multi-gene circuits in a single day. Additionally, 3G allows generation of variant libraries enabling efficient screening of different possible circuit constructions. We characterize the efficiency and accuracy of 3G assembly for various construct sizes, and demonstrate 3G by characterizing variants of an inducible cell-lysis circuit.

Large-scale -omics data are now ubiquitously utilized to capture and interpret global responses to perturbations in biological systems, such as the impact of disease states on cells, tissues, and whole organs. Metabolomics data, in particular, are difficult to interpret for providing physiological insight because predefined biochemical pathways used for analysis are inherently biased and fail to capture more complex network interactions that span multiple canonical pathways. In this study, we introduce a nov-el approach coined Metabolomic Modularity Analysis (MMA) as a graph-based algorithm to systematically identify metabolic modules of reactions enriched with metabolites flagged to be statistically significant. A defining feature of the algorithm is its ability to determine modularity that highlights interactions between reactions mediated by the production and consumption of cofactors and other hub metabolites. As a case study, we evaluated the metabolic dynamics of discarded human livers using time-course metabolomics data and MMA to identify modules that explain the observed physiological changes leading to liver recovery during subnormothermic machine perfusion (SNMP). MMA was performed on a large scale liver-specific human metabolic network that was weighted based on metabolomics data and identified cofactor-mediated modules that would not have been discovered by traditional metabolic pathway analyses.

In systems and synthetic biology, it is common to build chemical reaction network (CRN) models of biochemical circuits and networks. Although automation and other high-throughput techniques have led to an abundance of data enabling data-driven quantitative modeling and parameter estimation, the intense amount of simulation needed for these methods still frequently results in a computational bottleneck. Here we present bioscrape (Bio-circuit Stochastic Single-cell Reaction Analysis and Parameter Estimation) - a Python package for fast and flexible modeling and simulation of highly customizable chemical reaction networks. Specifically, bioscrape supports deterministic and stochastic simulations, which can incorporate delay, cell growth, and cell division. All functionalities - reaction models, simulation algorithms, cell growth models, partioning models, and Bayesian inference - are implemented as interfaces in an easily extensible and modular object-oriented framework. Models can be constructed via Systems Biology Markup Language (SBML) or specified programmatically via a Python API. Simulation run times obtained with the package are comparable to those obtained using C code - this is particularly advantageous for computationally expensive applications such as Bayesian inference or simulation of cell lineages. We first show the package’s simulation capabilities on a variety of example simulations of stochastic gene expression. We then further demonstrate the package by using it to do parameter inference on a model of integrase enzyme-mediated DNA recombination dynamics with experimental data. The bioscrape package is publicly available online (https://github.com/biocircuits/bioscrape) along with more detailed documentation and examples.

An ongoing area of study in synthetic biology has been the design and construction of synthetic circuits that maintain homeostasis at the population level. Here, we are interested in designing a synthetic control circuit that regulates the total cell population and the relative ratio between cell strains in a culture containing two different cell strains. We have developed a dual feedback control strategy that uses two separate control loops to achieve the two functions respectively. By combining both of these control loops, we have created a population regulation circuit where both the total population size and relative cell type ratio can be set by reference signals. The dynamics of the regulation circuit show robustness and adaptation to perturbations in cell growth rate and changes in cell numbers. The control architecture is general and could apply to any organism for which synthetic biology tools for quorum sensing, comparison between outputs, and growth control are available.

In this paper, we are concerned with doing system identification for biological systems using time series distributional measurements and Markov chain models. By distributional measurements, we mean measurements provided by assays such as flow cytometry or FISH (fluorescence in situ hybridization) that allow us to make the same single cell measurements over a number of different cells. We focus here on the problem of estimating the transition probabilities of a Markov chain from distributional measurements. We set this problem up using Bayes' rule and make simplifying assumptions to reduce the problem to a non-convex optimization problem over finitely many variables. We propose methods for locally solving this non-convex optimization problem. For a special case, we discuss necessary and sufficient conditions for the Markov chain to be identifiable. Finally, we demonstrate our procedure on instructive toy examples as well as on simulated stochastic data for a genetic toggle switch. [Gardner et al. (2000)].