Memory and Combinatorial Logic Based on DNA Inversions: Dynamics and Evolutionary Stability

Genetic memory can be implemented using enzymes that catalyze DNA inversions, where each orientation corresponds to a “bit”. Here, we use two DNA invertases (FimE and HbiF) that reorient DNA irreversibly between two states with opposite directionality. First, we construct memory that is set by FimE and reset by HbiF. Next, we build a NOT gate where the input promoter drives FimE and in the absence of signal the reverse state is maintained by the constitutive expression of HbiF. The gate requires ∼3 h to turn on and off. The evolutionary stabilities of these circuits are measured by passaging cells while cycling function. The memory switch is stable over 400 h (17 days, 14 state changes); however, the gate breaks after 54 h (>2 days) due to continuous invertase expression. Genome sequencing reveals that the circuit remains intact, but the host strain evolves to reduce invertase expression. This work highlights the need to evaluate the evolutionary robustness and failure modes of circuit designs, especially as more complex multigate circuits are implemented

Schematic representation of how active learning and ML surrogates can work together.

Initially, an ML model is trained on a set of data generated by some initial simulations of a mechanistic model (Xinit, yinit), which are equivalent to (X, y) for this initial step. The ML model is used to make predictions (ypred). The estimated error between the prediction of the mechanistic model (y) and that of the ML model (ypred) is used to select a subset from all the possible input data that has not been used to make predictions using the mechanistic model in the past (X’). The mechanistic model is run using X’ as input to obtain a new set of input-output pairs (X, y), equivalent to the newly generated (X′, y′), that when included in the ML pipeline are expected to reduce the estimated error (y−ypred).</p

Using Synthetic Biological Parts and Microbioreactors to Explore the Protein Expression Characteristics of <i>Escherichia coli</i>

Synthetic biology has developed numerous parts for the precise control of protein expression. However, relatively little is known about the burden these place on a host, or their reliability under varying environmental conditions. To address this, we made use of synthetic transcriptional and translational elements to create a combinatorial library of constructs that modulated expression strength of a green fluorescent protein. Combining this library with a microbioreactor platform, we were able to perform a detailed large-scale assessment of transient expression and growth characteristics of two <i>Escherichia coli</i> strains across several temperatures. This revealed significant differences in the robustness of both strains to differing types of protein expression, and a complex response of transcriptional and translational elements to differing temperatures. This study supports the development of reliable synthetic biological systems capable of working across different hosts and environmental contexts. Plasmids developed during this work have been made publicly available to act as a reference set for future research

An example of the DBTL pipeline where the metabolic or whole-cell models can be replaced by surrogate models.

An example of the DBTL pipeline where the metabolic or whole-cell models can be replaced by surrogate models.</p

Summary of the performance and methodologies of the ML surrogates of the systems biology models.

Summary of the performance and methodologies of the ML surrogates of the systems biology models.</p

Summary of the performance and methodologies of the ML surrogate models that describe engineering processes with methodologies that can be extended to surrogates of biological models.

Summary of the performance and methodologies of the ML surrogate models that describe engineering processes with methodologies that can be extended to surrogates of biological models.</p

DNAplotlib: Programmable Visualization of Genetic Designs and Associated Data

DNAplotlib (www.dnaplotlib.org) is a computational toolkit for the programmable visualization of highly customizable, standards-compliant genetic designs. Functions are provided to aid with both visualization tasks and to extract and overlay associated experimental data. High-quality output is produced in the form of vector-based PDFs, rasterized images, and animated movies. All aspects of the rendering process can be easily customized or extended by the user to cover new forms of genetic part or regulation. DNAplotlib supports improved communication of genetic design information and offers new avenues for static, interactive and dynamic visualizations that map and explore the links between the structure and function of genetic parts, devices and systems; including metabolic pathways and genetic circuits. DNAplotlib is cross-platform software developed using Python

Synchronized genetic oscillators.

<p><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0042790#s3" target="_blank">Results</a> from studying the synchronization of a population of 200 bacteria, each containing a repressilator GRN model <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0042790#pone.0042790-GarciaOjalvo1" target="_blank">[28]</a>. A) Repressilator GRN with external coupling. B–D) Simulations performed with a chemical field diffusivity of 100 and a cell wall diffusion constant of 1 . B) Left to right, simulation output for times 0, 5.5 and 40, where mins is the GRN period of oscillation. The color of the bacteria corresponds to their internal level of <i>lacI</i> mRNA, yellow for low and red for high. External autoinducer level is represented by the intensity of the blue field surrounding the bacteria. Initial mRNA and protein levels for each bacterium were chosen at random. However, synchronization quickly increases over time. Also see Video S2. C) Phase portraits for 3 pairs of bacteria. For clarity the first 2.5 hours of data, where the bacteria were extremely asynchronous, are omitted. Over time, each pair becomes more synchronized. D) Amplitude spectra for all bacteria with colors representing (amplitude) in arbitrary units (a.u.). The clear peaks correspond to the fundamental frequencies of the GRN where phase locked synchronization has occurred. E) Phase transition to synchronization as the cell wall diffusion constant is increased.</p

Multi-level effects of the <i>lac</i> operon.

<p><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0042790#s3" target="_blank">Results</a> from a model of the <i>lac</i> operon that considers the states of individual cells as well as the population as a whole. A) Bimodal state distributions including the external inducer concentration and level of <i>lac</i> permease, . Since the model does not explicitly include an indicator, was used as a proxy measure. Low and high external inducer concentrations bias the population toward an uninduced or induced state respectively, and all concentrations see coexistence of states in the form of a bimodal distribution of . Dashed line indicates the overall population induction (average) that would be measured by purely observing at the population level, i.e., not taking into account the bimodal distribution of individual states. B) Effect of growth rate on coexistence of induced and uninduced states within the population. Line color indicates external inducer concentration, , (yellow = 30 M, green = 80 M, blue = 110 M,), solid and dashed lines indicate simulations where induction did and did not inhibit growth respectively. C) Bifurcation diagram showing bistability in the intracellular inducer concentration, . Red line illustrates the equilibrium state of in the deterministic GRN equations for a single cell as a function of external inducer, , computed via numerical continuation (solid and dashed lines indicate stable and unstable equilibrium respectively); blue line illustrates ensemble average concentration in a BSim simulation which incorporates this deterministic GRN and stochastic agent creation and removal in which was slowly varied (dashed lines indicate population minimum and maximum).</p

Schematic of a BSim model.

<p>BSim models consist of two main levels: 1. the individual agent (top) and 2. the shared environment (bottom). Individual agents are used to model any autonomous entity, such as a bacterium, outer membrane vesicle, etc, and contain an internal state vector which can change over time. BSim provides support for ordinary differential equations or user defined rules when specifying agent dynamics. Agents can sense various environmental factors as inputs and generate outputs within the local environment. The environment provides a shared medium in which agents can move, communicate (using chemical signaling), interact (through physical contact) with other agents or objects, and can be detailed and heterogeneous.</p