Control-based continuation: a new approach to prototype synthetic gene networks

Control-Based Continuation (CBC) is a general and systematic method to carry out the bifurcation analysis of physical experiments. CBC does not rely on a mathematical model and thus overcomes the uncertainty introduced when identifying bifurcation curves indirectly through modelling and parameter estimation. We demonstrate, in silico, CBC applicability to biochemical processes by tracking the equilibrium curve of a toggle switch which includes additive process noise and exhibits bistability. We compare results obtained when CBC uses a model-free and model-based control strategy and show that both can track stable and unstable solutions, revealing bistability. We then demonstrate CBC in conditions more representative of a real experiment using an agent-based simulator describing cells growth and division, cell-to-cell variability, spatial distribution, and diffusion of chemicals. We further show how the identified curves can be used for parameter estimation and discuss how CBC can significantly accelerate the prototyping of synthetic gene regulatory networks.

A genetic toolkit and gene switches to limit Mycoplasma growth for a synthetic vaccine chassis

Mycoplasmas have exceptionally streamlined genomes and are strongly adapted to their many hosts, which provide them with essential nutrients. Owing to their relative genomic simplicity, Mycoplasmas have been used for the development of chassis to deploy tailored vaccines. However, the dearth of robust and precise toolkits for genomic manipulation and tight regulation has hindered any substantial advance. Herein we describe the construction of a robust genetic toolkit for M. pneumoniae, and its successful deployment to engineer synthetic gene switches that control and limit Mycoplasma growth, for biosafety containment applications. We found these synthetic gene circuits to be stable and robust in the long-term, in the context of a minimal cell. With this work, we lay a foundation to develop viable and robust biosafety systems to exploit a synthetic Mycoplasma chassis for live attenuated vaccines or even for live vectors for biotherapeutics.

Synthetic neuromorphic computing in living cells

Biological regulatory networks in cells and neuronal networks employ complex circuit topologies with highly interconnected nodes to perform sophisticated information processing. Despite the complexity of neuronal networks, their information processing and computational capabilities can be recapitulated using simplified models comprising repeated connected nodes, e.g., perceptrons, with decision-making capabilities and flexible weighted links. Here, we argue that analogous to their revolutionary impact on computing, neuro-inspired models can similarly transform synthetic gene circuit design in a manner that is reliable, efficient in resource utilization, and can be readily reconfigurable for new tasks. We introduce neuromorphic design for synthetic gene circuits by first defining the perceptgene, a perceptron that computes in the logarithmic domain, which enables efficient implementation of artificial neural networks in the cellular milieu. Working in Escherichia coli cells, we experimentally demonstrated logarithmic scale analog multiplication using a single perceptgene. We modified perceptgene parameters (weights and biases) to create devices that compute a log-transformed negative rectifier encoding the minimum operation, log-transformed positive rectifier encoding the maximum operation, and log-transformed average of analog inputs. We then created multi-layer perceptgene circuits that compute a majority function, perform analog-to-digital conversion, and implement a ternary switch. Experimental and theoretical analysis showed that our approach enables circuit optimization via artificial intelligence algorithms such as gradient descent and backpropagation. Realizing neural-like computing in the noisy resource-limited environments of individual cells marks an important step towards synthetic biological systems that can be engineered effectively via supervised ANN optimization algorithms.

Data-driven network models for genetic circuits from time-series data with incomplete measurements

Synthetic gene networks are frequently conceptualized and visualized as static graphs. This view of biological programming stands in stark contrast to the transient nature of biomolecular interaction, which is frequently enacted by labile molecules that are often unmeasured. Thus, the network topology and dynamics of synthetic gene networks can be difficult to verify in vivo or in vitro , due to the presence of unmeasured biological states. Here we introduce the dynamical structure function as a new mesoscopic, data-driven class of models to describe gene networks with incomplete measurements of state dynamics. We develop a network reconstruction algorithm and a code base for reconstructing the dynamical structure function from data, to enable discovery and visualization of graphical relationships in a genetic circuit diagram as time-dependent functions rather than static, unknown weights. We prove a theorem, showing that dynamical structure functions can provide a data-driven estimate of the size of crosstalk fluctuations from an idealized model. We illustrate this idea with numerical examples. Finally, we show how data-driven estimation of dynamical structure functions can explain failure modes in two experimentally implemented genetic circuits, a previously reported in vitro genetic circuit and a new E. coli -based transcriptional event detector.

Synthetic Gene Circuits for Antimicrobial Resistance and Cancer Research

Mathematical models and synthetic gene circuits are powerful tools to develop novel treatments for patients with drug-resistant infections and cancers. Mathematical modeling guides the rational design of synthetic gene circuits. These systems are then assembled into unified constructs from existing and/or modified genetic components from a range of organisms. In this chapter, we describe modeling tools for the design and characterization of chemical- and light-inducible synthetic gene circuits in different organisms and highlight how synthetic gene circuits are advancing biomedical research. Specifically, we demonstrate how these quantitative model systems are being used to study drug resistance in microbes and to probe the spatial–temporal dimensions of cancer in mammalian cells.