Intelligent computation in cancer gene therapy.

In recent years, the use of gene therapy for the treatment of disease has gained substantial interest, both in academic research and in the biomedical industry. Initial experimentation in gene therapy has generated positive results, as well as questions regarding safety. However, lessons have been learned from these first investigations, among them a realization that such treatments require a method to fine-tune the expression of therapeutic genes in real-time. A logical solution to this problem arose through the field of synthetic biology in the form of synthetic gene circuits. Thus, the synthetic biology community today aims to create "smart cells" for a variety of gene therapy applications, in an attempt to precisely target malignant cells while avoiding harming healthy ones. To generate safer and more effective gene therapies, new approaches with emerging computational abilities are necessary. In this review, we present several computational approaches which allow demonstrating artificial intelligence in living cells. Specifically, we will focus on implementing artificial neural networks using synthetic gene regulatory networks for cancer therapy and discuss the state-of-the-art computational developments.

Synthetic neuromorphic computing in living cells.

Computational properties of neuronal networks have been applied to computing systems using simplified models comprising repeated connected nodes, e.g., perceptrons, with decision-making capabilities and flexible weighted links. Analogously to their revolutionary impact on computing, neuro-inspired models can transform synthetic gene circuit design in a manner that is reliable, efficient in resource utilization, and readily reconfigurable for different tasks. To this end, we introduce the perceptgene, a perceptron that computes in the logarithmic domain, which enables efficient implementation of artificial neural networks in Escherichia coli cells. We successfully modify perceptgene parameters to create devices that encode a minimum, maximum, and average of analog inputs. With these devices, we create multi-layer perceptgene circuits that compute a soft majority function, perform an analog-to-digital conversion, and implement a ternary switch. We also create a programmable perceptgene circuit whose computation can be modified from OR to AND logic using small molecule induction. Finally, we show that our approach enables circuit optimization via artificial intelligence algorithms.

Synthetic nonlinear computation for genetic circuit design.

Computation frameworks have been studied in synthetic biology to achieve biosignals integration and processing, for biosensing and therapeutics applications. Biological systems exhibit nonlinearity across scales from the molecular level, to biochemical network and intercellular systems. At the molecular level, cooperative bindings contribute to nonlinear molecular signal processing in a way similar to weight variables. At the intracellular network level, feedback and feedforward regulations result in cell behaviors such as multistability and adaptation. When biochemical networks are distributed in different cell groups, intercelluar networks can generate population dynamics. Here, we review works that highlight nonlinear computations in synthetic biology. We group the works according to the scale of implementations, from the cis-transcription level, to biochemical circuit level and cellular networks.

Synthetic neural-like computing in microbial consortia for pattern recognition.

Complex biological systems in nature comprise cells that act collectively to solve sophisticated tasks. Synthetic biological systems, in contrast, are designed for specific tasks, following computational principles including logic gates and analog design. Yet such approaches cannot be easily adapted for multiple tasks in biological contexts. Alternatively, artificial neural networks, comprised of flexible interactions for computation, support adaptive designs and are adopted for diverse applications. Here, motivated by the structural similarity between artificial neural networks and cellular networks, we implement neural-like computing in bacteria consortia for recognizing patterns. Specifically, receiver bacteria collectively interact with sender bacteria for decision-making through quorum sensing. Input patterns formed by chemical inducers activate senders to produce signaling molecules at varying levels. These levels, which act as weights, are programmed by tuning the sender promoter strength Furthermore, a gradient descent based algorithm that enables weights optimization was developed. Weights were experimentally examined for recognizing 3 × 3-bit pattern.

Topologies of synthetic gene circuit for optimal fold change activation.

Computations widely exist in biological systems for functional regulations. Recently, incoherent feedforward loop and integral feedback controller have been implemented into Escherichia coli to achieve a robust adaptation. Here, we demonstrate that an indirect coherent feedforward loop and mutual inhibition designs can experimentally improve the fold change of promoters, by reducing the basal level while keeping the maximum activity high. We applied both designs to six different promoters in E. coli, starting with synthetic inducible promoters as a proof-of-principle. Then, we examined native promoters that are either functionally specific or systemically involved in complex pathways such as oxidative stress and SOS response. Both designs include a cascade having a repressor and a construct of either transcriptional interference or antisense transcription. In all six promoters, an improvement of up to ten times in the fold change activation was observed. Theoretically, our unitless models show that when regulation strength matches promoter basal level, an optimal fold change can be achieved. We expect that this methodology can be applied in various biological systems for biotechnology and therapeutic applications.

Continuous Measurement of Biological Noise in Escherichia Coli Using Time-lapse Microscopy.

The protocol developed here offers a tool to enable computer tracking of Escherichia coli division and fluorescent levels over several hours. The process starts by screening for colonies that survive on minimal media, assuming that only Escherichia coli harboring the correct plasmid will be able to thrive in the specific conditions. Since the process of building large genetic circuits, requiring the assembly of many DNA parts, is challenging, circuit components are often distributed between multiple plasmids at different copy numbers requiring the use of several antibiotics. Mutations in the plasmid can destroy transcription of the antibiotic resistance genes and interject with resources management in the cell leading to necrosis. The selected colony is set on a glass-bottom Petri dish and a few focus planes are selected for microscopy tracking in both bright field and fluorescent domains. The protocol maintains the image focus for more than 12 hours under initial conditions that cannot be regulated, creating a few difficulties. For example, dead cells start to accumulate in the lenses' field of focus after a few hours of imaging, which causes toxins to buildup and the signal to blur and decay. Depletion of nutrients introduces new metabolic processes and hinder the desired response of the circuit. The experiment's temperature lowers the effectivity of inducers and antibiotics, which can further damage the reliability of the signal. The minimal media gel shrinks and dries, and as a result the optical focus changes over time. We developed this method to overcome these challenges in Escherichia coli, similar to previous works developing analogous methods for other micro-organisms. In addition, this method offers an algorithm to quantify the total stochastic noise in unaltered and altered cells, finding that the results are consistent with flow analyzer predictions as shown by a similar coefficient of variation (CV).

A Whole-Cell Bacterial Biosensor for Blood Markers Detection in Urine.

The early detection of blood in urine (hematuria) can play a crucial role in the treatment of serious diseases (e.g., infections, kidney disease, schistosomiasis, and cancer). Therefore, the development of low-cost portable biosensors for blood detection in urine has become necessary. Here, we designed an ultrasensitive whole-cell bacterial biosensor interfaced with an optoelectronic measurement module for heme detection in urine. Heme is a red blood cells (RBCs) component that is liberated from lysed cells. The bacterial biosensor includes Escherichia coli cells carrying a heme-sensitive synthetic promoter integrated with a luciferase reporter (luxCDABE) from Photorhabdus luminescens. To improve the bacterial biosensor performance, we re-engineered the genetic structure of luxCDABE operon by splitting it into two parts (luxCDE and luxAB). The luxCDE genes were regulated by the heme-sensitive promoter, and the luxAB genes were regulated by either constitutive or inducible promoters. We examined the genetic circuit's performance in synthetic urine diluent supplied with heme and in human urine supplied with lysed blood. Finally, we interfaced the bacterial biosensor with a light detection setup based on a commercial optical measurement single-photon avalanche photodiode (SPAD). The whole-cell biosensor was tested in human urine with lysed blood, demonstrating a low-cost, portable, and easy-to-use hematuria detection with an ON-to-OFF ratio of 6.5-fold for blood levels from 5 × 104 to 5 × 105 RBC per mL of human urine.

Quantifying the distribution of protein oligomerization degree reflects cellular information capacity.

The generation of information, energy and biomass in living cells involves integrated processes that optimally evolve into complex and robust cellular networks. Protein homo-oligomerization, which is correlated with cooperativity in biology, is one means of scaling the complexity of protein networks. It can play critical roles in determining the sensitivity of genetic regulatory circuits and metabolic pathways. Therefore, understanding the roles of oligomerization may lead to new approaches of probing biological functions. Here, we analyzed the frequency of protein oligomerization degree in the cell proteome of nine different organisms, and then, we asked whether there are design trade-offs between protein oligomerization, information precision and energy costs of protein synthesis. Our results indicate that there is an upper limit for the degree of protein oligomerization, possibly because of the trade-off between cellular resource limitations and the information precision involved in biochemical reaction networks. These findings can explain the principles of cellular architecture design and provide a quantitative tool to scale synthetic biological systems.

Cytomorphic Electronics With Memristors for Modeling Fundamental Genetic Circuits.

Cytomorphic engineering attempts to study the cellular behavior of biological systems using electronics. As such, it can be considered analogous to the study of neurobiological concepts for neuromorphic engineering applications. To date, digital and analog translinear electronics have commonly been used in the design of cytomorphic circuits; Such circuits could greatly benefit from lowering the area of the digital memory via memristive circuits. In this article, we propose a novel approach that utilizes the Boltzmann-exponential stochastic transport of ionic species through insulators to naturally model the nonlinear and stochastic behavior of biochemical reactions. We first show that two-terminal memristive devices can capture the non-linear and stochastic behavior of biochemical reactions. Then, we present the design of several building blocks based on analog memristive circuits that inherently model the biophysical mechanisms of gene expression. The circuits model induction by small molecules, activation and repression by transcription factors, biological promoters, cooperative binding, and transcriptional and translational regulation of gene expression. Finally, we utilize the building blocks to form complex mixed-signal networks that can simulate the delay-induced oscillator and the p53-mdm2 interaction in the cancer signaling pathway. Our approach can provide a fast and simple emulative framework for studying genetic circuits and arbitrary large-scale biological networks in systems and synthetic biology. Some challenges may be that memristive devices with frequent learning and programming do not have the same longevity as traditional transistor-based electron-transport devices, and operate with significantly slower time constants, which can limit emulation speed.

Synthetic metabolic computation in a bioluminescence-sensing system.

Bioluminescence is visible light produced and emitted by living cells using various biological systems (e.g. luxCDABE cassette). Today, this phenomenon is widely exploited in biological research, biotechnology and medical applications as a quantitative technique for the detection of biological signals. However, this technique has mostly been used to detect a single input only. In this work, we re-engineered the complex genetic structure of luxCDABE cassette to build a biological unit that can detect multi-inputs, process the cellular information and report the computation results. We first split the luxCDABE operon into several parts to create a genetic circuit that can compute a soft minimum in living cells. Then, we used the new design to implement an AND logic function with better performance as compared to AND logic functions based on protein-protein interactions. Furthermore, by controlling the reverse reaction of the luxCDABE cassette independently from the forward reaction, we built a comparator with a programmable detection threshold. Finally, we applied the redesigned cassette to build an incoherent feedforward loop that reduced the unwanted crosstalk between stress-responsive promoters (recA, katG). This work demonstrates the construction of genetic circuits that combine regulations of gene expression with metabolic pathways, for sensing and computing in living cells.

A Novel Bioelectronic Reporter System in Living Cells Tested with a Synthetic Biological Comparator.

As the fields of biotechnology and synthetic biology expand, cheap and sensitive tools are needed to measure increasingly complicated genetic circuits. In order to bypass some drawbacks of optical fluorescent reporting systems, we have designed and created a co-culture microbial fuel cell (MFC) system for electronic reporting. This system leverages the syntrophic growth of Escheriachia. coli (E. coli) and an electrogenic bacterium Shewanella oneidensis MR-1 (S. oneidensis). The fermentative products of E. coli provide a carbon and electron source for S. oneidensis MR-1, which then reports on such activity electrically at the anode of the MFC. To further test the capability of electrical reporting of complicated synthetic circuits, a novel synthetic biological comparator was designed and tested with both fluorescent and electrical reporting systems. The results suggest that the electrical reporting system is a good alternative to commonly used optical fluorescent reporter systems since it is a non-toxic reporting system with a much wider dynamic range.