Synthetic Biology Meets Machine Learning.

This chapter outlines the myriad applications of machine learning (ML) in synthetic biology, specifically in engineering cell and protein activity, and metabolic pathways. Though by no means comprehensive, the chapter highlights several prominent computational tools applied in the field and their potential use cases. The examples detailed reinforce how ML algorithms can enhance synthetic biology research by providing data-driven insights into the behavior of living systems, even without detailed knowledge of their underlying mechanisms. By doing so, ML promises to increase the efficiency of research projects by modeling hypotheses in silico that can then be tested through experiments. While challenges related to training dataset generation and computational costs remain, ongoing improvements in ML tools are paving the way for smarter and more streamlined synthetic biology workflows that can be readily employed to address grand challenges across manufacturing, medicine, engineering, agriculture, and beyond.

Tweak to Treat: Reprograming Bacteria for Cancer Treatment.

Recent advancements in cancer biology, microbiology, and bioengineering have spurred the development of engineered live biotherapeutics for targeted cancer therapy. In particular, natural tumor-targeting and probiotic bacteria have been engineered for controlled and sustained delivery of anticancer agents into the tumor microenvironment (TME). Here, we review the latest advancements in the development of engineered bacteria for cancer therapy and additional engineering strategies to potentiate the delivery of therapeutic payloads. We also explore the use of combination therapies comprising both engineered bacteria and conventional anticancer therapies for addressing intratumor heterogeneity. Finally, we discuss prospects for the development and clinical translation of engineered bacteria for cancer prevention and treatment.

Meta-Omics- and Metabolic Modeling-Assisted Deciphering of Human Microbiota Metabolism.

The human microbiota is a complex community of commensal, symbiotic, and pathogenic microbes that play a crucial role in maintaining the homeostasis of human health. Such a homeostasis is maintained through the collective functioning of enzymatic genes responsible for the production of metabolites, enabling the interaction and signaling within microbiota as well as between microbes and the human host. Understanding microbial genes, their associated chemistries and functions would be valuable for engineering systemic metabolic pathways within the microbiota to manage human health and diseases. Given that there are many unknown gene metabolic functions and interactions, increasing efforts have been made to gain insights into the underlying functions of microbiota metabolism. This can be achieved through culture-independent metagenomic approaches and metabolic modeling to simulate the microenvironment of human microbiota. In this article, the recent advances in metagenome mining and functional profiling for the discovery of the genetic and biochemical links in human microbiota metabolism as well as metabolic modeling for simulation and prediction of metabolic fluxes in the human microbiota are reviewed. This review provides useful insights into the understanding, reconstruction, and modulation of the human microbiota guided by the knowledge acquired from the basic understanding of the human microbiota metabolism.

Array-on-Array Strategy For Activity-Based Enzyme Profiling.

We describe a novel array on array strategy intended to enhance the throughput of enzymatic activity screening using microarrays. This strategy consists of spotting a first array with large droplets of enzymes with varying concentrations and subsequently spotting a second array with small droplets of fluorogenic substrate on top of the enzyme array. By varying the array on array spotting patterns of different classes of enzyme (e.g., proteases, phosphatases, kinases) and their corresponding fluorogenic substrates, we have the unprecedented ability for testing enzymes and mixed samples in a multiplexed fashion within a single microarray slide. This new approach enables rapid enzyme characterization building upon a one enzyme on one slide droplet-based screening concept previously established.

3D characterization and trend analysis of scents from EEG recordings of repeated scent exposure.

Scent plays an important role in influencing the brain and has been commonly used in psychological research. Much of such research has been conducted without the use of electroencephalography (EEG) to measure the response of the human brain to scent stimulus. Recent studies have involved the use of EEG to perform comparative studies on how different scents can affect brain activity. However, little has been done to analyze the trend of brain activity when a subject is repeatedly exposed to the same scent. This paper discusses the use of 4 features - Entropy Difference, Entropy Ratio, Entropy Time and Root Mean Square (RMS) to perform trend analysis of EEG signals in a repeated scent-exposure setting. The results show that different types of scents cause the brain to be stimulated at different degrees for each repeated exposure, giving rise to different trend patterns. It is also observed that the 4 features give similar trends for the same scent. This similarity allows us to combine the 4 features by forming a feature vector and plotting them in 3 dimensional (3D) space, using 3 repeated scent exposures as the axes. The region of space where the feature vector lies is represented by an ellipsoid, which can be used to characterize a particular scent. Unlike previous work, which did not characterize scent from EEG recordings, this paper investigates the different trends of scent after its repeated exposure to the human subject and by using the 3D representation to characterize the scent.