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While synthetic biology has revolutionized our approaches to medicine, agriculture, and energy, the design of completely novel biological circuit components beyond naturally-derived templates remains challenging due to poorly understood design rules. Toehold switches, which are programmable nucleic acid sensors, face an analogous design bottleneck; our limited understanding of how sequence impacts functionality often necessitates expensive, time-consuming screens to identify effective switches. Here, we introduce Sequence-based Toehold Optimization and Redesign Model (STORM) and Nucleic-Acid Speech (NuSpeak), two orthogonal and synergistic deep learning architectures to characterize and optimize toeholds. Applying techniques from computer vision and natural language processing, we ‘un-box’ our models using convolutional filters, attention maps, and in silico mutagenesis. Through transfer-learning, we redesign sub-optimal toehold sensors, even with sparse training data, experimentally validating their improved performance. This work provides sequence-to-function deep learning frameworks for toehold selection and design, augmenting our ability to construct potent biological circuit components and precision diagnostics., The design of synthetic biology circuits remains challenging due to poorly understood design rules. Here the authors introduce STORM and NuSpeak, two deep-learning architectures to characterize and optimize toehold switches.

In organ transplantation, infection and rejection are major causes of graft loss. They are linked by the net state of immunosuppression. To diagnose and treat these conditions earlier, and to improve long-term patient outcomes, refined strategies for the monitoring of patients after graft transplantation are needed. Here, we show that a fast and inexpensive assay based on CRISPR-Cas13 accurately detects BK polyomavirus DNA and cytomegalovirus DNA from patient-derived blood and urine samples, as well as CXCL9 messenger RNA (a marker of graft rejection) at elevated levels in urine samples from patients experiencing acute kidney transplant rejection. The assay, which we adapted for lateral-flow readout, enables-via simple visualization-the post-transplantation monitoring of common opportunistic viral infections and of graft rejection, and should facilitate point-of-care post-transplantation monitoring.

In organ transplantation, infection and rejection are major causes of graft loss. They are linked by the net state of immunosuppression. To diagnose and treat these conditions earlier, and to improve long-term patient outcomes, refined strategies for the monitoring of patients after graft transplantation are needed. Here, we show that a fast and inexpensive assay based on CRISPR–Cas13 accurately detects BK polyomavirus DNA and cytomegalovirus DNA from patient-derived blood and urine samples, as well as CXCL9 messenger RNA (a marker of graft rejection) at elevated levels in urine samples from patients experiencing acute kidney transplant rejection. The assay, which we adapted for lateral-flow readout, enables—via simple visualization—the post-transplantation monitoring of common opportunistic viral infections and of graft rejection, and should facilitate point-of-care post-transplantation monitoring., National Science Foundation (Award 1122374)

© 2019 Elsevier Inc. Current machine learning techniques enable robust association of biological signals with measured phenotypes, but these approaches are incapable of identifying causal relationships. Here, we develop an integrated “white-box” biochemical screening, network modeling, and machine learning approach for revealing causal mechanisms and apply this approach to understanding antibiotic efficacy. We counter-screen diverse metabolites against bactericidal antibiotics in Escherichia coli and simulate their corresponding metabolic states using a genome-scale metabolic network model. Regression of the measured screening data on model simulations reveals that purine biosynthesis participates in antibiotic lethality, which we validate experimentally. We show that antibiotic-induced adenine limitation increases ATP demand, which elevates central carbon metabolism activity and oxygen consumption, enhancing the killing effects of antibiotics. This work demonstrates how prospective network modeling can couple with machine learning to identify complex causal mechanisms underlying drug efficacy. Causal metabolic pathways underlying antibiotic lethality in bacteria are illuminated by a network model-driven machine learning approach, overcoming limitations of existing “black-box” approaches that cannot reveal causal relationships from large biological datasets.

Background The hallmark of heart failure is increased blood volume. Quantitative blood volume measures are not conveniently available and are not tested in heart failure management. We assess ferumoxytol, a marketed parenteral iron supplement having a long intravascular half-life, to measure the blood volume with cardiovascular magnetic resonance (CMR). Methods Swine were administered 0.7 mg/kg ferumoxytol and blood pool T 1 was measured repeatedly for an hour to characterize contrast agent extraction and subsequent effect on V blood estimates. We compared CMR blood volume with a standard carbon monoxide rebreathing method. We then evaluated three abbreviated acquisition protocols for bias and precision. Results Mean plasma volume estimated by ferumoxytol was 61.9 ± 4.3 ml/kg. After adjustment for hematocrit the resultant mean blood volume was 88.1 ± 9.4 ml/kg, which agreed with carbon monoxide measures (91.1 ± 18.9 ml/kg). Repeated measurements yielded a coefficient of variation of 6.9%, and Bland-Altman repeatability coefficient of 14%. The blood volume estimates with abbreviated protocols yielded small biases (mean differences between 0.01–0.06 L) and strong correlations (r 2 between 0.97–0.99) to the reference values indicating clinical feasibility. Conclusions In this swine model, ferumoxytol CMR accurately measures plasma volume, and with correction for hematocrit, blood volume. Abbreviated protocols can be added to diagnostic CMR examination for heart failure within 8 min.

Allosteric regulation has traditionally been described by mathematically-complex allosteric rate laws in the form of ratios of polynomials derived from the application of simplifying kinetic assumptions. Alternatively, an approach that explicitly describes all known ligand-binding events requires no simplifying assumptions while allowing for the computation of enzymatic states. Here, we employ such a modeling approach to examine the “catalytic potential” of an enzyme—an enzyme’s capacity to catalyze a biochemical reaction. The catalytic potential is the fundamental result of multiple ligand-binding events that represents a “tug of war” among the various regulators and substrates within the network. This formalism allows for the assessment of interacting allosteric enzymes and development of a network-level understanding of regulation. We first define the catalytic potential and use it to characterize the response of three key kinases (hexokinase, phosphofructokinase, and pyruvate kinase) in human red blood cell glycolysis to perturbations in ATP utilization. Next, we examine the sensitivity of the catalytic potential by using existing personalized models, finding that the catalytic potential allows for the identification of subtle but important differences in how individuals respond to such perturbations. Finally, we explore how the catalytic potential can help to elucidate how enzymes work in tandem to maintain a homeostatic state. Taken together, this work provides an interpretation and visualization of the dynamic interactions and network-level effects of interacting allosteric enzymes., Author summary Enzymatic rate laws have historically been used to simulate the dynamics of complex metabolic networks with regulated reactions represented by allosteric rate laws. Here, we use detailed elementary reaction descriptions of regulatory enzymes that allow for the explicit computation of the fraction of the enzymes that are in a catalytically-active state. The fraction of the enzyme that is in the active state represents the time-dependent utilization of the enzyme’s “catalytic potential,” its capacity to catalyze a reaction. We apply this interpretation to red blood cell glycolysis, examining how three key kinases with allosteric regulation modulate their utilization of their catalytic potential based on ligand-binding events throughout the network in order to maintain a homeostatic state. We then examine how an enzyme modulates its utilization of its catalytic potential using personalized data as a case study, visualizing the systems-level properties of a kinetic model.

The allosteric regulation of metabolic enzymes plays a key role in controlling the flux through metabolic pathways. The activity of such enzymes is traditionally described by allosteric rate laws in complex kinetic models of metabolic network function. As an alternative, we describe the fraction of the regulated enzyme that is in an active form by developing a detailed reaction network of all known ligand binding events to the enzyme. This fraction is the fundamental result of metabolic regulation as it represents the “tug of war” among the various regulators and substrates that determine the utilization of the enzyme. The active fraction corresponds to the utilization of the catalytic potential of the enzyme. Using well developed kinetic models of human red blood cell (RBC) glycolysis, we characterize the catalytic potential of its three key kinases: hexokinase (HEX), phosphofructokinase (PFK), and pyruvate kinase (PYK). We then compute their time-dependent interacting catalytic potentials. We show how detailed kinetic models of the management of the catalytic potential of the three kinases elucidates disturbance rejection capabilities of glycolysis. Further, we examine the sensitivity of the catalytic potential through an examination of existing personalized RBC models, providing a physiologically-meaningful sampling of the feasible parameter space. The graphical representation of the dynamic interactions of the individual kinase catalytic potential adjustment provides an easy way to understand how a robust homeostatic state is maintained through interacting allosteric regulatory mechanisms.