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W2802214405 abstract "Article26 April 2018Open Access Transparent process Computer-aided biochemical programming of synthetic microreactors as diagnostic devices Alexis Courbet Corresponding Author Alexis Courbet [email protected] orcid.org/0000-0003-0539-7011 Sys2diag UMR9005 CNRS/ALCEDIAG, Montpellier, France Department of Endocrinology, Diabetes, Nutrition and INSERM 1411 Clinical Investigation Center, University Hospital of Montpellier, Montpellier Cedex 5, France Search for more papers by this author Patrick Amar Patrick Amar orcid.org/0000-0003-0584-0546 Sys2diag UMR9005 CNRS/ALCEDIAG, Montpellier, France LRI, Université Paris Sud - UMR CNRS 8623, Orsay Cedex, France Search for more papers by this author François Fages François Fages EPI Lifeware, INRIA Saclay, Palaiseau, France Search for more papers by this author Eric Renard Eric Renard Department of Endocrinology, Diabetes, Nutrition and INSERM 1411 Clinical Investigation Center, University Hospital of Montpellier, Montpellier Cedex 5, France Institute of Functional Genomics, CNRS UMR 5203, INSERM U1191, University of Montpellier, Montpellier Cedex 5, France Search for more papers by this author Franck Molina Franck Molina Sys2diag UMR9005 CNRS/ALCEDIAG, Montpellier, France Search for more papers by this author Alexis Courbet Corresponding Author Alexis Courbet [email protected] orcid.org/0000-0003-0539-7011 Sys2diag UMR9005 CNRS/ALCEDIAG, Montpellier, France Department of Endocrinology, Diabetes, Nutrition and INSERM 1411 Clinical Investigation Center, University Hospital of Montpellier, Montpellier Cedex 5, France Search for more papers by this author Patrick Amar Patrick Amar orcid.org/0000-0003-0584-0546 Sys2diag UMR9005 CNRS/ALCEDIAG, Montpellier, France LRI, Université Paris Sud - UMR CNRS 8623, Orsay Cedex, France Search for more papers by this author François Fages François Fages EPI Lifeware, INRIA Saclay, Palaiseau, France Search for more papers by this author Eric Renard Eric Renard Department of Endocrinology, Diabetes, Nutrition and INSERM 1411 Clinical Investigation Center, University Hospital of Montpellier, Montpellier Cedex 5, France Institute of Functional Genomics, CNRS UMR 5203, INSERM U1191, University of Montpellier, Montpellier Cedex 5, France Search for more papers by this author Franck Molina Franck Molina Sys2diag UMR9005 CNRS/ALCEDIAG, Montpellier, France Search for more papers by this author Author Information Alexis Courbet *,1,2,6,7, Patrick Amar1,3, François Fages4, Eric Renard2,5 and Franck Molina1 1Sys2diag UMR9005 CNRS/ALCEDIAG, Montpellier, France 2Department of Endocrinology, Diabetes, Nutrition and INSERM 1411 Clinical Investigation Center, University Hospital of Montpellier, Montpellier Cedex 5, France 3LRI, Université Paris Sud - UMR CNRS 8623, Orsay Cedex, France 4EPI Lifeware, INRIA Saclay, Palaiseau, France 5Institute of Functional Genomics, CNRS UMR 5203, INSERM U1191, University of Montpellier, Montpellier Cedex 5, France 6Present address: Department of Biochemistry, University of Washington, Seattle, WA, USA 7Present address: Institute for Protein Design, University of Washington, Seattle, WA, USA *Corresponding author. E-mail: [email protected] Molecular Systems Biology (2018)14:e7845https://doi.org/10.15252/msb.20177845 Correction(s) for this article Computer-aided biochemical programming of synthetic microreactors as diagnostic devices28 June 2018 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract Biological systems have evolved efficient sensing and decision-making mechanisms to maximize fitness in changing molecular environments. Synthetic biologists have exploited these capabilities to engineer control on information and energy processing in living cells. While engineered organisms pose important technological and ethical challenges, de novo assembly of non-living biomolecular devices could offer promising avenues toward various real-world applications. However, assembling biochemical parts into functional information processing systems has remained challenging due to extensive multidimensional parameter spaces that must be sampled comprehensively in order to identify robust, specification compliant molecular implementations. We introduce a systematic methodology based on automated computational design and microfluidics enabling the programming of synthetic cell-like microreactors embedding biochemical logic circuits, or protosensors, to perform accurate biosensing and biocomputing operations in vitro according to temporal logic specifications. We show that proof-of-concept protosensors integrating diagnostic algorithms detect specific patterns of biomarkers in human clinical samples. Protosensors may enable novel approaches to medicine and represent a step toward autonomous micromachines capable of precise interfacing of human physiology or other complex biological environments, ecosystems, or industrial bioprocesses. Synopsis A systematic approach is presented to design and encapsulate biochemical logic circuits within synthetic phospholipid bilayers that operate as synthetic microreactors. As proof-of-concept, such devices were programmed to detect specific patterns of metabolic biomarkers for the diagnosis of diabetes. We introduce the first complete workflow based on computational design and microfluidics for the programming of synthetic cell-like microreactors using biochemical logic circuits, to perform biosensing and biocomputing operations in vitro. For the first time we show that the implementation of Boolean logic circuits with reactive biochemical species can be automated to satisfy user defined temporal logic specifications and their behavior optimized for robustness. We demonstrate the programming, synthesis and operability of three different instances of synthetic biochemical logic circuits encapsulated within synthetic phospholipid bilayers. Using these methodologies, we generate proof-of-concept diagnostic microreactors, or protosensors, biochemically programmed to detect specific patterns of biomarkers and classify pathological states in situ. We demonstrate their capabilities for the diagnosis of acute diabetes complications in human clinical samples. Introduction From nanoscale biomolecular machines to complex organisms, biological systems have evolved to sense, solve logical problems, and respond to their biochemical environment in optimized ways (Jacob & Monod, 1961). For more than decades, their unique information processing capabilities have fascinated both fundamental and engineering sciences (Feynman, 1960; Conrad, 1985; Bray, 1995). The field of synthetic biology has devoted considerable attention to expanding these biochemical mechanisms into scalable synthetic systems integrating modular biosensing and biocomputing with the hope to advance biotechnologies (Benenson et al, 2004; Benenson, 2012; Church et al, 2014; Pardee et al, 2014; Katz, 2015; Van Roekel et al, 2015a; Pardee et al, 2014). Indeed, biomolecular machines capable of dynamic probing and decision-making in situ could offer new ways to interface biology (Slomovic et al, 2015; Courbet et al, 2016) as well as unprecedented versatility in analytical and biomedical applications. For instance, synthetic cell-based devices can be designed and employed as programmable bioanalytical tools to detect molecular cues for diagnostic purposes (Courbet et al, 2015; Danino et al, 2015) or smart therapeutics (Ye & Fussenegger, 2014; Perez-Pinera et al, 2016; Roybal et al, 2016; Xie et al, 2016). While considerable success has been seen in the engineering of control circuits from standardized and composable genetic parts assembled into living cells (Canton et al, 2008; Shetty et al, 2008; Smolke, 2009), genetically modified organisms have intrinsic limits imposed by the cellular machinery and pose ethical, ecological, and industrial challenges (Chugh, 2013). Instead of repurposing living organisms, biomolecules can be used to build cell-free biochemical circuit-based solutions for information processing (Benenson, 2012). Following success with in vitro reconstitution of natural biochemical circuits (Nakajima, 2005), a variety of devices have been designed where biochemical programs are hard-coded within circuits' topology and kinetic parameters ruling the interactions between components, in order to perform useful biomolecular logic: digital/analog circuits (Ashkenasy & Ghadiri, 2004; Niazov et al, 2006; Katz & Privman, 2010; Rialle et al, 2010; Qian & Winfree, 2011; Orbach et al, 2012; Sarpeshkar, 2014; Genot et al, 2016), oscillators (Semenov et al, 2015), switches and memories (Kim et al, 2006; Padirac et al, 2012), noise filters (Tyson & Novák, 2010), neural networks (Qian et al, 2011), or universal Turing machines (Arkin & Ross, 1994) solving hard computational problems (Adleman, 1994; Faulhammer et al, 2000; Stojanovic et al, 2014). Biochemical signal processing has thus been explored, and metabolic cascades of enzymatic reactions or DNA strand displacement mechanisms have been successfully designed by hand and assembled in vitro to yield various useful devices (Sarpeshkar, 2010; Katz, 2012). However, systematically designing arbitrary sequences of logic operations using a variety of biochemical substrates with respect to time-dependent specifications, a strategy we refer to as biochemical programming, has remained challenging. The main reason that has so far prevented the programming of biochemical systems is the exponential growth of the parameter space that consequently cannot be naively sampled to identify robust design implementations. In the same way as electronic design automation enabled the growth in size and capacity of electronic devices (i.e. Moore's law), automated design frameworks are required to build biochemical control circuits de novo (Chandran et al, 2011; Chiang et al, 2014). Although progress in design automation of synthetic gene circuits has been made (Marchisio & Stelling, 2011; Van Roekel et al, 2015b; Delépine et al, 2016; Nielsen et al, 2016), to date no clear engineering principles or methodologies exist to design cell-free synthetic reaction-based logic systems according to specifications, while using a variety of reactive biochemical species of different nature. Furthermore, in the same way natural cells rely on membrane compartmentalization and localization of metabolons to support complex operations to be performed, microarchitectures are required within which biochemical circuitry can be insulated to allow spatial segregation, parallelization of processes, and multiplexed signal processing (Elani et al, 2014). Elegant approaches to synthetic cell-like microreactors containing cascaded circuits of enzymes or nucleic acids, often referred to as protocells, have shown recapitulation of complex behavior found in natural cells such as communication, information processing, metabolism, and reproduction (Noireaux & Libchaber, 2004; Caschera & Noireaux, 2014; Sun et al, 2015; Adamala et al, 2016; Küchler et al, 2016; Qiao et al, 2016). However, these approaches have so far remained unsuitable for scale-up and for potential use as functional devices, for the reason that the behavior and robustness of manually constructed entities could not be efficiently designed, sufficiently controlled, and maintained (Miller & Gulbis, 2015). Here, we propose to automate the programming of membrane-insulated synthetic biochemical circuits through computer-aided design and demonstrate that this strategy can be efficiently applied to build biosensing devices that solve bioanalytical problems at the microscale. In our approach, programming a biochemical circuit to exhibit a user-defined dynamic behavior amounts to identifying suitable reactions of kinetically favorable species for processing a signal from input substrates to output product molecules, together with their respective concentrations at which the robustness is maximized. Starting from previously established software suites for modeling and simulating large-scale biochemical systems with low computational requirements (i.e. BIOCHAM, BioNetCAD, and HSIM) (Mazière et al, 2004; Maziere et al, 2007; Amar et al, 2008; Rizk et al, 2009, 2011; Peres et al, 2010; Rialle et al, 2010; Peres et al, 2013; Amar & Paulevé, 2015), we scale up the capabilities of computer-aided design of biochemical logic circuits through the integration of automated implementation relying on a library of parts mined from natural biochemical networks, combined with model checking, sensitivity, and robustness analysis (Koeppl, 2011; Rizk et al, 2009, 2011). This enables to automate the search for biochemical circuit solutions to defined logic specifications while providing quantitative in silico assessment. Furthermore, we propose to exploit the advantages of digital microfluidic technologies that offer precise control over assembly mechanisms, compartment size and stoichiometry of content, high-throughput generation, and amenability to automation (Miller & Gulbis, 2015). We develop a directed self-assembly microfluidic method that allows us to accurately build picoliter scale cell-like reactors in which biochemical circuits can be insulated within synthetic phospholipidic membranes with respect to in silico models. Using this complete workflow, we show for the first time how to program and assemble in vitro discrete synthetic biochemical microreactors that behave according to arbitrary sensing and logic specifications (Fig 1). We coin the term protosensors, which we define as minimal cell-like biosensing–biocomputing microreactors that can be biochemically programmed with a wide range of biomolecules or synthetic machinery, into smart and autonomously functioning micromachine (Courbet et al, 2017). To our knowledge, our study is the first report of computer-automated design of synthetic cell-like information processing systems. Figure 1. General computer-aided design methodology for the programming of synthetic protosensorsA specific biotechnological problem, such as assaying for the presence of pathological biomarkers in a clinical sample, can be solved by an appropriate set of combined biosensing/biocomputing operations performed by biochemical species. An abstract Boolean function can be formalized along with a set of kinetic specifications, corresponding to the logic to perform on molecular components in situ to solve an analytical and decision problem. Desired Boolean functions can be hard-coded within a biochemical reaction circuit by finding appropriate biomolecular implementations, a process we refer to as biochemical programming. We introduce a systematic methodology based on automated computational design and microfluidics enabling the programming of synthetic cell-like microreactors using programmed biochemical logic circuits, or protosensors, to perform accurate and robust biosensing and biocomputing operations in vitro according to predefined temporal logic specifications. In order to navigate the multidimensional design space comprehensively, we developed computational tools used to automate the search for synthetic biochemical circuit solutions to formal abstractions. Biochemical circuits are then be experimentally insulated within synthetic membranes to yield autonomous, microscale, discrete protosensors behaving according to specifications. Download figure Download PowerPoint As a valuable proof-of-concept, we apply our framework to the biodetection of human pathologies. We demonstrate the capabilities of protosensor biochemical programming by implementing a diagnostic algorithm designed to discriminate between all acute metabolic complications of diabetes and achieve differential diagnosis. We further demonstrate the capabilities of this novel diagnostic approach in clinical context and propose that computer-aided biochemical programming of protosensors could provide versatile microscale solutions to complex analytical questions. Results Operation principles and architecture of protosensors Our first goal was to identify a universal and robust macromolecular architecture capable of supporting the modular implementation of in vitro biosensing/biocomputing processes in the form of synthetic biochemical circuits. This architecture should be capable of (i) stably encapsulating and protecting arbitrary biochemical circuits irrelevant of their biomolecular composition, (ii) discretizing space through the definition of an insulated interior containing the synthetic circuit, and an exterior consisting of the medium to operate in (e.g. a clinical sample), (iii) allowing signal transduction through selective mass transfer of molecular signals (i.e. biomarker inputs), and (iv) supporting accurate construction through thermodynamically favorable self-assembling mechanisms. The protosensor architecture we propose in this study consists of synthetic vesicles made of phospholipid bilayer membranes rendered permeable to small organic molecules through self-incorporation of α-hemolysin transmembrane protein pores. While nucleic acids have so far been favored due to the advantage of Watson–Crick base pairing-dependent programmability (Padirac et al, 2013), we decide to rely on proteins that are versatile computational elements offering a wide panel of kinetics and functionality (Bray, 1995). Similarly to natural cells, in this architecture we propose the biochemical work necessary to support signal sensing, processing, and output generation originates from redox reactions. Potential biochemical energy is either stored in encapsulated electrons donors or originates from energy-rich molecular inputs (e.g. glucose). Interestingly, enzyme-gated electron transfer displays useful thermodynamic similarities with current flow in electronics and behaves as elementary biomolecular transistors wired through binding kinetics determining logic, signal amplification, and memory (Eyring et al, 1981; Mehta et al, 2015). Proteins of specific functionality also offer the advantage to be identified through mining of databases of natural biochemical networks, and synthetic biochemical circuits can be easily designed to integrate catalytic activities that depend on specific molecular biomarkers, enabling the coupling of biosensing with decision-making algorithms in situ. Here, biochemical information processing under the digital domain to implement decision-making algorithms requires the definition of thresholds for concentration parameters, where a signal at a node of a biochemical circuit is encoded as the continuous valuation or absence of a particular species. A robust architecture would thus allow us to use a systematic design framework, which is detailed in Fig 1. As a proof-of-concept for the diagnosis of specific human pathologies through the biodetection of patterns of biomarkers in clinical samples, we chose to implement a clinically useful algorithm enabling to classify acute metabolic complications of diabetes, namely diabetic ketoacidosis, hyperglycemic hyperosmolar state, hypoglycemia, and lactic acidosis, which are known to be associated with a high medical and socioeconomic burden and with important mortality and morbidity (Fig 2A and B). Figure 2. Architecture and operational principle of protosensors for medical diagnosis Arising from a clinical need to detect pathologies associated with specific patterns of biomarkers in clinical samples, medical diagnosis can be abstracted to a computational process formalized using Boolean logic in vitro and programmed into synthetic biochemical circuits. These de novo circuits can be programmed and optimized in silico, assembled from naturally occurring building blocks, and insulated in synthetic containers in vitro to yield diagnostic devices, or protosensors. Protosensors are capable of detecting patterns of specific biomarkers in human clinical samples and integrate these signals in a medical decision algorithm. If a pathological pattern of biomarker is detected, protosensors generate specific colorimetric outputs. Different types of protosensors corresponding to different clinical questions can be used at the same time to enable multiplexed detection of pathological biomarkers, subsequent logic processing, and achieve differential diagnosis of pathologies in clinical samples. Proof-of-concept diagnostic algorithm used in this study and programmed into protosensors circuitry to achieve differential diagnosis of diabetes acute complications and screening for diabetes. Diabetes-associated acute complications, namely diabetic ketoacidosis, hyperglycemia hyperosmolar state, hypoglycemia, and lactic acidosis, are clinical emergencies that represent a major healthcare burden associated with severe mortality, morbidity, and frequent complications. Here, we propose a diagnostic algorithm enabling differential diagnosis of these complications, as well as a proof-of-concept screening assay, from markers present in urines. Logical abstraction and in silico-automated implementation of synthetic biochemical circuits for medical diagnosis. Top: formal Boolean description depicted using basic logic gates symbols, and theoretical truth tables for three models recapitulating the medical algorithm, bottom: biochemical circuit solutions found after automated in silico search for implementation. SBML models of the synthetic circuits can be found in Appendix. Download figure Download PowerPoint Computer-aided biochemical programming of useful algorithms in synthetic biochemical circuits: from in silico design to in vitro implementation Programming formal models of biosensing/biocomputing problems amounts to identifying precise biochemical implementations satisfying Boolean logic, molecular input/output, dynamic range, and kinetic specifications within a multidimensional design space. Therefore, a primary key step was to develop a systematic in silico framework supporting design automation of synthetic biochemical logic circuits. For this purpose, we developed a computational tool, NetGate, a part of the Silicell Maker software suite, which enables us to mine curated metabolic network databases for biochemical parts, devices, and circuits performing specific Boolean functions, and automates the search for more complex biochemical algorithms (Bouffard et al, 2015). In this context, we define mining as the automated implementation of a formal logic function by a set of biochemical reactive species extracted from natural metabolic networks. Since we aim at programming biochemical logic circuits using multiple reactions taking place simultaneously in a microreactor, we reason that mining for molecular species within the same metabolic context in vivo would minimize possible failure modes. Briefly, NetGate defines biochemical logic gates by their truth table, the set of molecular species representing input substrates, output products, and enzyme. NetGate takes as inputs a SBML file describing an input metabolic network and a list of truth tables corresponding to Boolean functions that are to be searched in the metabolic network. Additional details about this process can be found in Appendix, while an in-depth description of the algorithm behind NetGate can be found in Bouffard et al Second, we developed and refined HSIM (hyperstructure simulator) (Amar et al, 2008; Rialle et al, 2010; Amar & Paulevé, 2015), a flexible hybrid SSA and entity centered based stochastic and ODEs simulator, which enables fast and accurate model prediction incorporating common biochemical parameters, chemical reactivity (concentrations, Km, Kcat, molecule size, motion, diffusion), and spatial features of microscale structures for realistic physics-based simulation of three-dimensional complex environments. In order to model the selective permeation of small molecules' inputs through the α-hemolysin pores of the protosensors membrane, we implemented in HSIM Fick's equations of diffusion (see Appendix for details). In this study, we use HSIM to perform assessment of kinetically and functionally suitable logic devices circuits and verify the behavior of protosensors. The software environment BIOCHAM (Calzone et al, 2006; Soliman, 2012) (Biochemical Abstract Machine) then provides model checking, automated exploration of a multidimensional design space, and optimization of experimental parameters according to temporal logic specifications. Specifically, BIOCHAM supports sensitivity and robustness analysis of the biochemical parameters (e.g. enzyme concentrations) that have to be finely tuned with respect to each other to maximize robustness with respect to specific temporal logic behaviors (Fig 1). To generate the synthetic biochemical circuits described in this study, we performed an organism agnostic search of all the sets of natural metabolic networks of the BRENDA database with overlapping enzymes, substrates, or products related to the inputs and outputs of the circuits we aimed at designing. SBML files of these networks were then downloaded and merged into a large SBML network (Appendix Fig S1 and Code EV1). This large network was used as input and mined using the program NetGate to identify 775 biochemical logic gates solutions of <2 reactions. The program NetBuild was then used to find unique biochemical implementations satisfying the Boolean logic specifications of protosensors that could execute the particular acute diabetes diagnostic algorithm (Fig 2C, Appendix Figs S1 and S2). The medical algorithm is distributed through three distinct and orthogonal protosensors, each processing two biomarkers as inputs, which were named for convenience GluONe (Glucose and Acetone as inputs, Code EV2), LacOH (Lactate and Ethanol as inputs, Code EV3), and GluNOx (Glucose and Nitric oxides as inputs, Code EV4). The biochemical implementation for these three systems required 6, 5, and 4 different biochemical entities, comprising 4, 3, and 2 different enzymes, respectively. Biomolecular signal processing occurring in these circuits leads to the synthesis of the following measurable output signals molecules: NADH (output 1, 340 nm absorbance), Resorufin (output 2, 571–600 nm fluorescence), ABTS (output 3, 420 nm absorbance), and DAF (output 4, 488–515 nm fluorescence). The Boolean formalism and truth tables corresponding to the medical algorithm, as well as the biochemical implementation, are depicted in Fig 2C (Detailed in Appendix Fig S2), and SBML models of the synthetic circuits can be found in SI. Feeding HSIM with biochemical knowledge extracted from the BRENDA database, stochastic simulations were then performed to evaluate the behavior of the three circuits (See Appendix Table S1 and S2, Appendix Figs S2 and S3 for more detail). As a first step, we studied models of non-encapsulated synthetic circuits, where initial conditions (i.e. species concentrations) were determined empirically and non-optimized. Predictions of the evolution of these three biochemical circuits after induction with variable concentrations of input biomarkers are represented as computed molecular output signals heat maps (Fig 3A). The relation between computed molecular concentrations and experimental measured signal was calibrated beforehand (See Appendix and Appendix Fig S4). In silico models showed that the de novo biochemical circuits behaved according to Boolean logic specifications with large signal fold change and near-digital responses. In addition, switching thresholds were found to match useful clinical sensitivity for biomarker inputs (i.e. pathological thresholds: ketones > 17 μM (~10 mg/dl); glucose > 1.39 mM (~25 mg/dl); lactate > 10 μM; EtOH > 17.4 mM (~80 mg/dl)); NOx > 1,000 μM). Figure 3. In silico models and experimental assessment of programmed synthetic biochemical circuits Computed heat maps depicting the in silico transfer function of non-optimized implementations of synthetic biochemical circuits recapitulating the medical algorithm of interest. We used stochastic HSIM simulation to compute the concentration at 60 min, and therefore the absorption (Out1 and Out3) or fluorescence (Out2 and Out4) of outputs after induction with varying input concentrations. Concentration parameters were determined empirically, and kinetic parameters were extracted from BRENDA database. The mathematical relation between concentration of outputs and related absorption or fluorescence was calibrated beforehand (Appendix Fig S4). Heat maps were generated by plotting the mean of five simulations trajectories for each point. In vitro biochemical circuits implementations and experimental measurements of truth tables with photograph of tubes at 60 min showing human-readable outputs (left), and kinetic behavior compared to HSIM predictions (right). The concentrations of inputs used to induce the circuits were 1 mM acetone, 1 mM glucose, 20 mM ethanol, 0.5 mM lactate, 5 mM glucose, and 5 mM NOx. The experiments were carried out at 25°C, and the truth table reflects the values obtained at 60 min. All experiments were performed in triplicate wells for each condition and repeated three times on different weeks and different batches, reported is the mean with error bars showing SD. From top to bottom, two-sided Student's t-test of induced versus highest non-induced condition P = 1.01286E-06, P = 1.08059E-07, P = 1.2022E-04, P = 3.3309E-04, P = 4.27901E-05. Download figure Download PowerPoint Investigation of the experimental behavior of the synthetic circuits prior to encapsulation was then carried out. We proceeded to in vitro impleme" @default.
W2802214405 created "2018-05-17" @default.
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