Conditional sanctioning in a legume-Rhizobium mutualism.

Legumes are high in protein and form a valuable part of human diets due to their interaction with symbiotic nitrogen-fixing bacteria known as rhizobia. Plants house rhizobia in specialized root nodules and provide the rhizobia with carbon in return for nitrogen. However, plants usually house multiple rhizobial strains that vary in their fixation ability, so the plant faces an investment dilemma. Plants are known to sanction strains that do not fix nitrogen, but nonfixers are rare in field settings, while intermediate fixers are common. Here, we modeled how plants should respond to an intermediate fixer that was otherwise isogenic and tested model predictions using pea plants. Intermediate fixers were only tolerated when a better strain was not available. In agreement with model predictions, nodules containing the intermediate-fixing strain were large and healthy when the only alternative was a nonfixer, but nodules of the intermediate-fixing strain were small and white when the plant was coinoculated with a more effective strain. The reduction in nodule size was preceded by a lower carbon supply to the nodule even before differences in nodule size could be observed. Sanctioned nodules had reduced rates of nitrogen fixation, and in later developmental stages, sanctioned nodules contained fewer viable bacteria than nonsanctioned nodules. This indicates that legumes can make conditional decisions, most likely by comparing a local nodule-dependent cue of nitrogen output with a global cue, giving them remarkable control over their symbiotic partners.

Turing Patterning in Stratified Domains.

Reaction-diffusion processes across layered media arise in several scientific domains such as pattern-forming E. coli on agar substrates, epidermal-mesenchymal coupling in development, and symmetry-breaking in cell polarization. We develop a modeling framework for bilayer reaction-diffusion systems and relate it to a range of existing models. We derive conditions for diffusion-driven instability of a spatially homogeneous equilibrium analogous to the classical conditions for a Turing instability in the simplest nontrivial setting where one domain has a standard reaction-diffusion system, and the other permits only diffusion. Due to the transverse coupling between these two regions, standard techniques for computing eigenfunctions of the Laplacian cannot be applied, and so we propose an alternative method to compute the dispersion relation directly. We compare instability conditions with full numerical simulations to demonstrate impacts of the geometry and coupling parameters on patterning, and explore various experimentally relevant asymptotic regimes. In the regime where the first domain is suitably thin, we recover a simple modulation of the standard Turing conditions, and find that often the broad impact of the diffusion-only domain is to reduce the ability of the system to form patterns. We also demonstrate complex impacts of this coupling on pattern formation. For instance, we exhibit non-monotonicity of pattern-forming instabilities with respect to geometric and coupling parameters, and highlight an instability from a nontrivial interaction between kinetics in one domain and diffusion in the other. These results are valuable for informing design choices in applications such as synthetic engineering of Turing patterns, but also for understanding the role of stratified media in modulating pattern-forming processes in developmental biology and beyond.

Scaling up genetic circuit design for cellular computing: advances and prospects.

Synthetic biology aims to engineer and redesign biological systems for useful real-world applications in biomanufacturing, biosensing and biotherapy following a typical design-build-test cycle. Inspired from computer science and electronics, synthetic gene circuits have been designed to exhibit control over the flow of information in biological systems. Two types are Boolean logic inspired TRUE or FALSE digital logic and graded analog computation. Key principles for gene circuit engineering include modularity, orthogonality, predictability and reliability. Initial circuits in the field were small and hampered by a lack of modular and orthogonal components, however in recent years the library of available parts has increased vastly. New tools for high throughput DNA assembly and characterization have been developed enabling rapid prototyping, systematic in situ characterization, as well as automated design and assembly of circuits. Recently implemented computing paradigms in circuit memory and distributed computing using cell consortia will also be discussed. Finally, we will examine existing challenges in building predictable large-scale circuits including modularity, context dependency and metabolic burden as well as tools and methods used to resolve them. These new trends and techniques have the potential to accelerate design of larger gene circuits and result in an increase in our basic understanding of circuit and host behaviour.

Computing with biological switches and clocks.

The complex dynamics of biological systems is primarily driven by molecular interactions that underpin the regulatory networks of cells. These networks typically contain positive and negative feedback loops, which are responsible for switch-like and oscillatory dynamics, respectively. Many computing systems rely on switches and clocks as computational modules. While the combination of such modules in biological systems leads to a variety of dynamical behaviours, it is also driving development of new computing algorithms. Here we present a historical perspective on computation by biological systems, with a focus on switches and clocks, and discuss parallels between biology and computing. We also outline our vision for the future of biological computing.

Orthogonal intercellular signaling for programmed spatial behavior.

Bidirectional intercellular signaling is an essential feature of multicellular organisms, and the engineering of complex biological systems will require multiple pathways for intercellular signaling with minimal crosstalk. Natural quorum-sensing systems provide components for cell communication, but their use is often constrained by signal crosstalk. We have established new orthogonal systems for cell-cell communication using acyl homoserine lactone signaling systems. Quantitative measurements in contexts of differing receiver protein expression allowed us to separate different types of crosstalk between 3-oxo-C6- and 3-oxo-C12-homoserine lactones, cognate receiver proteins, and DNA promoters. Mutating promoter sequences minimized interactions with heterologous receiver proteins. We used experimental data to parameterize a computational model for signal crosstalk and to estimate the effect of receiver protein levels on signal crosstalk. We used this model to predict optimal expression levels for receiver proteins, to create an effective two-channel cell communication device. Establishment of a novel spatial assay allowed measurement of interactions between geometrically constrained cell populations via these diffusible signals. We built relay devices capable of long-range signal propagation mediated by cycles of signal induction, communication and response by discrete cell populations. This work demonstrates the ability to systematically reduce crosstalk within intercellular signaling systems and to use these systems to engineer complex spatiotemporal patterning in cell populations.

Is 'peak N' key to understanding the timing of flowering in annual plants?

Flowering time in annual plants has large fitness consequences and has been the focus of theoretical and empirical study. Previous theory has concluded that flowering time has evolved over evolutionary time to maximize fitness over a particular season length. We introduce a new model where flowering is cued by a growth-rate rule (peak nitrogen (N)). Flowering is therefore sensitive to physiological parameters and to current environmental conditions, including N availability and the presence of competitors. The model predicts that, when overall conditions are suitable for flowering, plants should never flower after 'peak N', the point during development when the whole-plant N uptake rate reaches its maximum. Our model further predicts correlations between flowering time and vegetative growth rates, and that the response to increased N depends heavily on how this extra N is made available. We compare our predictions to observations in the literature. We suggest that annual plants may have evolved to use growth-rate rules as part of the cue for flowering, allowing them to smoothly and optimally adjust their flowering time to a wide range of local conditions. If so, there are widespread implications for the study of the molecular biology behind flowering pathways.

The circadian clock has transient plasticity of period and is required for timing of nocturnal processes in Arabidopsis.

A circadian rhythm matched to the phase and period of the day-night cycle has measurable benefits for land plants. We assessed the contribution of circadian period to the phasing of cellular events with the light : dark cycle. We also investigated the plasticity of circadian period within the Arabidopsis circadian oscillator. We monitored the circadian oscillator in wild-type and circadian period mutants under light : dark cycles of varying total duration. We also investigated changes in oscillator dynamics during and after the transition from light : dark cycles to free running conditions. Under light : dark cycles, dawn and dusk were anticipated differently when the circadian period was not resonant with the environmental period ('T cycle'). Entrainment to T cycles differing from the free-running period caused a short-term alteration in oscillator period. The transient plasticity of period was described by existing mathematical models of the Arabidopsis circadian network. We conclude that a circadian period resonant with the period of the environment is particularly important for anticipation of dawn and the timing of nocturnal events; and there is short-term and transient plasticity of period of the Arabidopsis circadian network.

The circadian oscillator gene GIGANTEA mediates a long-term response of the Arabidopsis thaliana circadian clock to sucrose.

Circadian clocks are 24-h timing devices that phase cellular responses; coordinate growth, physiology, and metabolism; and anticipate the day-night cycle. Here we report sensitivity of the Arabidopsis thaliana circadian oscillator to sucrose, providing evidence that plant metabolism can regulate circadian function. We found that the Arabidopsis circadian system is particularly sensitive to sucrose in the dark. These data suggest that there is a feedback between the molecular components that comprise the circadian oscillator and plant metabolism, with the circadian clock both regulating and being regulated by metabolism. We used also simulations within a three-loop mathematical model of the Arabidopsis circadian oscillator to identify components of the circadian clock sensitive to sucrose. The mathematical studies identified GIGANTEA (GI) as being associated with sucrose sensing. Experimental validation of this prediction demonstrated that GI is required for the full response of the circadian clock to sucrose. We demonstrate that GI acts as part of the sucrose-signaling network and propose this role permits metabolic input into circadian timing in Arabidopsis.

Correct biological timing in Arabidopsis requires multiple light-signaling pathways.

Circadian oscillators provide rhythmic temporal cues for a range of biological processes in plants and animals, enabling anticipation of the day/night cycle and enhancing fitness-associated traits. We have used engineering models to understand the control principles of a plant's response to seasonal variation. We show that the seasonal changes in the timing of circadian outputs require light regulation via feed-forward loops, combining rapid light-signaling pathways with entrained circadian oscillators. Linear time-invariant models of circadian rhythms were computed for 3,503 circadian-regulated genes and for the concentration of cytosolic-free calcium to quantify the magnitude and timing of regulation by circadian oscillators and light-signaling pathways. Bioinformatic and experimental analysis show that rapid light-induced regulation of circadian outputs is associated with seasonal rephasing of the output rhythm. We identify that external coincidence is required for rephasing of multiple output rhythms, and is therefore important in general phase control in addition to specific photoperiod-dependent processes such as flowering and hypocotyl elongation. Our findings uncover a fundamental design principle of circadian regulation, and identify the importance of rapid light-signaling pathways in temporal control.

Understanding biological timing using mechanistic and black-box models.

The use of mathematical modelling in understanding and dissecting physiological mechanisms in plants has seen many successes. Notably, studies of the component interactions of the Arabidopsis circadian clock have yielded multiple insights into the roles of specific regulators at the transcriptional and post-transcriptional level. In this article, I review the use of mathematical techniques in dissecting the Arabidopsis clock mechanism, covering first the well-established use of mechanistic models implemented as systems of nonlinear ordinary differential equations. In situations where mechanistic models are not appropriate, I describe how linear time-invariant (LTI) systems, a type of black-box model, can offer quantitative descriptions of biological systems that provide a systems-level understanding without detailed descriptions of the underlying mechanism. A comparison of the two approaches is provided to exemplify when LTI systems modelling might offer advantages for interpreting biological measurements. In particular, formal analysis of large datasets with LTI systems can offer genome-scale inferences, which is of timely relevance as novel experimental techniques are generating increasingly large quantities of data.

A peptide filtering relation quantifies MHC class I peptide optimization.

Major Histocompatibility Complex (MHC) class I molecules enable cytotoxic T lymphocytes to destroy virus-infected or cancerous cells, thereby preventing disease progression. MHC class I molecules provide a snapshot of the contents of a cell by binding to protein fragments arising from intracellular protein turnover and presenting these fragments at the cell surface. Competing fragments (peptides) are selected for cell-surface presentation on the basis of their ability to form a stable complex with MHC class I, by a process known as peptide optimization. A better understanding of the optimization process is important for our understanding of immunodominance, the predominance of some T lymphocyte specificities over others, which can determine the efficacy of an immune response, the danger of immune evasion, and the success of vaccination strategies. In this paper we present a dynamical systems model of peptide optimization by MHC class I. We incorporate the chaperone molecule tapasin, which has been shown to enhance peptide optimization to different extents for different MHC class I alleles. Using a combination of published and novel experimental data to parameterize the model, we arrive at a relation of peptide filtering, which quantifies peptide optimization as a function of peptide supply and peptide unbinding rates. From this relation, we find that tapasin enhances peptide unbinding to improve peptide optimization without significantly delaying the transit of MHC to the cell surface, and differences in peptide optimization across MHC class I alleles can be explained by allele-specific differences in peptide binding. Importantly, our filtering relation may be used to dynamically predict the cell surface abundance of any number of competing peptides by MHC class I alleles, providing a quantitative basis to investigate viral infection or disease at the cellular level. We exemplify this by simulating optimization of the distribution of peptides derived from Human Immunodeficiency Virus Gag-Pol polyprotein.

Programming Molecular Systems To Emulate a Learning Spiking Neuron.

Hebbian theory seeks to explain how the neurons in the brain adapt to stimuli to enable learning. An interesting feature of Hebbian learning is that it is an unsupervised method and, as such, does not require feedback, making it suitable in contexts where systems have to learn autonomously. This paper explores how molecular systems can be designed to show such protointelligent behaviors and proposes the first chemical reaction network (CRN) that can exhibit autonomous Hebbian learning across arbitrarily many input channels. The system emulates a spiking neuron, and we demonstrate that it can learn statistical biases of incoming inputs. The basic CRN is a minimal, thermodynamically plausible set of microreversible chemical equations that can be analyzed with respect to their energy requirements. However, to explore how such chemical systems might be engineered de novo, we also propose an extended version based on enzyme-driven compartmentalized reactions. Finally, we show how a purely DNA system, built upon the paradigm of DNA strand displacement, can realize neuronal dynamics. Our analysis provides a compelling blueprint for exploring autonomous learning in biological settings, bringing us closer to realizing real synthetic biological intelligence.

Tapasin-mediated editing of the MHC I immunopeptidome is epitope specific and dependent on peptide off-rate, abundance, and level of tapasin expression.

Tapasin, a component of the major histocompatibility complex (MHC) I peptide loading complex, edits the repertoire of peptides that is presented at the cell surface by MHC I and thereby plays a key role in shaping the hierarchy of CD8+ T-cell responses to tumors and pathogens. We have developed a system that allows us to tune the level of tapasin expression and independently regulate the expression of competing peptides of different off-rates. By quantifying the relative surface expression of peptides presented by MHC I molecules, we show that peptide editing by tapasin can be measured in terms of "tapasin bonus," which is dependent on both peptide kinetic stability (off-rate) and peptide abundance (peptide supply). Each peptide has therefore an individual tapasin bonus fingerprint. We also show that there is an optimal level of tapasin expression for each peptide in the immunopeptidome, dependent on its off-rate and abundance. This is important, as the level of tapasin expression can vary widely during different stages of the immune response against pathogens or cancer and is often the target for immune escape.

A systematic approach to inserting split inteins for Boolean logic gate engineering and basal activity reduction.

Split inteins are powerful tools for seamless ligation of synthetic split proteins. Yet, their use remains limited because the already intricate split site identification problem is often complicated by the requirement of extein junction sequences. To address this, we augment a mini-Mu transposon-based screening approach and devise the intein-assisted bisection mapping (IBM) method. IBM robustly reveals clusters of split sites on five proteins, converting them into AND or NAND logic gates. We further show that the use of inteins expands functional sequence space for splitting a protein. We also demonstrate the utility of our approach over rational inference of split sites from secondary structure alignment of homologous proteins, and that basal activities of highly active proteins can be mitigated by splitting them. Our work offers a generalizable and systematic route towards creating split protein-intein fusions for synthetic biology.

Decentralizing Cell-Free RNA Sensing With the Use of Low-Cost Cell Extracts.

Cell-free gene expression systems have emerged as a promising platform for field-deployed biosensing and diagnostics. When combined with programmable toehold switch-based RNA sensors, these systems can be used to detect arbitrary RNAs and freeze-dried for room temperature transport to the point-of-need. These sensors, however, have been mainly implemented using reconstituted PURE cell-free protein expression systems that are difficult to source in the Global South due to their high commercial cost and cold-chain shipping requirements. Based on preliminary demonstrations of toehold sensors working on lysates, we describe the fast prototyping of RNA toehold switch-based sensors that can be produced locally and reduce the cost of sensors by two orders of magnitude. We demonstrate that these in-house cell lysates provide sensor performance comparable to commercial PURE cell-free systems. We further optimize these lysates with a CRISPRi strategy to enhance the stability of linear DNAs by knocking-down genes responsible for linear DNA degradation. This enables the direct use of PCR products for fast screening of new designs. As a proof-of-concept, we develop novel toehold sensors for the plant pathogen Potato Virus Y (PVY), which dramatically reduces the yield of this important staple crop. The local implementation of low-cost cell-free toehold sensors could enable biosensing capacity at the regional level and lead to more decentralized models for global surveillance of infectious disease.

A deep learning model for predicting next-generation sequencing depth from DNA sequence.

Targeted high-throughput DNA sequencing is a primary approach for genomics and molecular diagnostics, and more recently as a readout for DNA information storage. Oligonucleotide probes used to enrich gene loci of interest have different hybridization kinetics, resulting in non-uniform coverage that increases sequencing costs and decreases sequencing sensitivities. Here, we present a deep learning model (DLM) for predicting Next-Generation Sequencing (NGS) depth from DNA probe sequences. Our DLM includes a bidirectional recurrent neural network that takes as input both DNA nucleotide identities as well as the calculated probability of the nucleotide being unpaired. We apply our DLM to three different NGS panels: a 39,145-plex panel for human single nucleotide polymorphisms (SNP), a 2000-plex panel for human long non-coding RNA (lncRNA), and a 7373-plex panel targeting non-human sequences for DNA information storage. In cross-validation, our DLM predicts sequencing depth to within a factor of 3 with 93% accuracy for the SNP panel, and 99% accuracy for the non-human panel. In independent testing, the DLM predicts the lncRNA panel with 89% accuracy when trained on the SNP panel. The same model is also effective at predicting the measured single-plex kinetic rate constants of DNA hybridization and strand displacement.

Disruption of two defensive signaling pathways by a viral RNA silencing suppressor.

The Cucumber mosaic virus (CMV) 2b counter-defense protein disrupts plant antiviral mechanisms mediated by RNA silencing and salicylic acid (SA). We used microarrays to investigate defensive gene expression in 2b-transgenic Arabidopsis thaliana plants. Surprisingly, 2b inhibited expression of few SA-regulated genes and, in some instances, enhanced the effect of SA on certain genes. Strikingly, the 2b protein inhibited changes in the expression of 90% of genes regulated by jasmonic acid (JA). Consistent with this, infection of plants with CMV, but not the 2b gene-deletion mutant CMVDelta2b, strongly inhibited JA-inducible gene expression. JA levels were unaffected by infection with either CMV or CMVDelta2b. Although the CMV-Arabidopsis interaction is a compatible one, SA accumulation, usually considered to be an indicator of plant resistance, was increased in CMV-infected plants but not in CMVDelta2b-infected plants. Thus, the 2b protein inhibits JA signaling at a step downstream of JA biosynthesis but it primes induction of SA biosynthesis by another CMV gene product or by the process of infection itself. Like many plant viruses, CMV is aphid transmitted. JA is important in plant defense against insects. This raises the possibility that disruption of JA-mediated gene expression by the 2b protein may influence CMV transmission by aphids.

Stochastic pulsing of gene expression enables the generation of spatial patterns in Bacillus subtilis biofilms.

Stochastic pulsing of gene expression can generate phenotypic diversity in a genetically identical population of cells, but it is unclear whether it has a role in the development of multicellular systems. Here, we show how stochastic pulsing of gene expression enables spatial patterns to form in a model multicellular system, Bacillus subtilis bacterial biofilms. We use quantitative microscopy and time-lapse imaging to observe pulses in the activity of the general stress response sigma factor σB in individual cells during biofilm development. Both σB and sporulation activity increase in a gradient, peaking at the top of the biofilm, even though σB represses sporulation. As predicted by a simple mathematical model, increasing σB expression shifts the peak of sporulation to the middle of the biofilm. Our results demonstrate how stochastic pulsing of gene expression can play a key role in pattern formation during biofilm development.