Predicting partner fitness based on spatial structuring in a light-driven microbial community.

Microbial communities have vital roles in systems essential to human health and agriculture, such as gut and soil microbiomes, and there is growing interest in engineering designer consortia for applications in biotechnology (e.g., personalized probiotics, bioproduction of high-value products, biosensing). The capacity to monitor and model metabolite exchange in dynamic microbial consortia can provide foundational information important to understand the community level behaviors that emerge, a requirement for building novel consortia. Where experimental approaches for monitoring metabolic exchange are technologically challenging, computational tools can enable greater access to the fate of both chemicals and microbes within a consortium. In this study, we developed an in-silico model of a synthetic microbial consortia of sucrose-secreting Synechococcus elongatus PCC 7942 and Escherichia coli W. Our model was built on the NUFEB framework for Individual-based Modeling (IbM) and optimized for biological accuracy using experimental data. We showed that the relative level of sucrose secretion regulates not only the steady-state support for heterotrophic biomass, but also the temporal dynamics of consortia growth. In order to determine the importance of spatial organization within the consortium, we fit a regression model to spatial data and used it to accurately predict colony fitness. We found that some of the critical parameters for fitness prediction were inter-colony distance, initial biomass, induction level, and distance from the center of the simulation volume. We anticipate that the synergy between experimental and computational approaches will improve our ability to design consortia with novel function.

Inferring assembly-curving trends of bacterial micro-compartment shell hexamers from crystal structure arrangements.

Bacterial microcompartments (BMC) are complex macromolecular assemblies that participate in varied chemical processes in about one fourth of bacterial species. BMC-encapsulated enzymatic activities are segregated from other cell contents by means of semipermeable shells, justifying why BMC are viewed as prototype nano-reactors for biotechnological applications. Herein, we undertook a comparative study of bending propensities of BMC hexamers (BMC-H), the most abundant shell constituents. Published data show that some BMC-H, like ß-carboxysomal CcmK, tend to assemble flat whereas other BMC-H often build curved objects. Inspection of available crystal structures presenting BMC-H in tiled arrangements permitted us to identify two major assembly modes with a striking connection with experimental trends. All-atom molecular dynamics (MD) supported that BMC-H bending is triggered robustly only from the arrangement adopted in crystals by BMC-H that experimentally form curved objects, leading to very similar arrangements to those found in structures of recomposed BMC shells. Simulations on triplets of planar-behaving hexamers, which were previously reconfigured to comply with such organization, confirmed that bending propensity is mostly defined by the precise lateral positioning of hexamers, rather than by BMC-H identity. Finally, an interfacial lysine was pinpointed as the most decisive residue in controlling PduA spontaneous curvature. Globally, results presented herein should contribute to improve our understanding of the variable mechanisms of biogenesis characterized for BMC, and of possible strategies to regulate BMC size and shape.

Extraction of interaction parameters from specular neutron reflectivity in thin films of diblock copolymers: an "inverse problem".

Diblock copolymers have been shown to undergo microphase separation due to an interplay of repulsive interactions between dissimilar monomers, which leads to the stretching of chains and entropic loss due to the stretching. In thin films, additional effects due to confinement and monomer-surface interactions make microphase separation much more complicated than in that in bulks (i.e., without substrates). Previously, physics-based models have been used to interpret and extract various interaction parameters from the specular neutron reflectivities of annealed thin films containing diblock copolymers (J. P. Mahalik, J. W. Dugger, S. W. Sides, B. G. Sumpter, V. Lauter and R. Kumar, Interpreting neutron reflectivity profiles of diblock copolymer nanocomposite thin films using hybrid particle-field simulations, Macromolecules, 2018, 51(8), 3116; J. P. Mahalik, W. Li, A. T. Savici, S. Hahn, H. Lauter, H. Ambaye, B. G. Sumpter, V. Lauter and R. Kumar, Dispersity-driven stabilization of coexisting morphologies in asymmetric diblock copolymer thin films, Macromolecules, 2021, 54(1), 450). However, extracting Flory-Huggins χ parameters characterizing monomer-monomer, monomer-substrate, and monomer-air interactions has been labor-intensive and prone to errors, requiring the use of alternative methods for practical purposes. In this work, we have developed such an alternative method by employing a multi-layer perceptron, an autoencoder, and a variational autoencoder. These neural networks are used to extract interaction parameters not only from neutron scattering length density profiles constructed using self-consistent field theory-based simulations, but also from a noisy ad hoc model constructed previously. In particular, the variational autoencoder is shown to be the most promising tool when it comes to the reconstruction and extraction of parameters from an ad hoc neutron scattering length density profile of a thin film containing a symmetric di-block copolymer (poly(deuterated styrene-b-n-butyl methacrylate)). This work paves the way for automated analysis of specular neutron reflectivities from thin films of copolymers using machine learning tools.

Investigating Carboxysome Morphology Dynamics with a Rotationally Invariant Variational Autoencoder.

Carboxysomes are a class of bacterial microcompartments that form proteinaceous organelles within the cytoplasm of cyanobacteria and play a central role in photosynthetic metabolism by defining a cellular microenvironment permissive to CO2 fixation. Critical aspects of the assembly of the carboxysomes remain relatively unknown, especially with regard to the dynamics of this microcompartment. Progress in understanding carboxysome dynamics is impeded in part because analysis of the subtle changes in carboxysome morphology with microscopy remains a low-throughput and subjective process. Here we use deep learning techniques, specifically a Rotationally Invariant Variational Autoencoder (rVAE), to analyze fluorescence microscopy images of cyanobacteria bearing a carboxysome reporter and quantitatively evaluate how carboxysome shell remodelling impacts subtle trends in the morphology of the microcompartment over time. Toward this goal, we use a recently developed tool to control endogenous protein levels, including carboxysomal components, in the model cyanobacterium Synechococcous elongatus PCC 7942. By utilization of this system, proteins that compose the carboxysome can be tuned in real time as a method to examine carboxysome dynamics. We find that rVAEs are able to assist in the quantitative evaluation of changes in carboxysome numbers, shape, and size over time. We propose that rVAEs may be a useful tool to accelerate the analysis of carboxysome assembly and dynamics in response to genetic or environmental perturbation and may be more generally useful to probe regulatory processes involving a broader array of bacterial microcompartments.

Video frame prediction of microbial growth with a recurrent neural network.

The recent explosion of interest and advances in machine learning technologies has opened the door to new analytical capabilities in microbiology. Using experimental data such as images or videos, machine learning, in particular deep learning with neural networks, can be harnessed to provide insights and predictions for microbial populations. This paper presents such an application in which a Recurrent Neural Network (RNN) was used to perform prediction of microbial growth for a population of two Pseudomonas aeruginosa mutants. The RNN was trained on videos that were acquired previously using fluorescence microscopy and microfluidics. Of the 20 frames that make up each video, 10 were used as inputs to the network which outputs a prediction for the next 10 frames of the video. The accuracy of the network was evaluated by comparing the predicted frames to the original frames, as well as population curves and the number and size of individual colonies extracted from these frames. Overall, the growth predictions are found to be accurate in metrics such as image comparison, colony size, and total population. Yet, limitations exist due to the scarcity of available and comparable data in the literature, indicating a need for more studies. Both the successes and challenges of our approach are discussed.

Application of Machine Learning Techniques to an Agent-Based Model of Pantoea.

Agent-based modeling (ABM) is a powerful simulation technique which describes a complex dynamic system based on its interacting constituent entities. While the flexibility of ABM enables broad application, the complexity of real-world models demands intensive computing resources and computational time; however, a metamodel may be constructed to gain insight at less computational expense. Here, we developed a model in NetLogo to describe the growth of a microbial population consisting of Pantoea. We applied 13 parameters that defined the model and actively changed seven of the parameters to modulate the evolution of the population curve in response to these changes. We efficiently performed more than 3,000 simulations using a Python wrapper, NL4Py. Upon evaluation of the correlation between the active parameters and outputs by random forest regression, we found that the parameters which define the depth of medium and glucose concentration affect the population curves significantly. Subsequently, we constructed a metamodel, a dense neural network, to predict the simulation outputs from the active parameters and found that it achieves high prediction accuracy, reaching an R 2 coefficient of determination value up to 0.92. Our approach of using a combination of ABM with random forest regression and neural network reduces the number of required ABM simulations. The simplified and refined metamodels may provide insights into the complex dynamic system before their transition to more sophisticated models that run on high-performance computing systems. The ultimate goal is to build a bridge between simulation and experiment, allowing model validation by comparing the simulated data to experimental data in microbiology.

Self-Propulsion Enhances Polymerization.

Self-assembly is a spontaneous process through which macroscopic structures are formed from basic microscopic constituents (e.g., molecules or colloids). By contrast, the formation of large biological molecules inside the cell (such as proteins or nucleic acids) is a process more akin to self-organization than to self-assembly, as it requires a constant supply of external energy. Recent studies have tried to merge self-assembly with self-organization by analyzing the assembly of self-propelled (or active) colloid-like particles whose motion is driven by a permanent source of energy. Here we present evidence that points to the fact that self-propulsion considerably enhances the assembly of polymers: self-propelled molecules are found to assemble faster into polymer-like structures than non self-propelled ones. The average polymer length increases towards a maximum as the self-propulsion force increases. Beyond this maximum, the average polymer length decreases due to the competition between bonding energy and disruptive forces that result from collisions. The assembly of active molecules might have promoted the formation of large pre-biotic polymers that could be the precursors of the informational polymers we observe nowadays.

Visualizing in Vivo Dynamics of Designer Nanoscaffolds.

Enzymes of natural biochemical pathways are routinely subcellularly organized in space and time in order to improve pathway efficacy and control. Designer scaffolding platforms are under development to confer similar benefits upon engineered pathways. Herein, we evaluate bacterial microcompartment shell (pfam0936-domain) proteins as modules for constructing well-defined nanometer scale scaffolds in vivo. We use a suite of visualization techniques to evaluate scaffold assembly and dynamics. We demonstrate recruitment of target cargo molecules onto assembled scaffolds by appending reciprocally interacting adaptor domains. These interactions can be refined by fine-tuning the scaffold expression level. Real-time observation of this system reveals a nucleation-limited step where multiple scaffolds initially form within a cell. Over time, nucleated scaffolds reorganize into a single intracellular assembly, likely due to interscaffold competition for protein subunits. Our results suggest design considerations for using self-assembling proteins as building blocks to construct nanoscaffolds, while also providing a platform to visualize scaffold-cargo dynamics in vivo.

Surface, Interface, and Temperature Effects on the Phase Separation and Nanoparticle Self Assembly of Bi-Metallic Ni0.5Ag0.5: A Molecular Dynamics Study.

Classical molecular dynamics (MD) simulations were used to investigate how free surfaces, as well as supporting substrates, affect phase separation in a NiAg alloy. Bulk samples, droplets, and droplets deposited on a graphene substrate were investigated at temperatures that spanned regions of interest in the bulk NiAg phase diagram, i.e., miscible and immiscible liquid, liquid-crystal, and crystal-crystal regions. Using MD simulations to cool down a bulk sample from 3000 K to 800 K, it was found that phase separation below 2400 K takes place in agreement with the phase diagram. When free surface effects were introduced, phase separation was accompanied by a core-shell transformation: spherical droplets created from the bulk samples became core-shell nanoparticles with a shell made mostly of Ag atoms and a core made of Ni atoms. When such droplets were deposited on a graphene substrate, the phase separation was accompanied by Ni layering at the graphene interface and Ag at the vacuum interface. Thus, it should be possible to create NiAg core-shell and layer-like nanostructures by quenching liquid NiAg samples on tailored substrates. Furthermore, interesting bimetallic nanoparticle morphologies might be tuned via control of the surface and interface energies and chemical instabilities of the system.

External strain-enhanced cysteine enantiomeric separation ability on alloyed stepped surfaces.

Using density functional theory with an accurate treatment of van der Waals interactions, we investigate the enantioselective recognition and separation of chiral molecules on stepped metal surfaces. Our calculations demonstrate that the separation ability of metal substrates can be significantly enhanced by surface decoration and external strain. For example, applying 2% tensile strain to the Ag-alloyed Au(532) surface leads to a dramatic increase (by 89%) in cysteine enantioselectivity as compared to that of pristine Au(532). Analysis on the computed binding energies shows that the interaction energy is the predominant factor that affects the separation efficiency in strongly bound systems. Our study presents a new strategy to modify the enantioselectivity of stepped metal surfaces and paves the way for exploring high efficiency chiral separation technology in pharmaceutical industry.

Heterohexamers Formed by CcmK3 and CcmK4 Increase the Complexity of Beta Carboxysome Shells.

Bacterial microcompartments (BMCs) encapsulate enzymes within a selectively permeable, proteinaceous shell. Carboxysomes are BMCs containing ribulose-1,5-bisphosphate carboxylase oxygenase and carbonic anhydrase that enhance carbon dioxide fixation. The carboxysome shell consists of three structurally characterized protein types, each named after the oligomer they form: BMC-H (hexamer), BMC-P (pentamer), and BMC-T (trimer). These three protein types form cyclic homooligomers with pores at the center of symmetry that enable metabolite transport across the shell. Carboxysome shells contain multiple BMC-H paralogs, each with distinctly conserved residues surrounding the pore, which are assumed to be associated with specific metabolites. We studied the regulation of ß-carboxysome shell composition by investigating the BMC-H genes ccmK3 and ccmK4 situated in a locus remote from other carboxysome genes. We made single and double deletion mutants of ccmK3 and ccmK4 in Synechococcus elongatus PCC7942 and show that, unlike CcmK3, CcmK4 is necessary for optimal growth. In contrast to other CcmK proteins, CcmK3 does not form homohexamers; instead CcmK3 forms heterohexamers with CcmK4 with a 1:2 stoichiometry. The CcmK3-CcmK4 heterohexamers form stacked dodecamers in a pH-dependent manner. Our results indicate that CcmK3-CcmK4 heterohexamers potentially expand the range of permeability properties of metabolite channels in carboxysome shells. Moreover, the observed facultative formation of dodecamers in solution suggests that carboxysome shell permeability may be dynamically attenuated by "capping" facet-embedded hexamers with a second hexamer. Because ß-carboxysomes are obligately expressed, heterohexamer formation and capping could provide a rapid and reversible means to alter metabolite flux across the shell in response to environmental/growth conditions.

Self-assembly directed one-step synthesis of [4]radialene on Cu(100) surfaces.

The synthetic challenges of radialenes have precluded their practical applications. Here, we report a one-step synthetic protocol of [4]radialene on a copper surface. High-resolution scanning tunneling microscopy measurements reveal that such catalytic reaction proceeds readily with high selectivity at the temperature below 120 K. First-principles calculations show that the reaction pathway is characterized by firstly the cooperative inter-molecular hydrogen tautomerization and then the C-C bond formation. The feasibility of such cyclotetramerization reaction can be interpreted by the surface effect of Cu(100), which firstly plays an important role in directing the molecular assembly and then serves as an active catalyst in the hydrogen tautomerization and C-C coupling processes. This work presents not only a novel strategy to the scant number of synthetic methods to produce [4]radialenes via a novel [1 + 1 + 1 + 1] reaction pathway, but also a successful example of C-C bond coupling reactions guided by the surface-induced C-H/π assembly.

Principles of Design for Substrate-Supported Molecular Switches Based on Physisorbed and Chemisorbed States.

The physisorbed (precursor) and chemisorbed states of a molecule on metal surfaces can be utilized to build a logic switch at the single-molecule level, enabling further microminiaturization of electronic devices beyond the silicon limits. However, a serious drawback of this design is easy lateral diffusion of the molecule in the physisorbed state, which may destroy the normal switch operation. Here, we demonstrate that anchoring engineering can be an effective way to enhance the stability of molecular switches without degrading switching functionality. As exemplified by trans-ADT on Cu(111), we show that the lateral diffusion of such molecular switch can be obstructed by the anchoring of the ending thiophene groups, along with a rotation of the adsorbate during the switching process. More general, our results also suggest that when searching for molecular switches with reversible physisorbed and chemisorbed states with excellent bistability and lateral stability, the focus should be on finding molecules with a moderate HOMO-LUMO energy gap and anchoring atoms with positive charge that can then be deposited on substrates with which they interact moderately. This allows further improvement of the lateral and vertical stability of such a molecular switch by substituting the thiophene groups with selenophene, thus establishing trans-ADS on Cu(111) as a promising switch.

Ultrahigh Conductivity in Two-Dimensional InSe via Remote Doping at Room Temperature.

Conductivity of two-dimenstional (2D) materials, which largely determines the efficiency and reliability of nanodevices, is proportional to the product of carrier concentration and mobility. Conventional doping, such as ionic substitution or introduction of vacancies, increases carrier concentration and decreases carrier mobility due to the scattering or trapping of carriers. We propose a remote-doping strategy that enables the simultaneous enhancement of both parameters. Density functional theory calculations in 2D InSe reveal that adsorbing the molecule tetrathiafulvalene (TTF) and applying a 4% external tensile strain leads to an increase in the carrier concentration of the TTF-InSe system that is 13 orders of magnitude higher than that of the pristine counterpart, whereas the carrier mobility is enhanced by 35% compared with the InSe monolayer. As a consequence of the synergetic role of molecule doping and strain engineering, ultrahigh conductivity of 1.85 × 105 S/m is achieved in the TTF-InSe system at room temperature.

Direct atomic fabrication and dopant positioning in Si using electron beams with active real-time image-based feedback.

Semiconductor fabrication is a mainstay of modern civilization, enabling the myriad applications and technologies that underpin everyday life. However, while sub-10 nanometer devices are already entering the mainstream, the end of the Moore's law roadmap still lacks tools capable of bulk semiconductor fabrication on sub-nanometer and atomic levels, with probe-based manipulation being explored as the only known pathway. Here we demonstrate that the atomic-sized focused beam of a scanning transmission electron microscope can be used to manipulate semiconductors such as Si on the atomic level, inducing growth of crystalline Si from the amorphous phase, reentrant amorphization, milling, and dopant front motion. These phenomena are visualized in real-time with atomic resolution. We further implement active feedback control based on real-time image analytics to automatically control the e-beam motion, enabling shape control and providing a pathway for atom-by-atom correction of fabricated structures in the near future. These observations open a new epoch for atom-by-atom manufacturing in bulk, the long-held dream of nanotechnology.

A Microfluidics and Agent-Based Modeling Framework for Investigating Spatial Organization in Bacterial Colonies: The Case of Pseudomonas Aeruginosa and H1-Type VI Secretion Interactions.

The factors leading to changes in the organization of microbial assemblages at fine spatial scales are not well characterized or understood. However, they are expected to guide the succession of community development and function toward specific outcomes that could impact human health and the environment. In this study, we put forward a combined experimental and agent-based modeling framework and use it to interpret unique spatial organization patterns of H1-Type VI secretion system (T6SS) mutants of P. aeruginosa under spatial confinement. We find that key parameters, such as T6SS-mediated cell contact and lysis, spatial localization, relative species abundance, cell density and local concentrations of growth substrates and metabolites are influenced by spatial confinement. The model, written in the accessible programming language NetLogo, can be adapted to a variety of biological systems of interest and used to simulate experiments across a broad parameter space. It was implemented and run in a high-throughput mode by deploying it across multiple CPUs, with each simulation representing an individual well within a high-throughput microwell array experimental platform. The microfluidics and agent-based modeling framework we present in this paper provides an effective means by which to connect experimental studies in microbiology to model development. The work demonstrates progress in coupling experimental results to simulation while also highlighting potential sources of discrepancies between real-world experiments and idealized models.

Engineering the Bacterial Microcompartment Domain for Molecular Scaffolding Applications.

As synthetic biology advances the intricacy of engineered biological systems, the importance of spatial organization within the cellular environment must not be marginalized. Increasingly, biological engineers are investigating means to control spatial organization within the cell, mimicking strategies used by natural pathways to increase flux and reduce cross-talk. A modular platform for constructing a diverse set of defined, programmable architectures would greatly assist in improving yields from introduced metabolic pathways and increasing insulation of other heterologous systems. Here, we review recent research on the shell proteins of bacterial microcompartments and discuss their potential application as "building blocks" for a range of customized intracellular scaffolds. We summarize the state of knowledge on the self-assembly of BMC shell proteins and discuss future avenues of research that will be important to realize the potential of BMC shell proteins as predictively assembling and programmable biological materials for bioengineering.

Enhancing Enantiomeric Separation with Strain: The Case of Serine on Cu(531).

Serine has two enantiomers, d and l, which exhibit identical physical and chemical properties but have dramatically different physiological effects. For the pharmaceutical industry, it is very important to be able to separate both enantiomers. Here we study the enantioselectivity of the (531) surfaces of Cu, Ag, Au, and Pd using density functional theory with an accurate treatment of the van der Waals interactions. Among these surfaces, it is found that Cu(531) is the most efficient for energetically separating serine enantiomers. This greater efficiency is ultimately related to a conformational strain imposed in serine and most of all in the supporting substrate. Motivated by this, we decorated the step sites of Cu(531) with Ni atoms and showed that serine enantioselectivity increases by 36% as compared to that of pristine Cu(531). These results suggest that efficient enantiomeric separation of small chiral molecules could be achieved with bimetallic stepped surfaces for which strain, both in the surface and the molecule, increases significantly upon deposition.

Theoretical Study of the Initial Stages of Self-Assembly of a Carboxysome's Facet.

Bacterial microcompartments, BMCs, are organelles that exist within wide variety of bacteria and act as nanofactories. Among the different types of known BMCs, the carboxysome has been studied the most. The carboxysome plays an important role in the light-independent part of the photosynthesis process, where its icosahedral-like proteinaceous shell acts as a membrane that controls the transport of metabolites. Although a structural model exists for the carboxysome shell, it remains largely unknown how the shell proteins self-assemble. Understanding the self-assembly process can provide insights into how the shell affects the carboxysome's function and how it can be modified to create new functionalities, such as artificial nanoreactors and artificial protein membranes. Here, we describe a theoretical framework that employs Monte Carlo simulations with a coarse-grain potential that reproduces well the atomistic potential of mean force; employing this framework, we are able to capture the initial stages of the 2D self-assembly of CcmK2 hexamers, a major protein-shell component of the carboxysome's facet. The simulations reveal that CcmK2 hexamers self-assemble into clusters that resemble what was seen experimentally in 2D layers. Further analysis of the simulation results suggests that the 2D self-assembly of carboxysome's facets is driven by a nucleation-growth process, which in turn could play an important role in the hierarchical self-assembly of BMC shells in general.

Supramolecular polymerization of a prebiotic nucleoside provides insights into the creation of sequence-controlled polymers.

Self-assembly of a nucleoside on Au(111) was studied to ascertain whether polymerization on well-defined substrates constitutes a promising approach for making sequence-controlled polymers. Scanning tunneling microscopy and density functional theory were used to investigate the self-assembly on Au(111) of (RS)-N(9)-(2,3-dihydroxypropyl)adenine (DHPA), a plausibly prebiotic nucleoside analog of adenosine. It is found that DHPA molecules self-assemble into a hydrogen-bonded polymer that grows almost exclusively along the herringbone reconstruction pattern, has a two component sequence that is repeated over hundreds of nanometers, and is erasable with electron-induced excitation. Although the sequence is simple, more complicated ones are envisioned if two or more nucleoside types are combined. Because polymerization occurs on a substrate in a dry environment, the success of each combination can be gauged with high-resolution imaging and accurate modeling techniques. These characteristics make nucleoside self-assembly on a substrate an attractive approach for designing sequence-controlled polymers. Further, by choosing plausibly prebiotic nucleosides, insights may be provided into how nature created the first sequence-controlled polymers capable of storing information. Such insights, in turn, can inspire new ways of synthesizing sequence-controlled polymers.