Bayesian filtering for model predictive control of stochastic gene expression in single cells.

This study describes a method for controlling the production of protein in individual cells using stochastic models of gene expression. By combining modern microscopy platforms with optogenetic gene expression, experimentalists are able to accurately apply light to individual cells, which can induce protein production. Here we use a finite state projection based stochastic model of gene expression, along with Bayesian state estimation to control protein copy numbers within individual cells. We compare this method to previous methods that use population based approaches. We also demonstrate the ability of this control strategy to ameliorate discrepancies between the predictions of a deterministic model and stochastic switching system.

Transferring a Molecular Foundation Model for Polymer Property Predictions.

Transformer-based large language models have remarkable potential to accelerate design optimization for applications such as drug development and material discovery. Self-supervised pretraining of transformer models requires large-scale data sets, which are often sparsely populated in topical areas such as polymer science. State-of-the-art approaches for polymers conduct data augmentation to generate additional samples but unavoidably incur extra computational costs. In contrast, large-scale open-source data sets are available for small molecules and provide a potential solution to data scarcity through transfer learning. In this work, we show that using transformers pretrained on small molecules and fine-tuned on polymer properties achieves comparable accuracy to those trained on augmented polymer data sets for a series of benchmark prediction tasks.

Distribution shapes govern the discovery of predictive models for gene regulation.

Despite substantial experimental and computational efforts, mechanistic modeling remains more predictive in engineering than in systems biology. The reason for this discrepancy is not fully understood. One might argue that the randomness and complexity of biological systems are the main barriers to predictive understanding, but these issues are not unique to biology. Instead, we hypothesize that the specific shapes of rare single-molecule event distributions produce substantial yet overlooked challenges for biological models. We demonstrate why modern statistical tools to disentangle complexity and stochasticity, which assume normally distributed fluctuations or enormous datasets, do not apply to the discrete, positive, and nonsymmetric distributions that characterize mRNA fluctuations in single cells. As an example, we integrate single-molecule measurements and advanced computational analyses to explore mitogen-activated protein kinase induction of multiple stress response genes. Through systematic analyses of different metrics to compare the same model to the same data, we elucidate why standard modeling approaches yield nonpredictive models for single-cell gene regulation. We further explain how advanced tools recover precise, reproducible, and predictive understanding of transcription regulation mechanisms, including gene activation, polymerase initiation, elongation, mRNA accumulation, spatial transport, and decay.

The finite state projection based Fisher information matrix approach to estimate information and optimize single-cell experiments.

Modern optical imaging experiments not only measure single-cell and single-molecule dynamics with high precision, but they can also perturb the cellular environment in myriad controlled and novel settings. Techniques, such as single-molecule fluorescence in-situ hybridization, microfluidics, and optogenetics, have opened the door to a large number of potential experiments, which begs the question of how to choose the best possible experiment. The Fisher information matrix (FIM) estimates how well potential experiments will constrain model parameters and can be used to design optimal experiments. Here, we introduce the finite state projection (FSP) based FIM, which uses the formalism of the chemical master equation to derive and compute the FIM. The FSP-FIM makes no assumptions about the distribution shapes of single-cell data, and it does not require precise measurements of higher order moments of such distributions. We validate the FSP-FIM against well-known Fisher information results for the simple case of constitutive gene expression. We then use numerical simulations to demonstrate the use of the FSP-FIM to optimize the timing of single-cell experiments with more complex, non-Gaussian fluctuations. We validate optimal simulated experiments determined using the FSP-FIM with Monte-Carlo approaches and contrast these to experiment designs chosen by traditional analyses that assume Gaussian fluctuations or use the central limit theorem. By systematically designing experiments to use all of the measurable fluctuations, our method enables a key step to improve co-design of experiments and quantitative models.

Computational design and interpretation of single-RNA translation experiments.

Advances in fluorescence microscopy have introduced new assays to quantify live-cell translation dynamics at single-RNA resolution. We introduce a detailed, yet efficient sequence-based stochastic model that generates realistic synthetic data for several such assays, including Fluorescence Correlation Spectroscopy (FCS), ribosome Run-Off Assays (ROA) after Harringtonine application, and Fluorescence Recovery After Photobleaching (FRAP). We simulate these experiments under multiple imaging conditions and for thousands of human genes, and we evaluate through simulations which experiments are most likely to provide accurate estimates of elongation kinetics. Finding that FCS analyses are optimal for both short and long length genes, we integrate our model with experimental FCS data to capture the nascent protein statistics and temporal dynamics for three human genes: KDM5B, ß-actin, and H2B. Finally, we introduce a new open-source software package, RNA Sequence to NAscent Protein Simulator (rSNAPsim), to easily simulate the single-molecule translation dynamics of any gene sequence for any of these assays and for different assumptions regarding synonymous codon usage, tRNA level modifications, or ribosome pauses. rSNAPsim is implemented in Python and is available at: https://github.com/MunskyGroup/rSNAPsim.git.

SPI-Hub™: a gateway to scholarly publishing information.

BACKGROUND: Advances in the health sciences rely on sharing research and data through publication. As information professionals are often asked to contribute their knowledge to assist clinicians and researchers in selecting journals for publication, the authors recognized an opportunity to build a decision support tool, SPI-Hub: Scholarly Publishing Information Hub™, to capture the team's collective publishing industry knowledge, while carefully retaining the quality of service. CASE PRESENTATION: SPI-Hub's decision support functionality relies on a data framework that describes journal publication policies and practices through a newly designed metadata structure, the Knowledge Management Journal Record™. Metadata fields are populated through a semi-automated process that uses custom programming to access content from multiple sources. Each record includes 25 metadata fields representing best publishing practices. Currently, the database includes more than 24,000 health sciences journal records. To correctly capture the resources needed for both completion and future maintenance of the project, the team conducted an internal study to assess time requirements for completing records through different stages of automation. CONCLUSIONS: The journal decision support tool, SPI-Hub, provides an opportunity to assess publication practices by compiling data from a variety of sources in a single location. Automated and semi-automated approaches have effectively reduced the time needed for data collection. Through a comprehensive knowledge management framework and the incorporation of multiple quality points specific to each journal, SPI-Hub provides prospective users with both recommendations for publication and holistic assessment of the trustworthiness of journals in which to publish research and acquire trusted knowledge.

Why equating all evidence searches to systematic reviews defies their role in information seeking.

All too often the quality and rigor of topic investigations is inaccurately conveyed to information professionals, resulting in a mischaracterization of the research, which, if left unchecked and published, may in turn mislead potential readers. Accurately understanding and categorizing the types of topic investigation searches that are requested of information professionals is critical to both meeting requestors' needs and reflecting their intended methodological approaches. Information professionals' expertise can be an invaluable resource to guide users through the investigative and publication process.

Fetal lung transcriptome patterns in an ex vivo compression model of diaphragmatic hernia.

BACKGROUND: The purpose of this study was to employ a novel ex vivo lung model of congenital diaphragmatic hernia (CDH) to determine how a mechanical compression affects early pulmonary development. METHODS: Day-15 whole fetal rat lungs (n = 6-12/group) from nitrofen-exposed and normal (vehicle only) dams were explanted and cultured ex vivo in compression microdevices (0.2 or 0.4 kPa) for 16 h to mimic physiologic compression forces that occur in CDH in vivo. Lungs were evaluated with significance set at P < 0.05. RESULTS: Nitrofen-exposed lungs were hypoplastic and expressed lower levels of surfactant protein C at baseline. Although compression alone did not alter the α-smooth muscle actin (ACTA2) expression in normal lungs, nitrofen-exposed lungs had significantly increased ACTA2 transcripts (0.2 kPa: 2.04 ± 0.15; 0.4 kPa: 2.22 ± 0.11; both P < 0.001). Nitrofen-exposed lungs also showed further reductions in surfactant protein C expression at 0.2 and 0.4 kPa (0.53 ± 0.04, P < 0.01; 0.69 ± 0.23, P < 0.001; respectively). Whereas normal lungs exposed to 0.2 and 0.4 kPa showed significant increases in periostin (POSTN), a mechanical stress-response molecule (1.79 ± 0.10 and 2.12 ± 0.39, respectively; both P < 0.001), nitrofen-exposed lungs had a significant decrease in POSTN expression (0.4 kPa: 0.67 ± 0.15, P < 0.001), which was confirmed by immunohistochemistry. CONCLUSIONS: Collectively, these pilot data in a model of CDH lung hypoplasia suggest a primary aberration in response to mechanical stress within the nitrofen lung, characterized by an upregulation of ACTA2 and a downregulation in SPFTC and POSTN. This ex vivo compression system may serve as a novel research platform to better understand the mechanobiology and complex regulation of matricellular dynamics during CDH fetal lung development.

Echocardiographic determinants of LV functional improvement after transcatheter aortic valve replacement.

BACKGROUND: Transcatheter aortic valve replacement (TAVR) is an established therapy in high-risk patients with severe aortic stenosis. Among patients with reduced left ventricular ejection fraction (LVEF), it is unclear which patients will derive maximal benefit from TAVR. METHODS: Clinical and echocardiographic data of patients with severe aortic stenosis and low LVEF (≤50%) who underwent TAVR at a single institution during 2009-2013 were retrospectively analyzed. Patients were divided into 2 groups post-TAVR based on improved LV function (Group A = ΔLVEF ≥ 10%) versus persistent LV dysfunction (Group B = ΔLVEF<10%). Echocardiographic parameters were assessed for their association with LVEF change post-TAVR. Kaplan-Meier analysis was performed to generate survival estimates. RESULTS: Of 382 patients who underwent TAVR, 60 patients had low LVEF, LV function failed to improve ≥10% in 50% of patients following the procedure (Group B). At baseline echocardiograms, Group B had higher LVEF, stroke volume (SV), SV index; and lower E, E/E', and estimated pulmonary arterial systolic pressure (PASP) compared to Group A. Higher mortality was found in Group B compared to the Group A (p = 0.003) with a significantly shorter survival (Group A = 3.3 ± 0.1 years vs Group B = 2.7 ± 0.2 years, p = 0.003). One-year event free survival was 53.3% in Group B compared to 93.3% in Group A, with a stable trend over ensuing years (5-year survival; 53.3% versus 90.0%, p = 0.003). CONCLUSIONS: In patients undergoing TAVR with depressed LV function, those who failed to improve were more likely to have relatively higher LVEF, SV, and SVI; and lower E, E/E', and PASP at baseline. Mortality rates were found to be higher in persistent LV dysfunction group. © 2015 Wiley Periodicals, Inc.

Integrating single-molecule experiments and discrete stochastic models to understand heterogeneous gene transcription dynamics.

The production and degradation of RNA transcripts is inherently subject to biological noise that arises from small gene copy numbers in individual cells. As a result, cellular RNA levels can exhibit large fluctuations over time and from one cell to the next. This article presents a range of precise single-molecule experimental techniques, based upon RNA fluorescence in situ hybridization, which can be used to measure the fluctuations of RNA at the single-cell level. A class of models for gene activation and deactivation is postulated in order to capture complex stochastic effects of chromatin modifications or transcription factor interactions. A computational tool, known as the finite state projection approach, is introduced to accurately and efficiently analyze these models in order to predict how probability distributions of RNA change over time in response to changing environmental conditions. These single-molecule experiments, discrete stochastic models, and computational analyses are systematically integrated to identify models of gene regulation dynamics. To illustrate the power and generality of our integrated experimental and computational approach, we explore cases that include different models for three different RNA types (sRNA, mRNA and nascent RNA), three different experimental techniques and three different biological species (bacteria, yeast and human cells).

Finite state projection based bounds to compare chemical master equation models using single-cell data.

Emerging techniques now allow for precise quantification of distributions of biological molecules in single cells. These rapidly advancing experimental methods have created a need for more rigorous and efficient modeling tools. Here, we derive new bounds on the likelihood that observations of single-cell, single-molecule responses come from a discrete stochastic model, posed in the form of the chemical master equation. These strict upper and lower bounds are based on a finite state projection approach, and they converge monotonically to the exact likelihood value. These bounds allow one to discriminate rigorously between models and with a minimum level of computational effort. In practice, these bounds can be incorporated into stochastic model identification and parameter inference routines, which improve the accuracy and efficiency of endeavors to analyze and predict single-cell behavior. We demonstrate the applicability of our approach using simulated data for three example models as well as for experimental measurements of a time-varying stochastic transcriptional response in yeast.

Hedgehog signaling regulates dental papilla formation and tooth size during zebrafish odontogenesis.

BACKGROUND: Intercellular communication by the hedgehog cell signaling pathway is necessary for tooth development throughout the vertebrates, but it remains unclear which specific developmental signals control cell behavior at different stages of odontogenesis. To address this issue, we have manipulated hedgehog activity during zebrafish tooth development and visualized the results using confocal microscopy. RESULTS: We first established that reporter lines for dlx2b, fli1, NF-κB, and prdm1a are markers for specific subsets of tooth germ tissues. We then blocked hedgehog signaling with cyclopamine and observed a reduction or elimination of the cranial neural crest derived dental papilla, which normally contains the cells that later give rise to dentin-producing odontoblasts. Upon further investigation, we observed that the dental papilla begins to form and then regresses in the absence of hedgehog signaling, through a mechanism unrelated to cell proliferation or apoptosis. We also found evidence of an isometric reduction in tooth size that correlates with the time of earliest hedgehog inhibition. CONCLUSIONS: We hypothesize that these results reveal a previously uncharacterized function of hedgehog signaling during tooth morphogenesis, regulating the number of cells in the dental papilla and thereby controlling tooth size.

Outcomes of transcatheter aortic valve replacement in patients with chronic liver disease.

OBJECTIVE: Transcatheter aortic valve replacement (TAVR) has emerged as an alternative to high-risk surgery in patients with comorbid conditions. The role of TAVR in patients with liver disease has not been examined. METHODS: We examined the procedural and intermediate to long-term follow-up outcomes of patients with severe symptomatic aortic valve stenosis and chronic liver disease, identified by liver biopsy or from a combination of clinical findings. All patients were treated with balloon-expandable transfemoral (TF) or transapical (TA) TAVR between November 2007 and February 2014. RESULTS: A total of 17 of 706 (2.41%) patients treated at our institution with TF [n=14] or TA [n=3] TAVR had chronic liver disease (mean age 77.65±9.06 years, 7 women, mean STS score 8.37, mean Charlson score 5.00, mean MELD score 11.35, Child-Turcotte-Pugh (CTP) Class A [n=11], B [n=6], C [n=0], biopsy proven liver disease [n=5]). Median follow-up was 466 days (range=12-1,403 days). The mean post-procedure length of hospital stay was 5.88±3.08 days. Procedural success was achieved in all cases. In-hospital mortality was 5.88% and 90-day mortality was 17.65%. Safety and efficacy endpoints as defined by the valve academic research consortium (VARC) were significant for one perioperative death from a proximate cardiac cause (post-operative day 14), one death after hospital discharge of unknown cause (post-operative day 12), two late deaths from non-cardiac causes (post-operative days 50 and 487, respectively), and one late death of unknown cause (post-operative day 1,005). There were no life-threatening or major bleeding complications. One patient had an MI, one had a transient ischemic attack, four had transient, Stage I, acute kidney injury and one had transient, Stage II, acute kidney injury. CONCLUSION: TF and TA TAVR are feasible methods for treating aortic stenosis in patients with chronic liver disease. In patients with mild to moderate chronic liver disease there are acceptable rates of early and late complications, however, outcomes in patients with advanced liver disease (MELD>20 or CTP class C) warrant further study.

Two-dimensional vibrational-electronic spectroscopy.

Two-dimensional vibrational-electronic (2D VE) spectroscopy is a femtosecond Fourier transform (FT) third-order nonlinear technique that creates a link between existing 2D FT spectroscopies in the vibrational and electronic regions of the spectrum. 2D VE spectroscopy enables a direct measurement of infrared (IR) and electronic dipole moment cross terms by utilizing mid-IR pump and optical probe fields that are resonant with vibrational and electronic transitions, respectively, in a sample of interest. We detail this newly developed 2D VE spectroscopy experiment and outline the information contained in a 2D VE spectrum. We then use this technique and its single-pump counterpart (1D VE) to probe the vibrational-electronic couplings between high frequency cyanide stretching vibrations (νCN) and either a ligand-to-metal charge transfer transition ([Fe(III)(CN)6](3-) dissolved in formamide) or a metal-to-metal charge transfer (MMCT) transition ([(CN)5Fe(II)CNRu(III)(NH3)5](-) dissolved in formamide). The 2D VE spectra of both molecules reveal peaks resulting from coupled high- and low-frequency vibrational modes to the charge transfer transition. The time-evolving amplitudes and positions of the peaks in the 2D VE spectra report on coherent and incoherent vibrational energy transfer dynamics among the coupled vibrational modes and the charge transfer transition. The selectivity of 2D VE spectroscopy to vibronic processes is evidenced from the selective coupling of specific νCN modes to the MMCT transition in the mixed valence complex. The lineshapes in 2D VE spectra report on the correlation of the frequency fluctuations between the coupled vibrational and electronic frequencies in the mixed valence complex which has a time scale of 1 ps. The details and results of this study confirm the versatility of 2D VE spectroscopy and its applicability to probe how vibrations modulate charge and energy transfer in a wide range of complex molecular, material, and biological systems.

Enhancing molecular design efficiency: Uniting language models and generative networks with genetic algorithms.

This study examines the effectiveness of generative models in drug discovery, material science, and polymer science, aiming to overcome constraints associated with traditional inverse design methods relying on heuristic rules. Generative models generate synthetic data resembling real data, enabling deep learning model training without extensive labeled datasets. They prove valuable in creating virtual libraries of molecules for material science and facilitating drug discovery by generating molecules with specific properties. While generative adversarial networks (GANs) are explored for these purposes, mode collapse restricts their efficacy, limiting novel structure variability. To address this, we introduce a masked language model (LM) inspired by natural language processing. Although LMs alone can have inherent limitations, we propose a hybrid architecture combining LMs and GANs to efficiently generate new molecules, demonstrating superior performance over standalone masked LMs, particularly for smaller population sizes. This hybrid LM-GAN architecture enhances efficiency in optimizing properties and generating novel samples.

Approaching Maximum Resolution in Structured Illumination Microscopy via Accurate Noise Modeling.

Biological images captured by microscopes are characterized by heterogeneous signal-to-noise ratios (SNRs) due to spatially varying photon emission across the field of view convoluted with camera noise. State-of-the-art unsupervised structured illumination microscopy (SIM) reconstruction algorithms, commonly implemented in the Fourier domain, do not accurately model this noise and suffer from high-frequency artifacts, user-dependent choices of smoothness constraints making assumptions on biological features, and unphysical negative values in the recovered fluorescence intensity map. On the other hand, supervised methods rely on large datasets for training, and often require retraining for new sample structures. Consequently, achieving high contrast near the maximum theoretical resolution in an unsupervised, physically principled, manner remains an open problem. Here, we propose Bayesian-SIM (B-SIM), an unsupervised Bayesian framework to quantitatively reconstruct SIM data, rectifying these shortcomings by accurately incorporating known noise sources in the spatial domain. To accelerate the reconstruction process, we use the finite extent of the point-spread-function to devise a parallelized Monte Carlo strategy involving chunking and restitching of the inferred fluorescence intensity. We benchmark our framework on both simulated and experimental images, and demonstrate improved contrast permitting feature recovery at up to 25% shorter length scales over state-of-the-art methods at both high- and low-SNR. B-SIM enables unsupervised, quantitative, physically accurate reconstruction without the need for labeled training data, democratizing high-quality SIM reconstruction and expands the capabilities of live-cell SIM to lower SNR, potentially revealing biological features in previously inaccessible regimes.

Adaptive language model training for molecular design.

The vast size of chemical space necessitates computational approaches to automate and accelerate the design of molecular sequences to guide experimental efforts for drug discovery. Genetic algorithms provide a useful framework to incrementally generate molecules by applying mutations to known chemical structures. Recently, masked language models have been applied to automate the mutation process by leveraging large compound libraries to learn commonly occurring chemical sequences (i.e., using tokenization) and predict rearrangements (i.e., using mask prediction). Here, we consider how language models can be adapted to improve molecule generation for different optimization tasks. We use two different generation strategies for comparison, fixed and adaptive. The fixed strategy uses a pre-trained model to generate mutations; the adaptive strategy trains the language model on each new generation of molecules selected for target properties during optimization. Our results show that the adaptive strategy allows the language model to more closely fit the distribution of molecules in the population. Therefore, for enhanced fitness optimization, we suggest the use of the fixed strategy during an initial phase followed by the use of the adaptive strategy. We demonstrate the impact of adaptive training by searching for molecules that optimize both heuristic metrics, drug-likeness and synthesizability, as well as predicted protein binding affinity from a surrogate model. Our results show that the adaptive strategy provides a significant improvement in fitness optimization compared to the fixed pre-trained model, empowering the application of language models to molecular design tasks.

Using Mechanistic Models and Machine Learning to Design Single-Color Multiplexed Nascent Chain Tracking Experiments.

mRNA translation is the ubiquitous cellular process of reading messenger-RNA strands into functional proteins. Over the past decade, large strides in microscopy techniques have allowed observation of mRNA translation at a single-molecule resolution for self-consistent time-series measurements in live cells. Dubbed Nascent chain tracking (NCT), these methods have explored many temporal dynamics in mRNA translation uncaptured by other experimental methods such as ribosomal profiling, smFISH, pSILAC, BONCAT, or FUNCAT-PLA. However, NCT is currently restricted to the observation of one or two mRNA species at a time due to limits in the number of resolvable fluorescent tags. In this work, we propose a hybrid computational pipeline, where detailed mechanistic simulations produce realistic NCT videos, and machine learning is used to assess potential experimental designs for their ability to resolve multiple mRNA species using a single fluorescent color for all species. Through simulation, we show that with careful application, this hybrid design strategy could in principle be used to extend the number of mRNA species that could be watched simultaneously within the same cell. We present a simulated example NCT experiment with seven different mRNA species within the same simulated cell and use our ML labeling to identify these spots with 90% accuracy using only two distinct fluorescent tags. The proposed extension to the NCT color palette should allow experimentalists to access a plethora of new experimental design possibilities, especially for cell signalling applications requiring simultaneous study of multiple mRNAs.

Using mechanistic models and machine learning to design single-color multiplexed nascent chain tracking experiments.

mRNA translation is the ubiquitous cellular process of reading messenger-RNA strands into functional proteins. Over the past decade, large strides in microscopy techniques have allowed observation of mRNA translation at a single-molecule resolution for self-consistent time-series measurements in live cells. Dubbed Nascent chain tracking (NCT), these methods have explored many temporal dynamics in mRNA translation uncaptured by other experimental methods such as ribosomal profiling, smFISH, pSILAC, BONCAT, or FUNCAT-PLA. However, NCT is currently restricted to the observation of one or two mRNA species at a time due to limits in the number of resolvable fluorescent tags. In this work, we propose a hybrid computational pipeline, where detailed mechanistic simulations produce realistic NCT videos, and machine learning is used to assess potential experimental designs for their ability to resolve multiple mRNA species using a single fluorescent color for all species. Our simulation results show that with careful application this hybrid design strategy could in principle be used to extend the number of mRNA species that could be watched simultaneously within the same cell. We present a simulated example NCT experiment with seven different mRNA species within the same simulated cell and use our ML labeling to identify these spots with 90% accuracy using only two distinct fluorescent tags. We conclude that the proposed extension to the NCT color palette should allow experimentalists to access a plethora of new experimental design possibilities, especially for cell Signaling applications requiring simultaneous study of multiple mRNAs.

Enabling reactive microscopy with MicroMator.

Microscopy image analysis has recently made enormous progress both in terms of accuracy and speed thanks to machine learning methods and improved computational resources. This greatly facilitates the online adaptation of microscopy experimental plans using real-time information of the observed systems and their environments. Applications in which reactiveness is needed are multifarious. Here we report MicroMator, an open and flexible software for defining and driving reactive microscopy experiments. It provides a Python software environment and an extensible set of modules that greatly facilitate the definition of events with triggers and effects interacting with the experiment. We provide a pedagogic example performing dynamic adaptation of fluorescence illumination on bacteria, and demonstrate MicroMator's potential via two challenging case studies in yeast to single-cell control and single-cell recombination, both requiring real-time tracking and light targeting at the single-cell level.