Complex Biological Systems Research Articles

Uncertainty quantified discovery of chemical reaction systems via Bayesian scientific machine learning

The recently proposed Chemical Reaction Neural Network (CRNN) discovers chemical reaction pathways from time resolved species concentration data in a deterministic manner. Since the weights and biases of a CRNN are physically interpretable, the CRNN acts as a digital twin of a classical chemical reaction network. In this study, we employ a Bayesian inference analysis coupled with neural ordinary differential equations (ODEs) on this digital twin to discover chemical reaction pathways in a probabilistic manner. This allows for estimation of the uncertainty surrounding the learned reaction network. To achieve this, we propose an algorithm which combines neural ODEs with a preconditioned stochastic gradient langevin descent (pSGLD) Bayesian framework, and ultimately performs posterior sampling on the neural network weights. We demonstrate the successful implementation of this algorithm on several reaction systems by not only recovering the chemical reaction pathways but also estimating the uncertainty in our predictions. We compare the results of the pSGLD with that of the standard SGLD and show that this optimizer more efficiently and accurately estimates the posterior of the reaction network parameters. Additionally, we demonstrate how the embedding of scientific knowledge improves extrapolation accuracy by comparing results to purely data-driven machine learning methods. Together, this provides a new framework for robust, autonomous Bayesian inference on unknown or complex chemical and biological reaction systems.

A genome-wide collection of barcoded single-gene deletion mutants in Salmonella enterica serovar Typhimurium.

Genetic screening of pools of mutants can reveal genetic determinants involved in complex biological interactions, processes, and systems. We previously constructed two single-gene deletion resources for Salmonella enterica serovar Typhimurium 14028s in which kanamycin (KanR) and chloramphenicol (CamR) cassettes were used to replace non-essential genes. We have now used lambda-red recombination to convert the antibiotic cassettes in these resources into a tetracycline-resistant (TetR) version where each mutant contains a different 21-base barcode flanked by Illumina Read1 and Read2 primer sequences. A motility assay of a pool of the entire library, followed by a single-tube processing of the bacterial pellet, PCR, and sequencing, was used to verify the performance of the barcoded TetR collection. The new resource is useful for experiments with defined subsets of barcoded mutant strains where biological bottlenecks preclude high numbers of founder bacteria, such as in animal infections. The TetR version of the library will also facilitate the construction of triple mutants by transduction. The resource of 6197 mutants covering 3490 genes is deposited at Biological and Emerging Infections Resources (beiresources.org).

Biomimetic Eu@HOF Photoactivated Sponge as Ultrasensitive Auditory and Olfactory Sensor for Infrasound Wave Monitoring and Bimodal Information Integration

AbstractThe combination of multisensory stimuli in the somatosensory system enables humans to understand object comprehensive characteristics and make precise judgments. However, the development of auditory–olfactory bimodal flexible biomimetic sensors (FBSs) to mimic the function of biological perception of external stimuli still presents complex challenges. Herein, an auditory–olfactory bimodal FBS (1 and 2) is fabricated based on hydrogen‐bonded organic framework (Eu@HOF‐BPTC) integrated with melamine sponge. During the auditory sensing process, 1 can monitor sound with a maximum response frequency of 400 Hz. The auditory FBS performs ultrahigh sensitivity (41335.995 cps Pa−1 cm−2), ultrahigh precision (relative standard deviation < 0.20%), ultrafast response time (20 ms), ultralow detection limit (1.1039 Hz and 0.1083 dB), and great recyclability (148 cycles), which can also real‐time monitor infrasound waves within 4–20 Hz. Based on artificial intelligence, 2 as an olfactory FBS demonstrates excellent sensitivity with a ppm‐level response limit and desirable selectivity toward four odor molecules and realizes wearable on‐site detection. The FBS also completes auditory and olfactory signals integration and interoperability by means of human–machine interaction. The bimodal auditory–olfactory FBS is highly promising for implementing bimodal interaction, simulating complex biological neural system, and promoting environmental monitoring, disaster warning and health care.

Oriented docking of the template for improved imprinting efficiency toward peptide with modifications

Molecular imprinting polymers (MIPs) are synthetic receptors as biomimetic materials for various applications ranging from sensing to separation and catalysis. However, currently existing MIPs are stuck to some of the issues including the longer preparation steps and poor performance. In this report, a facile and one-pot strategy by integrating the in-situ growth of magnetic nanoparticles and reversed phase microemulsion oriented molecularly imprinting strategy to develop magnetic molecular imprinted nanocomposites was proposed. Through self-assembling of the template, it brought up highly ordered and uniform arrangement of the imprinting structure, which offered faster adsorption kinetic as adsorption equilibrium was achived within 15 min, higher adsorption capacity (Qmax = 48.78 ± 1.54 μmol/g) and high affinity (Kd = 127.63 ± 9.66 μM) toward paradigm molecule-adenosine monophosphate (AMP) compared to the conventional bulk imprinting. The developed MIPs offered better affinity and superior specificity which allowed the specific enrichment toward targeted phosphorylated peptides from complex samples containing 100-fold more abundant interfering peptides. Interestingly, different types of MIPs can be developed which could targetly enrich the specific phosphorylated peptides for mass spectrometry analysis by simply switching the templates, and this strategy also successfully achieved imprinting of macromolecular peptides. Collectively, the approach showed broad applicability to target specific enrichment from metabolites to phosphorylated peptides and providing an alternative choice for selective recognition and analysis from complex biological systems.

Holomics - a user-friendly R shiny application for multi-omics data integration and analysis

An organism’s observable traits, or phenotype, result from intricate interactions among genes, proteins, metabolites and the environment. External factors, such as associated microorganisms, along with biotic and abiotic stressors, can significantly impact this complex biological system, influencing processes like growth, development and productivity. A comprehensive analysis of the entire biological system and its interactions is thus crucial to identify key components that support adaptation to stressors and to discover biomarkers applicable in breeding programs or disease diagnostics. Since the genomics era, several other ’omics’ disciplines have emerged, and recent advances in high-throughput technologies have facilitated the generation of additional omics datasets. While traditionally analyzed individually, the last decade has seen an increase in multi-omics data integration and analysis strategies aimed at achieving a holistic understanding of interactions across different biological layers. Despite these advances, the analysis of multi-omics data is still challenging due to their scale, complexity, high dimensionality and multimodality. To address these challenges, a number of analytical tools and strategies have been developed, including clustering and differential equations, which require advanced knowledge in bioinformatics and statistics. Therefore, this study recognizes the need for user-friendly tools by introducing Holomics, an accessible and easy-to-use R shiny application with multi-omics functions tailored for scientists with limited bioinformatics knowledge. Holomics provides a well-defined workflow, starting with the upload and pre-filtering of single-omics data, which are then further refined by single-omics analysis focusing on key features. Subsequently, these reduced datasets are subjected to multi-omics analyses to unveil correlations between 2-n datasets. This paper concludes with a real-world case study where microbiomics, transcriptomics and metabolomics data from previous studies that elucidate factors associated with improved sugar beet storability are integrated using Holomics. The results are discussed in the context of the biological background, underscoring the importance of multi-omics insights. This example not only highlights the versatility of Holomics in handling different types of omics data, but also validates its consistency by reproducing findings from preceding single-omics studies.

A dynamical perspective: moving towards mechanism in single-cell transcriptomics.

As the field of single-cell transcriptomics matures, research is shifting focus from phenomenological descriptions of cellular phenotypes to a mechanistic understanding of the gene regulation underneath. This perspective considers the value of capturing dynamical information at single-cell resolution for gaining mechanistic insight; reviews the available technologies for recording and inferring temporal information in single cells; and explores whether better dynamical resolution is sufficient to adequately capture the causal relationships driving complex biological systems. This article is part of a discussion meeting issue 'Causes and consequences of stochastic processes in development and disease'.

Minimalist Tetrazine N-Acetyl Muramic Acid Probes for Rapid and Efficient Labeling of Commensal and Pathogenic Peptidoglycans in Living Bacterial Culture and During Macrophage Invasion.

N-Acetyl muramic acid (NAM) probes containing alkyne or azide groups are commonly used to investigate aspects of cell wall synthesis because of their small size and ability to incorporate into bacterial peptidoglycan (PG). However, copper-catalyzed alkyne-azide cycloaddition (CuAAC) reactions are not compatible with live cells, and strain-promoted alkyne-azide cycloaddition (SPAAC) reaction rates are modest and, therefore, not as desirable for tracking the temporal alterations of bacterial cell growth, remodeling, and division. Alternatively, the tetrazine-trans-cyclooctene ligation (Tz-TCO), which is the fastest known bioorthogonal reaction and not cytotoxic, allows for rapid live-cell labeling of PG at biologically relevant time scales and concentrations. Previous work to increase reaction kinetics on the PG surface by using tetrazine probes was limited because of low incorporation of the probe. Described here are new approaches to construct a minimalist tetrazine (Tz)-NAM probe utilizing recent advancements in asymmetric tetrazine synthesis. This minimalist Tz-NAM probe was successfully incorporated into pathogenic and commensal bacterial PG where fixed and rapid live-cell, no-wash labeling was successful in both free bacterial cultures and in coculture with human macrophages. Overall, this probe allows for expeditious labeling of bacterial PG, thereby making it an exceptional tool for monitoring PG biosynthesis for the development of new antibiotic screens. The versatility and selectivity of this probe will allow for real-time interrogation of the interactions of bacterial pathogens in a human host and will serve a broader utility for studying glycans in multiple complex biological systems.

The maximum capability of a topological feature in link prediction.

Networks offer a powerful approach to modeling complex systems by representing the underlying set of pairwise interactions. Link prediction is the task that predicts links of a network that are not directly visible, with profound applications in biological, social, and other complex systems. Despite intensive utilization of the topological feature in this task, it is unclear to what extent a feature can be leveraged to infer missing links. Here, we aim to unveil the capability of a topological feature in link prediction by identifying its prediction performance upper bound. We introduce a theoretical framework that is compatible with different indexes to gauge the feature, different prediction approaches to utilize the feature, and different metrics to quantify the prediction performance. The maximum capability of a topological feature follows a simple yet theoretically validated expression, which only depends on the extent to which the feature is held in missing and nonexistent links. Because a family of indexes based on the same feature shares the same upper bound, the potential of all others can be estimated from one single index. Furthermore, a feature's capability is lifted in the supervised prediction, which can be mathematically quantified, allowing us to estimate the benefit of applying machine learning algorithms. The universality of the pattern uncovered is empirically verified by 550 structurally diverse networks. The findings have applications in feature and method selection, and shed light on network characteristics that make a topological feature effective in link prediction.

Comprehensive Parameter Space Mapping of Cell Cycle Dynamics under Network Perturbations.

Studies of quantitative systems and synthetic biology have extensively utilized models to interpret data, make predictions, and guide experimental designs. However, models often simplify complex biological systems and lack experimentally validated parameters, making their reliability in perturbed systems unclear. Here, we developed a droplet-based synthetic cell system to continuously tune parameters at the single-cell level in multiple dimensions with full dynamic ranges, providing an experimental framework for global parameter space scans. We systematically perturbed a cell-cycle oscillator centered on cyclin-dependent kinase (Cdk1), enabling comprehensive mapping of period landscapes in response to network perturbations. The data allowed us to challenge existing models and refine a new model that matches the observed response. Our analysis demonstrated that Cdk1 positive feedback inhibition restricts the cell cycle frequency range, confirming model predictions; furthermore, it revealed new cellular responses to the inhibition of the Cdk1-counteracting phosphatase PP2A: monomodal or bimodal distributions across varying inhibition levels, underscoring the complex nature of cell cycle regulation that can be explained by our model. This comprehensive perturbation platform may be generalizable to exploring other complex dynamic systems.

Advancing Molecular Dynamics: Toward Standardization, Integration, and Data Accessibility in Structural Biology.

Molecular dynamics (MD) simulations have become a valuable tool in structural biology, offering insights into complex biological systems that are difficult to obtain through experimental techniques alone. The lack of available data sets and structures in most published computational work has limited other researchers' use of these models. In recent years, the emergence of online sharing platforms and MD database initiatives favor the deposition of ensembles and structures to accompany publications, favoring reuse of the data sets. However, the lack of uniform metadata collection, formats, and what data are deposited limits the impact and its use by different communities that are not necessarily experts in MD. This Perspective highlights the need for standardization and better resource sharing for processing and interpreting MD simulation results, akin to efforts in other areas of structural biology. As the field moves forward, we will see an increase in popularity and benefits of MD-based integrative approaches combining experimental data and simulations through probabilistic reasoning, but these too are limited by uniformity in experimental data availability and choices on how the data are modeled that are not trivial to decipher from papers. Other fields have addressed similar challenges comprehensively by establishing task forces with different degrees of success. The large scope and number of communities to represent the breadth of types of MD simulations complicates a parallel approach that would fit all. Thus, each group typically decides what data and which format to upload on servers like Zenodo. Uploading data with FAIR (findable, accessible, interoperable, reusable) principles in mind including optimal metadata collection will make the data more accessible and actionable by the community. Such a wealth of simulation data will foster method development and infrastructure advancements, thus propelling the field forward.

Theranostic potential of a novel aptamer specifically targeting HER2 in breast cancer cells.

The overexpression of HER2 is correlated with poorer outcomes and therapeutic resistance in breast cancer patients. While HER2-targeted therapies have shown improvement, prognosis remains poor for HER2-positive breast cancer patients, and these treatments have limitations. Therefore, it is crucial to explore effective molecular strategies for early detection and treatment of HER2-positive breast cancers. In this study, we employed the cell-SELEX method to generate a selective aptamer capable of recognizing HER2 in its native conformation within breast cancer cells, for theranostic applications. Utilizing an adherent cell-SELEX approach, we developed and explored a DNA aptamer, named HMAP7, which can specifically target HER2 in the MDA-MB-453 and SK-BR-3 human breast cancer cell lines. After sequencing, the binding affinities of 10 candidate aptamers to HER2 receptors were evaluated by measuring fluorescence intensities within intact cells using near-infrared optical imaging. The dissociation constant of HMAP7 was determined to be in the nanomolar range in both cell lines. The cell-SELEX-derived aptamer sequence, HMAP7 (41-mer), exhibited the highest binding affinity and specificity for HER2. HMAP7 was rapidly internalized into breast cancer cells overexpressing HER2 but showed no uptake in the HER2 receptor-deficient breast cancer cell line MDA-MB-231. Moreover, HMAP7 demonstrated remarkable selectivity for HER2, rendering it suitable for use in complex biological systems. Our findings suggest that the novel DNA aptamer HMAP7 holds promise for both therapeutic and diagnostic applications, enabling selective delivery of therapeutic agents or imaging of HER2-positive breast tumors.

Navigating the Nose-to-Brain Route: A Systematic Review on Lipid-Based Nanocarriers for Central Nervous System Disorders.

The blood-brain barrier (BBB) regulates brain substance entry, posing challenges for treating brain diseases. Traditional methods face limitations, leading to the exploration of non-invasive intranasal drug delivery. This approach exploits the direct nose-to-brain connection, overcoming BBB restrictions. Intranasal delivery enhances drug bioavailability, reduces dosage, and minimizes systemic side effects. Notably, lipid nanoparticles, such as solid lipid nanoparticles and nanostructured lipid carriers, offer advantages like improved stability and controlled release. Their nanoscale size facilitates efficient drug loading, enhancing solubility and bioavailability. Tailored lipid compositions enable optimal drug release, which is crucial for chronic brain diseases. This review assesses lipid nanoparticles in treating neuro-oncological and neurodegenerative conditions, providing insights for effective nose-to-brain drug delivery. A systematic search was conducted across major medical databases (PubMed, Ovid MEDLINE, and Scopus) up to 6 January 2024. The search strategy utilized relevant Medical Subject Heading (MeSH) terms and keywords related to "lipid nanoparticles", "intranasal administration", "neuro-oncological diseases", and "neurodegenerative disorders". This review consists of studies in vitro, in vivo, or ex vivo on the intranasal administration of lipid-based nanocarriers for the treatment of brain diseases. Out of the initial 891 papers identified, 26 articles met the eligibility criteria after a rigorous analysis. The exclusion of 360 articles was due to reasons such as irrelevance, non-reporting selected outcomes, the article being a systematic literature review or meta-analysis, and lack of method/results details. This systematic literature review, focusing on nose-to-brain drug delivery via lipid-based nanocarriers for neuro-oncological, neurodegenerative, and other brain diseases, encompassed 60 studies. A temporal distribution analysis indicated a peak in research interest between 2018 and 2020 (28.3%), with a steady increase over time. Regarding drug categories, Alzheimer's disease was prominent (26.7%), followed by antiblastic drugs (25.0%). Among the 65 drugs investigated, Rivastigmine, Doxorubicin, and Carmustine were the most studied (5.0%), showcasing a diverse approach to neurological disorders. Notably, solid lipid nanoparticles (SLNs) were predominant (65.0%), followed by nanostructured lipid carriers (NLCs) (28.3%), highlighting their efficacy in intranasal drug delivery. Various lipids were employed, with glyceryl monostearate being prominent (20.0%), indicating preferences in formulation. Performance assessment assays were balanced, with in vivo studies taking precedence (43.3%), emphasizing the translation of findings to complex biological systems for potential clinical applications. This systematic review reveals the transformative potential of intranasal lipid nanoparticles in treating brain diseases, overcoming the BBB. Positive outcomes highlight the effectiveness of SLNs and NLCs, which are promising new approaches for ailments from AD to stroke and gliomas. While celebrating progress, addressing challenges like nanoparticle toxicity is also crucial.

Symmetry-breaking malachite green as a near-infrared light-activated fluorogenic photosensitizer for RNA proximity labeling.

Cellular RNA is asymmetrically distributed in cells and the regulation of RNA localization is crucial for proper cellular functions. However, limited chemical tools are available to capture dynamic RNA localization in complex biological systems with high spatiotemporal resolution. Here, we developed a new method for RNA proximity labeling activated by near-infrared (NIR) light, which holds the potential for deep penetration. Our method, termed FAP-seq, utilizes a genetically encoded fluorogen activating protein (FAP) that selectively binds to a set of substrates known as malachite green (MG). FAP binding restricts the rotation of MG and rapidly activates its fluorescence in a wash-free manner. By introducing a monoiodo modification to MG, we created a photosensitizer (MG-HI) with the highest singlet oxygen generation ability among various MG derivatives, enabling both protein and RNA proximity labeling in live cells. New insights are provided inthe transcriptome analysis with FAP-seq, while a deeper understanding of the symmetry-breaking structural arrangement of FAP-MG-HI was obtained through molecular dynamics simulations. Overall, our wash-free and NIR light-inducible RNA proximity labeling method (FAP-seq) offers a powerful and versatile approach for investigating complex mechanisms underlying RNA-related biological processes.

Machine Learning Deciphered Molecular Mechanistics with Accurate Kinetic and Thermodynamic Prediction.

Time-lagged independent component analysis (tICA) and the Markov state model (MSM) have been extensively employed for extracting conformational dynamics and kinetic community networks from unbiased trajectory ensembles. However, these techniques may not be the optimal choice for elucidating transition mechanisms within low-dimensional representations, especially for intricate biosystems. Unraveling the association mechanism in such complex systems always necessitates permutations of several essential independent components or collective variables, a process that is inherently obscure and may require empirical knowledge for selection. To address these challenges, we have implemented an integrated unsupervised dimension reduction model: uniform manifold approximation and projection (UMAP) with hierarchy density-based spatial clustering of applications with noise (HDBSCAN). This approach effectively generates low-dimensional configurational embeddings. The hierarchical application of this architecture, in conjunction with MSM, reveals global kinetic connectivity while identifying local conformational states. Consequently, our methodology establishes a multiscale mechanistic elucidation framework. Leveraging the benefits of the uniform sample distribution and a denoising approach, our model demonstrates robustness in preserving global and local data structures compared to traditional dimension reduction methods in the field of MD analysis area. The interpretability of hyperparameter selection and compatibility with downstream tasks are cross-validated across various simulation data sets, utilizing both computational evaluation metrics and experimental kinetic observables. Furthermore, the predicted Mcl1-BH3 association kinetics (0.76 s-1) is in close agreement with surface plasmon resonance experiments (0.12 s-1), affirming the plausibility of the identified pathway composed of representative conformations. We anticipate that the devised workflow will serve as a foundational framework for studying recognition patterns in complex biological systems. Its contributions extend to the exploration of protein functional dynamics and rational drug design, offering a potent avenue for advancing research in these domains.

Evaluation of antimicrobial, anticancer, antidiabetic, antioxidant activities and silver nanoparticles synthesized from Indian Clove- Syzygium aromaticum leaf extract

The Indian Clove (Syzygium aromaticum) is a naturally occurring spice with significant biological properties. It is primarily found in India and Indonesia, but can also be cultivated in other coastal areas. Clove is commonly used as a natural preservative and in the production of natural medicines. One of the notable benefits of clove is its ability to help treat diseases caused by various bacteria and fungi, including E. coli, P. aeruginosa, S. aureus, Z. mobilis, and C. albicans. Additionally, clove possesses antioxidant properties that can help reduce cell damage caused by free radicals. Clove has also shown potential in inhibiting the growth of cancer cells and reducing the activity of enzymes such as amylase, glucosidase, and lipase, which can be beneficial for managing diabetes. The study demonstrated that Silver nanoparticles (AgNPs) synthesized from Indian clove leaf extract exhibited promising antimicrobial, anticancer, antidiabetic, and antioxidant activities. This suggests that these nanoparticles could have versatile applications in antimicrobial therapy, cancer treatment, diabetes management, and antioxidant supplementation. However, further research is needed to understand the underlying mechanisms of these activities and to evaluate the safety and efficacy of these nanoparticles in more complex biological systems. In summary, clove possesses diverse biological properties and has potential applications in various therapeutic areas. The synthesis of AgNPs from clove leaf extract shows promise in multiple fields, but more research is required for a deeper understanding of its mechanisms and broader applications.

Unveiling Gene Regulatory Networks That Characterize Difference of Molecular Interplays Between Gastric Cancer Drug Sensitive and Resistance Cell Lines.

Gastric cancer is a leading cause of cancer-related deaths globally and chemotherapy is widely accepted as the standard treatment for gastric cancer. However, drug resistance in cancer cells poses a significant obstacle to the success of chemotherapy, limiting its effectiveness in treating gastric cancer. Although many studies have been conducted to unravel the mechanisms of acquired drug resistance, the existing studies were based on abnormalities of a single gene, that is, differential gene expression (DGE) analysis. Single gene-based analysis alone is insufficient to comprehensively understand the mechanisms of drug resistance in cancer cells, because the underlying processes of the mechanism involve perturbations of the molecular interactions. To uncover the mechanism of acquired gastric cancer drug resistance, we perform for identification of differentially regulated gene networks between drug-sensitive and drug-resistant cell lines. We develop a computational strategy for identifying phenotype-specific gene networks by extending the existing method, CIdrgn, that quantifies the dissimilarity of gene networks based on comprehensive information of network structure, that is, regulatory effect between genes, structure of edge, and expression levels of genes. To enhance the efficiency of identifying differentially regulated gene networks and improve the biological relevance of our findings, we integrate additional information and incorporate knowledge of network biology, such as hubness of genes and weighted adjacency matrices. The outstanding capabilities of the developed strategy are validated through Monte Carlo simulations. By using our strategy, we uncover gene regulatory networks that specifically capture the molecular interplays distinguishing drug-sensitive and drug-resistant profiles in gastric cancer. The reliability and significance of the identified drug-sensitive and resistance-specific gene networks, as well as their related markers, are verified through literature. Our analysis for differentially regulated gene network identification has the capacity to characterize the drug-sensitive and resistance-specific molecular interplays related to mechanisms of acquired drug resistance that cannot be revealed by analysis based solely on abnormalities of a single gene, for example, DGE analysis. Through our analysis and comprehensive examination of relevant literature, we suggest that targeting the suppressors of the identified drug-resistant markers, such as the Melanoma Antigen (MAGE) family, Trefoil Factor (TFF) family, and Ras-Associated Binding 25 (RAB25), while enhancing the expression of inducers of the drug sensitivity markers [e.g., Serum Amyloid A (SAA) family], could potentially reduce drug resistance and enhance the effectiveness of chemotherapy for gastric cancer. We expect that the developed strategy will serve as a useful tool for uncovering cancer-related phenotype-specific gene regulatory networks that provide essential clues for uncovering not only drug resistance mechanisms but also complex biological systems of cancer.

The landscape of transcription factor promoter activity during vegetative development in Marchantia.

Transcription factors (TFs) are essential for the regulation of gene expression and cell fate determination. Characterizing the transcriptional activity of TF genes in space and time is a critical step toward understanding complex biological systems. The vegetative gametophyte meristems of bryophytes share some characteristics with the shoot apical meristems of flowering plants. However, the identity and expression profiles of TFs associated with gametophyte organization are largely unknown. With only ∼450 putative TF genes, Marchantia (Marchantia polymorpha) is an outstanding model system for plant systems biology. We have generated a near-complete collection of promoter elements derived from Marchantia TF genes. We experimentally tested reporter fusions for all the TF promoters in the collection and systematically analyzed expression patterns in Marchantia gemmae. This allowed us to build a map of expression domains in early vegetative development and identify a set of TF-derived promoters that are active in the stem-cell zone. The cell markers provide additional tools and insight into the dynamic regulation of the gametophytic meristem and its evolution. In addition, we provide an online database of expression patterns for all promoters in the collection. We expect that these promoter elements will be useful for cell-type-specific expression, synthetic biology applications, and functional genomics.

Non-Gaussian anomalous diffusion of optical vortices.

Anomalous diffusion of different particlelike entities, the deviation from typical Brownian motion, is ubiquitous in complex physical and biological systems. While optical vortices move randomly in evolving speckle fields, optical vortices have only been observed to exhibit pure Brownian motion in random speckle fields. Here we present direct experimental evidence of the anomalous diffusion of optical vortices in temporally varying speckle patterns from multiple-scattering viscoelastic media. Moreover, we observe two characteristic features, i.e., the self-similarity and the antipersistent correlation of the optical vortex motion, indicating that the mechanism of the observed subdiffusion of optical vortices can only be attributed to fractional Brownian motion (FBM). We further demonstrate that the vortex displacements exhibit a non-Gaussian heavy-tailed distribution. Additionally, we modulate the extent of subdiffusion, such as diffusive scaling exponents, and the non-Gaussianity of optical vortices by altering the viscoelasticity of samples. The discovery of the complex FBM but non-Gaussian subdiffusion of optical vortices may not only offer insight into certain fundamental physics, including the anomalous diffusion of vortices in fluids and the decoupling between Brownianity and Gaussianity, but also suggest a strong potential for utilizing optical vortices as tracers in microrheology instead of the introduced exogenous probe particles in particle tracking microrheology.

An effective hydrodynamic description of marching locusts

A fundamental question in complex systems is how to relate interactions between individual components (‘microscopic description’) to the global properties of the system (‘macroscopic description’). Furthermore, it is unclear whether such a macroscopic description exists and if such a description can capture large-scale properties. Here, we address the validity of a macroscopic description of a complex biological system using the collective motion of desert locusts as a canonical example. One of the world’s most devastating insect plagues begins when flightless juvenile locusts form ‘marching bands’. These bands display remarkable coordinated motion, moving through semiarid habitats in search of food. We investigated how well macroscopic physical models can describe the flow of locusts within a band. For this, we filmed locusts within marching bands during an outbreak in Kenya and automatically tracked all individuals passing through the camera frame. We first analyzed the spatial topology of nearest neighbors and found individuals to be isotropically distributed. Despite this apparent randomness, a local order was observed in regions of high density in the radial distribution function, akin to an ordered fluid. Furthermore, reconstructing individual locust trajectories revealed a highly aligned movement, consistent with the one-dimensional version of the Toner-Tu equations, a generalization of the Navier–Stokes equations for fluids, used to describe the equivalent macroscopic fluid properties of active particles. Using this effective Toner–Tu equation, which relates the gradient of the pressure to the acceleration, we show that the effective ‘pressure’ of locusts increases as a linear function of density in segments with the highest polarization (for which the one-dimensional approximation is most appropriate). Our study thus demonstrates an effective hydrodynamic description of flow dynamics in plague locust swarms.

The Metabolic Syndrome, a Human Disease.

This review focuses on the question of metabolic syndrome (MS) being a complex, but essentially monophyletic, galaxy of associated diseases/disorders, or just a syndrome of related but rather independent pathologies. The human nature of MS (its exceptionality in Nature and its close interdependence with human action and evolution) is presented and discussed. The text also describes the close interdependence of its components, with special emphasis on the description of their interrelations (including their syndromic development and recruitment), as well as their consequences upon energy handling and partition. The main theories on MS's origin and development are presented in relation to hepatic steatosis, type 2 diabetes, and obesity, but encompass most of the MS components described so far. The differential effects of sex and its biological consequences are considered under the light of human social needs and evolution, which are also directly related to MS epidemiology, severity, and relations with senescence. The triggering and maintenance factors of MS are discussed, with especial emphasis on inflammation, a complex process affecting different levels of organization and which is a critical element for MS development. Inflammation is also related to the operation of connective tissue (including the adipose organ) and the widely studied and acknowledged influence of diet. The role of diet composition, including the transcendence of the anaplerotic maintenance of the Krebs cycle from dietary amino acid supply (and its timing), is developed in the context of testosterone and β-estradiol control of the insulin-glycaemia hepatic core system of carbohydrate-triacylglycerol energy handling. The high probability of MS acting as a unique complex biological control system (essentially monophyletic) is presented, together with additional perspectives/considerations on the treatment of this 'very' human disease.