Complex Biological Systems Research Articles

Illuminating the Path to Enhanced Bioimaging by Phosphole-based Fluorophores.

As the research of biological systems becomes increasingly complex, there is a growing demand for fluorophores with a diverse range of wavelengths. In this study, we introduce phosphole-based fluorophores that surpass existing options like dansyl chloride. The reactive S-Cl bond in chlorosulfonylimino-5-phenylphosphole derivatives allows rapid and direct coupling to peptides making the fluorophores easily introducible to peptides. This coupling process occurs under mild conditions, demonstrated for [F7 ,P34 ]-NPY and its shorter analogues. Peptides linked with our fluorophores exhibit similar receptor activation to the control peptide, while maintaining high stability and low toxicity, making them ideal biolabeling reagents. In fluorescence microscopy experiments, they can be easily visualized even at low concentrations, without suffering from the typical issue of bleaching. These phosphole-based fluorophores represent a significant leap forward in the field. Their versatility, ease of modification, superior performance, and applicability in biological labeling make them a promising choice for researchers seeking advanced tools to unravel the details of complex biological systems.

Applications of information geometry to spiking neural network activity.

The space of possible behaviors that complex biological systems may exhibit is unimaginably vast, and these systems often appear to be stochastic, whether due to variable noisy environmental inputs or intrinsically generated chaos. The brain is a prominent example of a biological system with complex behaviors. The number of possible patterns of spikes emitted by a local brain circuit is combinatorially large, although the brain may not make use of all of them. Understanding which of these possible patterns are actually used by the brain, and how those sets of patterns change as properties of neural circuitry change is a major goal in neuroscience. Recently, tools from information geometry have been used to study embeddings of probabilistic models onto a hierarchy of model manifolds that encode how model outputs change as a function of their parameters, giving a quantitative notion of "distances" between outputs. We apply this method to a network model of excitatory and inhibitory neural populations to understand how the competition between membrane and synaptic response timescales shapes the network's information geometry. The hyperbolic embedding allows us to identify the statistical parameters to which the model behavior is most sensitive, and demonstrate how the ranking of these coordinates changes with the balance of excitation and inhibition in the network.

Current Understandings and Open Hypotheses on Extracellular Circular RNAs.

Circular RNAs (circRNAs) are closed RNA loops present in humans and other organisms. Various circRNAs have an essential role in diseases, including cancer. Cells can release circRNAs into the extracellular space of adjacent biofluids and can be present in extracellular vesicles. Due to their circular nature, extracellular circRNAs (excircRNAs) are more stable than their linear counterparts and are abundant in many biofluids, such as blood plasma and urine. circRNAs' link with disease suggests their extracellular counterparts have high biomarker potential. However, circRNAs and the extracellular space are challenging research domains, as they consist of complex biological systems plagued with nomenclature issues and a wide variety of protocols with different advantages and disadvantages. Here, we summarize what is known about excircRNAs, the current challenges in the field, and what is needed to improve extracellular circRNA research.

Machine Learning and Augmented Intelligence Enables Prognosis of Type 2 Diabetes Prior to Clinical Manifestation.

The global incidence of type 2 diabetes (T2D) persists at epidemic proportions. Early diagnosis and/or preventive efforts are critical to attenuate the multi-systemic clinical manifestation and consequent healthcare burden. Despite enormous strides in the understanding of pathophysiology and on-going therapeutic development, effectiveness and access are persistent limitations. Among the greatest challenges, the extensive research efforts have not promulgated reliable predictive biomarkers for early detection and risk assessment. The emerging fields of multi-omics combined with machine learning (ML) and augmented intelligence (AI) have profoundly impacted the capacity for predictive, preventive, and personalized medicine. This paper explores the current challenges associated with the identification of predictive biomarkers for T2D and discusses potential actionable solutions for biomarker identification and validation. The articles included were collected from PubMed queries. The selected topics of inquiry represented a wide range of themes in diabetes biomarker prediction and prognosis. The current criteria and cutoffs for T2D diagnosis are not optimal nor consider a myriad of contributing factors in terms of early detection. There is an opportunity to leverage AI and ML to significantly enhance the understanding of the underlying mechanisms of the disease and identify prognostic biomarkers. The innovative technologies being developed by GATC are expected to play a crucial role in this pursuit via algorithm training and validation, enabling comprehensive and in-depth analysis of complex biological systems. GATC is an emerging leader guiding the establishment of a systems approach towards research and predictive, personalized medicine. The integration of these technologies with clinical data can contribute to a more comprehensive understanding of T2D, paving the way for precision medicine approaches and improved patient outcomes.

MIWE: detecting the critical states of complex biological systems by the mutual information weighted entropy

Complex biological systems often undergo sudden qualitative changes during their dynamic evolution. These critical transitions are typically characterized by a catastrophic progression of the system. Identifying the critical point is critical to uncovering the underlying mechanisms of complex biological systems. However, the system may exhibit minimal changes in its state until the critical point is reached, and in the face of high throughput and strong noise data, traditional biomarkers may not be effective in distinguishing the critical state. In this study, we propose a novel approach, mutual information weighted entropy (MIWE), which uses mutual information between genes to build networks and identifies critical states by quantifying molecular dynamic differences at each stage through weighted differential entropy. The method is applied to one numerical simulation dataset and four real datasets, including bulk and single-cell expression datasets. The critical states of the system can be recognized and the robustness of MIWE method is verified by numerical simulation under the influence of different noises. Moreover, we identify two key transcription factors (TFs), CREB1 and CREB3, that regulate downstream signaling genes to coordinate cell fate commitment. The dark genes in the single-cell expression datasets are mined to reveal the potential pathway regulation mechanism.

Blinking characteristics of organic fluorophores for blink-based multiplexing

Single-molecule fluorescence experiments have transformed our understanding of complex materials and biological systems. Whether single molecules are used to report on their nano-environment or provide for localization, understanding their blinking dynamics (i.e., stochastic fluctuations in emission intensity under continuous illumination) is paramount. We recently demonstrated another use for blinking dynamics called blink-based multiplexing (BBM), where individual emitters are classified using a single excitation laser based on blinking dynamics, rather than color. This study elucidates the structure-activity relationships governing BBM performance in a series of model rhodamine, BODIPY, and anthraquinone fluorophores that undergo different photo-physical and-chemical processes during blinking. Change point detection and multinomial logistic regression analyses show that BBM can leverage spectral fluctuations, electron and proton transfer kinetics, as well as photostability for molecular classification—even within the context of a shared blinking mechanism. In doing so, we demonstrate two- and three-color BBM with ≥ 93% accuracy using spectrally-overlapped fluorophores.

A data-driven approach for timescale decomposition of biochemical reaction networks.

Understanding the dynamics of biological systems in evolving environments is a challenge due to their scale and complexity. Here, we present a computational framework for the timescale decomposition of biochemical reaction networks to distill essential patterns from their intricate dynamics. This approach identifies timescale hierarchies, concentration pools, and coherent structures from time-series data, providing a system-level description of reaction networks at physiologically important timescales. We apply this technique to kinetic models of hypothetical and biological pathways, validating it by reproducing analytically characterized or previously known concentration pools of these pathways. Moreover, by analyzing the timescale hierarchy of the glycolytic pathway, we elucidate the connections between the stoichiometric and dissipative structures of reaction networks and the temporal organization of coherent structures. Specifically, we show that glycolysis is a cofactor-driven pathway, the slowest dynamics of which are described by a balance between high-energy phosphate bond and redox trafficking. Overall, this approach provides more biologically interpretable characterizations of network dynamics than large-scale kinetic models, thus facilitating model reduction and personalized medicine applications. IMPORTANCE Complex interactions within interconnected biochemical reaction networks enable cellular responses to a wide range of unpredictable environmental perturbations. Understanding how biological functions arise from these intricate interactions has been a long-standing problem in biology. Here, we introduce a computational approach to dissect complex biological systems' dynamics in evolving environments. This approach characterizes the timescale hierarchies of complex reaction networks, offering a system-level understanding at physiologically relevant timescales. Analyzing various hypothetical and biological pathways, we show how stoichiometric properties shape the way energy is dissipated throughout reaction networks. Notably, we establish that glycolysis operates as a cofactor-driven pathway, where the slowest dynamics are governed by a balance between high-energy phosphate bonds and redox trafficking. This approach enhances our understanding of network dynamics and facilitates the development of reduced-order kinetic models with biologically interpretable components.

Materials Design Innovations in Optimizing Cellular Behavior on Melt Electrowritten (MEW) Scaffolds

AbstractThe field of melt electrowriting (MEW) has seen significant progress, bringing innovative advancements to the fabrication of biomaterial scaffolds, and creating new possibilities for applications in tissue engineering and beyond. Multidisciplinary collaboration across materials science, computational modeling, AI, bioprinting, microfluidics, and dynamic culture systems offers promising new opportunities to gain deeper insights into complex biological systems. As the focus shifts towards personalized medicine and reduced reliance on animal models, the multidisciplinary approach becomes indispensable. This review provides a concise overview of current strategies and innovations in controlling and optimizing cellular responses to MEW scaffolds, highlighting the potential of scaffold material, MEW architecture, and computational modeling tools to accelerate the development of efficient biomimetic systems. Innovations in material science and the incorporation of biologics into MEW scaffolds have shown great potential in adding biomimetic complexity to engineered biological systems. These techniques pave the way for exciting possibilities for tissue modeling and regeneration, personalized drug screening, and cell therapies.

Asymmetric Schottky Barrier-Generated MoS2/WTe2 FET Biosensor Based on a Rectified Signal.

Field-effect transistor (FET) biosensors can be used to measure the charge information carried by biomolecules. However, insurmountable hysteresis in the long-term and large-range transfer characteristic curve exists and affects the measurements. Noise signal, caused by the interference coefficient of external factors, may destroy the quantitative analysis of trace targets in complex biological systems. In this report, a "rectified signal" in the output characteristic curve, instead of the "absolute value signal" in the transfer characteristic curve, is obtained and analyzed to solve these problems. The proposed asymmetric Schottky barrier-generated MoS2/WTe2 FET biosensor achieved a 105 rectified signal, sufficient reliability and stability (maintained for 60 days), ultra-sensitive detection (10 aM) of the Down syndrome-related DYRK1A gene, and excellent specificity in base recognition. This biosensor with a response range of 10 aM-100 pM has significant application potential in the screening and rapid diagnosis of Down syndrome.

Four-dimensional computational ultrasound imaging of brain hemodynamics.

Four-dimensional ultrasound imaging of complex biological systems such as the brain is technically challenging because of the spatiotemporal sampling requirements. We present computational ultrasound imaging (cUSi), an imaging method that uses complex ultrasound fields that can be generated with simple hardware and a physical wave prediction model to alleviate the sampling constraints. cUSi allows for high-resolution four-dimensional imaging of brain hemodynamics in awake and anesthetized mice.

Porous Laser-Scribed Graphene Electrodes Modified with Zwitterionic Moieties: A Strategy for Antibiofouling and Low-Impedance Interfaces.

Laser-scribed graphene electrodes (LSGEs) are promising platforms for the development of electrochemical biosensors for point-of-care settings and continuous monitoring and wearable applications. However, the frequent occurrence of biofouling drastically reduces the sensitivity and selectivity of these devices, hampering their sensing performance. Herein, we describe a versatile, low-impedance, and robust antibiofouling interface based on sulfobetaine-zwitterionic moieties. The interface induces the formation of a hydration layer and exerts electrostatic repulsion, protecting the electrode surface from the nonspecific adsorption of various biofouling agents. We demonstrate through electrochemical and microscopy techniques that the modified electrode exhibits outstanding antifouling properties, preserving more than 90% of the original signal after 24 h of exposure to bovine serum albumin protein, HeLa cells, and Escherichia coli bacteria. The promising performance of this antifouling strategy suggests that it is a viable option for prolonging the lifetime of LSGEs-based sensors when operating on complex biological systems.

Modeling pH-Dependent Biomolecular Photochemistry.

The tuning mechanism of pH can be extremely challenging to model computationally in complex biological systems, especially with respect to the photochemical properties. This article reports a protocol aimed at modeling pH-dependent photodynamics using a combination of constant-pH molecular dynamics and semiclassical nonadiabatic molecular dynamics simulations. With retinal photoisomerization in Anabaena sensory rhodopsin (ASR) as a testbed, we show that our protocol produces pH-dependent photochemical properties, such as the isomerization quantum yield or decay rates. We decompose our results into single-titrated residue contributions, identifying some key tuning amino acids. Additionally, we assess the validity of the single protonation state picture to represent the system at a given pH and propose the most populated protein charge state as a compromise between cost and accuracy.

Heat Inactivation of Nipah Virus for Downstream Single-Cell RNA Sequencing Does Not Interfere with Sample Quality.

Single-cell RNA sequencing (scRNA-seq) technologies are instrumental to improving our understanding of virus-host interactions in cell culture infection studies and complex biological systems because they allow separating the transcriptional signatures of infected versus non-infected bystander cells. A drawback of using biosafety level (BSL) 4 pathogens is that protocols are typically developed without consideration of virus inactivation during the procedure. To ensure complete inactivation of virus-containing samples for downstream analyses, an adaptation of the workflow is needed. Focusing on a commercially available microfluidic partitioning scRNA-seq platform to prepare samples for scRNA-seq, we tested various chemical and physical components of the platform for their ability to inactivate Nipah virus (NiV), a BSL-4 pathogen that belongs to the group of nonsegmented negative-sense RNA viruses. The only step of the standard protocol that led to NiV inactivation was a 5 min incubation at 85 °C. To comply with the more stringent biosafety requirements for BSL-4-derived samples, we included an additional heat step after cDNA synthesis. This step alone was sufficient to inactivate NiV-containing samples, adding to the necessary inactivation redundancy. Importantly, the additional heat step did not affect sample quality or downstream scRNA-seq results.

BioModME for building and simulating dynamic computational models of complex biological systems.

Molecular mechanisms of biological functions and disease processes are exceptionally complex, and our ability to interrogate and understand relationships is becoming increasingly dependent on the use of computational modeling. We have developed "BioModME," a standalone R-based web application package, providing an intuitive and comprehensive graphical user interface to help investigators build, solve, visualize, and analyze computational models of complex biological systems. Some important features of the application package include multi-region system modeling, custom reaction rate laws and equations, unit conversion, model parameter estimation utilizing experimental data, and import and export of model information in the Systems Biology Matkup Language format. The users can also export models to MATLAB, R, and Python languages and the equations to LaTeX and Mathematical Markup Language formats. Other important features include an online model development platform, multi-modality visualization tool, and efficient numerical solvers for differential-algebraic equations and optimization. All relevant software information including documentation and tutorials can be found at https://mcw.marquette.edu/biomedical-engineering/computational-systems-biology-lab/biomodme.php. Deployed software can be accessed at https://biomodme.ctsi.mcw.edu/. Source code is freely available for download at https://github.com/MCWComputationalBiologyLab/BioModME.

SpatiAlign: an unsupervised contrastive learning model for data integration of spatially resolved transcriptomics.

Integrative analysis of spatially resolved transcriptomics datasets empowers a deeper understanding of complex biological systems. However, integrating multiple tissue sections presents challenges for batch effect removal, particularly when the sections are measured by various technologies or collected at different times. We propose spatiAlign, an unsupervised contrastive learning model that employs the expression of all measured genes and the spatial location of cells, to integrate multiple tissue sections. It enables the joint downstream analysis of multiple datasets not only in low-dimensional embeddings but also in the reconstructed full expression space. In benchmarking analysis, spatiAlign outperforms state-of-the-art methods in learning joint and discriminative representations for tissue sections, each potentially characterized by complex batch effects or distinct biological characteristics. Furthermore, we demonstrate the benefits of spatiAlign for the integrative analysis of time-series brain sections, including spatial clustering, differential expression analysis, and particularly trajectory inference that requires a corrected gene expression matrix.

CAT Bridge: an efficient toolkit for gene-metabolite association mining from multiomics data.

With advancements in sequencing and mass spectrometry technologies, multiomics data can now be easily acquired for understanding complex biological systems. Nevertheless, substantial challenges remain in determining the association between gene-metabolite pairs due to the nonlinear and multifactorial interactions within cellular networks. The complexity arises from the interplay of multiple genes and metabolites, often involving feedback loops and time-dependent regulatory mechanisms that are not easily captured by traditional analysis methods. Here, we introduce Compounds And Transcripts Bridge (abbreviated as CAT Bridge, available at https://catbridge.work), a free user-friendly platform for longitudinal multiomics analysis to efficiently identify transcripts associated with metabolites using time-series omics data. To evaluate the association of gene-metabolite pairs, CAT Bridge is a pioneering work benchmarking a set of statistical methods spanning causality estimation and correlation coefficient calculation for multiomics analysis. Additionally, CAT Bridge features an artificial intelligence agent to assist users interpreting the association results. We applied CAT Bridge to experimentally obtained Capsicum chinense (chili pepper) and public human and Escherichia coli time-series transcriptome and metabolome datasets. CAT Bridge successfully identified genes involved in the biosynthesis of capsaicin in C. chinense. Furthermore, case study results showed that the convergent cross-mapping method outperforms traditional approaches in longitudinal multiomics analyses. CAT Bridge simplifies access to various established methods for longitudinal multiomics analysis and enables researchers to swiftly identify associated gene-metabolite pairs for further validation.

Equations of Motion of Mechanical Systems with Switchable Constraints

The article deals with a numerical and analytical approach to solving the equations of motion, which enables to treat the considered problems with the change of system structure or number of degrees of freedom without interrupting the numerical integration process. The described methodology allows effectively incorporate switchable constraints in the systems in accordance with their flexible structures. The crucial idea is based on the formulation of the resulting differentialalgebraic equations into a saddle point system, where the switchable constraints are represented by a sign matrix with variable rank. In connection with this property, a pseudoinversion is applied to eliminate algebraic variables and transform the problem to the first order system of ordinary differential equations. Moreover, the time independent case leads to linear autonomous systems with non-diagonalizable matrices, as is proved. The relevant numerical scheme is based on Runge-Kutta methods, that correspond to the power series of the resulting matrix exponential for time independent problems. The methodology presented is illustrated on the idealized two-mass oscillator with a switchable constraint. The numerical experiments performed range from initial stages, through simple transient cases to damped intentional control. The advanced applications can be found in robotics, active and controlled systems, and in the simulations of complex systems in biology and related areas. Moreover, the methodology can also be applied in the simulation of transport systems, especially in relation to vehicle technology, a quarter car suspension system, a vibration control mechanism, a torsion system with a clutch, and machine balancing and storage should to be highlighted.

Dispersive micro solid phase extraction of glibenclamide from plasma, urine, and wastewater using a magnetic molecularly imprinted polymer followed by its determination by a high-performance liquid chromatography-photodiode array detector.

The present study describes the development of a simple and selective analytical method for dispersive micro solid phase extraction and determination of glibenclamide (GLB) using magnetic molecularly imprinted polymer (MMIP) as a sorbent. MMIP was fabricated by the non-covalent method on the surface of silicated Fe3O4 and had a high affinity for glibenclamide; dual monomers, itaconic acid and allylamine, were used for this. Polymerization was achieved by the precipitation method in the presence of glibenclamide as the template and ethylene glycol dimethacrylate as the cross-linker. The morphology and structural properties of the MMIP were characterized by different analytical methods. To achieve maximum extraction efficiency, influencing parameters were optimized. The linearity range was 1-2000 and 12-2000 μg L-1 by high-performance liquid chromatography-photodiode array detector (HPLC-PDA) and UV-vis spectroscopy, respectively. The detection and quantification limits with UV-vis and HPLC-PDA analyses were 4 and 12 μg L-1 and 0.3 and 1 μg L-1, respectively. Under optimized conditions, recovery of glibenclamide spiked in plasma, human urine, and wastewater was between 89.4 and 102.9% at the concentration levels of 25, 250, and 500 μg L-1; relative standard deviations were below 3.7% by HPLC-PDA. The developed method has a favorable pre-concentration factor of 140.0. Equilibrium data and sorption isotherms fitted well with the Langmuir model. A maximum sorption capacity of 24.260 mg g-1 was acquired based on the Langmuir model. The synthesized sorbent with high selectivity was used to separate GLB from complex biological systems and wastewater before measurement with UV-vis or HPLC-PDA.

Attractor-Transition Control of Complex Biological Networks: A Constant Control Approach.

This article presents attractor-transition control of complex biological networks represented by Boolean networks (BNs) wherein the BN is steered from a prescribed initial attractor toward a desired one. The proposed approach leverages the similarity between attractors and Boolean algebraic properties embedded in the underlying state transition equations. To enhance the clarity of expression regarding stabilization toward the desired attractor, a simple coordinate transformation is performed on the considered BN. Based on the characteristics of transformed state equations, self-stabilizing state variables requiring no control efforts are derived in the first. Next, by applying the feedback vertex set (FVS) control scheme, control inputs stabilizing the remaining state variables are determined. The proposed control scheme exhibits versatility by accommodating both fixed-point and cyclic attractors. We validate the effectiveness of the proposed strategy through extensive numerical experiments conducted on random BNs as well as complex biological systems. In adherence to the reproducible research initiative, detailed results of numerical experiments and all the implementation codes are provided on the authors' website: https://github.com/choonlog/AttractorTransition.

Mass spectrometry-intensive top-down proteomics: an update on technology advancements and biomedical applications.

Proteoforms are all forms of protein molecules from the same gene because of variations at the DNA, RNA, and protein levels, e.g., alternative splicing and post-translational modifications (PTMs). Delineation of proteins in a proteoform-specific manner is crucial for understanding their biological functions. Mass spectrometry (MS)-intensive top-down proteomics (TDP) is promising for comprehensively characterizing intact proteoforms in complex biological systems. It has achieved substantial progress in technological development, including sample preparation, proteoform separations, MS instrumentation, and bioinformatics tools. In a single TDP study, thousands of proteoforms can be identified and quantified from a cell lysate. It has also been applied to various biomedical research to better our understanding of protein function in regulating cellular processes and to discover novel proteoform biomarkers of diseases for early diagnosis and therapeutic development. This review covers the most recent technological development and biomedical applications of MS-intensive TDP.