Computational Systems Biology Research Articles

Drug repurposing in MASLD and MASH-cirrhosis: Targets and treatment approaches based on pathways analysis.

Designing and predicting novel drug targets to accelerate drug discovery for treating metabolic dysfunction-associated steatohepatitis (MASH)-cirrhosis is a challenging task. The presence of superimposed (nested) and co-occurring clinical and histological phenotypes, namely MASH and cirrhosis, may partly explain this. Thus, in this scenario, each sub-phenotype has its own set of pathophysiological mechanisms, triggers, and processes. Here, we used gene/protein and set enrichment analysis to predict druggable pathways for the treatment of MASH-cirrhosis. Our findings indicate that the pathogenesis of MASH-cirrhosis can be explained by perturbations in multiple, simultaneous, and overlapping molecular processes. In this scenario, each sub-phenotype has its own set of pathophysiological mechanisms, triggers, and processes. Therefore, we used systems biology modeling to provide evidence that MASH and cirrhosis paradoxically present unique and distinct as well as common disease mechanisms, including a network of molecular targets. More importantly, pathway analysis revealed straightforward results consistent with modulation of the immune response, cell cycle control, and epigenetic regulation. In conclusion, the selection of potential therapies for MASH-cirrhosis should be guided by a better understanding of the underlying biological processes and molecular perturbations that progressively damage liver tissue and its underlying structure. Therapeutic options for patients with MASH may not necessarily be of choice for MASH cirrhosis. Therefore, the biology of the disease and the processes associated with its natural history must be at the forefront of the decision-making process.

Dynamical behavior-based approach for the evaluation of treatment efficacy: The case of immuno-oncology.

Sophistication of mathematical models in the pharmacological context reflects the progress being made in understanding physiological, pharmacological, and disease relationships. This progress has illustrated once more the need for advanced quantitative tools able to efficiently extract information from these models. While dynamical systems theory has a long history in the analysis of systems biology models, as emphasized under the dynamical disease concept by Mackey and Glass [Science 197, 287-289 (1977)], its adoption in pharmacometrics is only at the beginning [Chae, Transl. Clin. Pharmacol. 28, 109 (2020)]. Using a quantitative systems pharmacology model of tumor immune dynamics as a case study [Kosinsky et al., J. Immunother. Cancer 6, 17 (2018)], we here adopt a dynamical systems analysis to describe, in an exhaustive way, six different statuses that refer to the response of the system to therapy, in the presence or absence of a tumor-free attractor. To evaluate the therapy success, we introduce the concept of TBA, related to the Time to enter the tumor-free Basin of Attraction, and corresponding to the earliest time at which the therapy can be stopped without jeopardizing its efficacy. TBA can determine the optimal time to stop drug administration and consequently quantify the reduction in drug exposure.

What we need to know about the germ-free animal models.

The gut microbiota (GM), as a forgotten organ, refers to the microbial community that resides in the gastrointestinal tract and plays a critical role in a variety of physiological activities in different body organs. The GM affects its targets through neurological, metabolic, immune, and endocrine pathways. The GM is a dynamic system for which exogenous and endogenous factors have negative or positive effects on its density and composition. Since the mid-twentieth century, laboratory animals are known as the major tools for preclinical research; however, each model has its own limitations. So far, two main models have been used to explore the effects of the GM under normal and abnormal conditions: the isolated germ-free and antibiotic-treated models. Both methods have strengths and weaknesses. In many fields of host-microbe interactions, research on these animal models are known as appropriate experimental subjects that enable investigators to directly assess the role of the microbiota on all features of physiology. These animal models present biological model systems to either study outcomes of the absence of microbes, or to verify the effects of colonization with specific and known microbial species. This paper reviews these current approaches and gives advantages and disadvantages of both models.

PyRates-A code-generation tool for modeling dynamical systems in biology and beyond.

The mathematical study of real-world dynamical systems relies on models composed of differential equations. Numerical methods for solving and analyzing differential equation systems are essential when complex biological problems have to be studied, such as the spreading of a virus, the evolution of competing species in an ecosystem, or the dynamics of neurons in the brain. Here we present PyRates, a Python-based software for modeling and analyzing differential equation systems via numerical methods. PyRates is specifically designed to account for the inherent complexity of biological systems. It provides a new language for defining models that mirrors the modular organization of real-world dynamical systems and thus simplifies the implementation of complex networks of interacting dynamic entities. Furthermore, PyRates provides extensive support for the various forms of interaction delays that can be observed in biological systems. The core of PyRates is a versatile code-generation system that translates user-defined models into "backend" implementations in various languages, including Python, Fortran, Matlab, and Julia. This allows users to apply a wide range of analysis methods for dynamical systems, eliminating the need for manual translation between code bases. PyRates may also be used as a model definition interface for the creation of custom dynamical systems tools. To demonstrate this, we developed two extensions of PyRates for common analyses of dynamic models of biological systems: PyCoBi for bifurcation analysis and RectiPy for parameter fitting. We demonstrate in a series of example models how PyRates can be used in combination with PyCoBi and RectiPy for model analysis and fitting. Together, these tools offer a versatile framework for applying computational modeling and numerical analysis methods to dynamical systems in biology and beyond.

Mechanical intelligence simplifies control in terrestrial limbless locomotion.

Limbless locomotors, from microscopic worms to macroscopic snakes, traverse complex, heterogeneous natural environments typically using undulatory body wave propagation. Theoretical and robophysical models typically emphasize body kinematics and active neural/electronic control. However, we contend that because such approaches often neglect the role of passive, mechanically controlled processes (those involving "mechanical intelligence"), they fail to reproduce the performance of even the simplest organisms. To uncover principles of how mechanical intelligence aids limbless locomotion in heterogeneous terradynamic regimes, here we conduct a comparative study of locomotion in a model of heterogeneous terrain (lattices of rigid posts). We used a model biological system, the highly studied nematode worm Caenorhabditis elegans, and a robophysical device whose bilateral actuator morphology models that of limbless organisms across scales. The robot's kinematics quantitatively reproduced the performance of the nematodes with purely open-loop control; mechanical intelligence simplified control of obstacle navigation and exploitation by reducing the need for active sensing and feedback. An active behavior observed in C. elegans, undulatory wave reversal upon head collisions, robustified locomotion via exploitation of the systems' mechanical intelligence. Our study provides insights into how neurally simple limbless organisms like nematodes can leverage mechanical intelligence via appropriately tuned bilateral actuation to locomote in complex environments. These principles likely apply to neurally more sophisticated organisms and also provide a design and control paradigm for limbless robots for applications like search and rescue and planetary exploration.

VSCode-Antimony: a source editor for building, analyzing, and translating antimony models.

Developing biochemical models in systems biology is a complex, knowledge-intensive activity. Some modelers (especially novices) benefit from model development tools with a graphical user interface. However, as with the development of complex software, text-based representations of models provide many benefits for advanced model development. At present, the tools for text-based model development are limited, typically just a textual editor that provides features such as copy, paste, find, and replace. Since these tools are not "model aware," they do not provide features for: (i) model building such as autocompletion of species names; (ii) model analysis such as hover messages that provide information about chemical species; and (iii) model translation to convert between model representations. We refer to these as BAT features. We present VSCode-Antimony, a tool for building, analyzing, and translating models written in the Antimony modeling language, a human readable representation of Systems Biology Markup Language (SBML) models. VSCode-Antimony is a source editor, a tool with language-aware features. For example, there is autocompletion of variable names to assist with model building, hover messages that aid in model analysis, and translation between XML and Antimony representations of SBML models. These features result from making VSCode-Antimony model-aware by incorporating several sophisticated capabilities: analysis of the Antimony grammar (e.g. to identify model symbols and their types); a query system for accessing knowledge sources for chemical species and reactions; and automatic conversion between different model representations (e.g. between Antimony and SBML). VSCode-Antimony is available as an open source extension in the VSCode Marketplace https://marketplace.visualstudio.com/items?itemName=stevem.vscode-antimony. Source code can be found at https://github.com/sys-bio/vscode-antimony.

Integrative gene expression studies on the path to precision medicine in Alzheimer’s and related disorders

AbstractBackgroundGenetic studies in Alzheimer’s disease and related disorders (ADRD) revealed numerous disease risk loci many of which reside in noncoding regions. To translate these discoveries to precision medicine therapies and biomarkers, integrative multi‐omics studies especially combining gene expression and disease phenotypes are necessary.MethodIn this presentation, we will review existing and emerging approaches in the discovery of functional disease risk variants, pathways and key disease molecules in ADRD via integrating multi‐omics and disease phenotypic data using computational systems biology followed by experimental validation.ResultsIntegrative genome‐wide association studies (GWAS) of ADRD risk, ADRD‐related phenotypes, gene expression and epigenetic factors such as methylation nominate disease variants and pinpoint risk mechanisms. Large scale differential gene and co‐expression network analyses discovered novel disease pathways unique to or shared between different ADRD. Experimental validations of these findings in model systems yielded high confidence molecular perturbations in ADRD.ConclusionADRD is characterized by molecular perturbations in myriad pathways including vascular, synaptic, immune, myelination and many others that are revealed through integrative multi‐omics studies in well‐characterized human cohorts and prioritized through experimental model systems. These molecules serve as candidate precision medicine therapeutic targets and biomarkers.

A New Approach to Designing Control of Dissolved Oxygen and Aeration System in Sequencing Batch Reactor by Applied Backstepping Control Algorithm

Abstract The wastewater treatment plant (WWTP) is a complex system due to its non-linearity, time-variance and multiple time scales in its dynamics among others. The most important control parameter in a WWTP is the dissolved oxygen (DO) concentration. The tracking problem of the DO concentration is one of the most fundamental issues in biological wastewater treatment. Proper control of DO concentration is necessary to achieve adequate biological conditions for microorganisms in the WWTP. Aeration is an important process to achieve those conditions, but it is expensive. It was performed using an aeration system, which includes blowers, pipelines and diffusers. This paper presents a new approach to designing a non-linear control system for controlling DO concentration using an adaptive backstepping algorithm. A model of biological processes and aeration system were applied in designing the control system. Simulation tests of the control system were performed and very good results on control were obtained. The proposed solution has proved to be effective and computationally efficient.

Skeleton phylogeny reconstructed with transcriptomes for the tribe Drosophilini (Diptera: Drosophilidae)

The family Drosophilidae is one of the most important model systems in evolutionary biology. Thanks to advances in high-throughput sequencing technology, a number of molecular phylogenetic analyses have been undertaken by using large data sets of many genes and many species sampled across this family. Especially, recent analyses using genome sequences have depicted the family-wide skeleton phylogeny with high confidence. However, the taxon sampling is still insufficient for minor lineages and non-Drosophila genera. In this study, we carried out phylogenetic analyses using a large number of transcriptome-based nucleotide sequences, focusing on the largest, core tribe Drosophilini in the Drosophilidae. In our analyses, some noise factors against phylogenetic reconstruction were taken into account by removing putative paralogy from the datasets and examining the effects of missing data, i.e. gene occupancy and site coverage, and incomplete lineage sorting. The inferred phylogeny has newly resolved the following phylogenetic positions/relationships at the genomic scale: (i) the monophyly of the subgenus Siphlodora including Zaprionus flavofasciatus to be transferred therein; (ii) the paraphyly of the robusta and melanica species groups within a clade comprised of the robusta, melanica and quadrisetata groups and Z. flavofasciatus; (iii) Drosophila curviceps (representing the curviceps group), D. annulipes (the quadrilineata subgroup of the immigrans group) and D. maculinotata clustered into a clade sister to the Idiomyia + Scaptomyza clade, forming together the expanded Hawaiian drosophilid lineage; (iv) Dichaetophora tenuicauda (representing the lineage comprised of the Zygothrica genus group and Dichaetophora) placed as the sister to the clade of the expanded Hawaiian drosophilid lineage and Siphlodora; and (v) relationships of the subgenus Drosophila and the genus Zaprionus as follows: (Zaprionus, (the quadrilineata subgroup, ((D. sternopleuralis, the immigrans group proper), (the quinaria radiation, the tripunctata radiation)))). These results are to be incorporated into the so-far published phylogenomic tree as a backbone (constraint) tree for grafting much more species based on sequences of a limited number of genes. Such a comprehensive, highly confident phylogenetic tree with extensive and dense taxon sampling will provide an essential framework for comparative studies of the Drosophilidae.

Empirical Quantification of Predictive Uncertainty Due to Model Discrepancy by Training with an Ensemble of Experimental Designs: An Application to Ion Channel Kinetics

When using mathematical models to make quantitative predictions for clinical or industrial use, it is important that predictions come with a reliable estimate of their accuracy (uncertainty quantification). Because models of complex biological systems are always large simplifications, model discrepancy arises—models fail to perfectly recapitulate the true data generating process. This presents a particular challenge for making accurate predictions, and especially for accurately quantifying uncertainty in these predictions. Experimentalists and modellers must choose which experimental procedures (protocols) are used to produce data used to train models. We propose to characterise uncertainty owing to model discrepancy with an ensemble of parameter sets, each of which results from training to data from a different protocol. The variability in predictions from this ensemble provides an empirical estimate of predictive uncertainty owing to model discrepancy, even for unseen protocols. We use the example of electrophysiology experiments that investigate the properties of hERG potassium channels. Here, ‘information-rich’ protocols allow mathematical models to be trained using numerous short experiments performed on the same cell. In this case, we simulate data with one model and fit it with a different (discrepant) one. For any individual experimental protocol, parameter estimates vary little under repeated samples from the assumed additive independent Gaussian noise model. Yet parameter sets arising from the same model applied to different experiments conflict—highlighting model discrepancy. Our methods will help select more suitable ion channel models for future studies, and will be widely applicable to a range of biological modelling problems.

Synthesis of poly-(N-phenylenbenzimidazole) and the production of nanofiber materials based on it

The work is devoted to the synthesis of poly-(N-phenylenbenzimidazoles), poly(benzodiimidazoles) and the production of nanofiber materials based on them. The relevance of the work is due to the fact that nanofibers obtained from polymer can be used to cover wound dressings. Bisbenzymidazoles are being developed as agents that bind small DNA grooves with antitumor activity, and can act as ligands to transition metals for modeling biological systems.

Searching for significant reactions and subprocesses in models of biological systems based on Petri nets

The primary aim of this research was to propose algorithms enabling the identification of significant reactions and subprocesses within models of biological systems constructed using classical Petri nets. These solutions allow to performance of two analysis methods: an importance analysis for identifying individual reactions critical to the functioning of the model and an occurrence analysis for finding essential subprocesses. To demonstrate the utility of these methods, analyses of an example model have been performed. In this case, it was a model related to the DNA damage response mechanism. It is worth noting that the proposed analyses can be applied to any biological phenomenon represented using the Petri net formalism. The presented analysis methods represent an extension of classical Petri net-based analyses. Their utility lies in their potential to enhance our comprehension of the biological phenomena under investigation. Furthermore, they can lead to the development of more effective medical therapies, as they can aid in the identification of potential molecular targets for drugs.

PTM-Psi: A python package to facilitate the computational investigation of post-translational modification on protein structures and their impacts on dynamics and functions.

Post-translational modification (PTM) of a protein occurs after it has been synthesized from its genetic template, and involves chemical modifications of the protein's specific amino acid residues. Despite of the central role played by PTM in regulating molecular interactions, particularly those driven by reversible redox reactions, it remains challenging to interpret PTMs in terms of protein dynamics and function because there are numerous combinatorially enormous means for modifying amino acids in response to changes in the protein environment. In this study, we provide a workflow that allows users to interpret how perturbations caused by PTMs affect a protein's properties, dynamics, and interactions with its binding partners based on inferred or experimentally determined protein structure. This Python-based workflow, called PTM-Psi, integrates several established open-source software packages, thereby enabling the user to infer protein structure from sequence, develop force fields for non-standard amino acids using quantum mechanics, calculate free energy perturbations through molecular dynamics simulations, and score the bound complexes via docking algorithms. Using the S-nitrosylation of several cysteines on the GAP2 protein as an example, we demonstrated the utility of PTM-Psi for interpreting sequence-structure-function relationships derived from thiol redox proteomics data. We demonstrate that the S-nitrosylated cysteine that is exposed to the solvent indirectly affects the catalytic reaction of another buried cysteine over a distance in GAP2 protein through the movement of the two ligands. Our workflow tracks the PTMs on residues that are responsive to changes in the redox environment and lays the foundation for the automation of molecular and systems biology modeling.

Application of the Finite Element Method in Modeling Biological Systems in Traumatology and Orthopedics

Surgical treatment of fractures of the proximal femur is a problem due to the lack of a uniform treatment strategy. Of particular importance is the analysis of the processes occurring in bone tissue during the healing of fractures under dynamic conditions, especially the analysis of mechanical stresses. The finite element method is an effective method for mathematical modeling of complex objects and describing the stress-strain states of these objects. In order to improve the results of treatment of fractures of the proximal femur and pelvic bones, mathematical modeling was carried out using the finite element method of a pertrochanteric femur fracture under conditions of fixation with a dynamic pin and a pelvic fracture under various conditions of its submersible fixation

Cellular communication among smooth muscle cells: The role of membrane potential via connexins

Communication via action potentials among neurons has been extensively studied. However, effective communication without action potentials is ubiquitous in biological systems, yet it has received much less attention in comparison. Multi-cellular communication among smooth muscles is crucial for regulating blood flow, for example. Understanding the mechanism of this non-action potential communication is critical in many cases, like synchronization of cellular activity, under normal and pathological conditions. In this paper, we employ a multi-scale asymptotic method to derive a macroscopic homogenized bidomain model from the microscopic electro-neutral (EN) model. This is achieved by considering different diffusion coefficients and incorporating nonlinear interface conditions. Subsequently, the homogenized macroscopic model is used to investigate communication in multi-cellular tissues. Our computational simulations reveal that the membrane potential of syncytia, formed by interconnected cells via connexins, plays a crucial role in propagating oscillations from one region to another, providing an effective means for fast cellular communication.Statement of Significance: In this study, we investigated cellular communication and ion transport in vascular smooth muscle cells, shedding light on their mechanisms under normal and abnormal conditions. Our research highlights the potential of mathematical models in understanding complex biological systems. We developed effective macroscale electro-neutral bi-domain ion transport models and examined their behavior in response to different stimuli. Our findings revealed the crucial role of connexinmediated membrane potential changes and demonstrated the effectiveness of cellular communication through syncytium membranes. Despite some limitations, our study provides valuable insights into these processes and emphasizes the importance of mathematical modeling in unraveling the complexities of cellular communication and ion transport.

A supramolecular system mimicking the infection process of an enveloped virus through membrane fusion

Membrane fusion is an essential step for the entry of enveloped viruses, such as human immunodeficiency virus and influenza virus, into the host cell, often triggered by the binding of membrane proteins on the viral envelope to host cell membrane. Recently, external stimuli was shown to trigger membrane fusion in an artificial system. Direct observation of artificial membrane fusion using a giant unilamellar vesicle (GUV), which is similar in size to a cell, is useful as a biological model system. However, there are no model systems for studying membrane fusion of enveloped viruses with host cells. Here, we report a supramolecular model system for viral entry into a GUV or cell through membrane fusion. The system was constructed by complexing a cationic lipid bilayer on an anionic artificial viral capsid, self-assembled from viral β-annulus peptides. We demonstrate that the cationic enveloped artificial viral capsid electrostatically interacts with the anionic GUV or cell, and the capsid enters the GUV or cell through membrane fusion. The model system established in this study will be important for analyzing membrane fusion during infection of a natural virus.

Computational study of the motor neuron protein KIF5A to identify nsSNPs, bioactive compounds, and its key regulators.

Introduction: Kinesin family member 5A (KIF5A) is a motor neuron protein expressed in neurons and involved in anterograde transportation of organelles, proteins, and RNA. Variations in the KIF5A gene that interfere with axonal transport have emerged as a distinguishing feature in several neurodegenerative disorders, including hereditary spastic paraplegia (HSP10), Charcot-Marie-Tooth disease type 2 (CMT2), and Amyotrophic Lateral Sclerosis (ALS). Methods: In this study, we implemented a computational structural and systems biology approach to uncover the role of KIF5A in ALS. Using the computational structural biology method, we explored the role of non-synonymous Single Nucleotide Polymorphism (nsSNPs) in KIF5A. Further, to identify the potential inhibitory molecule against the highly destabilizing structure variant, we docked 24 plant-derived phytochemicals involved in ALS. Results: We found KIF5AS291F variant showed the most structure destabilizing behavior and the phytocompound "epigallocatechin gallate" showed the highest binding affinity (-9.0Kcal/mol) as compared to wild KIF5A (-8.4Kcal/mol). Further, with the systems biology approach, we constructed the KIF5A protein-protein interaction (PPI) network to identify the associated Kinesin Families (KIFs) proteins, modules, and their function. We also constructed a transcriptional and post-transcriptional regulatory network of KIF5A. With the network topological parameters of PPIN (Degree, Bottleneck, Closeness, and MNC) using CytoHubba and computational knock-out experiment using Network Analyzer, we found KIF1A, 5B, and 5C were the significant proteins. The functional modules were highly enriched with microtubule motor activity, chemical synaptic transmission in neurons, GTP binding, and GABA receptor activity. In regulatory network analysis, we found KIF5A post-transcriptionally down-regulated by miR-107 which is further transcriptionally up-regulated by four TFs (HIF1A, PPARA, SREBF1, and TP53) and down-regulated by three TFs (ZEB1, ZEB2, and LIN28A). Discussion: We concluded our study by finding a crucial variant of KIF5A and its potential therapeutic target (epigallocatechin gallate) and KIF5A associated significant genes with important regulators which could decrypt the novel therapeutics in ALS and other neurodegenerative diseases.

Draft genome sequences of Neurospora crassa clade B, isolated from burned Cytisus sp. plants in France.

Neurospora crassa clade A is a model system for genetics, biochemistry, molecular biology, and experimental evolution. Here, we present the draft genome sequences of four isolates of N. crassa clade B. These data represent a valuable resource to investigate the population biology and evolutionary history of N. crassa sensu lato.

Multi-scale models of whole cells: progress and challenges.

Whole-cell modeling is "the ultimate goal" of computational systems biology and "a grand challenge for 21st century" (Tomita, Trends in Biotechnology, 2001, 19(6), 205-10). These complex, highly detailed models account for the activity of every molecule in a cell and serve as comprehensive knowledgebases for the modeled system. Their scope and utility far surpass those of other systems models. In fact, whole-cell models (WCMs) are an amalgam of several types of "system" models. The models are simulated using a hybrid modeling method where the appropriate mathematical methods for each biological process are used to simulate their behavior. Given the complexity of the models, the process of developing and curating these models is labor-intensive and to date only a handful of these models have been developed. While whole-cell models provide valuable and novel biological insights, and to date have identified some novel biological phenomena, their most important contribution has been to highlight the discrepancy between available data and observations that are used for the parametrization and validation of complex biological models. Another realization has been that current whole-cell modeling simulators are slow and to run models that mimic more complex (e.g., multi-cellular) biosystems, those need to be executed in an accelerated fashion on high-performance computing platforms. In this manuscript, we review the progress of whole-cell modeling to date and discuss some of the ways that they can be improved.

A yeast cell cycle pulse generator model shows consistency with multiple oscillatory and checkpoint mutant datasets

Modeling biological systems holds great promise for speeding up the rate of discovery in systems biology by predicting experimental outcomes and suggesting targeted interventions. However, this process is dogged by an identifiability issue, in which network models and their parameters are not sufficiently constrained by coarse and noisy data to ensure unique solutions. In this work, we evaluated the capability of a simplified yeast cell-cycle network model to reproduce multiple observed transcriptomic behaviors under genomic mutations. We matched time-series data from both cycling and checkpoint arrested cells to model predictions using an asynchronous multi-level Boolean approach. We showed that this single network model, despite its simplicity, is capable of exhibiting dynamical behavior similar to the datasets in most cases, and we demonstrated the drop in severity of the identifiability issue that results from matching multiple datasets.