Computational Systems Biology Research Articles

Automatic construction of Petri net models for computational simulations of molecular interaction network

Petri nets are commonly applied in modeling biological systems. However, construction of a Petri net model for complex biological systems is often time consuming, and requires expertise in the research area, limiting their application. To address this challenge, we developed GINtoSPN, an R package that automates the conversion of multi-omics molecular interaction network extracted from the Global Integrative Network (GIN) into Petri nets in GraphML format. These GraphML files can be directly used for Signaling Petri Net (SPN) simulation. To demonstrate the utility of this tool, we built a Petri net model for neurofibromatosis type I. Simulation of NF1 gene knockout, compared to normal skin fibroblast cells, revealed persistent accumulation of Ras-GTPs as expected. Additionally, we identified several other genes substantially affected by the loss of NF1’s function, exhibiting individual-specific variability. These results highlight the effectiveness of GINtoSPN in streamlining the modeling and simulation of complex biological systems.

Physicochemical and electrical properties of DPPC bilayer membranes in the presence of oleanolic or asiatic acid

This study aimed to investigate the effect of selected compounds from the group of triterpene sapogenins on model phosphatidylcholine membranes. Two types of biological membrane model systems were used in the work, i.e., liposomes (microelectrophoresis method) and spherical bilayers (interfacial tension method). Each model was modified with the tested sapogenin compounds, and the change in their physicochemical and electrical parameters was analyzed. Parameters characterizing the equilibrium in the membrane of 1,2-dipalmitoyl-sn-glycero-3-phosphocholine (DPPC)-oleanolic acid (OA) and DPPC-asiatic acid (AA) were determined). Based on the Young-Laplace equation, the interfacial tensions of spherical lipid bilayers were measured. The formation of 1:1 complexes was assumed in the DPPC-OA and DPPC-AA membrane systems, and the parameters characterizing the interactions in the formed complexes were calculated. Microelectrophoresis was used to study the surface charge density of lipid membranes. These values were obtained from electrophoretic mobility data using Smoluchowsky’s equation. The influence of pH on the electrolyte solution and the composition of the membranes was investigated. The results indicate that modifying DPPC membranes with selected triterpene sapogenins, both OA and AA, causes changes in the surface charge density and shifts of the isoelectric point. Data presented in this work, obtained through mathematical derivation and confirmed experimentally, are of great importance for interpreting phenomena occurring in lipid membranes. A quantitative description of equilibria between phosphatidylcholine and sapogenins lets us understand the processes on the membrane surface. The equilibria are particularly significant from the standpoint of cell functioning. Phosphatidylcholine-sapogenin interactions modulate a range of physicochemical properties of membranes, and they are important in the course of the multiple processes involving membranes in the living cell (e.g., transport mechanism).

A mechanistic systems biology model of brain microvascular endothelial cell signaling reveals dynamic pathway-based therapeutic targets for brain ischemia

Ischemic stroke is a significant threat to human health. Currently, there is a lack of effective treatments for stroke, and progress in new neuron-centered drug target development is relatively slow. On the other hand, studies have demonstrated that brain microvascular endothelial cells (BMECs) are crucial components of the neurovascular unit and play pivotal roles in ischemic stroke progression. To better understand the complex multifaceted roles of BMECs in the regulation of ischemic stroke pathophysiology and facilitate BMEC-based drug target discovery, we utilized a transcriptomics-informed systems biology modeling approach and constructed a mechanism-based computational multipathway model to systematically investigate BMEC function and its modulatory potential. Extensive multilevel data regarding complex BMEC pathway signal transduction and biomarker expression under various pathophysiological conditions were used for quantitative model calibration and validation, and we generated dynamic BMEC phenotype maps in response to various stroke-related stimuli to identify potential determinants of BMEC fate under stress conditions. Through high-throughput model sensitivity analyses and virtual target perturbations in model-based single cells, our model predicted that targeting succinate could effectively reverse the detrimental cell phenotype of BMECs under oxygen and glucose deprivation/reoxygenation, a condition that mimics stroke pathogenesis, and we experimentally validated the utility of this new target in terms of regulating inflammatory factor production, free radical generation and tight junction protection in vitro and in vivo. Our work is the first that complementarily couples transcriptomic analysis with mechanistic systems-level pathway modeling in the study of BMEC function and endothelium-based therapeutic targets in ischemic stroke.

Defining Golden Batches in Biomanufacturing Processes From Internal Metabolic Activity to Detect Process Changes That May Affect Product Quality.

Cellular metabolism plays a role in the observed variability of a drug substance's Critical Quality Attributes (CQAs) made by biomanufacturing processes. Therefore, here we describe a new approach for monitoring biomanufacturing processes that measures a set of metabolic reaction rates (named Critical Metabolic Parameters (CMP) in addition to the macroscopic process conditions currently being used as Critical Process Parameters (CPP) for biomanufacturing. Constraint-based systems biology models like Flux Balance Analysis (FBA) are used to estimate metabolic reaction rates, and metabolic rates are used as inputs for multivariate Batch Evolution Models (BEM). Metabolic activity was reproducible among batches and could be monitored to detect a deliberately induced macroscopic process shift (i.e., temperature change). The CMP approach has the potential to enable "golden batches" in biomanufacturing processes to be defined from the internal metabolic activity and to aid in detecting process changes that may impact the quality of the product. Overall, the data suggested that monitoring of metabolic activity has promise for biomanufacturing process control.

Thermostatted Kinetic Theory Structures in Biophysics: Generalizations and Perspectives

The mathematical modeling of multicellular systems is an important branch of biophysics, which focuses on how the system properties emerge from the elementary interaction between the constituent elements. Recently, mathematical structures have been proposed within the thermostatted kinetic theory for the modeling of complex living systems and have been profitably employed for the modeling of various complex biological systems at the cellular scale. This paper deals with a class of generalized thermostatted kinetic theory frameworks that can stand in as background paradigms for the derivation of specific models in biophysics. Specifically, the fundamental homogeneous thermostatted kinetic theory structures of the recent literature are recovered and generalized in order to take into consideration further phenomena in biology. The generalizations concern the conservative, the nonconservative, and the mutative interactions between the inner system and the outer environment. In order to sustain the strength of the new structures, some specific models of the literature are reset into the style of the new frameworks of the thermostatted kinetic theory. The selected models deal with breast cancer, genetic mutations, immune system response, and skin fibrosis. Future research directions from the theoretical and modeling viewpoints are discussed in the whole paper and are mainly devoted to the well-posedness in the Hadamard sense of the related initial boundary value problems, to the spatial–velocity dynamics and to the derivation of macroscopic-scale dynamics.

Molecular and functional landscape of malignant serous effusions for precision oncology

Personalized treatment for patients with advanced solid tumors critically depends on the deep characterization of tumor cells from patient biopsies. Here, we comprehensively characterize a pan-cancer cohort of 150 malignant serous effusion (MSE) samples at the cellular, molecular, and functional level. We find that MSE-derived cancer cells retain the genomic and transcriptomic profiles of their corresponding primary tumors, validating their use as a patient-relevant model system for solid tumor biology. Integrative analyses reveal that baseline gene expression patterns relate to global ex vivo drug sensitivity, while high-throughput drug-induced transcriptional changes in MSE samples are indicative of drug mode of action and acquired treatment resistance. A case study exemplifies the added value of multi-modal MSE profiling for patients who lack genetically stratified treatment options. In summary, our study provides a functional multi-omics view on a pan-cancer solid tumor cohort and underlines the feasibility and utility of MSE-based precision oncology.

The ecological and evolutionary dynamics of inselbergs.

Islands are fundamental model systems in ecology, biogeography, and evolutionary biology. However, terrestrial islands, unlike their aquatic counterparts, have received comparatively less attention. Among these land islands, inselbergs (i.e. isolated rock outcrops with diverse lithologies and a modest topographical prominence) stand out as iconic examples distributed worldwide across global biomes. Due to their durable lithology, inselbergs change slowly, persisting for tens of millions of years. In this review, we propose a biological definition for inselbergs that captures three fundamental characteristics of inselbergs from the perspective of biota. These are old age, isolation and the presence of unique microhabitats that are rare or absent in the surrounding matrix, fostering distinct communities often with unique and endemic biota. We synthesise the state of the art and formulate a set of testable hypotheses to deepen our understanding of the origins and maintenance of diversity on inselbergs, which are increasingly exposed to anthropogenic threats. By offering different habitats compared to the surrounding habitat matrix (e.g. moist microhabitats in dryland landscapes and xeric environments in humid tropical landscapes), inselbergs may allow specific lineages to thrive beyond their typical geographical limits. Particularly in drylands and degraded landscapes, inselbergs may not just provide different habitats but also act as ecological refuges or evolutionary refugia by providing a wider range of potential microhabitats than the surrounding matrix, enhancing resilience and promoting regional biodiversity. The central role of the matrix ensures that the ecological and evolutionary dynamics of inselbergs differ from those of true islands such as oceanic islands. Given that inselberg biota coexist within a terrestrial matrix, interactions between inselberg and matrix populations impact each other significantly. Over evolutionary timescales, matrix species may contract to inselberg refugia, preserving lineages while cycles of isolation and reconnection may drive speciation via a species pump. Although inselberg biodiversity has been studied predominantly from an island biogeography perspective, we argue that depending on the spatial scale, habitat specificity and mobility of the organisms considered, a range of different theories and paradigms can help explain the biogeography and local distribution patterns of different taxonomic and functional groups of inselberg species.

A new high-quality genome assembly and annotation for the threatened Florida Scrub-Jay (Aphelocoma coerulescens).

The Florida Scrub-Jay (Aphelocoma coerulescens), a Federally Threatened, cooperatively-breeding bird, is an emerging model system in evolutionary biology and ecology. Extensive individual-based monitoring and genetic sampling for decades has yielded a wealth of data, allowing for the detailed study of social behavior, demography, and population genetics of this natural population. Here, we report a linkage map and a chromosome-level genome assembly and annotation for a female Florida Scrub-Jay made with long-read sequencing technology, chromatin conformation data, and the linkage map. We constructed a linkage map comprising 4,468 SNPs that had 34 linkage groups and a total sex-averaged autosomal genetic map length of 2446.78 cM. The new genome assembly is 1.33 Gb in length, consisting of 33 complete or near-complete autosomes and the sex chromosomes (ZW). This highly contiguous assembly has an NG50 of 68 Mb and a Benchmarking Universal Single-Copy Orthologs (BUSCO) completeness score of 97.1% with respect to the Aves database. The annotated gene set has a BUSCO transcriptome completeness score of 95.5% and 17,964 identified protein-coding genes, 92.5% of which have associated functional annotations. This new, high-quality genome assembly and linkage map of the Florida Scrub-Jay provides valuable tools for future research into the evolutionary dynamics of small, natural populations of conservation concern.

Combining Zhegalkin Polynomials and SAT Solving for Context-specific Boolean Modeling of Biological Systems.

Large amounts of knowledge regarding biological processes are readily available in the literature and aggregated in diverse databases. Boolean networks are powerful tools to render that knowledge into models that can mimic and simulate biological phenomena at multiple scales. Yet, when a model is required to understand or predict the behavior of a biological system in given conditions, existing information often does not completely match this context. Networks built from only prior knowledge can overlook mechanisms, lack specificity, and just partially recapitulate experimental observations. To address this limitation, context-specific data needs to be integrated. However, the brute-force identification of qualitative rules matching these data becomes infeasible as the number of candidates explodes for increasingly complex systems. Here, we used Zhegalkin polynomials to transform this identification into a binary value assignment for exponentially fewer variables, which we addressed with a state-of-the-art SAT solver. We evaluated our implemented method alongside two widely recognized tools, CellNetOptimizer and Caspo-ts, on both artificial toy models and large-scale models based on experimental data from the HPN-DREAM challenge. Our approach demonstrated benchmark-leading capabilities on networks of significant size and intricate complexity. It thus appears promising for the in silico modeling of ever more comprehensive biological systems.

Editorial: Dynamics of Deterministic Models of Biological Systems.

Editorial: Dynamics of Deterministic Models of Biological Systems.

Fenestrated Endothelial Cells across Organs: Insights into Kidney Function and Disease.

In the human body, the vascular system plays an indispensable role in maintaining homeostasis by supplying oxygen and nutrients to cells and organs and facilitating the removal of metabolic waste and toxins. Blood vessels-the key constituents of the vascular system-are composed of a layer of endothelial cells on their luminal surface. In most organs, tightly packed endothelial cells serve as a barrier separating blood and lymph from surrounding tissues. Intriguingly, endothelial cells in some tissues and organs (e.g., choroid plexus, liver sinusoids, small intestines, and kidney glomerulus) form transcellular pores called fenestrations that facilitate molecular and ionic transport across the vasculature and mediate immune responses through leukocyte transmigration. However, the development and unique functions of endothelial cell fenestrations across organs are yet to be fully uncovered. This review article provides an overview of fenestrated endothelial cells in multiple organs. We describe their development and organ-specific roles, with expanded discussions on their contributions to glomerular health and disease. We extend these discussions to highlight the dynamic changes in endothelial cell fenestrations in diabetic nephropathy, focal segmental glomerulosclerosis, Alport syndrome, and preeclampsia, and how these unique cellular features could be targeted for therapeutic development. Finally, we discuss emerging technologies for in vitro modeling of biological systems, and their relevance for advancing the current understanding of endothelial cell fenestrations in health and disease.

Electronic Polarizability Tunes the Function of the Human Bestrophin 1 Cl- Channel.

Mechanisms of anion permeation within ion channels and nanopores remain poorly understood. Recent cryo-electron microscopy structures of the human bestrophin 1 Cl- channel (hBest1) provide an opportunity to evaluate ion interactions predicted by molecular dynamics (MD) simulations against experimental observations. Here, we implement the fully polarizable forcefield AMOEBA in MD simulations on different conformations of hBest1. This forcefield models multipole moments up to the quadrupole; therefore, it captures induced dipole and anion-π interactions. We show that key biophysical properties of the channel can only be simulated when electronic polarization is included in the molecular models and that Cl- permeation through the neck of the pore is achieved through hydrophobic solvation concomitant with partial ion dehydration. Furthermore, we demonstrate how such polarizable simulations can help determine the identity of ion-like densities within high-resolution cryo-EM structures and that neglecting polarization places Cl- at positions that do not correspond with their experimentally resolved location. Overall, our results demonstrate the importance of including electronic polarization in realistic and physically accurate models of biological systems, especially channels and pores that selectively permeate anions.

A Computational Model of the Secondary Hemostasis Pathway in Reaction Systems

Reaction Systems (RSs) are a computational framework inspired by biochemical mechanisms. An RS defines a finite set of reactions over a finite set of entities (molecules, proteins, etc). Starting from an initial set of entities (the initial state), a computation is performed by applying all reactions to a state in order to produce the following state, giving rise to a sequence of sets of entities. RSs have shown to be a general computational framework whose application ranges from the modeling of biological phenomena to molecular chemistry and computer science. In this paper, we contribute to research on the application of RSs for modeling biological systems. We consider the problem of modeling hemostasis, for which several models have been defined, starting from the 1960s. Previous models are based on sets of ordinary differential equations, while we develop a discrete model in RSs for pathways of the secondary hemostasis. Then, we implement our model in BioReSolve, a computational framework for RSs that we have previously defined which provides tools for the specification and verification of properties. By using the tools in BioReSolve we derive important observations on the model behaviour for hemostasis, and in particular, we study the role of three important inhibitors, verifying that their presence or absence leads to phenomena such as thrombophilia, or thromboembolism, or excessive coagulation, etc. We can also study computationally the causality relations between the molecules involved in the reactions showing which entities play a fundamental role, thus contributing to the design of more effective and specialized drugs. Our work can hence help to show how to model complex biological systems in RSs and derive computationally and biologically relevant properties of the systems.

Meaning, concept and application of bioinformatics

Bioinformatics, originating from the term coined in 1979 by Paulien Hogeweg and Ben Hesper, is a burgeoning interdisciplinary field at the nexus of biology, computer science, and information technology. It encompasses the collection, classification, storage, and analysis of vast biological datasets using computational methods, aiming to unravel the complexities of biological systems. This abstract provides a comprehensive overview of bioinformatics, emphasizing its definition, scope, branches, aims, research areas, databases, and applications, particularly highlighting its role in drug discovery through structural bioinformatics. Bioinformatics integrates diverse branches such as computational biology, genomics, proteomics, structural biology, systems biology, pharmacogenomics, and bioprogramming. Each branch applies mathematical modeling, molecular interactions analysis, or genomic data interpretation to advance understanding in biology and medicine. Key aims include applying advanced computational technologies to biological problems, presenting complex data clearly, and providing robust statistical tools for genomic analysis. The field also fosters collaborations across academic, commercial, and government sectors to leverage resources and expertise. Research areas span computational evolutionary analysis, genome annotation, gene expression analysis, cancer mutation studies, and comparative genomics, illuminating disease mechanisms and evolutionary relationships. Bioinformatics databases play a crucial role by storing and organizing biological data. Primary repositories like GenBank and the Protein Data Bank (PDB), secondary databases such as SWISS-Prot, and specialized databases support research and clinical applications by providing access to genomic, proteomic, and structural information. In drug discovery, structural bioinformatics techniques like homology modeling, molecular docking, and simulations predict protein structures, analyze interactions, and aid in designing therapies. These computational methods accelerate drug development, optimize drug-target interactions, and reduce costs associated with bringing new drugs to market. Overall, bioinformatics drives innovation in biological sciences and healthcare by leveraging computational methods to interpret biological data. As technology advances, bioinformatics will continue to play a pivotal role in addressing global health challenges, advancing personalized medicine, and facilitating groundbreaking discoveries in biology and medicine. The evolving field of bioinformatics promises continued growth and impact, shaping the future of biological research and its applications in improving human health and understanding the natural world.

2D electrical admittance lattice model of biological cellular system for modeling electroporation

In this work, a new modeling approach is presented to obtain a two-dimensional transport lattice of a biological cellular system for the calculation of the potential distribution throughout the system and investigation of the corresponding membrane electroporation. The presented model has been obtained by a modified bilayer model of the cell membrane. This modified membrane model allows for an effective inclusion of the shape of the cell membrane in the potential calculation. The results of the model have shown good agreement with the results of the well-known Schwan equation and COMSOL Multiphysics for the circular cell. The simulation results show that both membranes of a mitochondrion can be simultaneously electroporated by an alternating voltage source with frequencies between 1 MHz and 1 GHz.

Systems modeling of oncogenic G-protein and GPCR signaling reveals unexpected differences in downstream pathway activation

Mathematical models of biochemical reaction networks are an important and emerging tool for the study of cell signaling networks involved in disease processes. One promising potential application of such mathematical models is the study of how disease-causing mutations promote the signaling phenotype that contributes to the disease. It is commonly assumed that one must have a thorough characterization of the network readily available for mathematical modeling to be useful, but we hypothesized that mathematical modeling could be useful when there is incomplete knowledge and that it could be a tool for discovery that opens new areas for further exploration. In the present study, we first develop a mechanistic mathematical model of a G-protein coupled receptor signaling network that is mutated in almost all cases of uveal melanoma and use model-driven explorations to uncover and explore multiple new areas for investigating this disease. Modeling the two major, mutually-exclusive, oncogenic mutations (Gαq/11 and CysLT2R) revealed the potential for previously unknown qualitative differences between seemingly interchangeable disease-promoting mutations, and our experiments confirmed oncogenic CysLT2R was impaired at activating the FAK/YAP/TAZ pathway relative to Gαq/11. This led us to hypothesize that CYSLTR2 mutations in UM must co-occur with other mutations to activate FAK/YAP/TAZ signaling, and our bioinformatic analysis uncovers a role for co-occurring mutations involving the plexin/semaphorin pathway, which has been shown capable of activating this pathway. Overall, this work highlights the power of mechanism-based computational systems biology as a discovery tool that can leverage available information to open new research areas.

Adaptive optical third-harmonic generation microscopy for in vivo imaging of tissues.

Third-harmonic generation microscopy is a powerful label-free nonlinear imaging technique, providing essential information about structural characteristics of cells and tissues without requiring external labelling agents. In this work, we integrated a recently developed compact adaptive optics module into a third-harmonic generation microscope, to measure and correct for optical aberrations in complex tissues. Taking advantage of the high sensitivity of the third-harmonic generation process to material interfaces and thin membranes, along with the 1,300-nm excitation wavelength used here, our adaptive optical third-harmonic generation microscope enabled high-resolution in vivo imaging within highly scattering biological model systems. Examples include imaging of myelinated axons and vascular structures within the mouse spinal cord and deep cortical layers of the mouse brain, along with imaging of key anatomical features in the roots of the model plant Brachypodium distachyon. In all instances, aberration correction led to enhancements in image quality.

Systems-level computational modeling in ischemic stroke: from cells to patients.

Ischemic stroke, a significant threat to human life and health, refers to a class of conditions where brain tissue damage is induced following decreased cerebral blood flow. The incidence of ischemic stroke has been steadily increasing globally, and its disease mechanisms are highly complex and involve a multitude of biological mechanisms at various scales from genes all the way to the human body system that can affect the stroke onset, progression, treatment, and prognosis. To complement conventional experimental research methods, computational systems biology modeling can integrate and describe the pathogenic mechanisms of ischemic stroke across multiple biological scales and help identify emergent modulatory principles that drive disease progression and recovery. In addition, by running virtual experiments and trials in computers, these models can efficiently predict and evaluate outcomes of different treatment methods and thereby assist clinical decision-making. In this review, we summarize the current research and application of systems-level computational modeling in the field of ischemic stroke from the multiscale mechanism-based, physics-based and omics-based perspectives and discuss how modeling-driven research frameworks can deliver insights for future stroke research and drug development.

MoNETA: MultiOmics Network Embedding for SubType Analysis.

Cells are complex systems whose behavior emerges from a huge number of reactions taking place within and among different molecular districts. The availability of bulk and single-cell omics data fueled the creation of multi-omics systems biology models capturing the dynamics within and between omics layers. Powerful modeling strategies are needed to cope with the increased amount of data to be interrogated and the relative research questions. Here, we present MultiOmics Network Embedding for SubType Analysis (MoNETA) for fast and scalable identification of relevant multi-omics relationships between biological entities at the bulk and single-cells level. We apply MoNETA to show how glioma subtypes previously described naturally emerge with our approach. We also show how MoNETA can be used to identify cell types in five multi-omic single-cell datasets.

Data-driven identification of stable sparse differential operators using constrained regression

Identifying differential operators from data is essential for the mathematical modeling of complex physical and biological systems where massive datasets are available. These operators must be stable for accurate predictions for dynamics forecasting problems. In this article, we propose a novel methodology for learning differential operators that are theoretically linearly stable and have sparsity patterns of common discretization schemes. These differential operators are obtained by solving a constrained regression problem, involving local constraints to ensure the linear stability of the global dynamical system. We further extend this approach for learning nonlinear differential operators by determining linear stability constraints for linearized equations around an equilibrium point. The applicability of the proposed method is demonstrated for both linear and nonlinear partial differential equations such as 1-D scalar advection-diffusion equation, 1-D Burgers equation and 2-D advection equation. The results indicated that solutions to constrained regression problems with linear stability constraints provide accurate and linearly stable sparse differential operators.