Computational Systems Biology Research Articles

Understanding the biosynthesis and regulatory mechanisms of bioactive compounds in Taraxacum species (dandelions), a model system for natural rubber, food, and medicinal plant biology

Plants that belong to the genus Taraxacum are commonly referred to as dandelions; these are some of the most evolved plants in the Asteraceae, with more than 2000 species distributed worldwide. Dandelions not only include medicinal plants but also rubber-producing plants. Recent advances in biotechnology and the growing demand for dandelions have increased research attention toward dandelions. Owing to the availability of the whole genome sequence of Taraxacum kok-saghyz, and Taraxacum mongolicum (accession no. GWHBCHF00000000 and GWHAAAA00000000), and large transcriptome and metabolome databases for T. kok-saghyz, Taraxacum officinale, T. mongolicum, and Taraxacum antungense (accession no. PRJNA378120, PRJEB48186, PRJNA897666, and PRJNA578773), significant progress has been made in understanding the biosynthesis and regulatory mechanisms of phenolic acids, flavonoids, terpenoids, rubber, and other bioactive compounds. This review focuses on Taraxacum spp.; specifically, We describe the following: (1) the recent results of genetic transformation systems, (2) biosynthetic pathways of bioactive compounds and natural rubber, (3) cross-talk in the biosynthesis of different secondary metabolites, (4) the relevance of the environment and the reproductive mode of dandelions in secondary metabolite biosynthesis, and (5) future research directives for dandelions. This review also lays the foundation for further research on other medicinal and edible plants.

From Microphone to Phoneme: An End-to-End Computational Neural Model for Predicting Speech Perception With Cochlear Implants.

Advances in computational models of biological systems and artificial neural networks enable rapid virtual prototyping of neuroprostheses, accelerating innovation in the field. Here, we present an end-to-end computational model for predicting speech perception with cochlear implants (CI), the most widely-used neuroprosthesis. The model integrates CI signal processing, a finite element model of the electrically-stimulated cochlea, and an auditory nerve model to predict neural responses to speech stimuli. An automatic speech recognition neural network is then used to extract phoneme-level speech perception from these neural response patterns. Compared to human CI listener data, the model predicts similar patterns of speech perception and misperception, captures between-phoneme differences in perceptibility, and replicates effects of stimulation parameters and noise on speech recognition. Information transmission analysis at different stages along the CI processing chain indicates that the bottleneck of information flow occurs at the electrode-neural interface, corroborating studies in CI listeners. An end-to-end model of CI speech perception replicated phoneme-level CI speech perception patterns, and was used to quantify information degradation through the CI processing chain. This type of model shows great promise for developing and optimizing new and existing neuroprostheses.

Developmental atlas of the indirect-developing sea urchin Paracentrotus lividus: From fertilization to juvenile stages.

The sea urchin Paracentrotus lividus has been used as a model system in biology for more than a century. Over the past decades, it has been at the center of a number of studies in cell, developmental, ecological, toxicological, evolutionary, and aquaculture research. Due to this previous work, a significant amount of information is already available on the development of this species. However, this information is fragmented and rather incomplete. Here, we propose a comprehensive developmental atlas for this sea urchin species, describing its ontogeny from fertilization to juvenile stages. Our staging scheme includes three periods divided into 33 stages, plus 15 independent stages focused on the development of the coeloms and the adult rudiment. For each stage, we provide a thorough description based on observations made on live specimens using light microscopy, and when needed on fixed specimens using confocal microscopy. Our descriptions include, for each stage, the main anatomical characteristics related, for instance, to cell division, tissue morphogenesis, and/or organogenesis. Altogether, this work is the first of its kind providing, in a single study, a comprehensive description of the development of P. lividus embryos, larvae, and juveniles, including details on skeletogenesis, ciliogenesis, myogenesis, coelomogenesis, and formation of the adult rudiment as well as on the process of metamorphosis in live specimens. Given the renewed interest for the use of sea urchins in ecotoxicological, developmental, and evolutionary studies as well as in using marine invertebrates as alternative model systems for biomedical investigations, this study will greatly benefit the scientific community and will serve as a reference for specialists and non-specialists interested in studying sea urchins.

Temporary and permanent control of partially specified Boolean networks

Boolean networks (BNs) are a well-accepted modelling formalism in computational systems biology. Nevertheless, modellers often cannot identify only a single BN that matches the biological reality. The typical reasons for this is insufficient knowledge or a lack of experimental data. Formally, this uncertainty can be expressed using partially specified Boolean networks (PSBNs), which encode the wide range of network candidates into a single structure.In this paper, we target the control of PSBNs. The goal of BN control is to find perturbations which guarantee stabilisation of the system in the desired state. Specifically, we consider variable perturbations (gene knock-out and over-expression) with three types of application time-window: one-step, temporary, and permanent.While the control of fully specified BNs is a thoroughly explored topic, control of PSBNs introduces additional challenges that we address in this paper. In particular, the unspecified components of the model cause a significant amount of additional state space explosion. To address this issue, we propose a fully symbolic methodology that can represent the numerous system variants in a compact form.In fully specified models, the efficiency of a perturbation is characterised by the count of perturbed variables (the perturbation size). However, in the case of a PSBN, a perturbation might work only for a subset of concrete BN models. To that end, we introduce and quantify perturbation robustness. This metric characterises how efficient the given perturbation is with respect to the model uncertainty.Finally, we evaluate the novel control methods using non-trivial real-world PSBN models. We inspect the method’s scalability and efficiency with respect to the size of the state space and the number of unspecified components. We also compare the robustness metrics for all three perturbation types. Our experiments support the hypothesis that one-step perturbations are significantly less robust than temporary and permanent ones.

Bayesian parameter estimation for dynamical models in systems biology.

Dynamical systems modeling, particularly via systems of ordinary differential equations, has been used to effectively capture the temporal behavior of different biochemical components in signal transduction networks. Despite the recent advances in experimental measurements, including sensor development and '-omics' studies that have helped populate protein-protein interaction networks in great detail, modeling in systems biology lacks systematic methods to estimate kinetic parameters and quantify associated uncertainties. This is because of multiple reasons, including sparse and noisy experimental measurements, lack of detailed molecular mechanisms underlying the reactions, and missing biochemical interactions. Additionally, the inherent nonlinearities with respect to the states and parameters associated with the system of differential equations further compound the challenges of parameter estimation. In this study, we propose a comprehensive framework for Bayesian parameter estimation and complete quantification of the effects of uncertainties in the data and models. We apply these methods to a series of signaling models of increasing mathematical complexity. Systematic analysis of these dynamical systems showed that parameter estimation depends on data sparsity, noise level, and model structure, including the existence of multiple steady states. These results highlight how focused uncertainty quantification can enrich systems biology modeling and enable additional quantitative analyses for parameter estimation.

Brain and Its Universal Logical Model of Multi-Agent Biological Systems

We build a topological model, based on intuitionistic logic, for multi-agent biological systems (such as Physarum polycephalum, bacterial colonies or any other swarm), reacting to external nourishment stimuli. Our construction follows the topological description of brain activity, where particles (neurons) are activated by an external environment, represented by a topological space X with an open cover {U_i:iin I}. The brain builds the model of this external space via the nerve (trace) of a topological space X. Here the body of Physarum polycephalum or a swarm made of networks of tubular structures represents a nerve (trace) of X also which means that Physarum polycephalum or a swarm gains orientation in the space of external stimuli even in the absence of any neural system. The logic of living organisms is based on open subsets of X and thus can be represented by Heyting algebra (i.e. intuitionistically). We also consider the generalisation of the nerve construction to a categorical context, where the category is determined by the network structures of multi-agent biological system. This model can be generalised up to simulating the behaviour of any swarm by means of intuitionistic logic.

On anti-occurrence of subsets of transitions in Petri net-based models of complex biological systems

Background and Objective:In the last two decades there can be observed a rapid development of systems biology. The basis of systems methods is a formal model of an analyzed system. It can be created in a language of some branch of mathematics and recently Petri net-based biological models seem to be especially promising since they have a great expressive power. One of the methods of analysis of such models is based on transition invariants. They correspond to some subprocesses which do not change a state of the modeled biological system. During such analysis, a need arose to study the subsets of transitions, what leads to interesting combinatorial problems — which have been considered in theory and practice. Methods & Results:Two problems of anti-occurrence were considered. These problems concern a set of transitions which is not a subset of any of t-invariant supports or is not a subset of t-invariant supports from some collection of such supports. They are defined in a formal way, their computational complexity is analyzed and an exact algorithm is provided for one of them. Conclusions:A comprehensive analysis of complex biological phenomena is challenging. Finding elementary processes that do not affect subprocesses belonging to the entire studied biological system may be necessary for a complete understanding of such a model and it is possible thanks to the proposed algorithm.

High iodine promotes autoimmune thyroid disease by activating hexokinase 3 and inducing polarization of macrophages towards M1.

Autoimmune thyroid disease (AITD), the most common autoimmune disease, includes Graves' disease (GD) and Hashimoto's thyroiditis (HT). Currently, the pathogenesis of AITD is not fully understood. Our study aimed to examine the presence of macrophage polarization imbalance in AITD patients, to investigate whether high iodine can cause macrophage polarization imbalance, and to investigate the role of key genes of metabolic reprogramming in macrophage polarization imbalance caused by high iodine. We synergistically used various research strategies such as systems biology, clinical studies, cell culture and mouse disease models. Gene set enrichment analysis (GSEA) revealed that M1 macrophage hyperpolarization was involved in the pathogenesis of AITD. In vitro and in vivo experiments showed that high iodine can affect the polarization of M1 or M2 macrophages and their related cytokines. Robust rank aggregation (RRA) method revealed that hexokinase 3 (HK3) was the most aberrantly expressed metabolic gene in autoimmune diseases. In vitro and in vivo studies revealed HK3 could mediate macrophage polarization induced by high iodine. In summary, hyperpolarization of M1-type macrophages is closely related to the pathogenesis of AITD. High iodine can increase HK3 expression in macrophages and promote macrophage polarization towards M1. Targeting HK3 can inhibit M1 polarization induced by high iodine.

Complementary Dual Approach for In Silico Target Identification of Potential Pharmaceutical Compounds in Cystic Fibrosis.

Cystic fibrosis is a genetic disease caused by mutation of the CFTR gene, which encodes a chloride and bicarbonate transporter in epithelial cells. Due to the vast range of geno- and phenotypes, it is difficult to find causative treatments; however, small-molecule therapeutics have been clinically approved in the last decade. Still, the search for novel therapeutics is ongoing, and thousands of compounds are being tested in different assays, often leaving their mechanism of action unknown. Here, we bring together a CFTR-specific compound database (CandActCFTR) and systems biology model (CFTR Lifecycle Map) to identify the targets of the most promising compounds. We use a dual inverse screening approach, where we employ target- and ligand-based methods to suggest targets of 309 active compounds in the database amongst 90 protein targets from the systems biology model. Overall, we identified 1038 potential target–compound pairings and were able to suggest targets for all 309 active compounds in the database.

The Involvement of Cell-Type-Specific Glycans in Hydra Temporary Adhesion Revealed by a Lectin Screen

Hydra is a freshwater solitary polyp, capable of temporary adhesion to underwater surfaces. The reversible attachment is based on an adhesive material that is secreted from its basal disc cells and left behind on the substrate as a footprint. Despite Hydra constituting a standard model system in stem cell biology and tissue regeneration, few studies have addressed its bioadhesion. This project aimed to characterize the glycan composition of the Hydra adhesive, using a set of 23 commercially available lectins to label Hydra cells and footprints. The results indicated the presence of N-acetylglucosamine, N-acetylgalactosamine, fucose, and mannose in the adhesive material. The labeling revealed a meshwork-like substructure in the footprints, implying that the adhesive is mainly formed by fibers. Furthermore, lectins might serve as a marker for Hydra cells and structures, e.g., many labeled as glycan-rich nematocytes. Additionally, some unexpected patterns were uncovered, such as structures associated with radial muscle fibers and endodermal gland cells in the hypostome of developing buds.

Machine Learning and Hybrid Methods for Metabolic Pathway Modeling.

Computational cell metabolism models seek to provide metabolic explanations of cell behavior under different conditions or following genetic alterations, help in the optimization of in vitro cell growth environments, or predict cellular behavior in vivo and in vitro. In the extremes, mechanistic models can include highly detailed descriptions of a small number of metabolic reactions or an approximate representation of an entire metabolic network. To date, all mechanistic models have required details of individual metabolic reactions, either kinetic parameters or metabolic flux, as well as information about extracellular and intracellular metabolite concentrations. Despite the extensive efforts and the increasing availability of high-quality data, required in vivo data are not available for the majority of known metabolic reactions; thus, mechanistic models are based primarily on ex vivo kinetic measurements and limited flux information. Machine learning approaches provide an alternative for derivation of functional dependencies from existing data. The increasing availability of metabolomic and lipidomic data, with growing feature coverage as well as sample set size, is expected to provide new data options needed for derivation of machine learning models of cell metabolic processes. Moreover, machine learning analysis of longitudinal data can lead to predictive models of cell behaviors over time. Conversely, machine learning models trained on steady-state data can provide descriptive models for the comparison of metabolic states in different environments or disease conditions. Additionally, inclusion of metabolic network knowledge in these analyses can further help in the development of models with limited data.This chapter will explore the application of machine learning to the modeling of cell metabolism. We first provide a theoretical explanation of several machine learning and hybrid mechanistic machine learning methods currently being explored to model metabolism. Next, we introduce several avenues for improving these models with machine learning. Finally, we provide protocols for specific examples of the utilization of machine learning in the development of predictive cell metabolism models using metabolomic data. We describe data preprocessing, approaches for training of machine learning models for both descriptive and predictive models, and the utilization of these models in synthetic and systems biology. Detailed protocols provide a list of software tools and libraries used for these applications, step-by-step modeling protocols, troubleshooting, as well as an overview of existing limitations to these approaches.

In Silico Modeling and Quantification of Synergistic Effects of Multi-Combination Compounds: Case Study of the Attenuation of Joint Pain Using a Combination of Phytonutrients

The quantification of synergistic effects of multi-combination compounds is critical in developing “cocktails” that are efficacious. In this research, a method for in silico modeling and the quantification of synergistic effects of multi-combination compounds is applied for assessing a combination of phytonutrients for joint pain. Joint pain is the most prominent and disabling symptom of arthritis. Arthritic pain leads to a reduced quality of life. This research explores the efficacy of a synergistic combination of two plant-based flavonoids—apigenin and hesperidin—on joint pain. The study employs computational systems biology: (1) to identify biomolecular mechanisms of joint pain; (2) to identify the specific effects of apigenin and hesperidin, individually and in combination, on the mechanisms of joint pain; and (3) to predict the quantitative effects of apigenin and hesperidin, individually and in combination, on joint pain and whether these combination effects are synergistic or additive. Four molecular pathways that are affected by apigenin and hesperidin include the following: (1) arachidonic acid metabolism, (2) PGE2 signaling, (3) COX-2 synthesis, and (4) oxidative stress. The combination of apigenin and hesperidin significantly lowered PGE2 production, CGRP production, TRVP-1 synthesis, COX-2 production, and reactive oxygen species (ROS) production. Our results indicate that the apigenin and hesperidin combination synergistically affected four of the five modalities to attenuate joint pain.

Saccharomyces cerevisiae as a Model System for Eukaryotic Cell Biology, from Cell Cycle Control to DNA Damage Response.

The yeast Saccharomyces cerevisiae has been used for bread making and beer brewing for thousands of years. In addition, its ease of manipulation, well-annotated genome, expansive molecular toolbox, and its strong conservation of basic eukaryotic biology also make it a prime model for eukaryotic cell biology and genetics. In this review, we discuss the characteristics that made yeast such an extensively used model organism and specifically focus on the DNA damage response pathway as a prime example of how research in S. cerevisiae helped elucidate a highly conserved biological process. In addition, we also highlight differences in the DNA damage response of S. cerevisiae and humans and discuss the challenges of using S. cerevisiae as a model system.

Species diversity and phylogeography of the Australoheros autrani group (Teleostei, Cichlidae) in eastern Brazil

Cichlid fishes are an important model system in evolutionary biology, primarily because of their exceptional diversity. However, while some cichlid assemblages, such as the ones of the African Great Lakes, have received considerable attention, others are not well studied, including many riverine species. Here, we focus on the Australoheros autrani species group and first report a new record of Australoheros in the upper Paranaíba River drainage, extending the known distribution range of this genus. Through Bayesian inference and maximum likelihood phylogenetic analyses of the mitochondrial cytochrome b gene of these specimens as well as available sequences, we assigned the newly discovered population to Australoheros barbosae. We corroborate the monophyly of the A. autrani species group and the presence of three species in the upper/middle Paraíba do Sul River basin as well as molecular diagnostic characters for each. Finally, we provide evidence for a recent expansion of A. barbosae.

Computational modelling in health and disease: highlights of the 6th annual SysMod meeting

Abstract Summary The Community of Special Interest (COSI) in Computational Modelling of Biological Systems (SysMod) brings together interdisciplinary scientists interested in combining data-driven computational modelling, multi-scale mechanistic frameworks, large-scale -omics data and bioinformatics. SysMod’s main activity is an annual meeting at the Intelligent Systems for Molecular Biology (ISMB) conference, a meeting for computer scientists, biologists, mathematicians, engineers and computational and systems biologists. The 2021 SysMod meeting was conducted virtually due to the ongoing COVID-19 pandemic (coronavirus disease 2019). During the 2-day meeting, the development of computational tools, approaches and predictive models was discussed, along with their application to biological systems, emphasizing disease mechanisms. This report summarizes the meeting. Availability and implementation All resources and further information are freely accessible at https://sysmod.info.

75 The Power of Theoretical and Practical Identifiability Analysis for Modeling (Micro-) Biological Processes

Abstract When modeling biological systems, one major step of the modeling exercise is the confrontation between the theory (the model) and the reality (the data). Such a confrontation passes through the resolution of the parameter identification (model calibration) problem, which aims at finding a set of parameters that best fits the variables predicted by the model to the data. The parameter identification step is often addressed like a downstream process. Using this approach, once the data are collected, the modeler has a minimal degree of freedom to improve the accuracy of her model. Instead, the modeler can identify opportunities for improving model accuracy if the parameter identification problem is addressed like an upstream process. For instance, one of these opportunities is to determine for a given experimental setup (before the actual data collection), whether the optimization problem is well-posed (that it is, if the problem has unique solution). Knowing this information beforehand is useful to set up an experimental protocol that guarantees the uniqueness of the parameter estimates. This question of uniqueness is related to the notion of structural (theoretical) identifiability. The modeler might also be interested in knowing whether the available data that will result from the experimental protocol will be informative enough to identify the model parameters accurately. The question of parameter accuracy based on the quality of available data is related to the notion of practical identifiability. This work aims to present the theoretical basis of structural and practical identifiability analysis. By capitalizing on the use of dedicated software tools, we will illustrate further the power of structural and practical identifiability analysis in the modeling exercise using relevant examples for the modeling of microbiological processes such as the rumen microbial fermentation.

New two-derivative implicit-explicit Runge-Kutta methods for stiff reaction-diffusion systems

Reaction-diffusion systems are extensively used in the mathematical modeling of biological and chemical systems to explain the Turing instability. Generally, reaction-diffusion systems are highly stiff in both reaction and diffusion terms. This paper discusses a new class of two-derivative implicit-explicit (IMEX) Runge-Kutta (RK) type methods for the numerical simulations of stiff reaction-diffusion systems. The present methods do not require numerical inversion of the coefficient matrix - computationally explicit. Stability properties of the developed methods are compared with the similar methods discussed in the literature. Moreover, accuracy and efficiency of the developed methods are validated by numerical simulations of spatiotemporal pattern formations for different reaction-diffusion systems, such as, phase-separation, Schnakenberg model and electrodeposition process.

Design centering enables robustness screening of pattern formation models.

MotivationAccess to unprecedented amounts of quantitative biological data allows us to build and test biochemically accurate reaction–diffusion models of intracellular processes. However, any increase in model complexity increases the number of unknown parameters and, thus, the computational cost of model analysis. To efficiently characterize the behavior and robustness of models with many unknown parameters remains, therefore, a key challenge in systems biology.ResultsWe propose a novel computational framework for efficient high-dimensional parameter space characterization of reaction–diffusion models in systems biology. The method leverages the Lp-Adaptation algorithm, an adaptive-proposal statistical method for approximate design centering and robustness estimation. Our approach is based on an oracle function, which predicts for any given point in parameter space whether the model fulfills given specifications. We propose specific oracles to efficiently predict four characteristics of Turing-type reaction–diffusion models: bistability, instability, capability of spontaneous pattern formation and capability of pattern maintenance. We benchmark the method and demonstrate that it enables global exploration of a model’s ability to undergo pattern-forming instabilities and to quantify robustness for model selection in polynomial time with dimensionality. We present an application of the framework to pattern formation on the endosomal membrane by the small GTPase Rab5 and its effectors, and we propose molecular mechanisms underlying this system.Availability and implementationOur code is implemented in MATLAB and is available as open source under https://git.mpi-cbg.de/mosaic/software/black-box-optimization/rd-parameter-space-screening.Supplementary information Supplementary data are available at Bioinformatics online.

Computational pharmacology: New avenues for COVID-19 therapeutics search and better preparedness for future pandemic crises

The COVID-19 pandemic created an unprecedented global healthcare emergency prompting the exploration of new therapeutic avenues, including drug repurposing. A large number of ongoing studies revealed pervasive issues in clinical research, such as the lack of accessible and organised data. Moreover, current shortcomings in clinical studies highlighted the need for a multi-faceted approach to tackle this health crisis. Thus, we set out to explore and develop new strategies for drug repositioning by employing computational pharmacology, data mining, systems biology, and computational chemistry to advance shared efforts in identifying key targets, affected networks, and potential pharmaceutical intervention options. Our study revealed that formulating pharmacological strategies should rely on both therapeutic targets and their networks. We showed how data mining can reveal regulatory patterns, capture novel targets, alert about side-effects, and help identify new therapeutic avenues. We also highlighted the importance of the miRNA regulatory layer and how this information could be used to monitor disease progression or devise treatment strategies. Importantly, our work bridged the interactome with the chemical compound space to better understand the complex landscape of COVID-19 drugs. Machine and deep learning allowed us to showcase limitations in current chemical libraries for COVID-19 suggesting that both in silico and experimental analyses should be combined to retrieve therapeutically valuable compounds. Based on the gathered data, we strongly advocate for taking this opportunity to establish robust practices for treating today's and future infectious diseases by preparing solid analytical frameworks.

Integrating Text Mining into the Curation of Disease Maps.

An adequate visualization form is required to gain an overview and ultimately understand the complex and diverse biological mechanisms of diseases. Recently, disease maps have been introduced for this purpose. A disease map is defined as a systems biological map or model that combines metabolic, signaling, and physiological pathways to create a comprehensive overview of known disease mechanisms. With the increase in publications describing biological interactions, efforts in creating and curating comprehensive disease maps is growing accordingly. Therefore, new computational approaches are needed to reduce the time that manual curation takes. Test mining algorithms can be used to analyse the natural language of scientific publications. These types of algorithms can take humanly readable text passages and convert them into a more ordered, machine-usable data structure. To support the creation of disease maps by text mining, we developed an interactive, user-friendly disease map viewer. The disease map viewer displays text mining results in a systems biology map, where the user can review them and either validate or reject identified interactions. Ultimately, the viewer brings together the time-saving advantages of text mining with the accuracy of manual data curation.