Figure 4 - available via license: Creative Commons Attribution 3.0 Unported
Content may be subject to copyright.
Examples of implicit reaction rules. Illustrative presentation of different rules between two molecules. Both displayed rules describe an interaction between the elementary molecules, A and B. The difference is in the participating sites. This example could lead to a double connection between A and B if there were no steric restraints. Another possibility is the formation of polymers of indefinite length. Please note that the binding sites in grey are not part of the rule, i.e., the bond and modification state of the grey sites is not considered for the rule to be applicable. All that is required for the upper rule to fire is that a molecule, A, has a free binding site, c, and that a molecule, B, has a free binding site, a. Then, these free binding sites are bound together. Via the grey binding sites, large molecule graphs could be connected, which would not interfere with the reaction.
Source publication
A common problem in the analysis of biological systems is the combinatorial explosion that emerges from the complexity of multi-protein assemblies. Conventional formalisms, like differential equations, Boolean networks and Bayesian networks, are unsuitable for dealing with the combinatorial explosion, because they are designed for a restricted stat...
Similar publications
Boolean networks are a widely used qualitative modelling approach which allows the abstract description of a biological system. One issue with the application of Boolean networks is the state space
explosion problem which limits the applicability of the approach to large realistic systems. In this paper we investigate developing a compositional fra...
In systems biology, models of cellular regulatory processes such as gene regulatory networks or signalling pathways are crucial to understanding the behaviour of living cells. Available biological data are however often insufficient for full model specification. In this paper, we focus on partially specified models where the missing information is...
Boolean networks have been widely used to model biological processes lacking detailed kinetic information. Despite their simplicity, Boolean network dynamics can still capture some important features of biological systems such as stable cell phenotypes represented by steady states. For small models, steady states can be determined through exhaustiv...
![Fig. 6: Wiring of the MAPK logical model of [20]. The diagram contains...](profile/Andrzej-Mizera/publication/316428835/figure/fig1/AS:486506520420352@1493003601940/Wiring-of-the-MAPK-logical-model-of-20-The-diagram-contains-three-types-of-nodes_Q320.jpg)
Boolean networks is a well-established formalism for modelling biological systems. A vital challenge for analysing a Boolean network is to identify all the attractors. This becomes more challenging for large asynchronous Boolean networks, due to the asynchronous updating scheme. Existing methods are prohibited due to the well-known state-space expl...
Citations
... Molecules diffuse either within volumes or on membrane surfaces and may affect each other by reacting upon collision. A review of currently maintained particle-based stochastic simulators which describes Smoldyn [7], eGFRD [8], SpringSaLaD [9], ReaDDy [10], and MCell3 was recently published in [11]. It should be noted that some of the new features of MCell4 that we report here are available in the current version of Smoldyn, v2.72. ...
... For better clarity, we adopt the name "elementary molecule" for the base building blocks of complexes. The tool SpringSa-LaD [9] uses the same distinction. ...
Biochemical signaling pathways in living cells are often highly organized into spatially segregated volumes, membranes, scaffolds, subcellular compartments, and organelles comprising small numbers of interacting molecules. At this level of granularity stochastic behavior dominates, well-mixed continuum approximations based on concentrations break down and a particle-based approach is more accurate and more efficient. We describe and validate a new version of the open-source MCell simulation program (MCell4), which supports generalized 3D Monte Carlo modeling of diffusion and chemical reaction of discrete molecules and macromolecular complexes in solution, on surfaces representing membranes, and combinations thereof. The main improvements in MCell4 compared to the previous versions, MCell3 and MCell3-R, include a Python interface and native BioNetGen reaction language (BNGL) support. MCell4’s Python interface opens up completely new possibilities for interfacing with external simulators to allow creation of sophisticated event-driven multiscale/multiphysics simulations. The native BNGL support, implemented through a new open-source library libBNG (also introduced in this paper), provides the capability to run a given BNGL model spatially resolved in MCell4 and, with appropriate simplifying assumptions, also in the BioNetGen simulation environment, greatly accelerating and simplifying model validation and comparison.
... The set rules are then customized to describe and characterize the interactions among components of the system whose dynamics are being mimicked through deterministic or stochastic simulations. Although its application is common in studies of biochemical systems, its usage may be extended to address problems in the context of ecology, agriculture, or spatial modeling (Ibrahim et al., 2013;Vodovotz and An, 2015). The advantage presented by allowing the inclusion of mechanistic and detailed interaction that affect insect pest dynamics makes data science with rule-based modeling the most suitable approach to address the purpose of this work. ...
Fall armyworm (FAW), Spodoptera frugiperda (J.E. Smith) threatens maize, sorghum, and millet production in Africa. Despite rigorous work done to reduce FAW prevalence, the dynamics and invasion mechanisms are still poorly understood. This study applied interdisciplinary tools, analytics, and algorithms on a FAW dataset to provide insights and projections on the intensity of FAW infestation across Africa. The data collected between January 2018 and December 2020 were matched with the monthly average data of the climatic and environmental variables. The multilevel analytics identified the key factors that influence the dynamics of spatial and temporal pest density and occurrence at a 2 km x 2 km grid resolution. The seasonal variations of the identified factors and dynamics were used to calibrate rule-based analytics employed to simulate the monthly densities and occurrence of the FAW for the years 2018, 2019, and 2020. Three FAW density level classes were inferred, i.e., low (0–10), moderate (11–30), and high (>30). Results show that monthly density projections were sensitive to the type of FAW host vegetation and the seasonal variability of climatic factors. Moreover, the diversity in the climate patterns and cropping systems across the African sub-regions are considered the main drivers of FAW abundance and variation. An optimum overall accuracy of 53% was obtained across the three years and at a continental scale, however, a gradual increase in prediction accuracy was observed among the years, with 2020 predictions providing accuracies greater than 70%. Apart from the low amount of data in 2018 and 2019, the average level of accuracy obtained could also be explained by the non-inclusion of data related to certain key factors such as the influence of natural enemies into the analysis. Further detailed data on the occurrence and efficiency of FAW natural enemies in the region may help to complete the tri-trophic
... Molecules diffuse either in volumes or on membranes and may affect each other by reacting upon collision. A review of the currently maintained particle-based stochastic simulators that describes Smoldyn [7], eGFRD [8], SpringSaLaD [9], ReaDDy [10], and MCell was recently published in [11]. ...
... For better clarity, we adopt the name elementary molecule for the base building blocks of complexes. The tool SpringSaLaD[9] uses the same distinction. ...
A bstract
Biochemical signaling pathways in living cells are often highly organized into spatially segregated volumes and surfaces of scaffolds, subcellular compartments, and organelles comprising small numbers of interacting molecules. At this level of granularity stochastic behavior dominates, well-mixed continuum approximations based on concentrations break down and a particle-based approach is more accurate and more efficient. We describe and validate a new version of the open-source MCell simulation program (MCell4), which supports generalized 3D Monte Carlo modeling of diffusion and chemical reaction of discrete molecules and macromolecular complexes in solution, on surfaces representing membranes, and combinations thereof. The main improvements in MCell4 compared to the previous versions, MCell3 and MCell3-R, include a Python interface and native BioNetGen reaction language (BNGL) support. MCell4’s Python interface opens up completely new possibilities of interfacing with external simulators and implementing sophisticated event-driven multiscale/multiphysics simulations. The native BNGL support through a new open-source library libBNG (also introduced in this paper) provides the capability to run a given BNGL model spatially resolved in MCell4 and, with appropriate simplifying assumptions, in the BioNetGen simulation environment, greatly accelerating and simplifying model validation and comparison.
... This can be achieved by incorporating simulations of complex models, which are based on artificial neural networks [46,47] or partial differential Equations [48][49][50][51]. The stochastic spatial effects can also be considered using rule-and agent-based modeling [52][53][54][55][56]. Another direction for future research is the focus on other sources of water, such as atmospheric water generation, pumping ground and underground water or greywater recycling. ...
In this article, the performance of a solar-powered multi-purpose supply container used as a service module for first-aid, showering, freezing, refrigeration and water generation purposes in areas of social emergency is analyzed. The average daily energy production of the solar panel is compared to the average daily energy demands of the above-mentioned types of service modules. The comparison refers to five different locations based on the Köppen–Geiger classification of climatic zones with the data for energy demand being taken from another publication. It is shown that in locations up to mid-latitudes, the supply container is not only able to power all types of modules all year round but also to provide up to 15 m3 of desalinated water per day for drinking, domestic use and irrigation purposes. This proves and quantifies the possibility of combining basic supply with efficient transport and self-sufficiency by using suitably equipped shipping containers. Thus, flexible solutions are provided to some of the most challenging problems humans will face in the future, such as natural disasters, water scarcity, starvation and homelessness.
... The set rules are then customized to describe and characterize the interactions among components of the system whose dynamics are being mimicked through deterministic or stochastic simulations. Although its application is common in studies of biochemical systems, its usage may be extended to address problems in the context of ecology, agriculture, or spatial modeling (Ibrahim et al., 2013;Vodovotz and An, 2015). The advantage presented by allowing the inclusion of mechanistic and detailed interaction that affect insect pest dynamics makes data science with rule-based modeling the most suitable approach to address the purpose of this work. ...
After five years of its first report on the African continent, Fall armyworm (FAW), Spodoptera frugiperda (J.E. Smith) is considered a major threat to maize, sorghum, and millet production in sub-Saharan Africa. Despite the rigorous work already conducted to reduce FAW prevalence, the dynamics and invasion mechanisms of FAW in Africa are still poorly understood. This study applied interdisciplinary tools, analytics, and algorithms on a FAW dataset within a spatial lens to provide insights and project the intensity of FAW infestation across Africa. The data collected between January 2018 and December 2020 in selected locations were matched with the monthly average data of the climatic and environmental variables. The multilevel analytics aimed to identify the key factors that influence the dynamics of spatial and temporal pest density and occurrence at a 2 km x 2 km grid resolution. The seasonal variations of the identified factors and dynamics were used to calibrate rule-based analytics employed to simulate the monthly densities and occurrence of the FAW for the years 2018, 2019, and 2020. Three FAW density level classes were inferred, i.e., low (0–10 FAW moth per trap), moderate (11–30 FAW moth per trap), and high (>30 FAW moth per trap). Results show that monthly density projections were sensitive to the type of FAW host vegetation and the seasonal variability of climatic factors. Moreover, the diversity in the climate patterns and cropping systems across the African sub-regions are considered the main drivers of FAW abundance and variation. An optimum overall accuracy of 53% was obtained across the three years and at a continental scale, however, a gradual increase in prediction accuracy was observed among the years, with 2020 predictions providing accuracies greater than 70%. Apart from the low amount of data in 2018 and 2019, the average level of accuracy obtained could also be explained by the non-inclusion of data related to certain key factors such as the influence of natural enemies (predators, parasitoids, and pathogens) into the analysis. Further detailed data on the occurrence and efficiency of FAW natural enemies in the region may help to complete the tri-trophic interactions between the host plants, pests, and beneficial organisms. Nevertheless, the tool developed in this study provides a framework for field monitoring of FAW in Africa that may be a basis for a future decision support system (DSS).
... Hi-C maps (available for example from Gene Expression Omnibus (Ibrahim et al. 2013) can be converted into a matrix of average pairwise distances that can be used as restraints in many simulation models. This is analogous to when NOE distances are added to all-atom force fields in determination of protein structures using NMR. ...
Synthetic signal transduction is an exciting new research field that applies supramolecular chemistry in a membrane environment to provide insight into the physical processes involved in natural signal transduction and to open new opportunities in synthetic biology, for example the integration of artificial signaling pathways into cells. Although it is still a developing field, we discuss a selection of recent stimuli-responsive supramolecular constructs that, when embedded in the phospholipid bilayer, can mimic aspects of the behavior of different natural signaling proteins, including ligand-gated ion channels, G-protein coupled receptors and receptor tyrosine kinases. The lipid bilayer plays a key part in these biomimetic systems, as this complex anisotropic environment provides challenges both when designing supramolecular systems that function in the bilayer and when analyzing the data they provide. Nonetheless these recent studies have provided key insights into how the bilayer affects binding to, the conformation of, and catalysis by membrane-embedded supramolecular constructs. If successful, these model systems promise to be key components for bottom-up synthetic biology, the creation of artificial cells and devices starting from molecular components.
... Models of the infection dynamics can help understand SARS-CoV-2 pathogenesis, develop optimal treatments, and introduce appropriate measures to prevent the spread of the virus. There are a multitude of modeling approaches with different properties, applications and aims that can be classed into categories of in-host models (e.g., [1][2][3][4][5][6][7][8]) versus host-to-host models (such as [9][10][11][12]), discrete versus continuous models and ODE versus PDE models (for an overview we refer to [13][14][15][16]). There is an accumulating body of literature on SARS-CoV-2 infection dynamics that make use of these various tools and provide datasets that can be analyzed retrospectively once consensus modeling strategies have been derived [17,18]. ...
This work provides a mathematical technique for analyzing and comparing infection dynamics models with respect to their potential long-term behavior, resulting in a hierarchy integrating all models. We apply our technique to coupled ordinary and partial differential equation models of SARS-CoV-2 infection dynamics operating on different scales, that is, within a single organism and between several hosts. The structure of a model is assessed by the theory of chemical organizations, not requiring quantitative kinetic information. We present the Hasse diagrams of organizations for the twelve virus models analyzed within this study. For comparing models, each organization is characterized by the types of species it contains. For this, each species is mapped to one out of four types, representing uninfected, infected, immune system, and bacterial species, respectively. Subsequently, we can integrate these results with those of our former work on Influenza-A virus resulting in a single joint hierarchy of 24 models. It appears that the SARS-CoV-2 models are simpler with respect to their long term behavior and thus display a simpler hierarchy with little dependencies compared to the Influenza-A models. Our results can support further development towards more complex SARS-CoV-2 models targeting the higher levels of the hierarchy.
... Thus, there is a pressing need for computer simulators that could unify those different imaging modes in a unique framework, estimate their respective biases, and serve as a predictive tool for experimenters, with the aim to quantitatively decipher protein organization and dynamics in living cells. Several particle-based packages relying on Monte Carlo simulations already exist to predict random motion and multi-state reactions of biological molecules, but either they do not integrate fluorescence properties or are limited to a specific type of imaging mode, and are usually not performing real-time visualization [11][12][13][14][15][16][17][18] . ...
Fluorescence live-cell and super-resolution microscopy methods have considerably advanced our understanding of the dynamics and mesoscale organization of macro-molecular complexes that drive cellular functions. However, different imaging techniques can provide quite disparate information about protein motion and organization, owing to their respective experimental ranges and limitations. To address these issues, we present here a robust computer program, called FluoSim, which is an interactive simulator of membrane protein dynamics for live-cell imaging methods including SPT, FRAP, PAF, and FCS, and super-resolution imaging techniques such as PALM, dSTORM, and uPAINT. FluoSim integrates diffusion coefficients, binding rates, and fluorophore photo-physics to calculate in real time the localization and intensity of thousands of independent molecules in 2D cellular geometries, providing simulated data directly comparable to actual experiments. FluoSim was thoroughly validated against experimental data obtained on the canonical neurexin-neuroligin adhesion complex at cell–cell contacts. This unified software allows one to model and predict membrane protein dynamics and localization at the ensemble and single molecule level, so as to reconcile imaging paradigms and quantitatively characterize protein behavior in complex cellular environments.
... Thus, there is a pressing need for computer simulators that could unify those different imaging modes in a unique framework, estimate their respective biases, and serve as a predictive tool for experimenters, with the aim to quantitatively decipher protein organization and dynamics in living cells. Several particle-based packages relying on Monte Carlo simulations already exist to predict random motion and multi-state reactions of biological molecules, but either they do not integrate fluorescence properties or are limited to a specific type of imaging mode, and are usually not performing real-time visualization [11][12][13][14][15][16][17][18] . ...
Fluorescence live-cell and super-resolution microscopy methods have considerably advanced our understanding of the dynamics and mesoscale organization of macro-molecular complexes that drive cellular functions. However, different imaging techniques can provide quite disparate information about protein motion and organization, owing to their respective experimental ranges and limitations. To address these limitations, we present here a unified computer program that allows one to model and predict membrane protein dynamics at the ensemble and single molecule level, so as to reconcile imaging paradigms and quantitatively characterize protein behavior in complex cellular environments. FluoSim is an interactive real-time simulator of protein dynamics for live-cell imaging methods including SPT, FRAP, PAF, and FCS, and super-resolution imaging techniques such as PALM, dSTORM, and uPAINT. The software, thoroughly validated against experimental data on the canonical neurexin-neuroligin adhesion complex, integrates diffusion coefficients, binding rates, and fluorophore photo-physics to calculate in real time the distribution of thousands of independent molecules in 2D cellular geometries, providing simulated data of protein dynamics and localization directly comparable to actual experiments.
... NetLogo allows users to write their own extensions. However, it cannot incorporate formal rule-based languages such as BNGL (BioNetGen language) [10] or Kappa [16], nor molecular structure and geometry (for details, see [47,49,50,54,55,71,126]). In addition, it is challenging to handle very large network models or very low concentrations of agents with stochastic rules [68,74,75]. ...
Pathogenic microorganisms entail enormous problems for humans, livestock, and crop plants. A better understanding of the different infection strategies of the pathogens enables us to derive optimal treatments to mitigate infectious diseases or develop vaccinations preventing the occurrence of infections altogether. In this review, we highlight the current trends in mathematical modeling approaches and related methods used for understanding host–pathogen interactions. Since these interactions can be described on vastly different temporal and spatial scales as well as abstraction levels, a variety of computational and mathematical approaches are presented. Particular emphasis is placed on dynamic optimization, game theory, and spatial modeling, as they are attracting more and more interest in systems biology. Furthermore, these approaches are often combined to illuminate the complexities of the interactions between pathogens and their host. We also discuss the phenomena of molecular mimicry and crypsis as well as the interplay between defense and counter defense. As a conclusion, we provide an overview of method characteristics to assist non-experts in their decision for modeling approaches and interdisciplinary understanding.
