PhysiCell Studio: a graphical tool to make agent-based modeling more accessible.

Defining a multicellular model can be challenging. There may be hundreds of parameters that specify the attributes and behaviors of objects. In the best case, the model will be defined using some format specification - a markup language - that will provide easy model sharing (and a minimal step toward reproducibility). PhysiCell is an open-source, physics-based multicellular simulation framework with an active and growing user community. It uses XML to define a model and, traditionally, users needed to manually edit the XML to modify the model. PhysiCell Studio is a tool to make this task easier. It provides a GUI that allows editing the XML model definition, including the creation and deletion of fundamental objects: cell types and substrates in the microenvironment. It also lets users build their model by defining initial conditions and biological rules, run simulations, and view results interactively. PhysiCell Studio has evolved over multiple workshops and academic courses in recent years, which has led to many improvements. There is both a desktop and cloud version. Its design and development has benefited from an active undergraduate and graduate research program. Like PhysiCell, the Studio is open-source software and contributions from the community are encouraged.

Building multiscale models with PhysiBoSS, an agent-based modeling tool.

Multiscale models provide a unique tool for studying complex processes that study events occurring at different scales across space and time. In the context of biological systems, such models can simulate mechanisms happening at the intracellular level such as signaling, and at the extracellular level where cells communicate and coordinate with other cells. They aim to understand the impact of genetic or environmental deregulation observed in complex diseases, describe the interplay between a pathological tissue and the immune system, and suggest strategies to revert the diseased phenotypes. The construction of these multiscale models remains a very complex task, including the choice of the components to consider, the level of details of the processes to simulate, or the fitting of the parameters to the data. One additional difficulty is the expert knowledge needed to program these models in languages such as C++ or Python, which may discourage the participation of non-experts. Simplifying this process through structured description formalisms - coupled with a graphical interface - is crucial in making modeling more accessible to the broader scientific community, as well as streamlining the process for advanced users. This article introduces three examples of multiscale models which rely on the framework PhysiBoSS, an add-on of PhysiCell that includes intracellular descriptions as continuous time Boolean models to the agent-based approach. The article demonstrates how to easily construct such models, relying on PhysiCell Studio, the PhysiCell Graphical User Interface. A step-by-step tutorial is provided as a Supplementary Material and all models are provided at: https://physiboss.github.io/tutorial/.

Maboss for HPC environments: implementations of the continuous time Boolean model simulator for large CPU clusters and GPU accelerators.

BACKGROUND: Computational models in systems biology are becoming more important with the advancement of experimental techniques to query the mechanistic details responsible for leading to phenotypes of interest. In particular, Boolean models are well fit to describe the complexity of signaling networks while being simple enough to scale to a very large number of components. With the advance of Boolean model inference techniques, the field is transforming from an artisanal way of building models of moderate size to a more automatized one, leading to very large models. In this context, adapting the simulation software for such increases in complexity is crucial. RESULTS: We present two new developments in the continuous time Boolean simulators: MaBoSS.MPI, a parallel implementation of MaBoSS which can exploit the computational power of very large CPU clusters, and MaBoSS.GPU, which can use GPU accelerators to perform these simulations. CONCLUSION: These implementations enable simulation and exploration of the behavior of very large models, thus becoming a valuable analysis tool for the systems biology community.

Solid phase speciation controls copper mobilisation from marine sediments by methanobactin.

Although marine environments represent huge reservoirs of the potent greenhouse gas methane, they currently contribute little to global net methane emissions. Most of the methane is oxidized by methanotrophs, minimizing escape to the atmosphere. Aerobic methanotrophs oxidize methane mostly via the copper (Cu)-bearing enzyme particulate methane monooxygenase (pMMO). Therefore, aerobic methane oxidation depends on sufficient Cu acquisition by methanotrophs. Because they require both oxygen and methane, aerobic methanotrophs reside at oxic-anoxic interfaces, often close to sulphidic zones where Cu bioavailability can be limited by poorly soluble Cu sulphide mineral phases. Under Cu-limiting conditions, certain aerobic methanotrophs exude Cu-binding ligands termed chalkophores, such as methanobactin (mb) exuded by Methylosinus trichosporium OB3b. Our main objective was to establish whether chalkophores can mobilise Cu from Cu sulphide-bearing marine sediments to enhance Cu bioavailability. Through a series of kinetic batch experiments, we investigated Cu mobilisation by mb from a set of well-characterized sulphidic marine sediments differing in sediment properties, including Cu content and phase distribution. Characterization of solid-phase Cu speciation included X-ray absorption spectroscopy and a targeted sequential extraction. Furthermore, in batch experiments, we investigated to what extent adsorption of metal-free mb and Cu-mb complexes to marine sediments constrains Cu mobilisation. Our results are the first to show that both solid phase Cu speciation and chalkophore adsorption can constrain methanotrophic Cu acquisition from marine sediments. Only for certain sediments did mb addition enhance dissolved Cu concentrations. Cu mobilisation by mb was not correlated to the total Cu content of the sediment, but was controlled by solid-phase Cu speciation. Cu was only mobilised from sediments containing a mono-Cu-sulphide (CuSx) phase. We also show that mb adsorption to sediments limits Cu acquisition by mb to less compact (surface) sediments. Therefore, in sulphidic sediments, mb-mediated Cu acquisition is presumably constrained to surface-sediment interfaces containing mono-Cu-sulphide phases.

A Critical Look at Colloid Generation, Stability, and Transport in Redox-Dynamic Environments: Challenges and Perspectives.

Colloid generation, stability, and transport are important processes that can significantly influence the fate and transport of nutrients and contaminants in environmental systems. Here, we critically review the existing literature on colloids in redox-dynamic environments and summarize the current state of knowledge regarding the mechanisms of colloid generation and the chemical controls over colloidal behavior in such environments. We also identify critical gaps, such as the lack of universally accepted cross-discipline definition and modeling infrastructure that hamper an in-depth understanding of colloid generation, behavior, and transport potential. We propose to go beyond a size-based operational definition of colloids and consider the functional differences between colloids and dissolved species. We argue that to predict colloidal transport in redox-dynamic environments, more empirical data are needed to parametrize and validate models. We propose that colloids are critical components of element budgets in redox-dynamic systems and must urgently be considered in field as well as lab experiments and reactive transport models. We intend to bring further clarity and openness in reporting colloidal measurements and fate to improve consistency. Additionally, we suggest a methodological toolbox for examining impacts of redox dynamics on colloids in field and lab experiments.

Dynamic Boolean modeling of molecular and cellular interactions in psoriasis predicts drug target candidates.

Psoriasis arises from complex interactions between keratinocytes and immune cells, leading to uncontrolled inflammation, immune hyperactivation, and a perturbed keratinocyte life cycle. Despite the availability of drugs for psoriasis management, the disease remains incurable. Treatment response variability calls for new tools and approaches to comprehend the mechanisms underlying disease development. We present a Boolean multiscale population model that captures the dynamics of cell-specific phenotypes in psoriasis, integrating discrete logical formalism and population dynamics simulations. Through simulations and network analysis, the model predictions suggest that targeting neutrophil activation in conjunction with inhibition of either prostaglandin E2 (PGE2) or STAT3 shows promise comparable to interleukin-17 (IL-17) inhibition, one of the most effective treatment options for moderate and severe cases. Our findings underscore the significance of considering complex intercellular interactions and intracellular signaling in psoriasis and highlight the importance of computational approaches in unraveling complex biological systems for drug target identification.

PhysiCell Studio: a graphical tool to make agent-based modeling more accessible.

Defining a multicellular model can be challenging. There may be hundreds of parameters that specify the attributes and behaviors of objects. Hopefully the model will be defined using some format specification, e.g., a markup language, that will provide easy model sharing (and a minimal step toward reproducibility). PhysiCell is an open source, physics-based multicellular simulation framework with an active and growing user community. It uses XML to define a model and, traditionally, users needed to manually edit the XML to modify the model. PhysiCell Studio is a tool to make this task easier. It provides a graphical user interface that allows editing the XML model definition, including the creation and deletion of fundamental objects, e.g., cell types and substrates in the microenvironment. It also lets users build their model by defining initial conditions and biological rules, run simulations, and view results interactively. PhysiCell Studio has evolved over multiple workshops and academic courses in recent years which has led to many improvements. Its design and development has benefited from an active undergraduate and graduate research program. Like PhysiCell, the Studio is open source software and contributions from the community are encouraged.

PhysiBoSS 2.0: a sustainable integration of stochastic Boolean and agent-based modelling frameworks.

In systems biology, mathematical models and simulations play a crucial role in understanding complex biological systems. Different modelling frameworks are employed depending on the nature and scales of the system under study. For instance, signalling and regulatory networks can be simulated using Boolean modelling, whereas multicellular systems can be studied using agent-based modelling. Herein, we present PhysiBoSS 2.0, a hybrid agent-based modelling framework that allows simulating signalling and regulatory networks within individual cell agents. PhysiBoSS 2.0 is a redesign and reimplementation of PhysiBoSS 1.0 and was conceived as an add-on that expands the PhysiCell functionalities by enabling the simulation of intracellular cell signalling using MaBoSS while keeping a decoupled, maintainable and model-agnostic design. PhysiBoSS 2.0 also expands the set of functionalities offered to the users, including custom models and cell specifications, mechanistic submodels of substrate internalisation and detailed control over simulation parameters. Together with PhysiBoSS 2.0, we introduce PCTK, a Python package developed for handling and processing simulation outputs, and generating summary plots and 3D renders. PhysiBoSS 2.0 allows studying the interplay between the microenvironment, the signalling pathways that control cellular processes and population dynamics, suitable for modelling cancer. We show different approaches for integrating Boolean networks into multi-scale simulations using strategies to study the drug effects and synergies in models of cancer cell lines and validate them using experimental data. PhysiBoSS 2.0 is open-source and publicly available on GitHub with several repositories of accompanying interoperable tools.

Variable renewable energy penetration impact on productivity: A case study of poultry farming.

Like all current industrial systems, agriculture overwhelmingly relies on energy supply from controllable sources, mainly fossil fuels and grid electricity. Power supply from these sources can be adapted to perfectly match the timing of power requirements of demand systems. The energy transition largely consists in substituting renewable power-which is intermittent by nature-to controllable sources, leading to disconnection between instantaneous power production and demand. Energy storage is a potential solution for balancing production and demand and safeguarding the operating conditions of the demand system. In this paper we quantify the effects of renewable power supply (solar and wind) on the operation of a standard poultry farm. We model the balance of power generation and demand considering the growth conditions of poultry and local weather data including temperatures, wind speed and solar radiation. We assess scenarios of renewable power supply in function of the size of the power plant, the wind-to-solar power generation mix and energy storage, and assess the impact of power supply patterns on the operating intensity (productivity) of the demand system. We show that, with a limited storage capacity, it is possible to achieve non-negligible shares of renewable power penetration without major loss in farm productivity. However, a full transition to renewable power would require the combination of i)-large energy storage compared to the annual demand, ii)- significant oversizing of the power production plant, and iii)-the exclusion of power generation combinations (wind/solar) that deviate from the timing of demand. Storage and power plant oversizing is all the more critical as production and demand are uncorrelated over the year. The ratio of useful to unused energy storage by the end of the year varies with the energy mix and operating intensity (productivity) of the farm. We discuss the implications of different energy configurations on the performance of the demand system.

Consider the Anoxic Microsite: Acknowledging and Appreciating Spatiotemporal Redox Heterogeneity in Soils and Sediments.

Reduction-oxidation (redox) reactions underlie essentially all biogeochemical cycles. Like most soil properties and processes, redox is spatiotemporally heterogeneous. However, unlike other soil features, redox heterogeneity has yet to be incorporated into mainstream conceptualizations of soil biogeochemistry. Anoxic microsites, the defining feature of redox heterogeneity in bulk oxic soils and sediments, are zones of oxygen depletion in otherwise oxic environments. In this review, we suggest that anoxic microsites represent a critical component of soil function and that appreciating anoxic microsites promises to advance our understanding of soil and sediment biogeochemistry. In sections 1 and 2, we define anoxic microsites and highlight their dynamic properties, specifically anoxic microsite distribution, redox gradient magnitude, and temporality. In section 3, we describe the influence of anoxic microsites on several key elemental cycles, organic carbon, nitrogen, iron, manganese, and sulfur. In section 4, we evaluate methods for identifying and characterizing anoxic microsites, and in section 5, we highlight past and current approaches to modeling anoxic microsites. Finally, in section 6, we suggest steps for incorporating anoxic microsites and redox heterogeneities more broadly into our understanding of soils and sediments.

Environmental impact of solution pH on the formation and migration of iron colloids in deep subsurface energy systems.

Deep subsurface stimulation processes often promote fluid-rock interactions that can lead to the formation of small colloidal particles that are suspected to migrate through the rock matrix, partially or fully clog pores and microfractures, and promote the mobilization of contaminants. Thus, the goal of this work is to understand the geochemical changes of the host rock in response to reservoir stimulation that promote the formation and migration of colloids. Two different carbonate-rich shales were exposed to different solution pHs (pH = 2 and 7). Iron and other mineral transformations at the shale-fluid interface were first characterized by synchrotron-based XRF mapping. Then, colloids that were able to migrate from the shale into the bulk fluid were characterized by synchrotron-based extended X-ray absorption structure (EXAFS), scanning electron microscopy (SEM), and single-particle inductively coupled plasma time-of-flight mass spectrometry (sp-icpTOF-MS). When exposed to the pH = 2 solution, extensive mineral dissolution and secondary precipitation was observed; iron-(oxyhydr)oxide colloids colocated with silicates were observed by SEM at the fluid-shale interfaces, and the mobilization of chromium and nickel with these iron colloids into the bulk fluid was detected by sp-icpTOF-MS. Iron EXAFS spectra of the solution at the shale-fluid interface suggests the rapid (within minutes) formation of ferrihydrite-like nanoparticles. Thus, we demonstrate that the pH neutralization promotes the mobilization of existing silicate minerals and the rapid formation of new iron colloids. These Fe colloids have the potential to migrate through the shale matrix and mobilize other heavy metals (such as Cr and Ni, in this study) and impacting groundwater quality, as well produced waters from these hydraulic fracturing operations.

Corrigendum to "Modeling signaling pathways in biology with MaBoSS: From one single cell to a dynamic population of heterogeneous interacting cells" [Comput. Struct. Biotechnol. 20 (2022) 5661-5671].

[This corrects the article DOI: 10.1016/j.csbj.2022.10.003.].

Multiscale model of the different modes of cancer cell invasion.

MOTIVATION: Mathematical models of biological processes altered in cancer are built using the knowledge of complex networks of signaling pathways, detailing the molecular regulations inside different cell types, such as tumor cells, immune and other stromal cells. If these models mainly focus on intracellular information, they often omit a description of the spatial organization among cells and their interactions, and with the tumoral microenvironment. RESULTS: We present here a model of tumor cell invasion simulated with PhysiBoSS, a multiscale framework, which combines agent-based modeling and continuous time Markov processes applied on Boolean network models. With this model, we aim to study the different modes of cell migration and to predict means to block it by considering not only spatial information obtained from the agent-based simulation but also intracellular regulation obtained from the Boolean model.Our multiscale model integrates the impact of gene mutations with the perturbation of the environmental conditions and allows the visualization of the results with 2D and 3D representations. The model successfully reproduces single and collective migration processes and is validated on published experiments on cell invasion. In silico experiments are suggested to search for possible targets that can block the more invasive tumoral phenotypes. AVAILABILITY AND IMPLEMENTATION: https://github.com/sysbio-curie/Invasion_model_PhysiBoSS.

Structure and composition of natural ferrihydrite nano-colloids in anoxic groundwater.

Fe-rich mobile colloids play vital yet poorly understood roles in the biogeochemical cycling of Fe in groundwater by influencing organic matter (OM) preservation and fluxes of Fe, OM, and other essential (micro-)nutrients. Yet, few studies have provided molecular detail on the structures and compositions of Fe-rich mobile colloids and factors controlling their persistence in natural groundwater. Here, we provide comprehensive new information on the sizes, molecular structures, and compositions of Fe-rich mobile colloids that accounted for up to 72% of aqueous Fe in anoxic groundwater from a redox-active floodplain. The mobile colloids are multi-phase assemblages consisting of Si-coated ferrihydrite nanoparticles and Fe(II)-OM complexes. Ferrihydrite nanoparticles persisted under both oxic and anoxic conditions, which we attribute to passivation by Si and OM. These findings suggest that mobile Fe-rich colloids generated in floodplains can persist during transport through redox-variable soils and could be discharged to surface waters. These results shed new light on their potential to transport Fe, OM, and nutrients across terrestrial-aquatic interfaces.

Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches.

Introduction: The COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing. Methods: Extensive community work allowed an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework can link biomolecules from omics data analysis and computational modelling to dysregulated pathways in a cell-, tissue- or patient-specific manner. Drug repurposing using text mining and AI-assisted analysis identified potential drugs, chemicals and microRNAs that could target the identified key factors. Results: Results revealed drugs already tested for anti-COVID-19 efficacy, providing a mechanistic context for their mode of action, and drugs already in clinical trials for treating other diseases, never tested against COVID-19. Discussion: The key advance is that the proposed framework is versatile and expandable, offering a significant upgrade in the arsenal for virus-host interactions and other complex pathologies.

Anti-counterfeiting SERS security labels derived from silver nanoparticles and aryl diazonium salts.

The development of anti-counterfeiting inks based on surface-enhanced Raman scattering (SERS) labels have attracted great interest in recent years for their use as security labels in anti-counterfeiting applications. Indeed, they are promising alternatives to luminescent inks, which suffer from several limitations including emission peak overlap, toxicity and photobleaching. Most of the reported SERS security labels developed so far rely on the use of thiolate self-assembled monolayers (SAMs) for the immobilization of Raman reporters on metallic nanoparticle surface. However, SAMs are prone to spontaneous desorption and degradation under laser irradiation, thereby compromising the ink long-term stability. To overcome this issue, we develop herein a new generation of SERS security labels based on silver nanoparticles (Ag NPs) functionalized by aryl diazonium salts, carrying various substituents (-NO2, -CN, -CCH) with distinguishable Raman fingerprints. The resulting SERS tags were fully characterized by scanning electron microscopy (SEM), transmission electron microscopy (TEM), UV-vis absorption and SERS. Then, they were incorporated into ink formulations to be printed on polyethylene naphthalate (PEN) substrates, using handwriting or inkjet printing. Proof-of-concept Raman imaging experiments confirmed the remarkable potential of diazonium salt chemistry to design Ag NPs-based SERS security labels.

Nitrate Controls on the Extent and Type of Metal Retention in Fine-Grained Sediments of a Simulated Aquifer.

Aquifer groundwater quality is largely controlled by sediment composition and physical heterogeneity, which commonly sustains a unique redox gradient pattern. Attenuation of heavy metals within these heterogeneous aquifers is reliant on multiple factors, including redox conditions and redox-active species that can further influence biogeochemical cycling. Here, we simulated an alluvial aquifer system using columns filled with natural coarse-grained sediments and two domains of fine-grained sediment lenses. Our goal was to examine heavy metal (Ni and Zn) attenuation within a complex aquifer network and further explore nitrate-rich groundwater conditions. The fine-grained sediment lenses sustained reducing conditions and served as a sink for Ni sequestration─in the form of Ni-silicates, Ni-organic matter, and a dominant Ni-sulfide phase. The silicate clay and sulfide pools were also important retention mechanisms for Zn; however, Ni was associated more extensively with organic matter compared to Zn, which formed layered double hydroxides. Nitrate-rich conditions promoted denitrification within the lenses that was coupled to the oxidation of Fe(II) and the concomitant precipitation of an Fe(III) phase with higher structural distortion. A decreased metal sulfide pool also resulted, where nitrate-rich conditions generated an average 20% decrease in solid-phase Ni, Zn, and Fe. Ultimately, nitrate plays a significant role in the aquifer's biogeochemical cycling and the capacity to retain heavy metals.

Modeling signaling pathways in biology with MaBoSS: From one single cell to a dynamic population of heterogeneous interacting cells.

As a result of the development of experimental technologies and the accumulation of data, biological and molecular processes can be described as complex networks of signaling pathways. These networks are often directed and signed, where nodes represent entities (genes/proteins) and arrows interactions. They are translated into mathematical models by adding a dynamic layer onto them. Such mathematical models help to understand and interpret non-intuitive experimental observations and to anticipate the response to external interventions such as drug effects on phenotypes. Several frameworks for modeling signaling pathways exist. The choice of the appropriate framework is often driven by the experimental context. In this review, we present MaBoSS, a tool based on Boolean modeling using a continuous time approach, which predicts time-dependent probabilities of entities in different biological contexts. MaBoSS was initially built to model the intracellular signaling in non-interacting homogeneous cell populations. MaBoSS was then adapted to model heterogeneous cell populations (EnsembleMaBoSS) by considering families of models rather than a unique model. To account for more complex questions, MaBoSS was extended to simulate dynamical interacting populations (UPMaBoSS), with a precise spatial distribution (PhysiBoSS). To illustrate all these levels of description, we show how each of these tools can be used with a running example of a simple model of cell fate decisions. Finally, we present practical applications to cancer biology and studies of the immune response.

Mechanism of Arsenic Partitioning During Sulfidation of As-Sorbed Ferrihydrite Nanoparticles.

Knowledge of how arsenic (As) partitions among various phases in Fe-rich sulfidic environments is critical for understanding the fate and mobility of As in such environments. We studied the reaction of arsenite and arsenate sorbed on ferrihydrite nanoparticle surfaces with dissolved sulfide at varying S/Fe ratios (0.1-2.0) to understand the fate and transformation mechanism of As during sulfidation of ferrihydrite. By using aqueous As speciation analysis by IC-ICP-MS and solid-phase As speciation analysis by synchrotron-based X-ray absorption spectroscopy (XAS), we were able to discern the mechanism and pathways of As partitioning and thio-arsenic species formation. Our results provide a mechanistic understanding of the fate and transformation of arsenic during the codiagenesis of As, Fe, and S in reducing environments. Our aqueous-phase As speciation data, combined with solid-phase speciation data, indicate that sulfidation of As-sorbed ferrihydrite nanoparticles results in their transformation to trithioarsenate and arsenite, independent of the initial arsenic species used. The nature and extent of transformation and the thioarsenate species formed were controlled by S/Fe ratios in our experiments. However, arsenate was reduced to arsenite before transformation to trithioarsenate.

Addressing barriers in comprehensiveness, accessibility, reusability, interoperability and reproducibility of computational models in systems biology.

Computational models are often employed in systems biology to study the dynamic behaviours of complex systems. With the rise in the number of computational models, finding ways to improve the reusability of these models and their ability to reproduce virtual experiments becomes critical. Correct and effective model annotation in community-supported and standardised formats is necessary for this improvement. Here, we present recent efforts toward a common framework for annotated, accessible, reproducible and interoperable computational models in biology, and discuss key challenges of the field.