Biology subjects

Blinov, M. L.

Publications and source records attributed to Blinov, M. L..

8 recordsLinked to original sources

bioRxiv2025

Using SED-ML for reproducible curation: Verifying BioModels across multiple simulation engines

The BioModels Repository contains over 1000 manually curated mechanistic models from published literature, most often encoded in the Systems Biology Markup Language (SBML). This community-based standard formally specifies each model, but does not describe the computational experimental conditions to run a simulation. Therefore, it can be challenging to reproduce any figure or result from a publication with an SBML model alone. The Simulation Experiment Description Markup Language (SED-ML) provides a solution: a standard way to specify exactly how to run an experiment corresponding to a specific figure or result. BioModels was established years before SED-ML, and both systems evolved over time, both in content and acceptance. Hence, only about half of the entries in BioModels contained SED-ML files, and these files reflected the version of SED-ML that was available at the time. Additionally, almost all of these SED-ML files had at least one minor mistake that made them invalid. To make these models and their results more reproducible, we report here on our work updating, correcting and generating new SED-ML files for 1055 curated mechanistic models in BioModels. In addition, because SED-ML is implementation-independent, it can be used for verification, demonstrating that results hold across multiple simulation engines. We tested, corrected, and improved over 450 existing SED-ML files in the BioModels database, and created basic files for the rest of the entries. Then, we used a wrapper architecture for interpreting SED-ML, and report verification results across five different ODE-based biosimulation engines, after further improving the models, the wrappers, and the engines themselves. Our work with SED-ML and the BioModels collection aims to improve the utility of these models by making them more reproducible and credible. Improved reproducibility means these models are now even more fit for re-use, such as in new investigations and as components of multiscale models. Author summaryReproducing computationally-derived scientific results seems like it should be straightforward, but is often elusive. Code is lost, file formats change, and knowledge of what was done is only partially recorded and/or forgotten. Model repositories such as BioModels address this failing in the Systems Biology domain by encoding models in a standard format that can reproduce a figure from the paper from which it was drawn. Here, we delved into the BioModels repository to create and correct the instructions on what to do with every curated model, and then tested those instructions on a variety of simulation platforms, allowing us to find and correct issues with the platforms and the simulators themselves. Not only did this improve the BioModels repository, but also improved the infrastructure necessary to run these verification comparisons in the future, and improved the fitness of the models for re-use by other researchers.

systems biology↗

bioRxiv2025

From spots to stripes: Evolution of pigmentation patterns in monkeyflowers via modulation of a reaction-diffusion system and its prepatterns

The reaction-diffusion (RD) system is widely assumed to account for many complex, self- organized pigmentation patterns in natural organisms. However, the specific configurations of such RD networks and how RD systems interact with positional information (i.e., prepatterns) that may specify the initiation conditions for the RD operation remain largely unknown. Here, we introduced a three-substance RD system underlying the formation of repetitive pigment spots and stripes in Mimulus flowers. It consists of an R2R3-MYB activator (NEGAN), an R3-MYB inhibitor (RTO), and a coactivator represented by two paralogous bHLH proteins. Through fine- scale genetic analyses, transgenic experiments, and computer simulations, we identified the causal loci contributing to the evolutionary transition from sparsely dispersed spots to longitudinal stripes. Genetic changes at these loci modulate the prepatterns of the activator and coactivator expression and the promoter activities of the inhibitor and one of the coactivator paralogs. Our findings highlight the importance of prepatterns towards a realistic description of RD systems in natural organisms, and reveal the genetic mechanism generating pattern variation through modulation of the kinetics of the RD system and its prepatterns.

developmental biology↗

bioRxiv2023

MolClustPy: A Python Package to Characterize Multivalent Biomolecular Clusters

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSSO_SCPLOWUMMARYC_SCPLOWC_ST_ABSLow-affinity interactions among multivalent biomolecules may lead to the formation of molecular complexes that undergo phase transitions to become extra-large clusters. Characterizing the physical properties of these clusters is important in recent biophysical research. Due to weak interactions such clusters are highly stochastic, demonstrating a wide range of sizes and compositions. We have developed a Python package to perform multiple stochastic simulation runs using NFsim (Network-Free stochastic simulator), characterize and visualize the distribution of cluster sizes, molecular composition, and bonds across molecular clusters and individual molecules of different types. AO_SCPLOWVAILABILITYC_SCPLOWO_SCPCAP C_SCPCAPO_SCPLOWANDC_SCPLOWO_SCPCAP C_SCPCAPO_SCPLOWIMPLEMENTATIONC_SCPLOWThe software is implemented in Python. A detailed Jupyter notebook is provided to enable convenient running. Code, user guide and examples are freely available at https://molclustpy.github.io/ CO_SCPLOWONTACTC_SCPLOWachattaraj007@gmail.com, blinov@uchc.edu SO_SCPLOWUPPLEMENTARYC_SCPLOWO_SCPCAP C_SCPCAPO_SCPLOWINFORMATIONC_SCPLOWAvailable at https://molclustpy.github.io/

systems biology↗

bioRxiv2021

runBioSimulations: an extensible web application that simulates a wide range of computational modeling frameworks, algorithms, and formats

Comprehensive, predictive computational models have significant potential for science, bioengineering, and medicine. One promising way to achieve more predictive models is to combine submodels of multiple subsystems. To capture the multiple scales of biology, these submodels will likely require multiple modeling frameworks and simulation algorithms. Several community resources are already available for working with many of these frameworks and algorithms. However, the variety and sheer number of these resources make it challenging to find and use appropriate tools for each model, especially for novice modelers and experimentalists. To make these resources easier to use, we developed runBioSimulations (https://run.biosimulations.org), a single web application for executing a broad range of models. runBioSimulations leverages community resources, including BioSimulators, a new open registry of simulation tools. These resources currently enable runBioSimulations to execute nine frameworks and 44 algorithms, and they make runBioSimulations extensible to additional frameworks and algorithms. runBioSimulations also provides features for sharing simulations and interactively visualizing their results. We anticipate that runBioSimulations will foster reproducibility, stimulate collaboration, and ultimately facilitate the creation of more predictive models.

systems biology↗

bioRxiv2020

Solubility product constant directs the formation of biomolecular condensates

Biomolecular condensates, formed by liquid-liquid phase separation (LLPS), are important cellular structures. Using stochastic network-free kinetic models, we establish a physical-chemical basis for the concentration threshold of heterotypic multivalent molecules required for LLPS. We associate phase separation with a bimodal partitioning of the cluster distribution into small oligomers vs. huge polymers. The simulations reveal that LLPS obeys the solubility product constant (Ksp): the product of monomer concentrations, accounting for ideal stoichiometries, does not exceed a threshold no matter how much additional monomer is added to the system - additional monomer is funneled into large clusters. The Ksp applies over a range of valencies and stoichiometries. However, consistent with the importance of disordered domains for LLPS, removing flexible linker domains funnels valency-matched monomers into a "dimer trap", and Ksp no longer defines a threshold for large cluster formation. We propose Ksp as a new tool for elucidating biomolecular condensate biophysics.

biophysics↗

bioRxiv2020

SBGN Bricks Ontology as a tool to describe recurring concepts in molecular networks

A comprehensible representation of a molecular network is key to communicating and understanding scientific results in systems biology. The Systems Biology Graphical Notation (SBGN) has emerged as the main standard to represent such networks graphically. It has been implemented by different software tools, and is now largely used to communicate maps in scientific publications. However, learning the standard, and using it to build large maps, can be tedious. Moreover, SBGN maps are not grounded on a formal semantic layer and therefore do not enable formal analysis. Here, we introduce a new set of patterns representing recurring concepts encountered in molecular networks, called SBGN bricks. The bricks are structured in a new ontology, the BricKs Ontology (BKO), to define clear semantics for each of the biological concepts they represent. We show the usefulness of the bricks and BKO for both the template-based construction and the semantic annotation of molecular networks. The SBGN bricks and BKO can be freely explored and downloaded at sbgnbricks.org.

systems biology↗

bioRxiv2018

Molecular Process Diagram: a precise, scalable and compact visualization of rule-based models

Rule-based modeling allows representation and simulation of biological systems where molecular features (such as protein domains) and feature-specific details of molecular interactions are accounted for. The rule-based description is precise and can define very fine molecular details (e.g. how phosphorylation status of a single residue in a multi-protein complex can affect affinity of another binding site of another protein within the same complex), but makes it difficult to effectively combine the assumptions scribed within the multiple rules of a model into a diagrammatic view. Various visualization schemas have been suggested, but they are all highly rule-based centric (e.g. a visual list of unconnected rules, extended contact maps, or atom-rule graphs). None of them match the clarity of traditional reaction/pathway diagrams, where a researcher can easily visually track the transitions and modifications of chemical species occurring in the biological systems being modeled. Here we present a novel approach and software for precise, scalable and compact representation of rule-based models that we call Molecular Process Diagram. It is based on the three basic elements: interacting molecular complexes, molecular sites directly modified by a rule, and molecular sites that are not modified but contribute to a rule mechanism (e.g. a site that in a phosphorylated state changes binding affinity of another site). Multiple levels of resolution are available: pathway-like diagram of interactions among molecules, optional site-specific interactions, and additional contingencies for interactions. Inclusion of molecular sites enables unambiguous reconstruction of the rule descriptions from the visual diagram without additional supporting documentation, while still keeping a pathway-like visual appearance. The proposed approach for visualization has been implemented in the Virtual Cell (VCell) modeling and simulation framework. Our Molecular Process Diagrams extend the notion of Systems Biology Graphical Notation (SBGN) process diagrams and use SBGN-compliant conventions.\n\nSummaryKinetic models have provided significant insights into biological regulatory mechanisms even though they typically did not take into consideration the details of protein subcomponents such as binding domains and phosphorylation sites. However, these details are often required for an accurate understanding of the events that occur during cell signaling. Without such detailed understanding, intervention strategies to act on signaling pathways in pathological conditions are bound to have limited success. This need to include site-specific details into models led to the advance of rule-based modeling. While rules describe the details of interactions with unmatched precision, they often obscure the \"big picture\", i.e. a pathway-like description of the information flow through the biological system. An intuitive visual diagram is crucial for understanding the assumptions embodied into a model. Here we present a novel approach and software for precise, scalable and compact representation of rule-based models that we call Molecular Process Diagram. It allows visualizing in a pathway-like diagram of the interacting molecules, the molecular sites modified, and the molecular sites that affect the interactions. The approach is implemented in the Virtual Cell (VCell) modeling and simulation framework and suggested as an extension for the Systems Biology Graphical Notations (SBGN) standard.

systems biology↗

bioRxiv2017

Compartmental and spatial rule-based modeling with Virtual cell (VCell)

In rule-based modeling, molecular interactions are systematically specified in the form of reaction rules that serve as generators of reactions. This provides a way to account for all the potential molecular complexes and interactions among multivalent or multistate molecules. Recently, we introduced rule- based modeling into the Virtual Cell (VCell) modeling framework, permitting graphical specification of rules and merger of networks generated automatically (using the BioNetGen modeling engine) with hand-specified reaction networks. VCell provides a number of ordinary differential equation (ODE) and stochastic numerical solvers for single-compartment simulations of the kinetic systems derived from these networks, and agent-based network-free simulation of the rules. In this work, compartmental and spatial modeling of rule-based models has been implemented within VCell. To enable rule-based deterministic and stochastic spatial simulations and network-free agent-based compartmental simulations, the BioNetGen and NFSim engines were each modified to support compartments. In the new rule-based formalism, every reactant and product pattern and every reaction rule are assigned locations. We also introduce the novel rule-based concept of molecular anchors. This assures that any species that has a molecule anchored to a predefined compartment will remain in this compartment. Importantly, in addition to formulation of compartmental models, this now permits VCell users to seamlessly connect reaction networks derived from rules to explicit geometries to automatically generate a system of reaction-diffusion equations. These may then be simulated using either the VCell partial differential equations (PDE) deterministic solvers or the Smoldyn stochastic simulator.

biophysics↗