scholarly journals Multiscale Modeling in Systems Biology: Methods and Perspectives

2021 ◽  
Author(s):  
Adrien Coulier
Keyword(s):  
Systems Biology ◽  
Chemical Kinetics ◽  
High Performance ◽  
Computational Models ◽  
Numerical Study ◽  
Operator Splitting ◽  
Computational Cost ◽  
Biological Tissues ◽  
Biological Processes ◽  
Cellular Mechanics

In the last decades, mathematical and computational models have become ubiquitous to the field of systems biology. Specifically, the multiscale nature of biological processes makes the design and simulation of such models challenging. In this thesis we offer a perspective on available methods to study and simulate such models and how they can be combined to handle biological processes evolving at different scales. The contribution of this thesis is threefold. First, we introduce Orchestral, a multiscale modular framework to simulate multicellular models. By decoupling intracellular chemical kinetics, cell-cell signaling, and cellular mechanics by means of operator-splitting, it is able to combine existing software into one massively parallel simulation. Its modular structure makes it easy to replace its components, e.g. to adjust the level of modeling details. We demonstrate the scalability of our framework on both high performance clusters and in a cloud environment. We then explore how center-based models can be used to study cellular mechanics in biological tissues. We show how modeling and numerical choices can affect the results of the simulation and mislead modelers into incorrect biological conclusions if these errors are not monitored properly. We then propose CBMOS, a Python framework specifically designed for the numerical study of such models. Finally, we study how spatial details in intracellular chemical kinetics can be efficiently approximated in a multiscale compartment-based model. We evaluate how this model compares to two other alternatives in terms of accuracy and computational cost. We then propose a computational pipeline to study and compare such models in the context of Bayesian parameter inference and illustrate its usage in three case studies.

Hierarchical Multiscale Modeling of Tire–Soil Interaction for Off-Road Mobility Simulation

2019 ◽  
Vol 14 (6) ◽  
Author(s):  
Hiroki Yamashita ◽  
Guanchu Chen ◽  
Yeefeng Ruan ◽  
Paramsothy Jayakumar ◽  
Hiroyuki Sugiyama
Keyword(s):  
High Performance ◽  
Computational Models ◽  
Computational Cost ◽  
Interaction Model ◽  
High Fidelity ◽  
Wheel Load ◽  
Mobility Performance ◽  
Wide Range ◽  
Computationally Intensive ◽  
Soil Bin

A high-fidelity computational terrain dynamics model plays a crucial role in accurate vehicle mobility performance prediction under various maneuvering scenarios on deformable terrain. Although many computational models have been proposed using either finite element (FE) or discrete element (DE) approaches, phenomenological constitutive assumptions in FE soil models make the modeling of complex granular terrain behavior very difficult and DE soil models are computationally intensive, especially when considering a wide range of terrain. To address the limitations of existing deformable terrain models, this paper presents a hierarchical FE–DE multiscale tire–soil interaction simulation capability that can be integrated in the monolithic multibody dynamics solver for high-fidelity off-road mobility simulation using high-performance computing (HPC) techniques. It is demonstrated that computational cost is substantially lowered by the multiscale soil model as compared to the corresponding pure DE model while maintaining the solution accuracy. The multiscale tire–soil interaction model is validated against the soil bin mobility test data under various wheel load and tire inflation pressure conditions, thereby demonstrating the potential of the proposed method for resolving challenging vehicle-terrain interaction problems.

scholarly journals Identifying frequent patterns in biochemical reaction networks - a workflow

2017 ◽  
Author(s):  
Fabienne Lambusch ◽  
Dagmar Waltemath ◽  
Olaf Wolkenhauer ◽  
Kurt Sandkuhl ◽  
Christian Rosenke ◽  
...  
Keyword(s):  
Systems Biology ◽  
Computational Models ◽  
Model Identification ◽  
Biochemical Reaction ◽  
Reaction Networks ◽  
Frequent Patterns ◽  
Biological Processes ◽  
Biochemical Reaction Networks ◽  
Structural Patterns ◽  
Biologically Relevant

Computational models in biology encode molecular and cell biological processes. These models often can be represented as biochemical reaction networks. Studying such networks, one is mostly interested in systems that share similar reactions and mechanisms. Typical goals of an investigation include understanding of the parts of a model, identification of reoccurring patterns, and recognition of biologically relevant motifs. The large number and size of available models, however, require automated methods to support researchers in achieving their goals. Specifically for the problem of finding patterns in large networks only partial solutions exist. We propose a workflow that identifies frequent structural patterns in biochemical reaction networks encoded in the Systems Biology Markup Language. The workflow utilises a subgraph mining algorithm to detect frequent network patterns. Once patterns are identified, the textual pattern description can automatically be converted into a graphical representation.Furthermore, information about the distribution of patterns among the selected set of models can be retrieved.The workflow was validated with 575 models from the curated branch of BioModels. In this paper, we highlight interesting and frequent structural patterns. Further, we provide exemplary patterns that incorporate terms from the Systems Biology Ontology. Our workflow can be applied to a custom set of models or to models already existing in our graph database MaSyMoS. The occurrences of frequent patterns may give insight into the encoding of central biological processes, evaluate postulated biological motifs, or serve as a similarity measure for models that share common structures. Availability: https://github.com/FabienneL/BioNet-Mining[p] Contact: [email protected]

scholarly journals Identifying frequent patterns in biochemical reaction networks - a workflow

2018 ◽  
Author(s):  
Fabienne Lambusch ◽  
Dagmar Waltemath ◽  
Olaf Wolkenhauer ◽  
Kurt Sandkuhl ◽  
Christian Rosenke ◽  
...  
Keyword(s):  
Systems Biology ◽  
Computational Models ◽  
Model Identification ◽  
Biochemical Reaction ◽  
Reaction Networks ◽  
Frequent Patterns ◽  
Biological Processes ◽  
Biochemical Reaction Networks ◽  
Structural Patterns ◽  
Biologically Relevant

Computational models in biology encode molecular and cell biological processes. These models often can be represented as biochemical reaction networks. Studying such networks, one is mostly interested in systems that share similar reactions and mechanisms. Typical goals of an investigation include understanding of the parts of a model, identification of reoccurring patterns, and recognition of biologically relevant motifs. The large number and size of available models, however, require automated methods to support researchers in achieving their goals. Specifically for the problem of finding patterns in large networks only partial solutions exist. We propose a workflow that identifies frequent structural patterns in biochemical reaction networks encoded in the Systems Biology Markup Language. The workflow utilises a subgraph mining algorithm to detect frequent network patterns. Once patterns are identified, the textual pattern description can automatically be converted into a graphical representation.Furthermore, information about the distribution of patterns among the selected set of models can be retrieved.The workflow was validated with 575 models from the curated branch of BioModels. In this paper, we highlight interesting and frequent structural patterns. Further, we provide exemplary patterns that incorporate terms from the Systems Biology Ontology. Our workflow can be applied to a custom set of models or to models already existing in our graph database MaSyMoS. The occurrences of frequent patterns may give insight into the encoding of central biological processes, evaluate postulated biological motifs, or serve as a similarity measure for models that share common structures. Availability: https://github.com/FabienneL/BioNet-Mining Contact: [email protected]

scholarly journals From numerical prototypes to real models: a progressive study of aerodynamic parameters of nonconventional concrete structures with Computational Fluid Dynamics

2020 ◽  
Vol 13 (3) ◽  
pp. 628-643
Author(s):  
C. V. S. SARMENTO ◽  
A. O. C. FONTE ◽  
L. J. PEDROSO ◽  
P. M. V. RIBEIRO
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
High Performance ◽  
Large Scale ◽  
Computational Models ◽  
Computational Cost ◽  
Concrete Structures ◽  
Navier Stokes ◽  
Small Scale ◽  
Aerodynamic Parameters

Abstract The practical evaluation of aerodynamic coefficients in unconventional concrete structures requires specific studies, which are small-scale models evaluated in wind tunnels. Sophisticated facilities and special sensors are needed, and the tendency is for modern and slender constructions to arise with specific demands on their interaction with the wind. On the other hand, the advances obtained in modern multi-core processors emerge as an alternative for the construction of sophisticated computational models, where the Navier-Stokes differential equations are solved for fluid flow using numerical methods. Computations of this kind require specialized theoretical knowledge, efficient computer programs, and high-performance computers for large-scale calculations. This paper presents recent results involving two real-world applications in concrete structures, where the aerodynamic parameters were estimated with the aid of computational fluid dynamics. Conventional quad-core computers were applied in simulations with the Finite Volume Method and a progressive methodology is presented, highlighting the main aspects of the simulation and allowing its generalization to other types of problems. The results confirm that the proposed methodology is promising in terms of computational cost, drag coefficient estimation and versatility of simulation parameters. These results also indicate that mid-performance computers can be applied for preliminary studies of aerodynamic parameters in design offices.

scholarly journals HICOLM: High-Performance Platform of Physical Simulations by Using Low Computational Cost Methods

2019 ◽  
Vol 26 (3) ◽  
pp. 90-103
Author(s):  
Flaviano Williams Fernandes
Keyword(s):  
Physical Properties ◽  
High Performance ◽  
Computational Models ◽  
Computational Simulation ◽  
Tight Binding ◽  
Computational Cost ◽  
Physical Analysis ◽  
Binding Model ◽  
Condensed Systems ◽  
Low Computational Cost

For decades, computational simulation models have been used by scientists in search for new materials with technological applications in several areas of knowledge. For this, software based on several theoretical-computational models were developed in order to obtain an analysis of the physical properties at atomic levels. The objective of this work is proposing a widely functional software to analyze the physical properties of nanostructures based on carbon and condensed systems using theories of low computational cost. Therefore, a Fortran language computational program called HICOLM was developed, whose theoretical bases are based on two commonly known models (Tight-binding and Molecular Dynamics). The physical properties of condensed systems can be obtained in the thermodynamic equilibrium in several statistical ensembles, and possible to obtain an analysis of the properties of the material and its evolution in the time-dependent on its thermodynamic conditions like temperature and pressure. Moreover, from the tight-binding model, the HICOLM program is also capable of performing a physical analysis of carbon-based nanostructures from the calculation of the material band structure.

scholarly journals Identifying frequent patterns in biochemical reaction networks - a workflow

2018 ◽  
Author(s):  
Fabienne Lambusch ◽  
Dagmar Waltemath ◽  
Olaf Wolkenhauer ◽  
Kurt Sandkuhl ◽  
Christian Rosenke ◽  
...  
Keyword(s):  
Systems Biology ◽  
Computational Models ◽  
Model Identification ◽  
Biochemical Reaction ◽  
Reaction Networks ◽  
Frequent Patterns ◽  
Biological Processes ◽  
Biochemical Reaction Networks ◽  
Structural Patterns ◽  
Biologically Relevant

Computational models in biology encode molecular and cell biological processes. These models often can be represented as biochemical reaction networks. Studying such networks, one is mostly interested in systems that share similar reactions and mechanisms. Typical goals of an investigation include understanding of the parts of a model, identification of reoccurring patterns, and recognition of biologically relevant motifs. The large number and size of available models, however, require automated methods to support researchers in achieving their goals. Specifically for the problem of finding patterns in large networks only partial solutions exist. We propose a workflow that identifies frequent structural patterns in biochemical reaction networks encoded in the Systems Biology Markup Language. The workflow utilises a subgraph mining algorithm to detect frequent network patterns. Once patterns are identified, the textual pattern description can automatically be converted into a graphical representation.Furthermore, information about the distribution of patterns among the selected set of models can be retrieved.The workflow was validated with 575 models from the curated branch of BioModels. In this paper, we highlight interesting and frequent structural patterns. Further, we provide exemplary patterns that incorporate terms from the Systems Biology Ontology. Our workflow can be applied to a custom set of models or to models already existing in our graph database MaSyMoS. The occurrences of frequent patterns may give insight into the encoding of central biological processes, evaluate postulated biological motifs, or serve as a similarity measure for models that share common structures. Availability: https://github.com/FabienneL/BioNet-Mining Contact: [email protected]

Co-planar two-dimensional Metal-Insulator-Semiconductor Capacitor: Numerical study of the device electrostatics.

2017 ◽  
Author(s):  
Varun Bheemireddy
Keyword(s):  
Space Charge ◽  
High Performance ◽  
Numerical Study ◽  
2D Materials ◽  
Space Charge Density ◽  
Two Dimensional ◽  
Short Channel ◽  
Metal Insulator ◽  
Metal Insulator Semiconductor ◽  
New Family

The two-dimensional(2D) materials are highly promising candidates to realise elegant and e cient transistor. In the present letter, we conjecture a novel co-planar metal-insulator-semiconductor(MIS) device(capacitor) completely based on lateral 2D materials architecture and perform numerical study of the capacitor with a particular emphasis on its di erences with the conventional 3D MIS electrostatics. The space-charge density features a long charge-tail extending into the bulk of the semiconductor as opposed to the rapid decay in 3D capacitor. Equivalently, total space-charge and semiconductor capacitance densities are atleast an order of magnitude more in 2D semiconductor. In contrast to the bulk capacitor, expansion of maximum depletion width in 2D semiconductor is observed with increasing doping concentration due to lower electrostatic screening. The heuristic approach of performance analysis(2D vs 3D) for digital-logic transistor suggest higher ON-OFF current ratio in the long-channel limit even without third dimension and considerable room to maximise the performance of short-channel transistor. The present results could potentially trigger the exploration of new family of co-planar at transistors that could play a signi significant role in the future low-power and/or high performance electronics.<br>

PARALLEL SOLVER FOR THE SIMULATIONS OF DETONATION WAVES ON UNSTRUCTURED GRIDS

2020 ◽  
Author(s):  
A. I. Lopato ◽  
◽  
A. G. Eremenko ◽  
Keyword(s):  
Chemical Kinetics ◽  
Numerical Scheme ◽  
Unstructured Grids ◽  
Numerical Study ◽  
Numerical Approach ◽  
Detonation Waves ◽  
Detailed Chemical Kinetics ◽  
Parallel Solver ◽  
Further Development ◽  
Detailed Chemical

Recently, we developed a numerical approach for the simulation of detonation waves on fully unstructured grids and applied it to the numerical study of the mechanisms of detonation initiation in multifocusing systems. Current work is devoted to further development of our numerical approach, namely, parallelization of the numerical scheme and introduction of more comprehensive detailed chemical kinetics scheme.

scholarly journals Genome-wide analysis and expression profile of the bZIP gene family in poplar

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Kai Zhao ◽  
Song Chen ◽  
Wenjing Yao ◽  
Zihan Cheng ◽  
Boru Zhou ◽  
...  
Keyword(s):  
Salt Stress ◽  
Systems Biology ◽  
Gene Family ◽  
Stress Responses ◽  
Metal Ion ◽  
Gene Networks ◽  
Expression Patterns ◽  
Gene Set Enrichment Analysis ◽  
Specific Gene ◽  
Biological Processes

Abstract Background The bZIP gene family, which is widely present in plants, participates in varied biological processes including growth and development and stress responses. How do the genes regulate such biological processes? Systems biology is powerful for mechanistic understanding of gene functions. However, such studies have not yet been reported in poplar. Results In this study, we identified 86 poplar bZIP transcription factors and described their conserved domains. According to the results of phylogenetic tree, we divided these members into 12 groups with specific gene structures and motif compositions. The corresponding genes that harbor a large number of segmental duplication events are unevenly distributed on the 17 poplar chromosomes. In addition, we further examined collinearity between these genes and the related genes from six other species. Evidence from transcriptomic data indicated that the bZIP genes in poplar displayed different expression patterns in roots, stems, and leaves. Furthermore, we identified 45 bZIP genes that respond to salt stress in the three tissues. We performed co-expression analysis on the representative genes, followed by gene set enrichment analysis. The results demonstrated that tissue differentially expressed genes, especially the co-expressing genes, are mainly involved in secondary metabolic and secondary metabolite biosynthetic processes. However, salt stress responsive genes and their co-expressing genes mainly participate in the regulation of metal ion transport, and methionine biosynthetic. Conclusions Using comparative genomics and systems biology approaches, we, for the first time, systematically explore the structures and functions of the bZIP gene family in poplar. It appears that the bZIP gene family plays significant roles in regulation of poplar development and growth and salt stress responses through differential gene networks or biological processes. These findings provide the foundation for genetic breeding by engineering target regulators and corresponding gene networks into poplar lines.

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