Computational Molecular Biology 2024, Vol.14, No.4, 134-144 http://bioscipublisher.com/index.php/cmb 140 6 Computational Tools and Platforms 6.1 Software for pathway and dynamic modeling The development of dynamic models in systems biology has been significantly advanced by various software tools designed to handle the complexity and scale of biological systems. One notable tool is PhysiCell, an open-source, physics-based cell simulator for 3-D multicellular systems. PhysiCell allows for the simulation of both the biochemical microenvironment and the interactions of many cells within this environment. It includes sub-models for cell cycling, apoptosis, necrosis, and motility, and is capable of handling simulations involving up to millions of cells on high-performance computing platforms (Ghaffarizadeh et al., 2017). Another important tool is the Cell Collective platform, which provides a web-based environment for creating and simulating dynamic models of biological processes. This platform is particularly useful in educational settings, allowing students to engage with and understand complex biological systems through interactive modeling (Helikar et al., 2015). SBML (Systems Biology Markup Language) Level 3 is another critical development, providing a standardized format for the exchange and reuse of biological models. This extensible format supports various model types, including reaction-based, constraint-based, and rule-based models, facilitating the integration and sharing of complex systems biology models across different platforms (Keating et al., 2020). Modular assembly approaches using bond graphs have also been proposed to enhance the reusability and scalability of dynamic models. This method ensures that submodels are consistent with each other and with fundamental conservation laws, making it easier to construct large-scale models that are both accurate and detailed (Pan et al., 2021). 6.2 Cloud-based platforms for whole-cell simulations Whole-cell models (WCMs) represent the pinnacle of systems biology modeling, integrating diverse intracellular pathways and processes. These models are computationally intensive, often requiring high-performance computing resources to simulate the vast networks of biochemical reactions involved. To address these challenges, cloud-based platforms have emerged as a viable solution. One such platform is the parallel implementation of the stochastic simulation algorithm (SSA), which has been applied to whole-cell reaction networks. This approach significantly speeds up the simulation process, making it feasible to handle the large-scale networks typical of WCMs (Yeom et al., 2021). The Cell Collective platform also supports cloud-based simulations, enabling high-throughput studies and the exploration of biological possibilities on a large scale. This platform's accessibility and ease of use make it a valuable tool for both research and education (Helikar et al., 2015). Additionally, the PhysiCell simulator, with its parallelized code and scalability, can be deployed on cloud-based high-performance computing platforms to simulate complex multicellular systems. This capability allows researchers to conduct large-scale simulations that would be otherwise infeasible on standard desktop workstations (Ghaffarizadeh et al., 2017). 7 Challenges and Future Directions 7.1 Data integration and model complexity The integration of diverse biological data into comprehensive models remains a significant challenge in systems biology. Whole-cell models (WCMs), which aim to simulate the entirety of cellular processes, exemplify the complexity involved. These models require the assimilation of various types of data, including genomic, transcriptomic, proteomic, and metabolomic information, to accurately represent cellular functions (Yeom et al., 2021; Georgouli et al., 2023). The development of such models is labor-intensive and necessitates sophisticated computational methods to manage and integrate the vast amounts of data (Georgouli et al., 2023). Additionally, ensuring consistency and compatibility among submodels, which are often developed independently, is crucial. Approaches like bond graphs, which apply physical conservation laws to model integration, have been proposed to address these issues, enhancing the modularity and reusability of models (Pan et al., 2021).
RkJQdWJsaXNoZXIy MjQ4ODYzNA==