Computational Molecular Biology 2024, Vol.14, No.2, 45-53 http://bioscipublisher.com/index.php/cmb 51 effectively (Lee et al., 2020). New approaches like Most Permissive Boolean Networks (MPBNs) have been proposed to reduce the complexity of dynamical analysis, enabling the modeling of genome-scale networks (Paulevé et al., 2020). 7.2 Data integration and interoperability The integration of heterogeneous data types is another major challenge. Advances in high-throughput techniques have generated vast amounts of diverse omics data, which need to be integrated to provide a holistic view of biological systems. However, the complexity, heterogeneity, and high-dimensionality of these data pose significant challenges for data integration and interoperability (Lee et al., 2020). Methods for collective mining of various types of networked biological data have been proposed, but they still face limitations in dealing with heterogeneous networked data (Gligorijević and Przulj, 2015). The development of heterogeneous multi-layered networks (HMLNs) has shown promise in integrating diverse biological data, but new computational challenges arise in establishing causal genotype-phenotype associations and understanding environmental impacts on organisms (Wang et al., 2021). 7.3 Advances in computational techniques To address the challenges of scalability, complexity, and data integration, advances in computational techniques are essential. Probabilistic models like ProbRules, which combine probabilities and logical rules, have been developed to represent the dynamics of biological systems across multiple scales (Grob et al., 2019). These models have shown robustness in predicting gene expression readouts and clarifying molecular mechanisms. Additionally, non-negative matrix factorization-based approaches have been highlighted for their potential in dealing with heterogeneous data and providing accurate integrative analyses (Pham et al., 2008). The application of machine learning methods to network biology has also been emphasized, offering new biological insights and aiding in the development of more accurate in silico representations of biological systems (Liu et al., 2020). Acknowledgments We would like to thank Ms Kim for reading the manuscript and providing valuable feedback that improved the clarity of the text. We also appreciate two anonymous peer reviewers who contributed to the evaluation of this manuscript. Conflict of Interest Disclosure The authors affirm that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. References Aittokallio T., and Schwikowski B., 2006, Graph-based methods for analysing networks in cell biology, Briefings in Bioinformatics, 7(3): 243-255. https://doi.org/10.1093/BIB/BBL022 Blazier A.S., and Papin J.A., 2012, Integration of expression data in genome-scale metabolic network reconstructions, Frontiers in Physiology, 3: 299. https://doi.org/10.3389/fphys.2012.00299 Boccaletti S., Latora V., Moreno Y., Chavez M., and Hwang D., 2006, Complex networks: structure and dynamics, Physics Reports, 424: 175-308. https://doi.org/10.1016/J.PHYSREP.2005.10.009 Bocci F., Jia D., Nie Q., Jolly M.K., and Onuchic J., 2023, Theoretical and computational tools to model multistable gene regulatory networks, Reports on, Progress in Physics, 2023: 86. https://doi.org/10.1088/1361-6633/acec88. Covert M., Knight E., Reed J., Herrgård M., and Palsson B., 2004, Integrating high-throughput and computational data elucidates bacterial networks, Nature, 429: 92-96. https://doi.org/10.1038/nature02456. Gao W., Wu H., Siddiqui M., and Baig A., 2017, Study of biological networks using graph theory, Saudi Journal of Biological Sciences, 25: 1212-1219. https://doi.org/10.1016/j.sjbs.2017.11.022. Glass K., Huttenhower C., Quackenbush J., and Yuan G., 2013, Passing messages between biological networks to refine predicted interactions, PLoS ONE, 8(5): e64832. https://doi.org/10.1371/journal.pone.0064832. Gligorijević V., and Przulj N., 2015, Methods for biological data integration: perspectives and challenges, Journal of The Royal Society Interface, 12(112): 20150571. https://doi.org/10.1098/rsif.2015.0571.
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