Emerging Trends in Systems Biology: Toward a Comprehensive Understanding of Life

Systems biology is an interdisciplinary field that seeks to decipher the complex interactions and principles governing biological systems. Emerging trends involve the use of artificial intelligence (AI) and Machine Learning (ML). By employing rigorous computational models, scientists aim to address the challenges of biological complexity and predict its impacts on different disciplines.

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What is Systems Biology?

Systems biology is an interdisciplinary research field that involves close collaboration between various disciplines, including biology, medicine, engineering, computer science, chemistry, physics, and mathematics.1

The field is dedicated to comprehensively characterizing biological entities by quantitatively integrating cellular and molecular information into models.1 The creation and evaluation of these models are fundamental in understanding cell complexity, as they serve to predict underlying mechanisms.1

Systems biology plays a crucial role in understanding the connection between the genome and the phenome and the mechanisms of biological processes.1 By using computational modeling and data analysis, it aims to identify the most plausible mechanisms from a range of potential candidates.1 In cases where the mechanisms are not yet fully understood, systems biology provides a framework for integrating existing knowledge about cellular systems to offer probabilistic predictions.1

The Power of Multi-Omics Data Integration

Omics data encompasses the comprehensive characterization and quantification of pools of biological molecules that make up the structure and function of organisms.2 This includes the study of genomes (genomics), transcriptomes (transcriptomics), proteomes (proteomics), and metabolomes (metabolomics).2 Each of these omics represents a different aspect of the biological system, from the genetic blueprint to the active proteins and the resulting metabolites.2

The integration of omics data is a complex yet crucial process in systems biology.2It involves combining heterogeneous and large datasets from various omics studies to gain a more comprehensive and holistic understanding of biological systems.2This integration is not only a conceptual challenge but also a practical one due to the sheer volume and diversity of the data.2

There are many examples of multi-omics studies where data integration provides key insights into the mechanism of different phenomena, from disease characterization to drug testing and more.

One of these examples is from the study by Zhao et al. (2020)3, which integrated genome-wide association studies (GWAS), expression quantitative trait loci (eQTL), and methylation quantitative trait loci (MQTL) data to identify single nucleotide polymorphisms (SNPs) and genes related to different types of strokes.3 This study explored the genetic pathogenesis of strokes based on loci, genes, gene expression, and phenotypes, finding 38 SNPs affecting the expression of 14 genes associated with stroke.3

Deciphering Biological Networks and Pathways

Systems biology allows researchers to elucidate gene regulatory networks, protein interactomes, as well as metabolic and signaling pathways, all these by integrating experimental and computational methods.

Several approaches are applied in this field, such as Weighted Gene Co-expression Network Analysis (WGCNA)4, Bayesian network modeling5, and Protein-Protein Interaction (PPI) Network Analysis4.

These systems biology approaches have been applied in a variety of fields, especially in cancer research. One example of this is in Colorectal cancer (CRC).6 CRC is a prevalent malignancy with most cases diagnosed at advanced stages; this makes the need for early biomarkers detection an urgent matter.6

In a study aimed at identifying early-stage CRC targets, proteomics analyses were conducted on tissues from stage II CRC patient, obtaining the expression of 2,968 proteins, which were cross-referenced with RNA-Seq data.6

Through differential expression, network analysis, and functional annotation, 111 proteins were pinpointed as key candidates, some of them emerged as potential biomarkers for diagnosis and prognosis.6

Predictive Insights through Computational Modeling and Simulation

Systems biology employs a diverse array of computational techniques, encompassing artificial intelligence (AI) and machine learning (ML), to model and forecast the behaviors of biological entities across multiple scales.7

Machine learning algorithms are instrumental in forecasting the outcomes of genetic alterations, evaluating protein-protein interactions, and categorizing cells using omics data.7

These algorithms excel at processing extensive datasets, which aids in the identification of potential drug targets and the prediction of compound efficacy.7 Additionally, they are pivotal in disease diagnosis and the creation of predictive models for assessing patient risks and treatment responses.7

Neural networks, such as convolutional neural networks (CNNs), are applied to tasks like sequence alignment, gene expression profiling, and protein structure prediction.7 Classification and regression problems usually apply random forest. Clustering algorithms are essential for the examination of unstructured data, which reveals underlying biological processes at the genomic level.7

Single-Cell Systems Biology

The advent of single cell sequencing technologies has elevated systems biology, enabling detailed exploration of intricate interactions at the individual cell level.8 This advancement transcends the scope of conventional omics techniques by tackling the inherent cellular diversity that is fundamental to cell biology.8

Merging AI and ML with single-cell omics is revolutionizing systems biology.8 AI-driven algorithms accurately manage the great amount of data produced by single cell technologies, facilitating the extraction of biological information and the integration of different omics datasets.8

Empowering Systems Biology with Bioinformatics and Data Analytics

The widespread adoption of high-throughput multi-omics techniques has revolutionized biological research. However, this generates vast amounts of complex omics data, presenting significant computational challenges that have been tackled by the advent of bioinformatics tools.

Bioinformatics is primarily concerned with the creation, design and improvement of informatics tools and methods for processing and managing biological data; while, data analysis involves statistics, and quantitative methods to analyze this data. In this field, the application of bioinformatics methods for data analysis is called computational biology.

Challenges and Future Directions in Systems Biology

Systems biology faces challenges in integrating diverse data types and computational models, reconciling bottom-up and top-down approaches, and calibrating models amidst biological noise. Multi-omics integration also present hurdles.9

Future directions include developing advanced computational tools, pursuing comprehensive models of biological systems, fostering interdisciplinary collaboration, and adhering to FAIR principles (Findable, Accessible, Interoperable, and Reusable) for data sharing, since this field aims to deepen the fundamental understanding of biological systems and improve predictive modeling.9


  1. Eddy J. A, et al. (2015). Fostering synergy between cell biology and systems biology. Trends in Cell Biology, 25(8), 440–445. https://doi.org/10.1016/j.tcb.2015.04.005
  2. Pinu F. R, et al. (2019). Systems Biology and Multi-Omics Integration: Viewpoints from the Metabolomics Research Community. Metabolites, 9(4), 76. https://doi.org/10.3390/metabo9040076
  3. Zhao S, et al. (2020). Integrating Multi-Omics Data to Identify Novel Disease Genes and Single-Neucleotide Polymorphisms. Frontiers in Genetics, 10. https://doi.org/10.3389/fgene.2019.01336
  4. Ovens K, et al. (2021). Comparative Analyses of Gene Co-expression Networks: Implementations and Applications in the Study of Evolution. Frontiers in Genetics, 12. https://doi.org/10.3389/fgene.2021.695399
  5. Fu C, et al. (2017). Bayesian network model for identification of pathways by integrating protein interaction with genetic interaction data. BMC Systems Biology, 11(S4). https://doi.org/10.1186/s12918-017-0454-9
  6. Yang W, et al. (2019). Integrating proteomics and transcriptomics for the identification of potential targets in early colorectal cancer. International Journal of Oncology. https://doi.org/10.3892/ijo.2019.4833
  7. Yue R. & Dutta A. (2022). Computational systems biology in disease modeling and control, review and perspectives. Npj Systems Biology and Applications, 8(1). https://doi.org/10.1038/s41540-022-00247-4
  8. Wang Y, et al. (2019). Systems biology intertwines with single cell and AI. BMC Bioinformatics, 20(S7). https://doi.org/10.1186/s12859-019-2731-7
  9. Saccenti E. (2021). Specialty Grand Challenge: Data and Model Integration in Systems Biology. Frontiers in Systems Biology, 1. https://doi.org/10.3389/fsysb.2021.800894

Further Reading 

Last Updated: Apr 2, 2024

Deliana Infante

Written by

Deliana Infante

I am Deliana, a biologist from the Simón Bolívar University (Venezuela). I have been working in research laboratories since 2016. In 2019, I joined The Immunopathology Laboratory of the Venezuelan Institute for Scientific Research (IVIC) as a research-associated professional, that is, a research assistant.


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