Tapping social networks to explore biological systems

Bharat Mishra is wrote this post as part of Dr. Stacy Krueger-Hadfield’s Science Communication course at the University of Alabama at Birmingham. He is currently pursuing his PhD in the lab of Dr. Shahid Mukhtar. He earned an undergraduate degree at SRM University and a MS in Bioinformatics from the Indian Institute of Information Technology – Allahabad. Bharat research interests are bioinformatics, non-coding RNAs and network systems biology.

Have you ever wondered how you received suggestions from a friend-of-a-friend or your distant family member in Facebook?

The answer lies in network science. The study of things that are connected to produce a focused result is called network science. Social networks, such as Facebook are extremely helpful for researchers exploring the complex processes performed by any biological system (Saqr and Alamro 2019). These biological processes are controlled by many highly synchronized and simultaneous biochemical reactions performed by proteins. These reactions can be demonstrated as proteins interacting with each other to perform a biological function. The interactions are represented based on graph theory and termed as protein-protein interaction networks (Caldera et al. 2019). 

Consider these proteins as your friend list in Facebook. The density of connecting you and your friends tells the degree of a person. If a person has high number of friends on Facebook, then he will be called hub. A hub protein is generally affected the most during stress. Similarly, consider a situation where your Facebook friends are from two different cities. Now, the person which is only connected through two hubs from two cities are called as bottlenecks. These bottlenecks are very important as they are central and lethal to pass an information from one city to another. Likewise, there are several different methods “centralities” through which you can understand social or biological network behavior (Alwash and Levine 2019). These high centrality proteins are generally the tapping point to study most influencers of any stress situation; pathogenic infectious disease, abiotic exposure disease, cancer and/or aging related disease in both animals and plants (Wang et al. 2019).

In last decade, large amount of system-scale data has been generated from biological sciences research due to unbelievable improvements in sequencing platforms (Metzker  2010). The fundamental challenge was to handle and analyze huge and multidimensional data sets. To effectively integrate multidimensional data sets; genomics, transcriptomics, and proteomics (Figure 1), systems biology has transpired as an effective and largely used practice with the remarkable advancement in network integration techniques (Altelaar et al. 2013). These integration techniques help us to understand the complexity of biology in a concentrated way to find novel associations between biological entities during disease or stress. We can study these associations which will helps us to counteract the abnormal condition through different therapeutics strategies; from drug development to block the most influencer in a disease to prevent the crop loss by introducing stress resistant variety through gene editing (Yang et al. 2019). 

Interestingly, each biological process changes its fate as time progresses. Even our body performs different functions throughout the day very precisely. This change in the biological system is called systems dynamics. There is a widespread dynamic change in the transcriptional reprogramming in highly synchronized manner of a plant cell after any stress including multifaceted signal transduction networks (Mukhtar et al. 2011). Dynamic Network modeling by large-scale static transcriptional regulatory network integrated with protein-protein interactions and temporal transcriptome expression data gives the high resolution dynamic transcriptional regulation in biological systems  ranging from immune-related to phylogenetic association between biomolecules (Figure 2)  (Soyer and O’Malley 2013). These system dynamics network study helps us to understand the progression of a disease in animals and plants (Hens et al. 2019).

Now, we can study the dynamic transcriptional regulatory networks in Human aging or Arabidopsis senescence based hidden Markov model, and time series expression data to identify the significant regulators (transcription factors and microRNAs) leading to the death of humans and plants (Wise and Bar-Joseph 2015). Similarly, we can investigate the system dynamics to target (block) the change in most influencer proteins/regulators by time through changing the drugs administered to treat long term disease.

Recently, Cholley et al. (2018) developed a new tool understand the reconstruction of cell fate during a given time period by identifying the time specific gene regulatory networks and cellular functional analysis. Altogether, dynamic regulatory network modeling tools acts as extended computational measures desired for comprehensive modeling of biotic and abiotic stress modalities. Implementation of this method will help us a lot to identify the culprits responsible for cell differentiation, survival or death. 

In conclusion, network biology is an applied network science approach that have been proven dependable to study the introduction and progression of disease (stress) in animal and plants. We can speed-up the identification of the collective system behavior and fasten the design of therapeutics to counter the most worrisome disease existing in the world through network biology.


Altelaar, A. F., J. Munoz, and A. J. Heck. 2013. ‘Next-generation proteomics: towards an integrative view of proteome dynamics’, Nat Rev Genet, 14: 35-48.

Alwash, N., and J. D. Levine. 2019. ‘Network analyses reveal structure in insect social groups’, Curr Opin Insect Sci, 35: 54-59.

Caldera, M., Müller, F., Kaltenbrunner, I., Licciardello, M., Lardeau, C., Kubicek, S., and  Menche, J.  2019. Mapping the perturbome network of cellular perturbations. Nat Commun 10, 5140.

Cholley, P. E., J. Moehlin, A. Rohmer, V. Zilliox, S. Nicaise, H. Gronemeyer, and M. A. Mendoza-Parra. 2018. ‘Modeling gene-regulatory networks to describe cell fate transitions and predict master regulators’, NPJ Syst Biol Appl, 4: 29.

Hens, C., Harush, U., Haber, S., Cohen, R., and Barzel, B. 2019. Spatiotemporal signal propagation in complex networks. Nat. Phys. 15, 403–412.

Metzker, M. L. 2010. ‘Sequencing technologies – the next generation’, Nat Rev Genet, 11: 31-46.

Mukhtar, M. S., et al. 2011. ‘Independently evolved virulence effectors converge onto hubs in a plant immune system network’, Science, 333: 596-601.

Saqr, M., and A. Alamro. 2019. ‘The role of social network analysis as a learning analytics tool in online problem based learning’, BMC Med Educ, 19: 160.

Soyer, O. S., and M. A. O’Malley. 2013. ‘Evolutionary systems biology: what it is and why it matters’, Bioessays, 35: 696-705.

Wang, Q., Zhang, Y., Wang, M. Song, W., Shen, Q., McKenzie, A., Choi, I., Zhou, X., Pan, P., Yue, Z., and Zhang, B. 2019. The landscape of multiscale transcriptomic networks and key regulators in Parkinson’s disease. Nat Commun 10, 5234.

Wise, A., and Z. Bar-Joseph. 2015. ‘SMARTS: reconstructing disease response networks from multiple individuals using time series gene expression data’, Bioinformatics, 31: 1250-7.

Yang, W., Zhao, X., Han, Y., Duan, L., Lu, X., Wang, X., Zhang,Y., Zhou,W., Liu, J., Zhang, H., Zhao, Q., Hong, L., and Fan, D. 2019. Identification of hub genes and therapeutic drugs in esophageal squamous cell carcinoma based on integrated bioinformatics strategy. Cancer Cell Int 19, 142.

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