Computational and Mathematical Biology Centre
Research Theme 3
Developing new tools related to potential target identification
Developing tools for identifying potential drug targets by exploring biological networks
Capturing disease mechanisms and identifying potential therapeutic targets through network analysis and machine learning algorithms
Traditional computational methods for investigating a disease system are limited by their inability to examine the in-depth association between different molecular layers. Moreover, with increased data volume due to technological advances, there is a demand for more computational tools backed by advanced mathematical methods. So, we used mathematical models and methods to develop tools for drug target identification from large datasets. Two features, the existence of bistability and noise, were used to develop the tool.
Bistability is one of the salient dynamical features in various all-or-none kinds of decision-making processes. Bistability in a cell signalling network plays a key role in the input-output (I/O) relation. We aim to capture and emphasize the role of motif structure influencing the I/O relation between two nodes in the context of bistability. Here, a model-based analysis is made to investigate the critical conditions responsible for the emergence of different bistable protein-protein interaction (PPI) motifs and their possible applications to find the potential drug targets. The bistable switching through hysteresis is explored to understand the underlying mechanisms involved in the cell signalling processes when significant motifs exhibiting bistability have emerged. Further, the results were elaborated on by the implication of the emerging PPI motifs to identify potential drug targets in three cancer networks, which were validated with existing databases. We also observed that the desired functionality of any signalling networks is hindered by the influence of stochastic perturbations or noise (Bioinformatics, 37(22), 4146-416, (2021)).
In the next study, we focused on the influence of noise on the functionality of the biological networks. Introducing noise to signals can alter central regulatory switches of cellular processes, leading to diseases. Noise is inherently present in the cellular signalling system and plays a decisive role in the input–output (I/O) relation. The noise tolerance of motif structures in the cell signalling processes is of interest, too. The vulnerability of a node to noise could be a significant factor in causing signalling errors and needs to be controlled. We developed stochastic differential equation (SDE) based mathematical models for network motifs with two nodes and studied the association between motif structure and signal–noise relation. A two-dimensional parameter space analysis on motif sensitivity with noise and input signal variation was performed to classify and rank the motifs. Identifying sensitive motifs and their high druggability infers their significance in screening potential drug-target candidates. Finally, we proposed a theoretical framework to identify nodes from a network as potential drug targets. We applied this mathematical formalism to three cancer networks to identify and validate drug targets with existing databases (Journal of Theoretical Biology, 555, 111298, (2022)).
The regulation of proteins governs the biological processes and functions and, therefore, the organisms’ phenotype. So, there is an unmet need for a systematic tool to identify the proteins that play a crucial role in information processing in a protein–protein interaction (PPI) network. However, the current protein databases and web servers still lag in providing an end-to-end pipeline that can leverage the topological understanding of a context-specific PPI network to identify the influential spreaders. Addressing this, we developed a web application, ‘konnect2prot’ (k2p), which can generate context-specific directional PPI networks from the input proteins and detect their biological and topological importance in the network. We pooled together a large amount of ontological knowledge, parsed it into a functional network, and gained insight into the molecular underpinnings of disease development by creating a one-stop junction for PPI data. k2p contains local and global information about a protein, such as protein class, disease mutations, ligands and PDB structure, enriched processes and pathways, multi-disease interactome and hubs and bottlenecks in the directional network. It also identifies spreaders in the network and maps them to disease hallmarks to determine whether they can affect the disease state or not (Bioinformatics, 39(1), btac815, (2023)). This is also copyrighted and is freely available on the THSTI website.