Data Incompleteness May form a Hard-to-Overcome Barrier to Decoding Life’s Mechanism

Abstract

In this brief review, we attempt to demonstrate that the incompleteness of data, as well as the intrinsic heterogeneity of biological systems, may form very strong and possibly insurmountable barriers for researchers trying to decipher the mechanisms of the functioning of live systems. We illustrate this challenge using the two most studied organisms: E. coli, with 34.6% genes lacking experimental evidence of function, and C. elegans, with identified proteins for approximately 50% of its genes. Another striking example is an artificial unicellular entity named JCVI-syn3.0, with a minimal set of genes. A total of 31.5% of the genes of JCVI-syn3.0 cannot be ascribed a specific biological function. The human interactome mapping project identified only 5–10% of all protein interactions in humans. In addition, most of the available data are static snapshots, and it is barely possible to generate realistic models of the dynamic processes within cells. Moreover, the existing interactomes reflect the de facto interaction but not its functional result, which is an unpredictable emerging property. Perhaps the completeness of molecular data on any living organism is beyond our reach and represents an unsolvable problem in biology.

Prospects of Using Protein Engineering for Selective Drug Delivery into a Specific Compartment of Target Cells

“…The available maps of the human interactome cover mainly binary interactions and do not take into account either their strength or the possible nonlinearity of these interactions [ 300 , 301 ]. The extraordinary complexity and lack of full data on the regulation of the body are additional barriers to the creation of constructs aimed at intracellular targets [ 302 ]. A typical way to solve this problem is to identify the main parameters that control the system.…”

AIGO-DTI: Predicting Drug–Target Interactions Based on Improved Drug Properties Combined with Adaptive Iterative Algorithms

“…14 On the other hand, the structural incompleteness further increases the difficulty of prediction. 15 Moreover, current optimization algorithms for drug−target association networks are mainly optimized from the perspective of the graph structure itself, overlooking the influence of drug−target features on the weights of DTI association networks for specific DTI prediction tasks. Additionally, excessive emphasis on the optimization process often leads to overfitting issues in DTI prediction.…”

“…Noise in the network may introduce false interactions, distorting the true relationships between drugs and targets, thereby affecting model learning and prediction . On the other hand, the structural incompleteness further increases the difficulty of prediction . Moreover, current optimization algorithms for drug–target association networks are mainly optimized from the perspective of the graph structure itself, overlooking the influence of drug–target features on the weights of DTI association networks for specific DTI prediction tasks.…”

A network embedding approach to identify active modules in biological interaction networks

“…The commonality between all these methods is that their effectiveness is very dependent on the network topology. Unfortunately, it is known that molecular interaction networks are noisy and incomplete ( Kondratyeva et al, 2022 ). In recent years, network embedding ( Cui et al, 2019 ) has proven to be a powerful network analysis approach by generating a very informative and compact vector representation for each vertex v in the network.…”