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Many biological systems across scales of size and complexity exhibit a time-varying complex network structure that emerges and self-organizes as a result of interactions with the environment. Network interactions optimize some intrinsic cost functions that are unknown and involve for example energy efficiency, robustness, resilience, and frailty. A wide range of networks exist in biology, from gene regulatory networks important for organismal development, protein interaction networks that govern physiology and metabolism, and neural networks that store and convey information to networks of microbes that form microbiomes within hosts, animal contact networks that underlie social systems, and networks of populations on the landscape connected by migration. Increasing availability of extensive (big) data is amplifying our ability to quantify biological networks. Similarly, theoretical methods that describe network structure and dynamics are being developed. Beyond static networks representing snapshots of biological systems, collections of longitudinal data series can help either at defining and characterizing network dynamics over time or analyzing the dynamics constrained to networked architectures. Moreover, due to interactions with the environment and other biological systems, a biological network may not be fully observable. Also, subnetworks may emerge and disappear as a result of the need for the biological system to cope with for example invaders or new information flows. The confluence of these developments renders tractable the question of how the structure of biological networks predicts and controls network dynamics. In particular, there may be structural features that result in homeostatic networks with specific higher-order statistics (e.g., multifractal spectrum), which maintain stability over time through robustness and/or resilience to perturbation. Alternative, plastic networks may respond to perturbation by (adaptive to catastrophic) shifts in structure. Here, we explore the opportunity for discovering universal laws connecting the structure of biological networks with their function, positioning them on the spectrum of time-evolving network structure, that is, dynamics of networks, from highly stable to exquisitely sensitive to perturbation. If such general laws exist, they could transform our ability to predict the response of biological systems to perturbations—an increasingly urgent priority in the face of anthropogenic changes to the environment that affect life across the gamut of organizational scales.
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Determining Dose-Response Characteristics of Molecular Perturbations in Whole-Organism Assays Using Biological Imaging and Machine Learning
Advances in microscopy and high-content imaging now offer a powerful way to profile the phenotypic response of intact systems to molecular perturbation and study the response irrespective of putative target activity and by preserving the physiological context in the living systems. An emerging challenge in bioinformatics and drug discovery is constituted by data generated from such studies that involve analyzing the effect of specific molecules at the system-wide organism level. In this paper we propose a novel automated approach that combines techniques from biological imaging and machine learning to automatically quantify a fundamental measure of molecular perturbation in an intact biological system, namely, its dose-response characteristics. We validate our results using phenotypic assay data involving post-infective larvae (schistosomula) of the parasitic Schistosoma mansoni flatworm. This parasite is one of the etiological agents of schistosomiasis – a significant neglected tropical disease, which puts at-risk nearly two billion people.
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- PAR ID:
- 10110724
- Date Published:
- Journal Name:
- IEEE International Conference on Bioinformatics and Biomedicine
- Page Range / eLocation ID:
- 283 to 290
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/BIBM.2018.8621083
