Next-Generation Technologies and Systems Biology for the Design of Novel Vaccines Against Apicomplexan Parasites

Parasites of the phylum Apicomplexa are the causative agents of important diseases such as malaria, toxoplasmosis or cryptosporidiosis in humans, and babesiosis and coccidiosis in animals. Whereas the first human recombinant vaccine against malaria has been approved and recently recommended for wide administration by the WHO, most other zoonotic parasitic diseases lack of appropriate immunoprophylaxis. Sequencing technologies, bioinformatics, and statistics, have opened the “omics” era into apicomplexan parasites, which has led to the development of systems biology, a recent field that can significantly contribute to more rational design for new vaccines. The discovery of novel antigens by classical approaches is slow and limited to very few antigens identified and analyzed by each study. High throughput approaches based on the expansion of the “omics”, mainly genomics and transcriptomics have facilitated the functional annotation of the genome for many of these parasites, improving significantly the understanding of the parasite biology, interactions with the host, as well as virulence and host immune response. Developments in genetic manipulation in apicomplexan parasites have also contributed to the discovery of new potential vaccine targets. The present minireview does a comprehensive summary of advances in “omics”, CRISPR/Cas9 technologies, and in systems biology approaches applied to apicomplexan parasites of economic and zoonotic importance, highlighting their potential of the holistic view in vaccine development.

Reconstructing Kinetic Models for Dynamical Studies of Metabolism using Generative Adversarial Networks

Kinetic models of metabolic networks relate metabolic fluxes, metabolite concentrations, and enzyme levels through well-defined mechanistic relations rendering them an essential tool for systems biology studies aiming to capture and understand the behavior of living organisms. However, due to the lack of information about the kinetic properties of enzymes and the uncertainties associated with available experimental data, traditional kinetic modeling approaches often yield only a few or no kinetic models with desirable dynamical properties making the computational analysis unreliable and computationally inefficient. We present REKINDLE (REconstruction of KINetic models using Deep LEarning), a deep-learning-based framework for efficiently generating large-scale kinetic models with dynamic properties matching the ones observed in living organisms. We showcase REKINDLE's efficiency and capabilities through three studies where we: (i) generate large populations of kinetic models that allow reliable in silico testing of hypotheses and systems biology designs, (ii) navigate the phenotypic space by leveraging the transfer learning capability of generative adversarial networks, demonstrating that the generators trained for one physiology can be fine-tuned for another physiology using a low amount of data, and (iii) expand upon existing datasets, making them amenable to thorough computational biology and data-science analyses. The results show that data-driven neural networks assimilate implicit kinetic knowledge and structure of metabolic networks and generate novel kinetic models with tailored properties and statistical diversity. We anticipate that our framework will advance our understanding of metabolism and accelerate future research in health, biotechnology, and systems and synthetic biology. REKINDLE is available as an open-access tool.