Modeling of the Glycolysis Pathway in Plasmodium falciparum using Petri Nets

Several supplementary concepts for applied category-theoretical states over an extended Petri net using an example relating to genetic coding: Toward an abstract algebraic formulation of molecular/genetic biology

algebraic concepts such as category are considered cornerstones on which logical consistency relies in any sophisticated study of natural phenomena. However, to the best of our knowledge, in molecular/genetic biology, their application is still severely limited because they capture neither the dynamics nor provide a visual form. The Petri net (PN) has often been used to illustrate visually parallel, asynchronous dynamic events in small data systems. A prototypal hybrid model combining both category theory and extended PNs may instead be indispensable for that purpose. This hybrid model incorporates 1) token-like elements of a group, 2) object-like places of a category, 3) square poles (rather than pentagon poles) that enable unique identifications of single-strand DNA sequences from the shape of its polygonal line, 4) creation/annihilation morphisms that generate/erase tokens, 5) Cartesian products 'Z5×Z2×…' that enable conversions between DNA and RNA sequences, 6) somatic recombinations (VDJ recombinations) for antibodies displayed concretely in category-theoretic form, 7) 'identity protein Δ' translated from a triplet of identity bases 'EEE' as an advanced concept from our previous display of the canonical central dogma, 8) illustrations of an incidence-matrix-like matrix A that includes operators as coordinates, and 9) basic topics concerning the canonical central dogma being displayed concretely using concepts of conventional category theory such as 'adjoint', 'adjoint functor', 'natural transformation', 'Yoneda's lemma' and 'Kan extension'. These ideas provide more advanced tools that expand our previous model concerning nucleic-acid-base sequences. Despite the nascent nature of our methodology, our hybrid model has potential in a variety of applications, illustrated using molecular/genetic sequences, in particular providing a simple dynamic/visual representation. With further improvements, this approach may prove effective in reducing the need for large data-storing systems.

Extracellular vesicles could be a putative posttranscriptional regulatory mechanism that shapes intracellular RNA levels in Plasmodium falciparum

Plasmodium falciparum secretes extracellular vesicles (PfEVs) that contain parasite-derived RNA. However, the significance of the secreted RNA remains unexplored. Here, we compare secreted and intracellular RNA from asexual cultures of six P. falciparum lines. We find that secretion of RNA via extracellular vesicles is not only periodic throughout the asexual intraerythrocytic developmental cycle but is also highly conserved across P. falciparum isolates. We further demonstrate that the phases of RNA secreted via extracellular vesicles are discernibly shifted compared to those of the intracellular RNA within the secreting whole parasite. Finally, transcripts of genes with no known function during the asexual intraerythrocytic developmental cycle are enriched in PfEVs compared to the whole parasite. We conclude that the secretion of extracellular vesicles could be a putative posttranscriptional RNA regulation mechanism that is part of or synergise the classic RNA decay processes to maintain intracellular RNA levels in P. falciparum.

An in vitro study on the differentiated metabolic mechanism of chloroquine-resistant Plasmodium falciparum using high-resolution metabolomics

Chloroquine (CQ) is an important drug used therapeutically for treatment of malaria. However, due to limited number of studies on metabolic targets of chloroquine (CQ), it is difficult to attribute mechanisms underlying resistance associated with usage of this drug. The present study aimed to investigate the metabolic signatures of CQ-resistant Plasmodium falciparum (PfDd2) compared to CQ-sensitive Plasmodium falciparum (Pf3D7). Both Pf3D7 and PfDd2 were treated with CQ at 200 nM for 48 hr; thereafter, the harvested red blood cells (RBCs) and media were subjected to microscopy and high-resolution metabolomics (HRM). Glutathione, γ-L-glutamyl-L-cysteine, spermidine, inosine monophosphate, alanine, and fructose-1,6-bisphosphate were markedly altered in PfDd2 of RBC. In the media, cysteine, cysteic acid, spermidine, phenylacetaldehyde, and phenylacetic acid were significantly altered in PfDd2. These differential metabolic signatures related signaling pathways of PfDd2, such as oxidative stress pathway and glycolysis may provide evidence for understanding the resistance mechanism and pathogenesis of the CQ-resistant parasite.

Study of the bioenergetics to identify the novel pathways as a drug target against Mycobacterium tuberculosis using Petri net

Tuberculosis is one of the life-threatening diseases globally, caused by the bacteria Mycobacterium tuberculosis. In order to control this epidemic globally, there is an urgent need to discover new drugs with novel mechanism of action that can help in shortening the duration of treatment for both drug resistant and drug sensitive tuberculosis. Mycobacterium essentially depends on oxidative phosphorylation for its growth and establishment of pathogenesis. This pathway is unique in Mycobacterium tuberculosis as compared to host due to the differences in some of the enzyme complexes carrying electron transfer. Hence, it serves as an important drug target area. The uncouplers which inhibit adenosine triphosphate synthesis, could play a vital role in serving as antimycobacterial agents and thus could help in eradicating this deadly disease. In this article, the bioenergetics of Mycobacterium tuberculosis are studied with and without uncouplers using Petri net. Petri net is among the most widely used mathematical and computational tools to model and study the complex biochemical networks. We first represented the bioenergetic pathway as a Petri net which is then validated and analyzed using invariant analysis techniques of Petri net. The valid mathematical models presented here are capable to explain the molecular mechanism of uncouplers and the processes occurring within the electron transport chain of Mycobacterium tuberculosis. The results explained the net behavior in agreement with the biological results and also suggested some possible processes and pathways to be studied as a drug target for developing antimycobacterials.

Computational Identification of Metabolic Pathways of Plasmodium falciparum using the k-Shortest Path Algorithm

Plasmodium falciparum, a malaria pathogen, has shown substantial resistance to treatment coupled with poor response to some vaccines thereby requiring urgent, holistic, and broad approach to prevent this endemic disease. Understanding the biology of the malaria parasite has been identified as a vital approach to overcome the threat of malaria. This study is aimed at identifying essential proteins unique to malaria parasites using a reconstructed iPfa genome-scale metabolic model (GEM) of the 3D7 strain of Plasmodium falciparum by filling gaps in the model with nineteen (19) metabolites and twenty-three (23) reactions obtained from the MetaCyc database. Twenty (20) currency metabolites were removed from the network because they have been identified to produce shortcuts that are biologically infeasible. The resulting modified iPfa GEM was a model using the k-shortest path algorithm to identify possible alternative metabolic pathways in glycolysis and pentose phosphate pathways of Plasmodium falciparum. Heuristic function was introduced for the optimal performance of the algorithm. To validate the prediction, the essentiality of the reactions in the reconstructed network was evaluated using betweenness centrality measure, which was applied to every reaction within the pathways considered in this study. Thirty-two (32) essential reactions were predicted among which our method validated fourteen (14) enzymes already predicted in the literature. The enzymatic proteins that catalyze these essential reactions were checked for homology with the host genome, and two (2) showed insignificant similarity, making them possible drug targets. In conclusion, the application of the intelligent search technique to the metabolic network of P. falciparum predicts potential biologically relevant alternative pathways using graph theory-based approach.

In Silico Knockout Screening of Plasmodium falciparum Reactions and Prediction of Novel Essential Reactions by Analysing the Metabolic Network

Malaria is an infectious disease that affects close to half a million individuals every year and Plasmodium falciparum is a major cause of malaria. The treatment of this disease could be done effectively if the essential enzymes of this parasite are specifically targeted. Nevertheless, the development of the parasite in resisting existing drugs now makes discovering new drugs a core responsibility. In this study, a novel computational model that makes the prediction of new and validated antimalarial drug target cheaper, easier, and faster has been developed. We have identified new essential reactions as potential targets for drugs in the metabolic network of the parasite. Among the top seven (7) predicted essential reactions, four (4) have been previously identified in earlier studies with biological evidence and one (1) has been with computational evidence. The results from our study were compared with an extensive list of seventy-seven (77) essential reactions with biological evidence from a previous study. We present a list of thirty-one (31) potential candidates for drug targets in Plasmodium falciparum which includes twenty-four (24) new potential candidates for drug targets.