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

Modeling and analyzing the action process of monoamine hormones in depression: a Petri nets-based intelligent approach

In contemporary society, the incidence of depression is increasing significantly around the world. At present, most of the treatment methods for depression are psychological counseling and drug therapy. However, this approach does not allow patients to visualize the logic of hormones at the pathological level. In order to better apply intelligence computing methods to the medical field, and to more easily analyze the relationship between norepinephrine and dopamine in depression, it is necessary to build an interpretable graphical model to analyze this relationship which is of great significance to help discover new treatment ideas and potential drug targets. Petri net (PN) is a mathematical and graphic tool used to simulate and study complex system processes. This article utilizes PN to study the relationship between norepinephrine and dopamine in depression. We use PN to model the relationship between the norepinephrine and dopamine, and then use the invariant method of PN to verify and analyze it. The mathematical model proposed in this article can explain the complex pathogenesis of depression and visualize the process of intracellular hormone-induced state changes. Finally, the experiment result suggests that our method provides some possible research directions and approaches for the development of antidepressant drugs.

Target Identification in Anti-Tuberculosis Drug Discovery

Mycobacterium tuberculosis (Mtb) is the etiological agent of tuberculosis (TB), a disease that, although preventable and curable, remains a global epidemic due to the emergence of resistance and a latent form responsible for a long period of treatment. Drug discovery in TB is a challenging task due to the heterogeneity of the disease, the emergence of resistance, and uncomplete knowledge of the pathophysiology of the disease. The limited permeability of the cell wall and the presence of multiple efflux pumps remain a major barrier to achieve effective intracellular drug accumulation. While the complete genome sequence of Mtb has been determined and several potential protein targets have been validated, the lack of adequate models for in vitro and in vivo studies is a limiting factor in TB drug discovery programs. In current therapeutic regimens, less than 0.5% of bacterial proteins are targeted during the biosynthesis of the cell wall and the energetic metabolism of two of the most important processes exploited for TB chemotherapeutics. This review provides an overview on the current challenges in TB drug discovery and emerging Mtb druggable proteins, and explains how chemical probes for protein profiling enabled the identification of new targets and biomarkers, paving the way to disruptive therapeutic regimens and diagnostic tools.

Mathematical modeling of regulatory networks of intracellular processes - Aims and selected methods

Regulatory networks structure and signaling pathways dynamics are uncovered in time- and resource consuming experimental work. However, it is increasingly supported by modeling, analytical and computational techniques as well as discrete mathematics and artificial intelligence applied to to extract knowledge from existing databases. This review is focused on mathematical modeling used to analyze dynamics and robustness of these networks. This paper presents a review of selected modeling methods that facilitate advances in molecular biology.

Quantitative analysis of the bioenergetics of Mycobacterium tuberculosis along with Glyoxylate cycle as a drug target under inhibition of enzymes using Petri net

The bacteria Mycobacterium tuberculosis is responsible for the infectious disease Tuberculosis. Targeting the tubercule bacteria is an important challenge in developing the antimycobacterials. The glyoxylate cycle is considered as a potential target for the development of anti-tuberculosis agents, due to its absence in the humans. Humans only possess tricarboxylic acid cycle, while this cycle gets connected to glyoxylate cycle in microbes. Glyoxylate cycle is essential to the Mycobacterium for its growth and survival. Due to this reason, it is considered as a potential therapeutic target for the development of anti-tuberculosis agents. Here, we explore the effect on the behavior of the tricarboxylic acid cycle, glyoxylate cycle and their integrated pathway with the bioenergetics of the Mycobacterium, under the inhibition of key glyoxylate cycle enzymes using Continuous Petri net. Continuous Petri net is a special Petri net used to perform the quantitative analysis of the networks. We first study the tricarboxylic acid cycle and glyoxylate cycle of the tubercule bacteria by simulating its Continuous Petri net model under different scenarios. Both the cycles are then integrated with the bioenergetics of the bacteria and the integrated pathway is again simulated under different conditions. The simulation graphs show the metabolic consequences of inhibiting the key glyoxylate cycle enzymes and adding the uncouplers on the individual as well as integrated pathway. The uncouplers that inhibit the synthesis of adenosine triphosphate, play an important role as anti-mycobacterials. The simulation study done here validates the proposed Continuous Petri net model as compared with the experimental outcomes and also explains the consequences of the enzyme inhibition on the biochemical reactions involved in the metabolic pathways of the mycobacterium.