Integrative Computational Framework for Understanding Metabolic Modulation in Leishmania

Background: The integration of computational and mathematical approaches is used to provide a key insight into the biological systems. Through systems biology approaches we seek to find detailed and more robust information on Leishmanial metabolic network. Forman/Forman-Ricci curvature measures were...

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Main Authors: Nutan Chauhan, Shailza Singh
Format: Article
Language:English
Published: Frontiers Media S.A. 2019-11-01
Series:Frontiers in Bioengineering and Biotechnology
Subjects:
Online Access:https://www.frontiersin.org/article/10.3389/fbioe.2019.00336/full
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spelling doaj-7cd0e469f1554d98bf7b20e1bb8ca1022020-11-24T21:24:07ZengFrontiers Media S.A.Frontiers in Bioengineering and Biotechnology2296-41852019-11-01710.3389/fbioe.2019.00336489301Integrative Computational Framework for Understanding Metabolic Modulation in LeishmaniaNutan ChauhanShailza SinghBackground: The integration of computational and mathematical approaches is used to provide a key insight into the biological systems. Through systems biology approaches we seek to find detailed and more robust information on Leishmanial metabolic network. Forman/Forman-Ricci curvature measures were applied to identify important nodes in the network(s). This was followed by flux balance analysis (FBA) to decipher important drug targets.Results: Our results revealed several key high curvature nodes (metabolites) belonging to common yet crucial metabolic networks, thus, maintaining the integrity of the network which signifies its robustness. Further analysis revealed the presence of some of these metabolites, MGO, in redox metabolism of the parasite. Being a component in the glyoxalase pathway and highly cytotoxic, we further attempted to study the outcome of the deletion of the key enzyme (GLOI) mainly involved in the neutralization of MGO by utilizing FBA. The model and the objective function kept as simple as possible demonstrated an interesting emergent behavior. The non-functional GLOI in the model contributed to “zero” flux which signifies the key role of GLOI as a rate limiting enzyme. This has led to several fold increase production of MGO, thereby, causing an increased level of MGO•− generation.Conclusions: The integrated computational approaches have deciphered GLOI as a potential target both from curvature measures as well as FBA which could further be explored for kinetic modeling by implying various redox-dependent constraints on the model. Furthermore, a constraint-based FBA on a larger model could further be explored to get broader picture to understand the exact underlying mechanisms. Designing various in vitro experimental perspectives could churn the therapeutic importance of GLOI.https://www.frontiersin.org/article/10.3389/fbioe.2019.00336/fullmetabolic networkForman curvatureForman-Ricci curvaturenetwork topologyLeishmaniaflux balance analysis
collection DOAJ
language English
format Article
sources DOAJ
author Nutan Chauhan
Shailza Singh
spellingShingle Nutan Chauhan
Shailza Singh
Integrative Computational Framework for Understanding Metabolic Modulation in Leishmania
Frontiers in Bioengineering and Biotechnology
metabolic network
Forman curvature
Forman-Ricci curvature
network topology
Leishmania
flux balance analysis
author_facet Nutan Chauhan
Shailza Singh
author_sort Nutan Chauhan
title Integrative Computational Framework for Understanding Metabolic Modulation in Leishmania
title_short Integrative Computational Framework for Understanding Metabolic Modulation in Leishmania
title_full Integrative Computational Framework for Understanding Metabolic Modulation in Leishmania
title_fullStr Integrative Computational Framework for Understanding Metabolic Modulation in Leishmania
title_full_unstemmed Integrative Computational Framework for Understanding Metabolic Modulation in Leishmania
title_sort integrative computational framework for understanding metabolic modulation in leishmania
publisher Frontiers Media S.A.
series Frontiers in Bioengineering and Biotechnology
issn 2296-4185
publishDate 2019-11-01
description Background: The integration of computational and mathematical approaches is used to provide a key insight into the biological systems. Through systems biology approaches we seek to find detailed and more robust information on Leishmanial metabolic network. Forman/Forman-Ricci curvature measures were applied to identify important nodes in the network(s). This was followed by flux balance analysis (FBA) to decipher important drug targets.Results: Our results revealed several key high curvature nodes (metabolites) belonging to common yet crucial metabolic networks, thus, maintaining the integrity of the network which signifies its robustness. Further analysis revealed the presence of some of these metabolites, MGO, in redox metabolism of the parasite. Being a component in the glyoxalase pathway and highly cytotoxic, we further attempted to study the outcome of the deletion of the key enzyme (GLOI) mainly involved in the neutralization of MGO by utilizing FBA. The model and the objective function kept as simple as possible demonstrated an interesting emergent behavior. The non-functional GLOI in the model contributed to “zero” flux which signifies the key role of GLOI as a rate limiting enzyme. This has led to several fold increase production of MGO, thereby, causing an increased level of MGO•− generation.Conclusions: The integrated computational approaches have deciphered GLOI as a potential target both from curvature measures as well as FBA which could further be explored for kinetic modeling by implying various redox-dependent constraints on the model. Furthermore, a constraint-based FBA on a larger model could further be explored to get broader picture to understand the exact underlying mechanisms. Designing various in vitro experimental perspectives could churn the therapeutic importance of GLOI.
topic metabolic network
Forman curvature
Forman-Ricci curvature
network topology
Leishmania
flux balance analysis
url https://www.frontiersin.org/article/10.3389/fbioe.2019.00336/full
work_keys_str_mv AT nutanchauhan integrativecomputationalframeworkforunderstandingmetabolicmodulationinleishmania
AT shailzasingh integrativecomputationalframeworkforunderstandingmetabolicmodulationinleishmania
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