Evidence map›Paper›PMID 39289347›Full record

ArticleNPJ systems biology and applications2024

Understanding flux switching in metabolic networks through an analysis of synthetic lethals.

Sowmya Manojna Narasimha, Tanisha Malpani, Omkar S Mohite, J Saketha Nath, Karthik Raman

Abstract read
In one paragraph

Article in NPJ systems biology and applications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Balancing Cell Growth and Product Synthesis for Efficient Microbial Cell Factories.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
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  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Sowmya Manojna Narasimha *Centre for Integrative Biology and Systems mEdicine (IBSE), Indian Institute of Technology (IIT) Madras, Chennai, 600 036, India.ORCID http://orcid.org/0000-0002-1312-2985
Tanisha Malpani *Centre for Integrative Biology and Systems mEdicine (IBSE), Indian Institute of Technology (IIT) Madras, Chennai, 600 036, India.
Omkar S MohiteCentre for Integrative Biology and Systems mEdicine (IBSE), Indian Institute of Technology (IIT) Madras, Chennai, 600 036, India.
J Saketha NathDepartment of Computer Science and Engineering, Indian Institute of Technology (IIT) Hyderabad, Hyderabad, 502 284, India.
Karthik RamanCentre for Integrative Biology and Systems mEdicine (IBSE), Indian Institute of Technology (IIT) Madras, Chennai, 600 036, India. kraman@iitm.ac.in.ORCID http://orcid.org/0000-0002-9311-7093

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Biological systems are robust and redundant. The redundancy can manifest as alternative metabolic pathways. Synthetic double lethals are pairs of reactions that, when deleted simultaneously, abrogate cell growth. However, removing one reaction allows the rerouting of metabolites through alternative pathways. Little is known about these hidden linkages between pathways. Understanding them in the context of pathogens is useful for therapeutic innovations. We propose a constraint-based optimisation approach to identify inter-dependencies between metabolic pathways. It minimises rerouting between two reaction deletions, corresponding to a synthetic lethal pair, and outputs the set of reactions vital for metabolic rewiring, known as the synthetic lethal cluster. We depict the results for different pathogens and show that the reactions span across metabolic modules, illustrating the complexity of metabolism. Finally, we demonstrate how the two classes of synthetic lethals play a role in metabolic networks and influence the different properties of a synthetic lethal cluster.

Indexed as

Metabolic Networks and PathwaysComputational BiologyModels, BiologicalSynthetic Lethal MutationsSystems Biology

Identifiers

PMID39289347
PMCPMC11408705

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.