Latest recommendations
| Id | Title * | Authors * | Abstract * | Picture * ▲ | Thematic fields * | Recommender | Reviewers | Submission date | |
|---|---|---|---|---|---|---|---|---|---|
27 Sep 2024
In silico identification of switching nodes in metabolic networksA computational method to identify key players in metabolic rewiringRecommended by Claudine Chaouiya based on reviews by 2 anonymous reviewers
Significant progress has been made in developing computational methods to tackle the analysis of the numerous (genome-wide scale) metabolic networks that have been documented for a wide range of species. Understanding the behaviours of these complex reaction networks is crucial in various domains such as biotechnology and medicine. Metabolic rewiring is essential as it enables cells to adapt their metabolism to changing environmental conditions. Identifying the metabolites around which metabolic rewiring occurs is certainly useful in the case of metabolic engineering, which relies on metabolic rewiring to transform micro-organisms into cellular factories [1], as well as in other contexts. This paper by F. Mairet [2] introduces a method to disclose these metabolites, named switch nodes, relying on the analysis of the flux distributions for different input conditions. Basically, considering fluxes for different inputs, which can be computed using e.g. Parsimonious Flux Balance Analysis (pFBA), the proposed method consists in identifying metabolites involved in reactions whose different flux vectors are not collinear. The approach is supported by four case studies, considering core and genome-scale metabolic networks of Escherichia coli, Saccharomyces cerevisiae and the diatom Phaeodactylum tricornutum. Whilst identified switch nodes may be biased because computed flux vectors satisfying given objectives are not necessarily unique, the proposed method has still a relevant predictive potential, complementing the current array of computational methods to study metabolism. References [1] Tao Yu, Yasaman Dabirian, Quanli Liu, Verena Siewers, Jens Nielsen (2019) Strategies and challenges for metabolic rewiring. Current Opinion in Systems Biology, Vol 15, pp 30-38. https://doi.org/10.1016/j.coisb.2019.03.004. [2] Francis Mairet (2024) In silico identification of switching nodes in metabolic networks. bioRxiv, ver.3 peer-reviewed and recommended by PCI Math Comp Biol https://doi.org/10.1101/2023.05.17.541195 | In silico identification of switching nodes in metabolic networks | Francis Mairet | <p>Cells modulate their metabolism according to environmental conditions. A major challenge to better understand metabolic regulation is to identify, from the hundreds or thousands of molecules, the key metabolites where the re-orientation of flux... | | Graph theory, Physiology, Systems biology | Claudine Chaouiya | Anonymous | 2023-05-26 17:24:26 | |
14 Mar 2023
Marker and source-marker reprogramming of Most Permissive Boolean networks and ensembles with BoNesisReprogramming of locally-monotone Boolean networks with BoNesisRecommended by Sergiu Ivanov based on reviews by Ismail Belgacem and 1 anonymous reviewerReprogramming of cellular networks is a well known challenge in computational biology consisting first of all in properly representing an ensemble of networks having a role in a phenomenon of interest, and secondly in designing strategies to alter the functioning of this ensemble in the desired direction. Important applications involve disease study: a therapy can be seen as a reprogramming strategy, and the disease itself can be considered a result of a series of adversarial reprogramming actions. The origins of this domain go back to the seminal paper by Barabási et al. [1] which formalized the concept of network medicine. An abstract tool which has gathered considerable success in network medicine and network biology are Boolean networks: sets of Boolean variables, each equipped with a Boolean update function describing how to compute the next value of the variable from the values of the other variables. Despite apparent dissimilarity with the biological systems which involve varying quantities and continuous processes, Boolean networks have been very effective in representing biological networks whose entities are typically seen as being on or off. Particular examples are protein signalling networks as well as gene regulatory networks. The paper [2] by Loïc Paulevé presents a versatile tool for tackling reprogramming of Boolean networks seen as models of biological networks. The problem of reprogramming is often formulated as the problem of finding a set of perturbations which guarantee some properties on the attractors. The work [2] relies on the most permissive semantics [3], which together with the modelling assumption allows for considerable speed-up in the practically relevant subclass of locally-monotone Boolean networks. The paper is structured as a tutorial. It starts by introducing the formalism, defining 4 different general variants of reprogramming under the most permissive semantics, and presenting evaluations of their complexity in terms of the polynomial hierarchy. The author then describes the software tool BoNesis which can handle different problems related to Boolean networks, and in particular the 4 reprogramming variants. The presentation includes concrete code examples with their output, which should be very helpful for future users. The paper [2] introduces a novel scenario: reprogramming of ensembles of Boolean networks delineated by some properties, including for example the property of having a given interaction graph. Ensemble reprogramming looks particularly promising in situations in which the biological knowledge is insufficient to fully determine all the update functions, i.e. in the majority of modelling situations. Finally, the author also shows how BoNesis can be used to deal with sequential reprogramming, which is another promising direction in computational controllability, potentially enabling more efficient therapies [4,5]. REFERENCES
| Marker and source-marker reprogramming of Most Permissive Boolean networks and ensembles with BoNesis | Loïc Paulevé | <p style="text-align: justify;">Boolean networks (BNs) are discrete dynamical systems with applications to the modeling of cellular behaviors. In this paper, we demonstrate how the software BoNesis can be employed to exhaustively identify combinat... | | Combinatorics, Computational complexity, Dynamical systems, Molecular Biology, Systems biology | Sergiu Ivanov | Ismail Belgacem, Anonymous | 2022-08-31 15:00:21 | |
02 Oct 2024
HairSplitter: haplotype assembly from long, noisy readsAccurate Haplotype Reconstruction from Long, Error-Prone, Reads with HairSplitterRecommended by Giulio Ermanno Pibiri based on reviews by Dmitry Antipov and 1 anonymous reviewer
A prominent challenge in computational biology is to distinguish microbial haplotypes -- closely related organisms with highly similar genomes -- due to small genomic differences that can cause significant phenotypic variations. Current genome assembly tools struggle with distinguishing these haplotypes, especially for long-read sequencing data with high error rates, such as PacBio or Oxford Nanopore Technology (ONT) reads. While existing methods work well for either viral or bacterial haplotypes, they often fail with low-abundance haplotypes and are computationally intensive. This work by Faure, Lavenier, and Flot [1] introduces a new tool -- HairSplitter -- that offers a solution for both viral and bacterial haplotype separation, even with error-prone long reads. It does this by efficiently calling variants, clustering reads into haplotypes, creating new separated contigs, and resolving the assembly graph. A key advantage of HairSplitter is that it is entirely parameter-free and does not require prior knowledge of the organism's ploidy. HairSplitter is designed to handle both metaviromes and bacterial metagenomes, offering a more versatile and efficient solution than existing tools, like stRainy [2], Strainberry [3], and hifiasm-meta [4]. References [1] Roland Faure, Dominique Lavenier, Jean-François Flot (2024) HairSplitter: haplotype assembly from long, noisy reads. bioRxiv, ver.3 peer-reviewed and recommended by PCI Math Comp Biol https://doi.org/10.1101/2024.02.13.580067 [2] Kazantseva E, A Donmez, M Pop, and M Kolmogorov (2023). stRainy: assembly-based metagenomic strain phasing using long reads. Bioinformatics. https://doi.org/10.1101/2023.01.31.526521 [3] Vicedomini R, C Quince, AE Darling, and R Chikhi (2021). Strainberry: automated strain separation in low complexity metagenomes using long reads. Nature Communications, 12, 4485. ISSN: 2041-1723. https://doi.org/10.1038/s41467-021-24515-9 [4] Feng X, H Cheng, D Portik, and H Li (2022). Metagenome assembly of high-fidelity long reads with hifiasm-meta. Nature Methods, 19, 1–4. https://doi.org/10.1038/s41592-022-01478-3 | HairSplitter: haplotype assembly from long, noisy reads | Roland Faure, Dominique Lavenier, Jean-François Flot | <p>Long-read assemblers face challenges in discerning closely related viral or<br>bacterial strains, often collapsing similar strains in a single sequence. This limitation has<br>been hampering metagenome analysis, where diverse strains may harbor... | | Design and analysis of algorithms, Development, Genomics and Transcriptomics, Probability and statistics | Giulio Ermanno Pibiri | 2024-02-15 10:17:04 |
