Latest recommendations
Id | Title * | Authors * | Abstract * | Picture * | Thematic fields * | Recommender▲ | Reviewers | Submission date | |
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19 Sep 2022
HMMploidy: inference of ploidy levels from short-read sequencing dataSamuele Soraggi, Johanna Rhodes, Isin Altinkaya, Oliver Tarrant, Francois Balloux, Matthew C Fisher, Matteo Fumagalli https://doi.org/10.1101/2021.06.29.450340Detecting variation in ploidy within and between genomesRecommended by Alan Rogers based on reviews by Barbara Holland, Benjamin Peter and Nicolas GaltierSoraggi et al. [2] describe HMMploidy, a statistical method that takes DNA sequencing data as input and uses a hidden Markov model to estimate ploidy. The method allows ploidy to vary not only between individuals, but also between and even within chromosomes. This allows the method to detect aneuploidy and also chromosomal regions in which multiple paralogous loci have been mistakenly assembled on top of one another. HMMploidy estimates genotypes and ploidy simultaneously, with a separate estimate for each genome. The genome is divided into a series of non-overlapping windows (typically 100), and HMMploidy provides a separate estimate of ploidy within each window of each genome. The method is thus estimating a large number of parameters, and one might assume that this would reduce its accuracy. However, it benefits from large samples of genomes. Large samples increase the accuracy of internal allele frequency estimates, and this improves the accuracy of genotype and ploidy estimates. In large samples of low-coverage genomes, HMMploidy outperforms all other estimators. It does not require a reference genome of known ploidy. The power of the method increases with coverage and sample size but decreases with ploidy. Consequently, high coverage or large samples may be needed if ploidy is high. The method is slower than some alternative methods, but run time is not excessive. Run time increases with number of windows but isn't otherwise affected by genome size. It should be feasible even with large genomes, provided that the number of windows is not too large. The authors apply their method and several alternatives to isolates of a pathogenic yeast, Cryptococcus neoformans, obtained from HIV-infected patients. With these data, HMMploidy replicated previous findings of polyploidy and aneuploidy. There were several surprises. For example, HMMploidy estimates the same ploidy in two isolates taken on different days from a single patient, even though sequencing coverage was three times as high on the later day as on the earlier one. These findings were replicated in data that were down-sampled to mimic low coverage. Three alternative methods (ploidyNGS [1], nQuire, and nQuire.Den [3]) estimated the highest ploidy considered in all samples from each patient. The present authors suggest that these results are artifactual and reflect the wide variation in allele frequencies. Because of this variation, these methods seem to have preferred the model with the largest number of parameters. HMMploidy represents a new and potentially useful tool for studying variation in ploidy. It will be of most use in studying the genetics of asexual organisms and cancers, where aneuploidy imposes little or no penalty on reproduction. It should also be useful for detecting assembly errors in de novo genome sequences from non-model organisms. References [1] Augusto Corrêa dos Santos R, Goldman GH, Riaño-Pachón DM (2017) ploidyNGS: visually exploring ploidy with Next Generation Sequencing data. Bioinformatics, 33, 2575–2576. https://doi.org/10.1093/bioinformatics/btx204 [2] Soraggi S, Rhodes J, Altinkaya I, Tarrant O, Balloux F, Fisher MC, Fumagalli M (2022) HMMploidy: inference of ploidy levels from short-read sequencing data. bioRxiv, 2021.06.29.450340, ver. 6 peer-reviewed and recommended by Peer Community in Mathematical and Computational Biology. https://doi.org/10.1101/2021.06.29.450340 [3] Weiß CL, Pais M, Cano LM, Kamoun S, Burbano HA (2018) nQuire: a statistical framework for ploidy estimation using next generation sequencing. BMC Bioinformatics, 19, 122. https://doi.org/10.1186/s12859-018-2128-z | HMMploidy: inference of ploidy levels from short-read sequencing data | Samuele Soraggi, Johanna Rhodes, Isin Altinkaya, Oliver Tarrant, Francois Balloux, Matthew C Fisher, Matteo Fumagalli | <p>The inference of ploidy levels from genomic data is important to understand molecular mechanisms underpinning genome evolution. However, current methods based on allele frequency and sequencing depth variation do not have power to infer ploidy ... | Design and analysis of algorithms, Evolutionary Biology, Genetics and population Genetics, Probability and statistics | Alan Rogers | 2021-07-01 05:26:31 | View | ||
02 May 2023
Population genetics: coalescence rate and demographic parameters inferenceOlivier Mazet, Camille Noûs https://doi.org/10.48550/arXiv.2207.02111Estimates of Effective Population Size in Subdivided PopulationsRecommended by Alan Rogers based on reviews by 2 anonymous reviewersWe often use genetic data from a single site, or even a single individual, to estimate the history of effective population size, Ne, over time scales in excess of a million years. Mazet and Noûs [2] emphasize that such estimates may not mean what they seem to mean. The ups and downs of Ne may reflect changes in gene flow or selection, rather than changes in census population size. In fact, gene flow may cause Ne to decline even if the rate of gene flow has remained constant. Consider for example the estimates of archaic population size in Fig. 1, which show an apparent decline in population size between roughly 700 kya and 300 kya. It is tempting to interpret this as evidence of a declining number of individuals, but that is not the only plausible interpretation. Each of these estimates is based on the genome of a single diploid individual. As we trace the ancestry of that individual backwards into the past, the ancestors are likely to remain in the same locale for at least a generation or two. Being neighbors, there’s a chance they will mate. This implies that in the recent past, the ancestors of a sampled individual lived in a population of small effective size. As we continue backwards into the past, there is more and more time for the ancestors to move around on the landscape. The farther back we go, the less likely they are to be neighbors, and the less likely they are to mate. In this more remote past, the ancestors of our sample lived in a population of larger effective size, even if neither the number of individuals nor the rate of gene flow has changed. For awhile then, Ne should increase as we move backwards into the past. This process does not continue forever, because eventually the ancestors will be randomly distributed across the population as a whole. We therefore expect Ne to increase towards an asymptote, which represents the effective size of the entire population. This simple story gets more complex if there is change in either the census size or the rate of gene flow. Mazet and Noûs [2] have shown that one can mimic real estimates of population history using models in which the rate of gene flow varies, but census size does not. This implies that the curves in Fig. 1 are ambiguous. The observed changes in Ne could reflect changes in census size, gene flow, or both. For this reason, Mazet and Noûs [2] would like to replace the term “effective population size” with an alternative, the “inverse instantaneous coalescent rate,” or IIRC. I don’t share this preference, because the same critique could be made of all definitions of Ne. For example, Wright [3, p. 108] showed in 1931 that Ne varies in response to the sex ratio, and this implies that changes in Ne need not involve any change in census size. This is also true when populations are geographically structured, as Mazet and Noûs [2] have emphasized, but this does not seem to require a new vocabulary. Figure 1: PSMC estimates of the history of population size based on three archaic genomes: two Neanderthals and a Denisovan [1]. Mazet and Noûs [2] also show that estimates of Ne can vary in response to selection. It is not hard to see why such an effect might exist. In genomic regions affected by directional or purifying selection, heterozygosity is low, and common ancestors tend to be recent. Such regions may contribute to small estimates of recent Ne. In regions under balancing selection, heterozygosity is high, and common ancestors tend to be ancient. Such regions may contribute to large estimates of ancient Ne. The magnitude of this effect presumably depends on the fraction of the genome under selection and the rate of recombination. In summary, this article describes several processes that can affect estimates of the history of effective population size. This makes existing estimates ambiguous. For example, should we interpret Fig. 1 as evidence of a declining number of archaic individuals, or in terms of gene flow among archaic subpopulations? But these questions also present research opportunities. If the observed decline reflects gene flow, what does this imply about the geographic structure of archaic populations? Can we resolve the ambiguity by integrating samples from different locales, or using archaeological estimates of population density or interregional trade? REFERENCES [1] Fabrizio Mafessoni et al. “A high-coverage Neandertal genome from Chagyrskaya Cave”. Proceedings of the National Academy of Sciences, USA 117.26 (2020), pp. 15132–15136. https://doi.org/10.1073/pnas.2004944117. [2] Olivier Mazet and Camille Noûs. “Population genetics: coalescence rate and demographic parameters inference”. arXiv, ver. 2 peer-reviewed and recommended by Peer Community In Mathematical and Computational Biology (2023). https://doi.org/10.48550/ARXIV.2207.02111. [3] Sewall Wright. “Evolution in mendelian populations”. Genetics 16 (1931), pp. 97–159. https://doi.org/10.48550/ARXIV.2207.0211110.1093/genetics/16.2.97. | Population genetics: coalescence rate and demographic parameters inference | Olivier Mazet, Camille Noûs | <p style="text-align: justify;">We propose in this article a brief description of the work, over almost a decade, resulting from a collaboration between mathematicians and biologists from four different research laboratories, identifiable as the c... | Genetics and population Genetics, Probability and statistics | Alan Rogers | Joseph Lachance, Anonymous | 2022-07-11 14:03:04 | View | |
10 Jan 2024
An approximate likelihood method reveals ancient gene flow between human, chimpanzee and gorillaNicolas Galtier https://doi.org/10.1101/2023.07.06.547897Aphid: A Novel Statistical Method for Dissecting Gene Flow and Lineage Sorting in Phylogenetic ConflictRecommended by Alan Rogers based on reviews by Richard Durbin and 2 anonymous reviewersGaltier [1] introduces “Aphid,” a new statistical method that estimates the contributions of gene flow (GF) and incomplete lineage sorting (ILS) to phylogenetic conflict. Aphid is based on the observation that GF tends to make gene genealogies shorter, whereas ILS makes them longer. Rather than fitting the full likelihood, it models the distribution of gene genealogies as a mixture of several canonical gene genealogies in which coalescence times are set equal to their expectations under different models. This simplification makes Aphid far faster than competing methods. In addition, it deals gracefully with bidirectional gene flow—an impossibility under competing models. Because of these advantages, Aphid represents an important addition to the toolkit of evolutionary genetics. In the interest of speed, Aphid makes several simplifying assumptions. Yet even when these were violated, Aphid did well at estimating parameters from simulated data. It seems to be reasonably robust. Aphid studies phylogenetic conflict, which occurs when some loci imply one phylogenetic tree and other loci imply another. This happens when the interval between successive speciation events is fairly short. If this interval is too short, however, Aphid’s approximations break down, and its estimates are biased. Galtier suggests caution when the fraction of discordant phylogenetic trees exceeds 50–55%. Thus, Aphids will be most useful when the interval between speciation events is short, but not too short. Galtier applies the new method to three sets of primate data. In two of these data sets (baboons and African apes), Aphid detects gene flow that would likely be missed by competing methods. These competing methods are primarily sensitive to gene flow that is asymmetric in two senses: (1) greater flow in one direction than the other, and (2) unequal gene flow connecting an outgroup to two sister species. Aphid finds evidence of symmetric gene flow in the ancestry of baboons and also in that of African apes. The data suggest that ancestral humans and chimpanzees both interbred with ancestral gorillas, and at about the same rate. Aphid’s ability to detect this signature sets it apart from competing methods. References [1] Nicolas Galtier (2023) “An approximate likelihood method reveals ancient gene flow between human, chimpanzee and gorilla”. bioRxiv, ver. 3 peer-reviewed and recommended by Peer Community in Mathematical and Computational Biology. https://doi.org/10.1101/2023.07.06.547897 | An approximate likelihood method reveals ancient gene flow between human, chimpanzee and gorilla | Nicolas Galtier | <p>Gene flow and incomplete lineage sorting are two distinct sources of phylogenetic conflict, i.e., gene trees that differ in topology from each other and from the species tree. Distinguishing between the two processes is a key objective of curre... | Evolutionary Biology, Genetics and population Genetics, Genomics and Transcriptomics | Alan Rogers | 2023-07-06 18:41:16 | View | ||
18 Apr 2023
Cancer phylogenetic tree inference at scale from 1000s of single cell genomesSohrab Salehi, Fatemeh Dorri, Kevin Chern, Farhia Kabeer, Nicole Rusk, Tyler Funnell, Marc J Williams, Daniel Lai, Mirela Andronescu, Kieran R. Campbell, Andrew McPherson, Samuel Aparicio, Andrew Roth, Sohrab Shah, and Alexandre Bouchard-Côté https://doi.org/10.1101/2020.05.06.058180Phylogenetic reconstruction from copy number aberration in large scale, low-depth genome-wide single-cell data.Recommended by Amaury Lambert based on reviews by 3 anonymous reviewersThe paper [1] presents and applies a new Bayesian inference method of phylogenetic reconstruction for multiple sequence alignments in the case of low sequencing coverage but diverse copy number aberrations (CNA), with applications to single cell sequencing of tumors. The idea is to take advantage of CNA to reconstruct the topology of the phylogenetic tree of sequenced cells in a first step (the `sitka' method), and in a second step to assign single nucleotide variants (SNV) to tree edges (and then calibrate their lengths) (the `sitka-snv' method). The data are summarized into a binary-valued CxL matrix Y, where C is the number of cells and L is the number of loci (here, loci are segments of prescribed length called `bins'). The entry of Y at row i and column j is 1 (otherwise 0) iff in the ancestral lineage of cell i, at least one genomic rearrangement has occurred, and more specifically the gain or loss of a segment with at least one endpoint in locus j or in locus j+1. The authors expect the infinite-allele assumption to approximately hold (i.e., that at most one mutation occurs at any given marker and that 0 is the ancestral state). They refer to this assumption as the `perfect phylogeny assumption'. By only recording from CNA events the endpoints at which they occur, the authors lose the information on copy number, but they gain the assumption of independence of the mutational processes occurring at different sites, which approximately holds for CNA endpoints. The goal of sitka is to produce a posterior distribution on phylogenetic trees conditional on the matrix Y , where here a phylogenetic tree is understood as containing the information on 1) the topology of the tree but not its edge lengths, and 2) for each edge, the identity of markers having undergone a mutation, in the sense of the previous paragraph. The results of the method are tested against synthetic datasets simulated under various assumptions, including conditions violating the perfect phylogeny assumption and compared to results obtained under other baseline methods. The method is extended to assign SNV to edges of the tree inferred by sitka. It is also applied to real datasets of single cell genomes of tumors. The manuscript is very well-written, with a high degree of detail. The method is novel, scalable, fast and appears to perform favorably compared to other approaches. It has been applied in independent publications, for example to multi-year time-series single-cell whole-genome sequencing of tumors, in order to infer the fitness landscape and its dynamics through time, see [2]. The reviewing process has taken too long, mainly because of other commitments I had during the period and to the difficulty of finding reviewers. Let me apologize to the authors and thank them for their patience as well as for the scientific rigor they brought to their revisions and answers to reviewers, who I also warmly thank for their quality work. REFERENCES [1] Sohrab Salehi, Fatemeh Dorri, Kevin Chern, Farhia Kabeer, Nicole Rusk, Tyler Funnell, Marc J Williams, Daniel Lai, Mirela Andronescu, Kieran R. Campbell, Andrew McPherson, Samuel Aparicio, Andrew Roth, Sohrab Shah, and Alexandre Bouchard-Côté. Cancer phylogenetic tree inference at scale from 1000s of single cell genomes (2023). bioRxiv, 2020.05.06.058180, ver. 4 peer-reviewed and recommended by Peer Community in Mathematical and Computational Biology. [2] Sohrab Salehi, Farhia Kabeer, Nicholas Ceglia, Mirela Andronescu, Marc J. Williams, Kieran R. Campbell, Tehmina Masud, Beixi Wang, Justina Biele, Jazmine Brimhall, David Gee, Hakwoo Lee, Jerome Ting, Allen W. Zhang, Hoa Tran, Ciara O’Flanagan, Fatemeh Dorri, Nicole Rusk, Teresa Ruiz de Algara, So Ra Lee, Brian Yu Chieh Cheng, Peter Eirew, Takako Kono, Jenifer Pham, Diljot Grewal, Daniel Lai, Richard Moore, Andrew J. Mungall, Marco A. Marra, IMAXT Consortium, Andrew McPherson, Alexandre Bouchard-Côté, Samuel Aparicio & Sohrab P. Shah. Clonal fitness inferred from time-series modelling of single-cell cancer genomes (2021). Nature 595, 585–590. https://doi.org/10.1038/s41586-021-03648-3 | Cancer phylogenetic tree inference at scale from 1000s of single cell genomes | Sohrab Salehi, Fatemeh Dorri, Kevin Chern, Farhia Kabeer, Nicole Rusk, Tyler Funnell, Marc J Williams, Daniel Lai, Mirela Andronescu, Kieran R. Campbell, Andrew McPherson, Samuel Aparicio, Andrew Roth, Sohrab Shah, and Alexandre Bouchard-Côté | <p style="text-align: justify;">A new generation of scalable single cell whole genome sequencing (scWGS) methods allows unprecedented high resolution measurement of the evolutionary dynamics of cancer cell populations. Phylogenetic reconstruction ... | Evolutionary Biology, Genetics and population Genetics, Genomics and Transcriptomics, Machine learning, Probability and statistics | Amaury Lambert | 2021-12-10 17:08:04 | View | ||
21 Feb 2022
Consistency of orthology and paralogy constraints in the presence of gene transfersMark Jones, Manuel Lafond, Celine Scornavacca https://doi.org/10.48550/arXiv.1705.01240Allowing gene transfers doesn't make life easier for inferring orthology and paralogyRecommended by Barbara Holland based on reviews by 2 anonymous reviewersDetermining if genes are orthologous (i.e. homologous genes whose most common ancestor represents a speciation) or paralogous (homologous genes whose most common ancestor represents a duplication) is a foundational problem in bioinformatics. For instance, the input to almost all phylogenetic methods is a sequence alignment of genes assumed to be orthologous. Understanding if genes are paralogs or orthologs can also be important for assigning function, for example genes that have diverged following duplication may be more likely to have neofunctionalised or subfunctionalised compared to genes that have diverged following speciation, which may be more likely to have continued in a similar role. This paper by Jones et al (2022) contributes to a wide range of literature addressing the inference of orthology/paralogy relations but takes a different approach to explaining inconsistency between an assumed species phylogeny and a relation graph (a graph where nodes represent genes and edges represent that the two genes are orthologs). Rather than assuming that inconsistencies are the result of incorrect assessment of orthology (i.e. incorrect edges in the relation graph) they ask if the relation graph could be consistent with a species tree combined with some amount of lateral (horizontal) gene transfer. The two main questions addressed in this paper are (1) if a network N and a relation graph R are consistent, and (2) if – given a species tree S and a relation graph R – transfer arcs can be added to S in such a way that it becomes consistent with R? The first question hinges on the concept of a reconciliation between a gene tree and a network (section 2.1) and amounts to asking if a gene tree can be found that can both be reconciled with the network and consistent with the relation graph. The authors show that the problem is NP hard. Furthermore, the related problem of attempting to find a solution using k or fewer transfers is NP-hard, and also W[1] hard implying that it is in a class of problems for which fixed parameter tractable solutions have not been found. The proof of NP hardness is by reduction to the k-multi-coloured clique problem via an intermediate problem dubbed “antichain on trees” (Section 3). The “antichain on trees” construction may be of interest to others working on algorithmic complexity with phylogenetic networks. In the second question the possible locations of transfers are not specified (or to put it differently any time consistent transfer arc is considered possible) and it is shown that it generally will be possible to add transfer edges to S in such a way that it can be consistent with R. However, the natural extension to this question of asking if it can be done with k or fewer added arcs is also NP hard. Many of the proofs in the paper are quite technical, but the authors have relegated a lot of this detail to the appendix thus ensuring that the main ideas and results are clear to follow in the main text. I am grateful to both reviewers for their detailed reviews and through checking of the proofs. References Jones M, Lafond M, Scornavacca C (2022) Consistency of orthology and paralogy constraints in the presence of gene transfers. arXiv:1705.01240 [cs], ver. 6 peer-reviewed and recommended by Peer Community in Mathematical and Computational Biology. https://arxiv.org/abs/1705.01240 | Consistency of orthology and paralogy constraints in the presence of gene transfers | Mark Jones, Manuel Lafond, Celine Scornavacca | <p style="text-align: justify;">Orthology and paralogy relations are often inferred by methods based on gene sequence similarity that yield a graph depicting the relationships between gene pairs. Such relation graphs frequently contain errors, as ... | Computational complexity, Design and analysis of algorithms, Evolutionary Biology, Graph theory | Barbara Holland | 2021-06-30 15:01:44 | View | ||
24 Dec 2020
A linear time solution to the Labeled Robinson-Foulds Distance problemSamuel Briand, Christophe Dessimoz, Nadia El-Mabrouk and Yannis Nevers https://doi.org/10.1101/2020.09.14.293522Comparing reconciled gene trees in linear timeRecommended by Céline Scornavacca based on reviews by Barbara Holland, Gabriel Cardona, Jean-Baka Domelevo Entfellner and 1 anonymous reviewerUnlike a species tree, a gene tree results not only from speciation events, but also from events acting at the gene level, such as duplications and losses of gene copies, and gene transfer events [1]. The reconciliation of phylogenetic trees consists in embedding a given gene tree into a known species tree and, doing so, determining the location of these gene-level events on the gene tree [2]. Reconciled gene trees can be seen as phylogenetic trees where internal node labels are used to discriminate between different gene-level events. Comparing them is of foremost importance in order to assess the performance of various reconciliation methods (e.g. [3]). References [1] Maddison, W. P. (1997). Gene trees in species trees. Systematic biology, 46(3), 523-536. doi: https://doi.org/10.1093/sysbio/46.3.523 | A linear time solution to the Labeled Robinson-Foulds Distance problem | Samuel Briand, Christophe Dessimoz, Nadia El-Mabrouk and Yannis Nevers | <p>Motivation Comparing trees is a basic task for many purposes, and especially in phylogeny where different tree reconstruction tools may lead to different trees, likely representing contradictory evolutionary information. While a large variety o... | Combinatorics, Design and analysis of algorithms, Evolutionary Biology | Céline Scornavacca | 2020-08-20 21:06:23 | View | ||
04 Feb 2022
Non-Markovian modelling highlights the importance of age structure on Covid-19 epidemiological dynamicsBastien Reyné, Quentin Richard, Camille Noûs, Christian Selinger, Mircea T. Sofonea, Ramsès Djidjou-Demasse, Samuel Alizon https://doi.org/10.1101/2021.09.30.21264339Importance of age structure on modeling COVID-19 epidemiological dynamicsRecommended by Chen Liao based on reviews by Facundo Muñoz, Kevin Bonham and 1 anonymous reviewerCOVID-19 spread around the globe in early 2020 and has deeply changed our everyday life [1]. Mathematical models allow us to estimate R0 (basic reproduction number), understand the progression of viral infection, explore the impacts of quarantine on the epidemic, and most importantly, predict the future outbreak [2]. The most classical model is SIR, which describes time evolution of three variables, i.e., number of susceptible people (S), number of people infected (I), and number of people who have recovered (R), based on their transition rates [3]. Despite the simplicity, SIR model produces several general predictions that have important implications for public health [3]. SIR model includes three populations with distinct labels and is thus compartmentalized. Extra compartments can be added to describe additional states of populations, for example, people exposed to the virus but not yet infectious. However, a model with more compartments, though more realistic, is also more difficult to parameterize and analyze. The study by Reyné et al. [4] proposed an alternative formalism based on PDE (partial differential equation), which allows modeling different biological scenarios without the need of adding additional compartments. As illustrated, the authors modeled hospital admission dynamics in a vaccinated population only with 8 general compartments. The main conclusion of this study is that the vaccination level till 2021 summer was insufficient to prevent a new epidemic in France. Additionally, the authors used alternative data sources to estimate the age-structured contact patterns. By sensitivity analysis on a daily basis, they found that the 9 parameters in the age-structured contact matrix are most variable and thus shape Covid19 pandemic dynamics. This result highlights the importance of incorporating age structure of the host population in modeling infectious diseases. However, a relevant potential limitation is that the contact matrix was assumed to be constant throughout the simulations. To account for time dependence of the contact matrix, social and behavioral factors need to be integrated [5]. References [1] Hu B, Guo H, Zhou P, Shi Z-L (2021) Characteristics of SARS-CoV-2 and COVID-19. Nature Reviews Microbiology, 19, 141–154. https://doi.org/10.1038/s41579-020-00459-7 [2] Jinxing G, Yongyue W, Yang Z, Feng C (2020) Modeling the transmission dynamics of COVID-19 epidemic: a systematic review. The Journal of Biomedical Research, 34, 422–430. https://doi.org/10.7555/JBR.34.20200119 [3] Tolles J, Luong T (2020) Modeling Epidemics With Compartmental Models. JAMA, 323, 2515–2516. https://doi.org/10.1001/jama.2020.8420 [4] Reyné B, Richard Q, Noûs C, Selinger C, Sofonea MT, Djidjou-Demasse R, Alizon S (2022) Non-Markovian modelling highlights the importance of age structure on Covid-19 epidemiological dynamics. medRxiv, 2021.09.30.21264339, ver. 3 peer-reviewed and recommended by Peer Community in Mathematical and Computational Biology. https://doi.org/10.1101/2021.09.30.21264339 [5] Bedson J, Skrip LA, Pedi D, Abramowitz S, Carter S, Jalloh MF, Funk S, Gobat N, Giles-Vernick T, Chowell G, de Almeida JR, Elessawi R, Scarpino SV, Hammond RA, Briand S, Epstein JM, Hébert-Dufresne L, Althouse BM (2021) A review and agenda for integrated disease models including social and behavioural factors. Nature Human Behaviour, 5, 834–846 https://doi.org/10.1038/s41562-021-01136-2 | Non-Markovian modelling highlights the importance of age structure on Covid-19 epidemiological dynamics | Bastien Reyné, Quentin Richard, Camille Noûs, Christian Selinger, Mircea T. Sofonea, Ramsès Djidjou-Demasse, Samuel Alizon | <p style="text-align: justify;">The Covid-19 pandemic outbreak was followed by a huge amount of modelling studies in order to rapidly gain insights to implement the best public health policies. Most of these compartmental models involved ordinary ... | Dynamical systems, Epidemiology, Systems biology | Chen Liao | 2021-10-04 13:49:51 | View | ||
27 Sep 2024
In silico identification of switching nodes in metabolic networksFrancis Mairet https://doi.org/10.1101/2023.05.17.541195A computational method to identify key players in metabolic rewiringRecommended by Claudine Chaouiya based on reviews by 2 anonymous reviewersSignificant 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 | View | |
13 Aug 2024
Phenotype control and elimination of variables in Boolean networksElisa Tonello, Loïc Paulevé https://doi.org/10.48550/arXiv.2406.02304Disclosing effects of Boolean network reduction on dynamical properties and control strategiesRecommended by Claudine Chaouiya based on reviews by Tomas Gedeon and David SafranekBoolean networks stem from seminal work by M. Sugita [1], S. Kauffman [2] and R. Thomas [3] over half a century ago. Since then, a very active field of research has been developed, leading to theoretical advances accompanied by a wealth of work on modelling genetic and signalling networks involved in a wide range of cellular processes. Boolean networks provide a successful formalism for the mathematical modelling of biological processes, with a qualitative abstraction particularly well adapted to handle the modelling of processes for which precise, quantitative data is barely available. Nevertheless, these abstract models reveal fundamental dynamical properties, such as the existence and reachability of attractors, which embody stable cellular responses (e.g. differentiated states). Analysing these properties still faces serious computational complexity. Reduction of model size was proposed as a mean to cope with this issue. Furthermore, to enhance the capacity of Boolean networks to produce relevant predictions, formal methods have been developed to systematically identify control strategies enforcing desired behaviours. In their paper, E. Tonello and L. Paulevé [4] assess the most popular reduction that consists in eliminating a model component. Considering three typical update schemes (synchronous, asynchronous and general asynchronous updates), they thoroughly study the effects of the reduction on attractors, minimal trap spaces (minimal subspaces from which the model dynamics cannot leave), and on phenotype controls (interventions which guarantee that the dynamics ends in a phenotype defined by specific component values). Because they embody potential behaviours of the biological process under study, these are all properties of great interest for a modeller. The authors show that eliminating a component can significantly affect some dynamical properties and may turn a control strategy ineffective. The different update schemes, targets of phenotype control and control strategies are carefully handled with useful supporting examples. Overall, E. Tonello and L. Paulevé’s contribution underlines the need for caution when defining a regulatory network and characterises the consequences on critical model properties when discarding a component [4]. References [1] Motoyosi Sugita (1963) Functional analysis of chemical systems in vivo using a logical circuit equivalent. II. The idea of a molecular automation. Journal of Theoretical Biology, 4, 179–92. https://doi.org/10.1016/0022-5193(63)90027-4 [2] Stuart Kauffman (1969) Metabolic stability and epigenesis in randomly constructed genetic nets. Journal of Theoretical Biology, 22, 437–67. https://doi.org/10.1016/0022-5193(69)90015-0 [3] René Thomas (1973) Boolean formalization of genetic control circuits. Journal of Theoretical Biology, 42, 563–85. https://doi.org/10.1016/0022-5193(73)90247-6 [4] Elisa Tonello, Loïc Paulevé (2024) Phenotype control and elimination of variables in Boolean networks. arXiv, ver.2 peer-reviewed and recommended by PCI Math Comp Biol https://arxiv.org/abs/2406.02304 | Phenotype control and elimination of variables in Boolean networks | Elisa Tonello, Loïc Paulevé | <p>We investigate how elimination of variables can affect the asymptotic dynamics and phenotype control of Boolean networks. In particular, we look at the impact on minimal trap spaces, and identify a structural condition that guarantees their pre... | Dynamical systems, Systems biology | Claudine Chaouiya | 2024-06-05 10:12:39 | View | ||
26 Feb 2024
A workflow for processing global datasets: application to intercroppingRémi Mahmoud, Pierre Casadebaig, Nadine Hilgert, Noémie Gaudio https://hal.science/hal-04145269Collecting, assembling and sharing data in crop sciencesRecommended by Eric Tannier based on reviews by Christine Dillmann and 2 anonymous reviewersIt is often the case that scientific knowledge exists but is scattered across numerous experimental studies. Because of this dispersion in different formats, it remains difficult to access, extract, reproduce, confirm or generalise. This is the case in crop science, where Mahmoud et al [1] propose to collect and assemble data from numerous field experiments on intercropping. It happens that the construction of the global dataset requires a lot of time, attention and a well thought-out method, inspired by the literature on data science [2] and adapted to the specificities of crop science. This activity also leads to new possibilities that were not available in individual datasets, such as the detection of full factorial designs using graph theory tools developed on top of the global dataset. The study by Mahmoud et al [1] has thus multiple dimensions:
I was particularly interested in the promotion of the FAIR principles, perhaps used a little too uncritically in my view, as an obvious solution to data sharing. On the one hand, I am admiring and grateful for the availability of these data, some of which have never been published, nor associated with published results. This approach is likely to unearth buried treasures. On the other hand, I can understand the reluctance of some data producers to commit to total, definitive sharing, facilitating automatic reading, without having thought about a certain reciprocity on the part of users and use by artificial intelligence. Reciprocity in terms of recognition, as is discussed by Mahmoud et al [1], but also in terms of contribution to the commons [5] or reading conditions for machine learning. References [1] Mahmoud R., Casadebaig P., Hilgert N., Gaudio N. A workflow for processing global datasets: application to intercropping. 2024. ⟨hal-04145269v2⟩ ver. 2 peer-reviewed and recommended by Peer Community in Mathematical and Computational Biology. https://hal.science/hal-04145269 [2] Wickham, H. 2014. Tidy data. Journal of Statistical Software 59(10) https://doi.org/10.18637/jss.v059.i10 [3] Gaudio, N., R. Mahmoud, L. Bedoussac, E. Justes, E.-P. Journet, et al. 2023. A global dataset gathering 37 field experiments involving cereal-legume intercrops and their corresponding sole crops. https://doi.org/10.5281/zenodo.8081577 [4] Mahmoud, R., Casadebaig, P., Hilgert, N. et al. Species choice and N fertilization influence yield gains through complementarity and selection effects in cereal-legume intercrops. Agron. Sustain. Dev. 42, 12 (2022). https://doi.org/10.1007/s13593-022-00754-y [5] Bernault, C. « Licences réciproques » et droit d'auteur : l'économie collaborative au service des biens communs ?. Mélanges en l'honneur de François Collart Dutilleul, Dalloz, pp.91-102, 2017, 978-2-247-17057-9. https://shs.hal.science/halshs-01562241 | A workflow for processing global datasets: application to intercropping | Rémi Mahmoud, Pierre Casadebaig, Nadine Hilgert, Noémie Gaudio | <p>Field experiments are a key source of data and knowledge in agricultural research. An emerging practice is to compile the measurements and results of these experiments (rather than the results of publications, as in meta-analysis) into global d... | Agricultural Science | Eric Tannier | 2023-06-29 15:38:28 | View |
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