Latest recommendations
Id | Title * | Authors * ▲ | Abstract * | Picture * | Thematic fields * | Recommender | Reviewers | Submission date | |
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26 May 2021
![]() An efficient algorithm for estimating population history from genetic dataAlan R. Rogers https://doi.org/10.1101/2021.01.23.427922An efficient implementation of legofit software to infer demographic histories from population genetic dataRecommended by Matteo FumagalliThe estimation of demographic parameters from population genetic data has been the subject of many scientific studies [1]. Among these efforts, legofit was firstly proposed in 2019 as a tool to infer size changes, subdivision and gene flow events from patterns of nucleotidic variation [2]. The first release of legofit used a stochastic algorithm to fit population parameters to the observed data. As it requires simulations to evaluate the fitting of each model, it is computationally intensive and can only be deployed on high-performance computing clusters. To overcome this issue, Rogers proposes a new implementation of legofit based on a deterministic algorithm that allows the estimation of demographic histories to be computationally faster and more accurate [3]. The new algorithm employs a continuous-time Markov chain that traces the ancestry of each sample into the past. The calculations are now divided into two steps, the first one being solved numerically. To test the hypothesis that the new implementation of legofit produces a more desirable performance, Rogers generated extensive simulations of genomes from African, European, Neanderthal and Denisovan populations with msprime [4]. Additionally, legofit was tested on real genetic data from samples of said populations, following a previously published study [5]. Based on simulations, the new deterministic algorithm is more than 1600 times faster than the previous stochastic model. Notably, the new version of legofit produces smaller residual errors, although the overall accuracy to estimate population parameters is comparable to the one obtained using the stochastic algorithm. When applied to real data, the new implementation of legofit was able to recapitulate previous findings of a complex demographic model with early gene flow from humans to Neanderthal [5]. Notably, the new implementation generates better discrimination between models, therefore leading to a better precision at predicting the population history. Some parameters estimated from real data point towards unrealistic scenarios, suggesting that the initial model could be misspecified. Further research is needed to fully explore the parameter range that can be evaluated by legofit, and to clarify the source of any associated bias. Additionally, the inclusion of data uncertainty in parameter estimation and model selection may be required to apply legofit to low-coverage high-throughput sequencing data [6]. Nevertheless, legofit is an efficient, accessible and user-friendly software to infer demographic parameters from genetic data and can be widely applied to test hypotheses in evolutionary biology. The new implementation of legofit software is freely available at https://github.com/alanrogers/legofit. References [1] Spence JP, Steinrücken M, Terhorst J, Song YS (2018) Inference of population history using coalescent HMMs: review and outlook. Current Opinion in Genetics & Development, 53, 70–76. https://doi.org/10.1016/j.gde.2018.07.002 [2] Rogers AR (2019) Legofit: estimating population history from genetic data. BMC Bioinformatics, 20, 526. https://doi.org/10.1186/s12859-019-3154-1 [3] Rogers AR (2021) An Efficient Algorithm for Estimating Population History from Genetic Data. bioRxiv, 2021.01.23.427922, ver. 5 peer-reviewed and recommended by Peer community in Mathematical and Computational Biology. https://doi.org/10.1101/2021.01.23.427922 [4] Kelleher J, Etheridge AM, McVean G (2016) Efficient Coalescent Simulation and Genealogical Analysis for Large Sample Sizes. PLOS Computational Biology, 12, e1004842. https://doi.org/10.1371/journal.pcbi.1004842 [5] Rogers AR, Harris NS, Achenbach AA (2020) Neanderthal-Denisovan ancestors interbred with a distantly related hominin. Science Advances, 6, eaay5483. https://doi.org/10.1126/sciadv.aay5483 [6] Soraggi S, Wiuf C, Albrechtsen A (2018) Powerful Inference with the D-Statistic on Low-Coverage Whole-Genome Data. G3 Genes|Genomes|Genetics, 8, 551–566. https://doi.org/10.1534/g3.117.300192 | An efficient algorithm for estimating population history from genetic data | Alan R. Rogers | <p style="text-align: justify;">The Legofit statistical package uses genetic data to estimate parameters describing population history. Previous versions used computer simulations to estimate probabilities, an approach that limited both speed and ... | ![]() | Combinatorics, Genetics and population Genetics | Matteo Fumagalli | 2021-01-26 20:04:35 | View | |
22 Jul 2024
![]() Genetic Evidence for Geographic Structure within the Neanderthal PopulationAlan R. Rogers https://doi.org/10.1101/2023.07.28.551046Decline in Neanderthal effective population size due to geographic structure and gene flowRecommended by Raquel Assis based on reviews by David Bryant and Guillaume AchazPublished PSMC estimates of Neanderthal effective population size (𝑁e) show an approximately five-fold decline over the past 20,000 years [1]. This observation may be attributed to a true decline in Neanderthal 𝑁e, statistical error that is notorious with PSMC estimation, or geographic subdivision and gene flow that has been hypothesized to occur within the Neanderthal population. Determining which of these factors contributes to the observed decline in Neanderthal 𝑁e is an important question that can provide insight into human evolutionary history. Though it is widely believed that the decline in Neanderthal 𝑁e is due to geographic subdivision and gene flow, no prior studies have theoretically examined whether these evolutionary processes can yield the observed pattern. In this paper [2], Rogers tackles this problem by employing two mathematical models to explore the roles of geographic subdivision and gene flow in the Neanderthal population. Results from both models show that geographic subdivision and gene flow can indeed result in a decline in 𝑁e that mirrors the observed decline estimated from empirical data. In contrast, Rogers argues that neither statistical error in PSMC estimates nor a true decline in 𝑁e are expected to produce the consistent decline in estimated 𝑁e observed across three distinct Neanderthal fossils. Statistical error would likely result in variation among these curves, whereas a true decline in 𝑁e would produce shifted curves due to the different ages of the three Neanderthal fossils. In summary, Rogers provides convincing evidence that the most reasonable explanation for the observed decline in Neanderthal 𝑁e is geographic subdivision and gene flow. Rogers also provides a basis for understanding this observation, suggesting that 𝑁e declines over time because coalescence times are shorter between more recent ancestors, as they are more likely to be geographic neighbors. Hence, Rogers’ theoretical findings shed light on an interesting aspect of human evolutionary history. References [1] Fabrizio Mafessoni, Steffi Grote, Cesare de Filippo, Svante Pääbo (2020) “A high-coverage Neandertal genome from Chagyrskaya Cave”. Proceedings of the National Academy of Sciences USA 117: 15132- 15136. https://doi.org/10.1073/pnas.2004944117 [2] Alan Rogers (2024) “Genetic evidence for geographic structure within the Neanderthal population”. bioRxiv, version 4 peer-reviewed and recommended by Peer Community in Mathematical and Computational Biology. https://doi.org/10.1101/2023.07.28.551046 | Genetic Evidence for Geographic Structure within the Neanderthal Population | Alan R. Rogers | <p>PSMC estimates of Neanderthal effective population size (N<sub>e</sub>)exhibit a roughly 5-fold decline across the most recent 20 ky before the death of each fossil. To explain this pattern, this article develops new theory relating... | ![]() | Evolutionary Biology, Genetics and population Genetics | Raquel Assis | 2023-10-17 18:06:38 | View | |
12 May 2025
![]() Mathematical modelling of the contribution of senescent fibroblasts to basement membrane digestion during carcinoma invasionAlmeida Luís, Poulain Alexandre, Pourtier Albin, Villa Chiara https://hal.science/hal-04574340v3Mathematical models: a key approach to understanding tumor-microenvironment interactions - The case of basement membrane digestion in carcinoma.Recommended by Benjamin MauroyThe local environment plays an important role in tumor progression. Not only can it hinder tumor development, but it can also promote it, as demonstrated by numerous studies over the past decades [1-3]. Tumor cells can interact with, modify, and utilize their local environment to enhance their ability to grow and invade. Angiogenesis, vasculogenesis, extracellular matrix components, other healthy cells, and even chronic inflammation are all examples of potential resources that tumors can exploit [4,5]. Several cancer therapies now aim to target the tumor's local environment in order to reduce its ability to take advantage of its surrounding [6,7].
The interactions between a tumor and its local environment involve many complex mechanisms, making the resulting dynamics difficult to capture and comprehend. Therefore, mathematical modeling serves as an efficient tool to analyze, identify, and quantify the roles of these mechanisms.
It has been recognized that healthy yet senescent cells can play a major role in cancer development [8]. The work of Almeida et al. aims to improve our understanding of the role these cells play in early cancer invasion [9]. They focus on carcinoma, an epithelial tumor. During the invasion process, tumor cells must escape their original compartment to reach the surrounding connective tissue. To do so, they must break through the basement membrane enclosing their compartment by digesting it using enzymatic proteins. These proteins are produced in an inactive form by senescent cells and activated by tumor cells. To analyze this process, the authors employ mathematical and numerical modeling, which allows them to fully control the system's complexity by carefully adjusting modeling hypotheses. This approach enables them to easily explore different invasion scenarios and compare their progression rates.
The authors propose an original model that provides a detailed temporal and spatial description of the biochemical reactions involved in basement membrane digestion. The model accounts for protein reactions and exchanges between the connective tissue and basement membrane. Their approach significantly enhances the accuracy of the biochemical description of basement membrane digestion. Additionally, through dimensionality reduction, they manage to represent the basement membrane as an infinitely thin layer while still maintaining an accurate biochemical and biophysical description of the system.
A clever modeling strategy is then employed. The authors first introduce a comprehensive model, which, due to its complexity, has low tractability. By analyzing the relative influence of various parameters, they derive a reduced model, which they validate using relevant data from the literature—a remarkable achievement in itself. Their results show that the reduced model accurately represents the system’s dynamics while being more manageable. However, the reduced model exhibits greater sensitivity to certain parameters, which the authors carefully analyze to establish safeguards for potential users.
The codes developed by the authors to analyze the models are open-source [10].
Almeida et al. explore several biological scenarios, and their results qualitatively align with existing literature. In addition to their impressive, consistent, and tractable modeling framework, Almeida et al.’s work provides a compelling explanation of why and how the presence of senescent cells in the stroma can accelerate basement membrane digestion and, consequently, tumor invasion. Moreover, the authors identify the key parameters—and thus, the essential tumor characteristics—that are central to basement membrane digestion.
This study represents a major step forward in understanding the role of senescent cells in carcinoma invasion and provides a powerful tool with significant potential. More generally, this work demonstrates that mathematical models are highly suited for studying the role of the stroma in cancer progression.
References
[1] J. Wu, Sheng ,Su-rui, Liang ,Xin-hua, et Y. and Tang, « The role of tumor microenvironment in collective tumor cell invasion », Future Oncology, vol. 13, no 11, p. 991‑1002, 2017, https://doi.org/10.2217/fon-2016-0501
[2] F. Entschladen, D. Palm, Theodore L. Drell IV, K. Lang, et K. S. Zaenker, « Connecting A Tumor to the Environment », Current Pharmaceutical Design, vol. 13, no 33, p. 3440‑3444, 2007, https://doi.org/10.2174/138161207782360573 [3] H. Li, X. Fan, et J. Houghton, « Tumor microenvironment: The role of the tumor stroma in cancer », Journal of Cellular Biochemistry, vol. 101, no 4, p. 805‑815, 2007, https://doi.org/10.1002/jcb.21159 [4] J. M. Brown, « Vasculogenesis: a crucial player in the resistance of solid tumours to radiotherapy », Br J Radiol, vol. 87, no 1035, p. 20130686, 2014, https://doi.org/10.1259/bjr.20130686 [5] P. Allavena, A. Sica, G. Solinas, C. Porta, et A. Mantovani, « The inflammatory micro-environment in tumor progression: The role of tumor-associated macrophages », Critical Reviews in Oncology/Hematology, vol. 66, no 1, p. 1‑9, 2008, https://doi.org/10.1016/j.critrevonc.2007.07.004 [6] L. Xu et al., « Reshaping the systemic tumor immune environment (STIE) and tumor immune microenvironment (TIME) to enhance immunotherapy efficacy in solid tumors », J Hematol Oncol, vol. 15, no 1, p. 87, 2022, https://doi.org/10.1186/s13045-022-01307-2 [7] N. E. Sounni et A. Noel, « Targeting the Tumor Microenvironment for Cancer Therapy », Clinical Chemistry, vol. 59, no 1, p. 85‑93, 2013, https://doi.org/10.1373/clinchem.2012.185363 [8] D. Hanahan, « Hallmarks of Cancer: New Dimensions », Cancer Discovery, vol. 12, no 1, p. 31‑46, 2022, https://doi.org/10.1158/2159-8290.CD-21-1059 [9] L. Almeida, A. Poulain, A. Pourtier, et C. Villa, « Mathematical modelling of the contribution of senescent fibroblasts to basement membrane digestion during carcinoma invasion », HAL, ver.3 peer-reviewed and recommended by PCI Mathematical and Computational Biology, 2025. https://hal.science/hal-04574340v3 [10] A. Poulain, alexandrepoulain/TumInvasion-BM: BM rupture code, 2024. Zenodo. https://doi.org/10.5281/zenodo.12654067 / https://github.com/alexandrepoulain/TumInvasion-BM | Mathematical modelling of the contribution of senescent fibroblasts to basement membrane digestion during carcinoma invasion | Almeida Luís, Poulain Alexandre, Pourtier Albin, Villa Chiara | <p>Senescent cells have been recognized to play major roles in tumor progression and are nowadays included in the hallmarks of cancer.Our work aims to develop a mathematical model capable of capturing a pro-invasion effect of senescent fibroblasts... | ![]() | Cell Biology | Benjamin Mauroy | 2024-07-09 14:50:00 | 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 | |
23 Jul 2024
![]() Alignment-free detection and seed-based identification of multi-loci V(D)J recombinations in Vidjil-algoCyprien Borée, Mathieu Giraud, Mikaël Salson https://hal.science/hal-04361907An accelerated Vidjil algorithm: up to 30X faster identification of V(D)J recombinations via spaced seeds and Aho-Corasick pattern matchingRecommended by Giulio Ermanno PibiriVDJ recombination is a crucial process in the immune system, where a V (variable) gene, a D (diversity) gene, and a J (joining) gene are randomly combined to create unique antigen receptor genes. This process generates a vast diversity of antibodies and T-cell receptors, essential for recognizing and combating a wide array of pathogens. By identifying and quantifying these VDJ recombinations, we can gain a deeper and more precise understanding of the immune response, enhancing our ability to monitor and manage immune-related conditions. It is therefore important to develop efficient methods to identify and extract VDJ recombinations from large sequences (e.g., several millions/billions of nucleotides). The work by Borée, Giraud, and Salson [2] contributes one such algorithm. As in previous work, the proposed algorithm employs the Aho-Corasick automaton to simultaneously match several patterns against a string but, differently from other methods, it also combines the efficiency of spaced seeds. Working with seeds rather than the original string has the net benefit of speeding up the algorithm and reducing its memory usage, sometimes at the price of a modest loss in accuracy. Experiments conducted on five different datasets demonstrate that these features grant the proposed method excellent practical performance compared to the best previous methods, like Vidjil [3] (up to 5X faster) and MiXCR [1] (up to 30X faster), with no quality loss. The method can also be considered an excellent example of a more general trend in scalable algorithmic design: adapt "classic" algorithms (in this case, the Aho-Corasick pattern matching algorithm) to work in sketch space (e.g., the spaced seeds used here), trading accuracy for efficiency. Sometimes, this compromise is necessary for the sake of scaling to very large datasets using modest computing power. References [1] D. A. Bolotin, S. Poslavsky, I. Mitrophanov, M. Shugay, I. Z. Mamedov, E. V. Putintseva, and D. M. Chudakov (2015). "MiXCR: software for comprehensive adaptive immunity profiling." Nature Methods 12, 380–381. ISSN: 1548-7091. https://doi.org/10.1038/nmeth.3364 [2] C. Borée, M. Giraud, M. Salson (2024) "Alignment-free detection and seed-based identification of multi-loci V(D)J recombinations in Vidjil-algo". https://hal.science/hal-04361907v2, version 2, peer-reviewed and recommended by Peer Community In Mathematical and Computational Biology. [3] M. Giraud, M. Salson, M. Duez, C. Villenet, S. Quief, A. Caillault, N. Grardel, C. Roumier, C. Preudhomme, and M. Figeac (2014). "Fast multiclonal clusterization of V(D)J recombinations from high-throughput sequencing". BMC Genomics 15, 409. https://doi.org/10.1186/1471-2164-15-409. | Alignment-free detection and seed-based identification of multi-loci V(D)J recombinations in Vidjil-algo | Cyprien Borée, Mathieu Giraud, Mikaël Salson | <p>The diversity of the immune repertoire is grounded on V(D)J recombinations in several loci. Many algorithms and software detect and designate these recombinations in high-throughput sequencing data. To improve their efficiency, we propose a mul... | ![]() | Combinatorics, Computational complexity, Design and analysis of algorithms, Genomics and Transcriptomics, Immunology | Giulio Ermanno Pibiri | 2023-12-28 18:03:42 | 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 ChaouiyaBoolean 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 | |
07 Dec 2021
![]() The emergence of a birth-dependent mutation rate in asexuals: causes and consequencesFlorian Patout, Raphaël Forien, Matthieu Alfaro, Julien Papaïx, Lionel Roques https://doi.org/10.1101/2021.06.11.448026A new perspective in modeling mutation rate for phenotypically structured populationsRecommended by Yuan Lou based on reviews by Hirohisa Kishino and 1 anonymous reviewerIn standard mutation-selection models for describing the dynamics of phenotypically structured populations, it is often assumed that the mutation rate is constant across the phenotypes. In particular, this assumption leads to a constant diffusion coefficient for diffusion approximation models (Perthame, 2007 and references therein). Patout et al (2021) study the dependence of the mutation rate on the birth rate, by introducing some diffusion approximations at the population level, derived from the large population limit of a stochastic, individual-based model. The reaction-diffusion model in this article is of the “cross-diffusion” type: The form of “cross-diffusion” also appeared in ecological literature as a type of biased movement behaviors for organisms (Shigesada et al., 1979). The key underlying assumption for “cross-diffusion” is that the transition probability at the individual level depends solely upon the condition at the departure point. Patout et al (2021) envision that a higher birth rate yields more mutations per unit of time. One of their motivations is that during cancer development, the mutation rates of cancer cells at the population level could be correlated with reproduction success. The reaction-diffusion approximation model derived in this article illustrates several interesting phenomena: For the time evolution situation, their model predicts different solution trajectories under various assumptions on the fitness function, e.g. the trajectory could initially move towards the birth optimum but eventually end up at the survival optimum. Their model also predicts that the mean fitness could be flat for some period of time, which might provide another alternative to explain observed data. At the steady-state level, their model suggests that the populations are more concentrated around the survival optimum, which agrees with the evolution of the time-dependent solution trajectories. Perhaps one of the most interesting contributions of the study of Patout et al (2021) is to give us a new perspective to model the mutation rate in phenotypically structured populations and subsequently, and to help us better understand the connection between mutation and selection. More broadly, this article offers some new insights into the evolutionary dynamics of phenotypically structured populations, along with potential implications in empirical studies. References Perthame B (2007) Transport Equations in Biology Frontiers in Mathematics. Birkhäuser, Basel. https://doi.org/10.1007/978-3-7643-7842-4_2 Patout F, Forien R, Alfaro M, Papaïx J, Roques L (2021) The emergence of a birth-dependent mutation rate in asexuals: causes and consequences. bioRxiv, 2021.06.11.448026, ver. 3 peer-reviewed and recommended by Peer Community in Mathematical and Computational Biology. https://doi.org/10.1101/2021.06.11.448026 Shigesada N, Kawasaki K, Teramoto E (1979) Spatial segregation of interacting species. Journal of Theoretical Biology, 79, 83–99. https://doi.org/10.1016/0022-5193(79)90258-3 | The emergence of a birth-dependent mutation rate in asexuals: causes and consequences | Florian Patout, Raphaël Forien, Matthieu Alfaro, Julien Papaïx, Lionel Roques | <p style="text-align: justify;">In unicellular organisms such as bacteria and in most viruses, mutations mainly occur during reproduction. Thus, genotypes with a high birth rate should have a higher mutation rate. However, standard models of asexu... | ![]() | Dynamical systems, Evolutionary Biology, Probability and statistics, Stochastic dynamics | Yuan Lou | Anonymous, Hirohisa Kishino | 2021-06-12 13:59:45 | 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 ChaouiyaSignificant 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 |
07 Sep 2021
![]() The origin of the allometric scaling of lung ventilation in mammalsFrédérique Noël, Cyril Karamaoun, Jerome A. Dempsey, Benjamin Mauroy https://doi.org/10.48550/arXiv.2005.12362How mammals adapt their breath to body activity – and how this depends on body sizeRecommended by Wolfram LiebermeisterHow fast and how deep do animals breathe, and how does this depend on how active they are? To answer this question, one needs to dig deeply into how breathing works and what biophysical processes it involves. And one needs to think about body size. It is impressive how nature adapts the same body plan – e.g. the skeletal structure of mammals – to various shapes and sizes. From mice to whales, also the functioning of most organs remains the same; they are just differently scaled. Scaling does not just mean “making bigger or smaller”. As already noted by Galilei, body shapes change as they are adapted to body dimensions, and the same holds for physiological variables. Many such variables, for instance, heartbeat rates, follow scaling laws of the form y~x^a, where x denotes body mass and the exponent a is typically a multiple of ¼ [1]. These unusual exponents – instead of multiples of ⅓, which would be expected from simple geometrical scaling – are why these laws are called “allometric”. Kleiber’s law for metabolic rates, with a scaling exponent of ¾, is a classic example [2]. As shown by G. West, allometric laws can be explained through a few simple steps [1]. In his models, he focused on network-like organs such as the vascular system and assumed that these systems show a self-similar structure, with a fixed minimal unit (for instance, capillaries) but varying numbers of hierarchy levels depending on body size. To determine the flow through such networks, he employed biophysical models and optimality principles (for instance, assuming that oxygen must be transported at a minimal mechanical effort), and showed that the solutions – and the physiological variables – respect the known scaling relations. The paper “The origin of the allometric scaling of lung ventilation in mammals“ by Noël et al. [3], applies this thinking to the depth and rate of breathing in mammals. Scaling laws describing breathing in resting animals have been known since the 1950s [4], with exponents of 1 (for tidal volume) and -¼ (for breathing frequency). Equipped with a detailed biophysical model, Noël et al. revisit this question, extending these laws to other metabolic regimes. Their starting point is a model of the human lung, developed previously by two of the authors [5], which assumes that we meet our oxygen demand with minimal lung movements. To state this as an optimization problem, the model combines two submodels: a mechanical model describing the energetic effort of ventilation and a highly detailed model of convection and diffusion in self-similar lung geometries. Breathing depths and rates are computed by numerical optimization, and to obtain results for mammals of any size many of the model parameters are described by known scaling laws. As expected, the depth of breathing (measured by tidal volume) scales almost proportionally with body mass and increases with metabolic demand, while the breathing rate decreases with body mass, with an exponent of about -¼. However, the laws for the breathing rate hold only for basal activity; at higher metabolic rates, which are modeled here for the first time, the exponent deviates strongly from this value, in line with empirical data. Why is this paper important? The authors present a highly complex model of lung physiology that integrates a wide range of biophysical details and passes a difficult test: the successful prediction of unexplained scaling exponents. These scaling relations may help us transfer insights from animal models to humans and in reverse: data for breathing during exercise, which are easy to measure in humans, can be extrapolated to other species. Aside from the scaling laws, the model also reveals physiological mechanisms. In the larger lung branches, oxygen is transported mainly by air movement (convection), while in smaller branches air flow is slow and oxygen moves by diffusion. The transition between these regimes can occur at different depths in the lung: as the authors state, “the localization of this transition determines how ventilation should be controlled to minimize its energetic cost at any metabolic regime”. In the model, the optimal location for the transition depends on oxygen demand [5, 6]: the transition occurs deeper in the lung in exercise regimes than at rest, allowing for more oxygen to be taken up. However, the effects of this shift depend on body size: while small mammals generally use the entire exchange surface of their lungs, large mammals keep a reserve for higher activities, which becomes accessible as their transition zone moves at high metabolic rates. Hence, scaling can entail qualitative differences between species! Altogether, the paper shows how the dynamics of ventilation depend on lung morphology. But this may also play out in the other direction: if energy-efficient ventilation depends on body activity, and therefore on ecological niches, a niche may put evolutionary pressures on lung geometry. Hence, by understanding how deep and fast animals breathe, we may also learn about how behavior, physiology, and anatomy co-evolve. References [1] West GB, Brown JH, Enquist BJ (1997) A General Model for the Origin of Allometric Scaling Laws in Biology. Science 276 (5309), 122–126. https://doi.org/10.1126/science.276.5309.122 [2] Kleiber M (1947) Body size and metabolic rate. Physiological Reviews, 27, 511–541. https://doi.org/10.1152/physrev.1947.27.4.511 [3] Noël F., Karamaoun C., Dempsey J. A. and Mauroy B. (2021) The origin of the allometric scaling of lung's ventilation in mammals. arXiv, 2005.12362, ver. 6 peer-reviewed and recommended by Peer community in Mathematical and Computational Biology. https://arxiv.org/abs/2005.12362 [4] Otis AB, Fenn WO, Rahn H (1950) Mechanics of Breathing in Man. Journal of Applied Physiology, 2, 592–607. https://doi.org/10.1152/jappl.1950.2.11.592 [5] Noël F, Mauroy B (2019) Interplay Between Optimal Ventilation and Gas Transport in a Model of the Human Lung. Frontiers in Physiology, 10, 488. https://doi.org/10.3389/fphys.2019.00488 [6] Sapoval B, Filoche M, Weibel ER (2002) Smaller is better—but not too small: A physical scale for the design of the mammalian pulmonary acinus. Proceedings of the National Academy of Sciences, 99, 10411–10416. https://doi.org/10.1073/pnas.122352499 | The origin of the allometric scaling of lung ventilation in mammals | Frédérique Noël, Cyril Karamaoun, Jerome A. Dempsey, Benjamin Mauroy | <p>A model of optimal control of ventilation has recently been developed for humans. This model highlights the importance of the localization of the transition between a convective and a diffusive transport of respiratory gas. This localization de... | ![]() | Biophysics, Evolutionary Biology, Physiology | Wolfram Liebermeister | 2020-08-28 15:18:03 | View | |
21 Oct 2024
![]() Benchmarking the identification of a single degraded protein to explore optimal search strategies for ancient proteinsIsmael Rodriguez-Palomo, Bharath Nair, Yun Chiang, Joannes Dekker, Benjamin Dartigues, Meaghan Mackie, Miranda Evans, Ruairidh Macleod, Jesper V. Olsen, Matthew J. Collins https://doi.org/10.1101/2023.12.15.571577Systematic investigation of software tools and design of a tailored pipeline for paleoproteomics researchRecommended by Raquel Assis based on reviews by Shevan Wilkin and 1 anonymous reviewerPaleoproteomics is a rapidly growing field with numerous challenges, many of which are due to the highly fragmented, modified, and degraded nature of ancient proteins. Though there are established standards for analysis, it is unclear how different software tools affect the identification and quantification of peptides, proteins, and post-translational modifications. To address this knowledge gap, Rodriguez Palomo et al. design a controlled system by experimentally degrading and purifying bovine beta-lactoglobulin, and then systematically compare the performance of many commonly used tools in its analysis. They present comprehensive investigations of false discovery rates, open and narrow searches, de novo sequencing coverage bias and accuracy, and peptide chemical properties and bias. In each investigation, they explore wide ranges of appropriate tools and parameters, providing guidelines and recommendations for best practices. Based on their findings, Rodriguez Palomo et al. develop a proposed pipeline that is tailored for the analysis of ancient proteins. This pipeline is an important contribution to paleoproteomics and is likely to be of great value to the research community, as it is designed to enhance power, accuracy, and consistency in studies of ancient proteins. References Ismael Rodriguez-Palomo, Bharath Nair, Yun Chiang, Joannes Dekker, Benjamin Dartigues, Meaghan Mackie, Miranda Evans, Ruairidh Macleod, Jesper V. Olsen, Matthew J. Collins (2023) Benchmarking the identification of a single degraded protein to explore optimal search strategies for ancient proteins. bioRxiv, ver.3 peer-reviewed and recommended by PCI Math Comp Biol https://doi.org/10.1101/2023.12.15.571577 | Benchmarking the identification of a single degraded protein to explore optimal search strategies for ancient proteins | Ismael Rodriguez-Palomo, Bharath Nair, Yun Chiang, Joannes Dekker, Benjamin Dartigues, Meaghan Mackie, Miranda Evans, Ruairidh Macleod, Jesper V. Olsen, Matthew J. Collins | <p style="text-align: justify;">Palaeoproteomics is a rapidly evolving discipline, and practitioners are constantly developing novel strategies for the analyses and interpretations of complex, degraded protein mixtures. The community has also esta... | ![]() | Genomics and Transcriptomics, Probability and statistics | Raquel Assis | Anonymous, Shevan Wilkin | 2024-03-12 15:17:08 | View |
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