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Chee FT, Harun S, Mohd Daud K, Sulaiman S, Nor Muhammad NA. Exploring gene regulation and biological processes in insects: Insights from omics data using gene regulatory network models. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2024; 189:1-12. [PMID: 38604435 DOI: 10.1016/j.pbiomolbio.2024.04.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 12/18/2023] [Accepted: 04/03/2024] [Indexed: 04/13/2024]
Abstract
Gene regulatory network (GRN) comprises complicated yet intertwined gene-regulator relationships. Understanding the GRN dynamics will unravel the complexity behind the observed gene expressions. Insect gene regulation is often complicated due to their complex life cycles and diverse ecological adaptations. The main interest of this review is to have an update on the current mathematical modelling methods of GRNs to explain insect science. Several popular GRN architecture models are discussed, together with examples of applications in insect science. In the last part of this review, each model is compared from different aspects, including network scalability, computation complexity, robustness to noise and biological relevancy.
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Affiliation(s)
- Fong Ting Chee
- Institute of Systems Biology, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia
| | - Sarahani Harun
- Institute of Systems Biology, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia
| | - Kauthar Mohd Daud
- Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600, UKM Bangi, Selangor, Malaysia
| | - Suhaila Sulaiman
- FGV R&D Sdn Bhd, FGV Innovation Center, PT23417 Lengkuk Teknologi, Bandar Baru Enstek, 71760 Nilai, Negeri Sembilan, Malaysia
| | - Nor Azlan Nor Muhammad
- Institute of Systems Biology, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia.
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Noordally ZB, Hindle MM, Martin SF, Seaton DD, Simpson TI, Le Bihan T, Millar AJ. A phospho-dawn of protein modification anticipates light onset in the picoeukaryote Ostreococcus tauri. JOURNAL OF EXPERIMENTAL BOTANY 2023; 74:5514-5531. [PMID: 37481465 PMCID: PMC10540734 DOI: 10.1093/jxb/erad290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 07/20/2023] [Indexed: 07/24/2023]
Abstract
Diel regulation of protein levels and protein modification had been less studied than transcript rhythms. Here, we compare transcriptome data under light-dark cycles with partial proteome and phosphoproteome data, assayed using shotgun MS, from the alga Ostreococcus tauri, the smallest free-living eukaryote. A total of 10% of quantified proteins but two-thirds of phosphoproteins were rhythmic. Mathematical modelling showed that light-stimulated protein synthesis can account for the observed clustering of protein peaks in the daytime. Prompted by night-peaking and apparently dark-stable proteins, we also tested cultures under prolonged darkness, where the proteome changed less than under the diel cycle. Among the dark-stable proteins were prasinophyte-specific sequences that were also reported to accumulate when O. tauri formed lipid droplets. In the phosphoproteome, 39% of rhythmic phospho-sites reached peak levels just before dawn. This anticipatory phosphorylation suggests that a clock-regulated phospho-dawn prepares green cells for daytime functions. Acid-directed and proline-directed protein phosphorylation sites were regulated in antiphase, implicating the clock-related casein kinases 1 and 2 in phase-specific regulation, alternating with the CMGC protein kinase family. Understanding the dynamic phosphoprotein network should be facilitated by the minimal kinome and proteome of O. tauri. The data are available from ProteomeXchange, with identifiers PXD001734, PXD001735, and PXD002909.
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Affiliation(s)
- Zeenat B Noordally
- SynthSys and School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3BF, UK
| | - Matthew M Hindle
- SynthSys and School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3BF, UK
| | - Sarah F Martin
- SynthSys and School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3BF, UK
| | - Daniel D Seaton
- SynthSys and School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3BF, UK
| | - T Ian Simpson
- Institute for Adaptive and Neural Computation, School of Informatics, University of Edinburgh, Edinburgh EH8 9AB, UK
| | - Thierry Le Bihan
- SynthSys and School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3BF, UK
| | - Andrew J Millar
- SynthSys and School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3BF, UK
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Guo Y, Mofrad MRK, Tepole AB. On modeling the multiscale mechanobiology of soft tissues: Challenges and progress. BIOPHYSICS REVIEWS 2022; 3:031303. [PMID: 38505274 PMCID: PMC10903412 DOI: 10.1063/5.0085025] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 07/12/2022] [Indexed: 03/21/2024]
Abstract
Tissues grow and remodel in response to mechanical cues, extracellular and intracellular signals experienced through various biological events, from the developing embryo to disease and aging. The macroscale response of soft tissues is typically nonlinear, viscoelastic anisotropic, and often emerges from the hierarchical structure of tissues, primarily their biopolymer fiber networks at the microscale. The adaptation to mechanical cues is likewise a multiscale phenomenon. Cell mechanobiology, the ability of cells to transform mechanical inputs into chemical signaling inside the cell, and subsequent regulation of cellular behavior through intra- and inter-cellular signaling networks, is the key coupling at the microscale between the mechanical cues and the mechanical adaptation seen macroscopically. To fully understand mechanics of tissues in growth and remodeling as observed at the tissue level, multiscale models of tissue mechanobiology are essential. In this review, we summarize the state-of-the art modeling tools of soft tissues at both scales, the tissue level response, and the cell scale mechanobiology models. To help the interested reader become more familiar with these modeling frameworks, we also show representative examples. Our aim here is to bring together scientists from different disciplines and enable the future leap in multiscale modeling of tissue mechanobiology.
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Affiliation(s)
- Yifan Guo
- School of Mechanical Engineering, Purdue University, West Lafayette, Indiana 47907, USA
| | - Mohammad R. K. Mofrad
- Departments of Bioengineering and Mechanical Engineering, University of California Berkeley, Berkeley, California 94720, USA
| | - Adrian Buganza Tepole
- School of Mechanical Engineering, Purdue University, West Lafayette, Indiana 47907, USA
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Lakhova TN, Kazantsev FV, Lashin SA, Matushkin YG. The finding and researching algorithm for potentially oscillating enzymatic systems. Vavilovskii Zhurnal Genet Selektsii 2021; 25:318-330. [PMID: 34901728 PMCID: PMC8627878 DOI: 10.18699/vj21.035] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Revised: 03/15/2021] [Accepted: 03/18/2021] [Indexed: 11/19/2022] Open
Abstract
Many processes in living organisms are subject to periodic oscillations at different hierarchical levels of their organization: from molecular-genetic to population and ecological. Oscillatory processes are responsible for cell cycles in both prokaryotes and eukaryotes, for circadian rhythms, for synchronous coupling of respiration with cardiac contractions, etc. Fluctuations in the numbers of organisms in natural populations can be caused by the populations' own properties, their age structure, and ecological relationships with other species. Along with experimental approaches, mathematical and computer modeling is widely used to study oscillating biological systems. This paper presents classical mathematical models that describe oscillatory behavior in biological systems. Methods for the search for oscillatory molecular-genetic systems are presented by the example of their special case - oscillatory enzymatic systems. Factors influencing the cyclic dynamics in living systems, typical not only of the molecular-genetic level, but of higher levels of organization as well, are considered. Application of different ways to describe gene networks for modeling oscillatory molecular-genetic systems is considered, where the most important factor for the emergence of cyclic behavior is the presence of feedback. Techniques for finding potentially oscillatory enzymatic systems are presented. Using the method described in the article, we present and analyze, in a step-by-step manner, first the structural models (graphs) of gene networks and then the reconstruction of the mathematical models and computational experiments with them. Structural models are ideally suited for the tasks of an automatic search for potential oscillating contours (linked subgraphs), whose structure can correspond to the mathematical model of the molecular-genetic system that demonstrates oscillatory behavior in dynamics. At the same time, it is the numerical study of mathematical models for the selected contours that makes it possible to confirm the presence of stable limit cycles in them. As an example of application of the technology, a network of 300 metabolic reactions of the bacterium Escherichia coli was analyzed using mathematical and computer modeling tools. In particular, oscillatory behavior was shown for a loop whose reactions are part of the tryptophan biosynthesis pathway.
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Affiliation(s)
- T N Lakhova
- Kurchatov Genomics Center of ICG SB RAS, Novosibirsk, Russia
| | - F V Kazantsev
- Kurchatov Genomics Center of ICG SB RAS, Novosibirsk, Russia
| | - S A Lashin
- Kurchatov Genomics Center of ICG SB RAS, Novosibirsk, Russia Novosibirsk State University, Novosibirsk, Russia
| | - Yu G Matushkin
- Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia Novosibirsk State University, Novosibirsk, Russia
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Zhao M, He W, Tang J, Zou Q, Guo F. A comprehensive overview and critical evaluation of gene regulatory network inference technologies. Brief Bioinform 2021; 22:6128842. [PMID: 33539514 DOI: 10.1093/bib/bbab009] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Revised: 12/11/2020] [Accepted: 01/06/2021] [Indexed: 12/12/2022] Open
Abstract
Gene regulatory network (GRN) is the important mechanism of maintaining life process, controlling biochemical reaction and regulating compound level, which plays an important role in various organisms and systems. Reconstructing GRN can help us to understand the molecular mechanism of organisms and to reveal the essential rules of a large number of biological processes and reactions in organisms. Various outstanding network reconstruction algorithms use specific assumptions that affect prediction accuracy, in order to deal with the uncertainty of processing. In order to study why a certain method is more suitable for specific research problem or experimental data, we conduct research from model-based, information-based and machine learning-based method classifications. There are obviously different types of computational tools that can be generated to distinguish GRNs. Furthermore, we discuss several classical, representative and latest methods in each category to analyze core ideas, general steps, characteristics, etc. We compare the performance of state-of-the-art GRN reconstruction technologies on simulated networks and real networks under different scaling conditions. Through standardized performance metrics and common benchmarks, we quantitatively evaluate the stability of various methods and the sensitivity of the same algorithm applying to different scaling networks. The aim of this study is to explore the most appropriate method for a specific GRN, which helps biologists and medical scientists in discovering potential drug targets and identifying cancer biomarkers.
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Affiliation(s)
- Mengyuan Zhao
- School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Wenying He
- School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Jijun Tang
- University of South Carolina, Tianjin, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Fei Guo
- School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China
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Kang X, Hajek B, Hanzawa Y. From graph topology to ODE models for gene regulatory networks. PLoS One 2020; 15:e0235070. [PMID: 32603340 PMCID: PMC7326199 DOI: 10.1371/journal.pone.0235070] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2020] [Accepted: 06/08/2020] [Indexed: 11/28/2022] Open
Abstract
A gene regulatory network can be described at a high level by a directed graph with signed edges, and at a more detailed level by a system of ordinary differential equations (ODEs). The former qualitatively models the causal regulatory interactions between ordered pairs of genes, while the latter quantitatively models the time-varying concentrations of mRNA and proteins. This paper clarifies the connection between the two types of models. We propose a property, called the constant sign property, for a general class of ODE models. The constant sign property characterizes the set of conditions (system parameters, external signals, or internal states) under which an ODE model is consistent with a signed, directed graph. If the constant sign property for an ODE model holds globally for all conditions, then the ODE model has a single signed, directed graph. If the constant sign property for an ODE model only holds locally, which may be more typical, then the ODE model corresponds to different graphs under different sets of conditions. In addition, two versions of constant sign property are given and a relationship between them is proved. As an example, the ODE models that capture the effect of cis-regulatory elements involving protein complex binding, based on the model in the GeneNetWeaver source code, are described in detail and shown to satisfy the global constant sign property with a unique consistent gene regulatory graph. Even a single gene regulatory graph is shown to have many ODE models of GeneNetWeaver type consistent with it due to combinatorial complexity and continuous parameters. Finally the question of how closely data generated by one ODE model can be fit by another ODE model is explored. It is observed that the fit is better if the two models come from the same graph.
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Affiliation(s)
- Xiaohan Kang
- Department of Electrical and Computer Engineering, and Coordinated Science Laboratory, University of Illinois at Urbana–Champaign, Urbana, Illinois, United States of America
| | - Bruce Hajek
- Department of Electrical and Computer Engineering, and Coordinated Science Laboratory, University of Illinois at Urbana–Champaign, Urbana, Illinois, United States of America
| | - Yoshie Hanzawa
- Department of Biology, California State University, Northridge, Northridge, California, United States of America
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A hypergraph-based method for large-scale dynamic correlation study at the transcriptomic scale. BMC Genomics 2019; 20:397. [PMID: 31117943 PMCID: PMC6530038 DOI: 10.1186/s12864-019-5787-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2018] [Accepted: 05/09/2019] [Indexed: 12/22/2022] Open
Abstract
Background The biological regulatory system is highly dynamic. Correlations between functionally related genes change over different biological conditions, which are often unobserved in the data. At the gene level, the dynamic correlations result in three-way gene interactions involving a pair of genes that change correlation, and a third gene that reflects the underlying cellular conditions. This type of ternary relation can be quantified by the Liquid Association statistic. Studying these three-way interactions at the gene triplet level have revealed important regulatory mechanisms in the biological system. Currently, due to the extremely large amount of possible combinations of triplets within a high-throughput gene expression dataset, no method is available to examine the ternary relationship at the biological system level and formally address the false discovery issue. Results Here we propose a new method, Hypergraph for Dynamic Correlation (HDC), to construct module-level three-way interaction networks. The method is able to present integrative uniform hypergraphs to reflect the global dynamic correlation pattern in the biological system, providing guidance to down-stream gene triplet-level analyses. To validate the method’s ability, we conducted two real data experiments using a melanoma RNA-seq dataset from The Cancer Genome Atlas (TCGA) and a yeast cell cycle dataset. The resulting hypergraphs are clearly biologically plausible, and suggest novel relations relevant to the biological conditions in the data. Conclusions We believe the new approach provides a valuable alternative method to analyze omics data that can extract higher order structures. The software is at https://github.com/yunchuankong/HypergraphDynamicCorrelation. Electronic supplementary material The online version of this article (10.1186/s12864-019-5787-x) contains supplementary material, which is available to authorized users.
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Millar AJ, Urquiza U, Freeman PL, Hume A, Plotkin GD, Sorokina O, Zardilis A, Zielinski T. Practical steps to digital organism models, from laboratory model species to 'Crops in silico. JOURNAL OF EXPERIMENTAL BOTANY 2019; 70:2403-2418. [PMID: 30615184 DOI: 10.1093/jxb/ery435] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2018] [Accepted: 11/28/2018] [Indexed: 05/20/2023]
Abstract
A recent initiative named 'Crops in silico' proposes that multi-scale models 'have the potential to fill in missing mechanistic details and generate new hypotheses to prioritize directed engineering efforts' in plant science, particularly directed to crop species. To that end, the group called for 'a paradigm shift in plant modelling, from largely isolated efforts to a connected community'. 'Wet' (experimental) research has been especially productive in plant science, since the adoption of Arabidopsis thaliana as a laboratory model species allowed the emergence of an Arabidopsis research community. Parts of this community invested in 'dry' (theoretical) research, under the rubric of Systems Biology. Our past research combined concepts from Systems Biology and crop modelling. Here we outline the approaches that seem most relevant to connected, 'digital organism' initiatives. We illustrate the scale of experimental research required, by collecting the kinetic parameter values that are required for a quantitative, dynamic model of a gene regulatory network. By comparison with the Systems Biology Markup Language (SBML) community, we note computational resources and community structures that will help to realize the potential for plant Systems Biology to connect with a broader crop science community.
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Affiliation(s)
- Andrew J Millar
- SynthSys and School of Biological Sciences, University of Edinburgh, Edinburgh, UK
| | - Uriel Urquiza
- SynthSys and School of Biological Sciences, University of Edinburgh, Edinburgh, UK
| | | | - Alastair Hume
- SynthSys and School of Biological Sciences, University of Edinburgh, Edinburgh, UK
- EPCC, Bayes Centre, University of Edinburgh, Edinburgh, UK
| | - Gordon D Plotkin
- Laboratory for the Foundations of Computer Science, School of Informatics, University of Edinburgh, Edinburgh, UK
| | - Oxana Sorokina
- Institute for Adaptive and Neural Computation, School of Informatics, University of Edinburgh, Edinburgh, UK
| | - Argyris Zardilis
- SynthSys and School of Biological Sciences, University of Edinburgh, Edinburgh, UK
| | - Tomasz Zielinski
- SynthSys and School of Biological Sciences, University of Edinburgh, Edinburgh, UK
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Shi M, Shen W, Chong Y, Wang HQ. Improving GRN re-construction by mining hidden regulatory signals. IET Syst Biol 2019; 11:174-181. [PMID: 29125126 PMCID: PMC8687237 DOI: 10.1049/iet-syb.2017.0013] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
Inferring gene regulatory networks (GRNs) from gene expression data is an important but challenging issue in systems biology. Here, the authors propose a dictionary learning-based approach that aims to infer GRNs by globally mining regulatory signals, known or latent. Gene expression is often regulated by various regulatory factors, some of which are observed and some of which are latent. The authors assume that all regulators are unknown for a target gene and the expression of the target gene can be mapped into a regulatory space spanned by all the regulators. Specifically, the authors modify the dictionary learning model, k-SVD, according to the sparse property of GRNs for mining the regulatory signals. The recovered regulatory signals are then used as a pool of regulatory factors to calculate a confidence score for a given transcription factor regulating a target gene. The capability of recovering hidden regulatory signals was verified on simulated data. Comparative experiments for GRN inference between the proposed algorithm (OURM) and some state-of-the-art algorithms, e.g. GENIE3 and ARACNE, on real-world data sets show the superior performance of OURM in inferring GRNs: higher area under the receiver operating characteristic curves and area under the precision-recall curves.
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Affiliation(s)
- Ming Shi
- State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430079, People's Republic of China
| | - Weiming Shen
- State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430079, People's Republic of China
| | - Yanwen Chong
- State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430079, People's Republic of China
| | - Hong-Qiang Wang
- Machine Intelligence and Computational Biology Laboratory, Institute of Intelligent Machines, Chinese Academy of Science, PO Box 1130, Hefei 230031, People's Republic of China.
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Huynh-Thu VA, Sanguinetti G. Tree-Based Learning of Regulatory Network Topologies and Dynamics with Jump3. Methods Mol Biol 2019; 1883:217-233. [PMID: 30547402 DOI: 10.1007/978-1-4939-8882-2_9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Inference of gene regulatory networks (GRNs) from time series data is a well-established field in computational systems biology. Most approaches can be broadly divided in two families: model-based and model-free methods. These two families are highly complementary: model-based methods seek to identify a formal mathematical model of the system. They thus have transparent and interpretable semantics but rely on strong assumptions and are rather computationally intensive. On the other hand, model-free methods have typically good scalability. Since they are not based on any parametric model, they are more flexible than model-based methods, but also less interpretable.In this chapter, we describe Jump3, a hybrid approach that bridges the gap between model-free and model-based methods. Jump3 uses a formal stochastic differential equation to model each gene expression but reconstructs the GRN topology with a nonparametric method based on decision trees. We briefly review the theoretical and algorithmic foundations of Jump3, and then proceed to provide a step-by-step tutorial of the associated software usage.
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Affiliation(s)
- Vân Anh Huynh-Thu
- Department of Electrical Engineering and Computer Science, University of Liège, Liège, Belgium.
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Herbach U, Bonnaffoux A, Espinasse T, Gandrillon O. Inferring gene regulatory networks from single-cell data: a mechanistic approach. BMC SYSTEMS BIOLOGY 2017; 11:105. [PMID: 29157246 PMCID: PMC5697158 DOI: 10.1186/s12918-017-0487-0] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/12/2017] [Accepted: 11/09/2017] [Indexed: 01/13/2023]
Abstract
Background The recent development of single-cell transcriptomics has enabled gene expression to be measured in individual cells instead of being population-averaged. Despite this considerable precision improvement, inferring regulatory networks remains challenging because stochasticity now proves to play a fundamental role in gene expression. In particular, mRNA synthesis is now acknowledged to occur in a highly bursty manner. Results We propose to view the inference problem as a fitting procedure for a mechanistic gene network model that is inherently stochastic and takes not only protein, but also mRNA levels into account. We first explain how to build and simulate this network model based upon the coupling of genes that are described as piecewise-deterministic Markov processes. Our model is modular and can be used to implement various biochemical hypotheses including causal interactions between genes. However, a naive fitting procedure would be intractable. By performing a relevant approximation of the stationary distribution, we derive a tractable procedure that corresponds to a statistical hidden Markov model with interpretable parameters. This approximation turns out to be extremely close to the theoretical distribution in the case of a simple toggle-switch, and we show that it can indeed fit real single-cell data. As a first step toward inference, our approach was applied to a number of simple two-gene networks simulated in silico from the mechanistic model and satisfactorily recovered the original networks. Conclusions Our results demonstrate that functional interactions between genes can be inferred from the distribution of a mechanistic, dynamical stochastic model that is able to describe gene expression in individual cells. This approach seems promising in relation to the current explosion of single-cell expression data. Electronic supplementary material The online version of this article (doi:10.1186/s12918-017-0487-0) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Ulysse Herbach
- Univ Lyon, ENS de Lyon, Univ Claude Bernard, CNRS UMR 5239, INSERM U1210, Laboratory of Biology and Modelling of the Cell, 46 allée d'Italie Site Jacques Monod, Lyon, F-69007, France.,Inria Team Dracula, Inria Center Grenoble Rhône-Alpes, Lyon, France.,Univ Lyon, Université Claude Bernard Lyon 1, CNRS UMR 5208, Institut Camille Jordan, 43 blvd. du 11 novembre 1918, Villeurbanne Cedex, F-6962, France
| | - Arnaud Bonnaffoux
- Univ Lyon, ENS de Lyon, Univ Claude Bernard, CNRS UMR 5239, INSERM U1210, Laboratory of Biology and Modelling of the Cell, 46 allée d'Italie Site Jacques Monod, Lyon, F-69007, France.,Inria Team Dracula, Inria Center Grenoble Rhône-Alpes, Lyon, France.,The CoSMo company, 5 passage du Vercors, Lyon, 69007, France
| | - Thibault Espinasse
- Univ Lyon, Université Claude Bernard Lyon 1, CNRS UMR 5208, Institut Camille Jordan, 43 blvd. du 11 novembre 1918, Villeurbanne Cedex, F-6962, France
| | - Olivier Gandrillon
- Univ Lyon, ENS de Lyon, Univ Claude Bernard, CNRS UMR 5239, INSERM U1210, Laboratory of Biology and Modelling of the Cell, 46 allée d'Italie Site Jacques Monod, Lyon, F-69007, France. .,Inria Team Dracula, Inria Center Grenoble Rhône-Alpes, Lyon, France.
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Kazantsev F, Akberdin I, Lashin S, Ree N, Timonov V, Ratushny A, Khlebodarova T, Likhoshvai V. MAMMOTh: A new database for curated mathematical models of biomolecular systems. J Bioinform Comput Biol 2017; 16:1740010. [PMID: 29172865 DOI: 10.1142/s0219720017400108] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
MOTIVATION Living systems have a complex hierarchical organization that can be viewed as a set of dynamically interacting subsystems. Thus, to simulate the internal nature and dynamics of the entire biological system, we should use the iterative way for a model reconstruction, which is a consistent composition and combination of its elementary subsystems. In accordance with this bottom-up approach, we have developed the MAthematical Models of bioMOlecular sysTems (MAMMOTh) tool that consists of the database containing manually curated MAMMOTh fitted to the experimental data and a software tool that provides their further integration. RESULTS The MAMMOTh database entries are organized as building blocks in a way that the model parts can be used in different combinations to describe systems with higher organizational level (metabolic pathways and/or transcription regulatory networks). The tool supports export of a single model or their combinations in SBML or Mathematica standards. The database currently contains 110 mathematical sub-models for Escherichia coli elementary subsystems (enzymatic reactions and gene expression regulatory processes) that can be combined in at least 5100 complex/sophisticated models concerning more complex biological processes as de novo nucleotide biosynthesis, aerobic/anaerobic respiration and nitrate/nitrite utilization in E. coli. All models are functionally interconnected and sufficiently complement public model resources. AVAILABILITY http://mammoth.biomodelsgroup.ru.
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Affiliation(s)
- Fedor Kazantsev
- * Institute of Cytology and Genetics SB RAS, Lavrentyev Avenue., 10, Novosibirsk 630090, Russia.,† Novosibirsk State University, Pirogova str. 2, Novosibirsk 630090, Russia
| | - Ilya Akberdin
- * Institute of Cytology and Genetics SB RAS, Lavrentyev Avenue., 10, Novosibirsk 630090, Russia.,† Novosibirsk State University, Pirogova str. 2, Novosibirsk 630090, Russia.,¶ Biology Department, San Diego State University, San Diego, CA 92182-4614, USA
| | - Sergey Lashin
- * Institute of Cytology and Genetics SB RAS, Lavrentyev Avenue., 10, Novosibirsk 630090, Russia.,† Novosibirsk State University, Pirogova str. 2, Novosibirsk 630090, Russia
| | - Natalia Ree
- * Institute of Cytology and Genetics SB RAS, Lavrentyev Avenue., 10, Novosibirsk 630090, Russia
| | - Vladimir Timonov
- † Novosibirsk State University, Pirogova str. 2, Novosibirsk 630090, Russia
| | - Alexander Ratushny
- ‡ Center for Infectious Disease Research (Formerly Seattle, Biomedical Research Institute), Seattle, WA 98109, USA.,§ Institute for Systems Biology, Seattle, WA 98109-5234, USA
| | - Tamara Khlebodarova
- * Institute of Cytology and Genetics SB RAS, Lavrentyev Avenue., 10, Novosibirsk 630090, Russia
| | - Vitaly Likhoshvai
- * Institute of Cytology and Genetics SB RAS, Lavrentyev Avenue., 10, Novosibirsk 630090, Russia.,† Novosibirsk State University, Pirogova str. 2, Novosibirsk 630090, Russia
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Liu W, Zhu W, Liao B, Chen H, Ren S, Cai L. Improving gene regulatory network structure using redundancy reduction in the MRNET algorithm. RSC Adv 2017. [DOI: 10.1039/c7ra01557g] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Inferring gene regulatory networks from expression data is a central problem in systems biology.
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Affiliation(s)
- Wei Liu
- College of Information Science and Engineering
- Hunan University
- Changsha
- China
| | - Wen Zhu
- College of Information Science and Engineering
- Hunan University
- Changsha
- China
| | - Bo Liao
- College of Information Science and Engineering
- Hunan University
- Changsha
- China
| | - Haowen Chen
- College of Information Science and Engineering
- Hunan University
- Changsha
- China
| | - Siqi Ren
- College of Information Science and Engineering
- Hunan University
- Changsha
- China
| | - Lijun Cai
- College of Information Science and Engineering
- Hunan University
- Changsha
- China
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14
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Gene Regulatory Network Inferences Using a Maximum-Relevance and Maximum-Significance Strategy. PLoS One 2016; 11:e0166115. [PMID: 27829000 PMCID: PMC5102470 DOI: 10.1371/journal.pone.0166115] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2016] [Accepted: 10/24/2016] [Indexed: 12/18/2022] Open
Abstract
Recovering gene regulatory networks from expression data is a challenging problem in systems biology that provides valuable information on the regulatory mechanisms of cells. A number of algorithms based on computational models are currently used to recover network topology. However, most of these algorithms have limitations. For example, many models tend to be complicated because of the "large p, small n" problem. In this paper, we propose a novel regulatory network inference method called the maximum-relevance and maximum-significance network (MRMSn) method, which converts the problem of recovering networks into a problem of how to select the regulator genes for each gene. To solve the latter problem, we present an algorithm that is based on information theory and selects the regulator genes for a specific gene by maximizing the relevance and significance. A first-order incremental search algorithm is used to search for regulator genes. Eventually, a strict constraint is adopted to adjust all of the regulatory relationships according to the obtained regulator genes and thus obtain the complete network structure. We performed our method on five different datasets and compared our method to five state-of-the-art methods for network inference based on information theory. The results confirm the effectiveness of our method.
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15
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Yan Y, Qiu S, Jin Z, Gong S, Bai Y, Lu J, Yu T. Detecting subnetwork-level dynamic correlations. Bioinformatics 2016; 33:256-265. [PMID: 27667792 DOI: 10.1093/bioinformatics/btw616] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2016] [Revised: 09/07/2016] [Accepted: 09/21/2016] [Indexed: 01/11/2023] Open
Abstract
MOTIVATION The biological regulatory system is highly dynamic. The correlations between many functionally related genes change over different biological conditions. Finding dynamic relations on the existing biological network may reveal important regulatory mechanisms. Currently no method is available to detect subnetwork-level dynamic correlations systematically on the genome-scale network. Two major issues hampered the development. The first is gene expression profiling data usually do not contain time course measurements to facilitate the analysis of dynamic relations, which can be partially addressed by using certain genes as indicators of biological conditions. Secondly, it is unclear how to effectively delineate subnetworks, and define dynamic relations between them. RESULTS Here we propose a new method named LANDD (Liquid Association for Network Dynamics Detection) to find subnetworks that show substantial dynamic correlations, as defined by subnetwork A is concentrated with Liquid Association scouting genes for subnetwork B. The method produces easily interpretable results because of its focus on subnetworks that tend to comprise functionally related genes. Also, the collective behaviour of genes in a subnetwork is a much more reliable indicator of underlying biological conditions compared to using single genes as indicators. We conducted extensive simulations to validate the method's ability to detect subnetwork-level dynamic correlations. Using a real gene expression dataset and the human protein-protein interaction network, we demonstrate the method links subnetworks of distinct biological processes, with both confirmed relations and plausible new functional implications. We also found signal transduction pathways tend to show extensive dynamic relations with other functional groups. AVAILABILITY AND IMPLEMENTATION The R package is available at https://cran.r-project.org/web/packages/LANDD CONTACTS: yunba@pcom.edu, jwlu33@hotmail.com or tianwei.yu@emory.eduSupplementary information: Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yan Yan
- School of Software Engineering, Tongji University, Shanghai, China
| | - Shangzhao Qiu
- School of Software Engineering, Tongji University, Shanghai, China
| | - Zhuxuan Jin
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, USA
| | - Sihong Gong
- School of Software Engineering, Tongji University, Shanghai, China
| | - Yun Bai
- Department of Pharmaceutical Sciences, School of Pharmacy, Philadelphia College of Osteopathic Medicine, Suwanee, GA, USA
| | - Jianwei Lu
- School of Software Engineering, Tongji University, Shanghai, China
- Institute of Advanced Translational Medicine, Tongji University, Shanghai, China
| | - Tianwei Yu
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, USA
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16
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Millar AJ. The Intracellular Dynamics of Circadian Clocks Reach for the Light of Ecology and Evolution. ANNUAL REVIEW OF PLANT BIOLOGY 2016; 67:595-618. [PMID: 26653934 DOI: 10.1146/annurev-arplant-043014-115619] [Citation(s) in RCA: 93] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
A major challenge for biology is to extend our understanding of molecular regulation from the simplified conditions of the laboratory to ecologically relevant environments. Tractable examples are essential to make these connections for complex, pleiotropic regulators and, to go further, to link relevant genome sequences to field traits. Here, I review the case for the biological clock in higher plants. The gene network of the circadian clock drives pervasive, 24-hour rhythms in metabolism, behavior, and physiology across the eukaryotes and in some prokaryotes. In plants, the scope of chronobiology is now extending from the most tractable, intracellular readouts to the clock's many effects at the whole-organism level and across the life cycle, including biomass and flowering. I discuss five research areas where recent progress might be integrated in the future, to understand not only circadian functions in natural conditions but also the evolution of the clock's molecular mechanisms.
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Affiliation(s)
- Andrew J Millar
- SynthSys and School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3BF, Scotland, United Kingdom;
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17
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Bayesian Computation Methods for Inferring Regulatory Network Models Using Biomedical Data. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2016; 939:289-307. [DOI: 10.1007/978-981-10-1503-8_12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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18
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Ocone A, Haghverdi L, Mueller NS, Theis FJ. Reconstructing gene regulatory dynamics from high-dimensional single-cell snapshot data. Bioinformatics 2015; 31:i89-96. [PMID: 26072513 PMCID: PMC4765871 DOI: 10.1093/bioinformatics/btv257] [Citation(s) in RCA: 104] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Motivation: High-dimensional single-cell snapshot data are becoming widespread in the systems biology community, as a mean to understand biological processes at the cellular level. However, as temporal information is lost with such data, mathematical models have been limited to capture only static features of the underlying cellular mechanisms. Results: Here, we present a modular framework which allows to recover the temporal behaviour from single-cell snapshot data and reverse engineer the dynamics of gene expression. The framework combines a dimensionality reduction method with a cell time-ordering algorithm to generate pseudo time-series observations. These are in turn used to learn transcriptional ODE models and do model selection on structural network features. We apply it on synthetic data and then on real hematopoietic stem cells data, to reconstruct gene expression dynamics during differentiation pathways and infer the structure of a key gene regulatory network. Availability and implementation: C++ and Matlab code available at https://www.helmholtz-muenchen.de/fileadmin/ICB/software/inferenceSnapshot.zip. Contact:fabian.theis@helmholtz-muenchen.de Supplementary information:Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Andrea Ocone
- Institute of Computational Biology, Helmholtz Zentrum München, 85764 Neuherberg, Germany and Department of Mathematics, Technical University Munich, 85747 Garching, Germany
| | - Laleh Haghverdi
- Institute of Computational Biology, Helmholtz Zentrum München, 85764 Neuherberg, Germany and Department of Mathematics, Technical University Munich, 85747 Garching, Germany
| | - Nikola S Mueller
- Institute of Computational Biology, Helmholtz Zentrum München, 85764 Neuherberg, Germany and Department of Mathematics, Technical University Munich, 85747 Garching, Germany
| | - Fabian J Theis
- Institute of Computational Biology, Helmholtz Zentrum München, 85764 Neuherberg, Germany and Department of Mathematics, Technical University Munich, 85747 Garching, Germany Institute of Computational Biology, Helmholtz Zentrum München, 85764 Neuherberg, Germany and Department of Mathematics, Technical University Munich, 85747 Garching, Germany
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19
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Huynh-Thu VA, Sanguinetti G. Combining tree-based and dynamical systems for the inference of gene regulatory networks. Bioinformatics 2015; 31:1614-22. [PMID: 25573916 PMCID: PMC4426834 DOI: 10.1093/bioinformatics/btu863] [Citation(s) in RCA: 84] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2014] [Revised: 12/05/2014] [Accepted: 12/23/2014] [Indexed: 12/21/2022] Open
Abstract
MOTIVATION Reconstructing the topology of gene regulatory networks (GRNs) from time series of gene expression data remains an important open problem in computational systems biology. Existing GRN inference algorithms face one of two limitations: model-free methods are scalable but suffer from a lack of interpretability and cannot in general be used for out of sample predictions. On the other hand, model-based methods focus on identifying a dynamical model of the system. These are clearly interpretable and can be used for predictions; however, they rely on strong assumptions and are typically very demanding computationally. RESULTS Here, we propose a new hybrid approach for GRN inference, called Jump3, exploiting time series of expression data. Jump3 is based on a formal on/off model of gene expression but uses a non-parametric procedure based on decision trees (called 'jump trees') to reconstruct the GRN topology, allowing the inference of networks of hundreds of genes. We show the good performance of Jump3 on in silico and synthetic networks and applied the approach to identify regulatory interactions activated in the presence of interferon gamma.
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Affiliation(s)
- Vân Anh Huynh-Thu
- School of Informatics, University of Edinburgh, Edinburgh EH8 9AB, and SynthSys - Systems and Synthetic Biology, University of Edinburgh, Edinburgh EH9 3JD, UK
| | - Guido Sanguinetti
- School of Informatics, University of Edinburgh, Edinburgh EH8 9AB, and SynthSys - Systems and Synthetic Biology, University of Edinburgh, Edinburgh EH9 3JD, UK School of Informatics, University of Edinburgh, Edinburgh EH8 9AB, and SynthSys - Systems and Synthetic Biology, University of Edinburgh, Edinburgh EH9 3JD, UK
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20
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Abstract
As major contributors to global oxygen levels and producers of fatty acids, carotenoids, sterols, and phycocolloids, algae have significant ecological and commercial roles. Early algal models have contributed much to our understanding of circadian clocks at physiological and biochemical levels. The genetic and molecular approaches that identified clock components in other taxa have not been as widely applied to algae. We review results from seven species: the chlorophytes Chlamydomonas reinhardtii, Ostreococcus tauri, and Acetabularia spp.; the dinoflagellates Lingulodinium polyedrum and Symbiodinium spp.; the euglenozoa Euglena gracilis; and the red alga Cyanidioschyzon merolae. The relative simplicity, experimental tractability, and ecological and evolutionary diversity of algal systems may now make them particularly useful in integrating quantitative data from "omic" technologies (e.g., genomics, transcriptomics, metabolomics, and proteomics) with computational and mathematical methods.
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Affiliation(s)
- Zeenat B Noordally
- SynthSys and School of Biological Sciences, University of Edinburgh , Edinburgh EH9 3BF, United Kingdom
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21
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Dixon LE, Hodge SK, van Ooijen G, Troein C, Akman OE, Millar AJ. Light and circadian regulation of clock components aids flexible responses to environmental signals. THE NEW PHYTOLOGIST 2014; 203:568-577. [PMID: 24842166 PMCID: PMC4286021 DOI: 10.1111/nph.12853] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/22/2013] [Accepted: 03/24/2014] [Indexed: 05/08/2023]
Abstract
The circadian clock measures time across a 24 h period, increasing fitness by phasing biological processes to the most appropriate time of day. The interlocking feedback loop mechanism of the clock is conserved across species; however, the number of loops varies. Mathematical and computational analyses have suggested that loop complexity affects the overall flexibility of the oscillator, including its responses to entrainment signals. We used a discriminating experimental assay, at the transition between different photoperiods, in order to test this proposal in a minimal circadian network (in Ostreococcus tauri) and a more complex network (in Arabidopsis thaliana). Transcriptional and translational reporters in O. tauri primarily tracked dawn or dusk, whereas in A. thaliana, a wider range of responses were observed, consistent with its more flexible clock. Model analysis supported the requirement for this diversity of responses among the components of the more complex network. However, these and earlier data showed that the O. tauri network retains surprising flexibility, despite its simple circuit. We found that models constructed from experimental data can show flexibility either from multiple loops and/or from multiple light inputs. Our results suggest that O. tauri has adopted the latter strategy, possibly as a consequence of genomic reduction.
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Affiliation(s)
- Laura E Dixon
- SynthSys, University of Edinburgh, Kings Buildings, Mayfield Road, Edinburgh, EH9 3JD, UK
- Department of Crop Genetics, John Innes Centre, Norwich Research Park, Norwich, NR4 7UH, UK
| | - Sarah K Hodge
- SynthSys, University of Edinburgh, Kings Buildings, Mayfield Road, Edinburgh, EH9 3JD, UK
| | - Gerben van Ooijen
- SynthSys, University of Edinburgh, Kings Buildings, Mayfield Road, Edinburgh, EH9 3JD, UK
| | - Carl Troein
- Department of Astronomy and Theoretical Physics, Lund University, 223 62, Lund, Sweden
| | - Ozgur E Akman
- SynthSys, University of Edinburgh, Kings Buildings, Mayfield Road, Edinburgh, EH9 3JD, UK
- Centre for Systems, Dynamics and Control, College of Engineering, Mathematics & Physical Sciences, University of Exeter, Exeter, EX4 4QF, UK
| | - Andrew J Millar
- SynthSys, University of Edinburgh, Kings Buildings, Mayfield Road, Edinburgh, EH9 3JD, UK
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22
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Extrapolating In Vitro Results to Predict Human Toxicity. METHODS IN PHARMACOLOGY AND TOXICOLOGY 2014. [DOI: 10.1007/978-1-4939-0521-8_24] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
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23
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Asif HMS, Sanguinetti G. Simultaneous inference and clustering of transcriptional dynamics in gene regulatory networks. Stat Appl Genet Mol Biol 2013; 12:545-57. [PMID: 24051920 DOI: 10.1515/sagmb-2012-0010] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
We present a novel method for simultaneous inference and nonparametric clustering of transcriptional dynamics from gene expression data. The proposed method uses gene expression data to infer time-varying TF profiles and cluster these temporal profiles according to the dynamics they exhibit. We use the latent structure of factorial hidden Markov model to model the transcription factor profiles as Markov chains and cluster these profiles using nonparametric mixture modeling. An efficient Gibbs sampling scheme is proposed for inference of latent variables and grouping of transcriptional dynamics into a priori unknown number of clusters. We test our model on simulated data and analyse its performance on two expression datasets; S. cerevisiae cell cycle data and E. coli oxygen starvation response data. Our results show the applicability of the method for genome wide analysis of expression data.
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