1
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Alicea B, Parent J, Singh U. Periodicity in the embryo: Emergence of order in space, diffusion of order in time. Biosystems 2021; 204:104405. [PMID: 33746021 DOI: 10.1016/j.biosystems.2021.104405] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Revised: 03/07/2021] [Accepted: 03/08/2021] [Indexed: 02/02/2023]
Abstract
Does embryonic development exhibit characteristic temporal features? This is apparent in evolution, where evolutionary change has been shown to occur in bursts of activity. Using two animal models (Nematode, Caenorhabditis elegans and Zebrafish, Danio rerio) and simulated data, we demonstrate that temporal heterogeneity exists in embryogenesis at the cellular level, and may have functional consequences. Cell proliferation and division from cell tracking data is subject to analysis to characterize specific features in each model species. Simulated data is then used to understand what role this variation might play in producing phenotypic variation in the adult phenotype. This goes beyond a molecular characterization of developmental regulation to provide a quantitative result at the phenotypic scale of complexity.
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Affiliation(s)
- Bradly Alicea
- OpenWorm Foundation, Boston, MA, USA; Orthogonal Research and Education Laboratory, Champaign, IL, USA.
| | - Jesse Parent
- Orthogonal Research and Education Laboratory, Champaign, IL, USA
| | - Ujjwal Singh
- OpenWorm Foundation, Boston, MA, USA; IIIT Delhi, Delhi, India
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2
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Pitt JA, Banga JR. Parameter estimation in models of biological oscillators: an automated regularised estimation approach. BMC Bioinformatics 2019; 20:82. [PMID: 30770736 PMCID: PMC6377730 DOI: 10.1186/s12859-019-2630-y] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2018] [Accepted: 01/14/2019] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Dynamic modelling is a core element in the systems biology approach to understanding complex biosystems. Here, we consider the problem of parameter estimation in models of biological oscillators described by deterministic nonlinear differential equations. These problems can be extremely challenging due to several common pitfalls: (i) a lack of prior knowledge about parameters (i.e. massive search spaces), (ii) convergence to local optima (due to multimodality of the cost function), (iii) overfitting (fitting the noise instead of the signal) and (iv) a lack of identifiability. As a consequence, the use of standard estimation methods (such as gradient-based local ones) will often result in wrong solutions. Overfitting can be particularly problematic, since it produces very good calibrations, giving the impression of an excellent result. However, overfitted models exhibit poor predictive power. Here, we present a novel automated approach to overcome these pitfalls. Its workflow makes use of two sequential optimisation steps incorporating three key algorithms: (1) sampling strategies to systematically tighten the parameter bounds reducing the search space, (2) efficient global optimisation to avoid convergence to local solutions, (3) an advanced regularisation technique to fight overfitting. In addition, this workflow incorporates tests for structural and practical identifiability. RESULTS We successfully evaluate this novel approach considering four difficult case studies regarding the calibration of well-known biological oscillators (Goodwin, FitzHugh-Nagumo, Repressilator and a metabolic oscillator). In contrast, we show how local gradient-based approaches, even if used in multi-start fashion, are unable to avoid the above-mentioned pitfalls. CONCLUSIONS Our approach results in more efficient estimations (thanks to the bounding strategy) which are able to escape convergence to local optima (thanks to the global optimisation approach). Further, the use of regularisation allows us to avoid overfitting, resulting in more generalisable calibrated models (i.e. models with greater predictive power).
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Affiliation(s)
- Jake Alan Pitt
- (Bio)Process Engineering Group, IIM-CSIC, Eduardo Cabello 6, Vigo, 36208 Spain
- RWTH Aachen University, Faculty of Medicine, Joint Research Centre for Computational Biomedicine (JRC-COMBINE), Aachen, Germany
| | - Julio R. Banga
- (Bio)Process Engineering Group, IIM-CSIC, Eduardo Cabello 6, Vigo, 36208 Spain
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3
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Tucker A, Li Y, Garway-Heath D. Reprint of "Updating Markov models to integrate cross-sectional and longitudinal studies". Artif Intell Med 2017; 81:33-40. [PMID: 28939301 DOI: 10.1016/j.artmed.2017.09.009] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2017] [Indexed: 11/27/2022]
Abstract
Clinical trials are typically conducted over a population within a defined time period in order to illuminate certain characteristics of a health issue or disease process. Cross-sectional studies provide a snapshot of these disease processes over a large number of people but do not allow us to model the temporal nature of disease, which is essential for modelling detailed prognostic predictions. Longitudinal studies, on the other hand, are used to explore how these processes develop over time in a number of people but can be expensive and time-consuming, and many studies only cover a relatively small window within the disease process. This paper explores the application of intelligent data analysis techniques for building reliable models of disease progression from both cross-sectional and longitudinal studies. The aim is to learn disease 'trajectories' from cross-sectional data by building realistic trajectories from healthy patients to those with advanced disease. We focus on exploring whether we can 'calibrate' models learnt from these trajectories with real longitudinal data using Baum-Welch re-estimation so that the dynamic parameters reflect the true underlying processes more closely. We use Kullback-Leibler distance and Wilcoxon rank metrics to assess how calibration improves the models to better reflect the underlying dynamics.
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Affiliation(s)
- Allan Tucker
- Department of Computer Science, Brunel University, UK.
| | - Yuanxi Li
- Department of Computer Science, Brunel University, UK
| | - David Garway-Heath
- Moorfields Eye Hospital and UCL Institute of Ophthalmology, University College London, UK
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4
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Tucker A, Li Y, Garway-Heath D. Updating Markov models to integrate cross-sectional and longitudinal studies. Artif Intell Med 2017; 77:23-30. [PMID: 28545609 DOI: 10.1016/j.artmed.2017.03.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2017] [Indexed: 01/09/2023]
Abstract
Clinical trials are typically conducted over a population within a defined time period in order to illuminate certain characteristics of a health issue or disease process. Cross-sectional studies provide a snapshot of these disease processes over a large number of people but do not allow us to model the temporal nature of disease, which is essential for modelling detailed prognostic predictions. Longitudinal studies, on the other hand, are used to explore how these processes develop over time in a number of people but can be expensive and time-consuming, and many studies only cover a relatively small window within the disease process. This paper explores the application of intelligent data analysis techniques for building reliable models of disease progression from both cross-sectional and longitudinal studies. The aim is to learn disease 'trajectories' from cross-sectional data by building realistic trajectories from healthy patients to those with advanced disease. We focus on exploring whether we can 'calibrate' models learnt from these trajectories with real longitudinal data using Baum-Welch re-estimation so that the dynamic parameters reflect the true underlying processes more closely. We use Kullback-Leibler distance and Wilcoxon rank metrics to assess how calibration improves the models to better reflect the underlying dynamics.
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Affiliation(s)
- Allan Tucker
- Department of Computer Science, Brunel University, UK.
| | - Yuanxi Li
- Department of Computer Science, Brunel University, UK
| | - David Garway-Heath
- Moorfields Eye Hospital and UCL Institute of Ophthalmology, University College London, UK
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5
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Computer simulations of in vitro morphogenesis. Biosystems 2012; 109:430-43. [DOI: 10.1016/j.biosystems.2012.06.002] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2012] [Revised: 06/15/2012] [Accepted: 06/15/2012] [Indexed: 01/08/2023]
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6
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Veldhoen N, Ikonomou MG, Helbing CC. Molecular profiling of marine fauna: integration of omics with environmental assessment of the world's oceans. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2012; 76:23-38. [PMID: 22036265 DOI: 10.1016/j.ecoenv.2011.10.005] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2011] [Revised: 09/16/2011] [Accepted: 10/06/2011] [Indexed: 05/31/2023]
Abstract
Many species that contribute to the commercial and ecological richness of our marine ecosystems are harbingers of environmental change. The ability of organisms to rapidly detect and respond to changes in the surrounding environment represents the foundation for application of molecular profiling technologies towards marine sentinel species in an attempt to identify signature profiles that may reside within the transcriptome, proteome, or metabolome and that are indicative of a particular environmental exposure event. The current review highlights recent examples of the biological information obtained for marine sentinel teleosts, mammals, and invertebrates. While in its infancy, such basal information can provide a systems biology framework in the detection and evaluation of environmental chemical contaminant effects on marine fauna. Repeated evaluation across different seasons and local marine environs will lead to discrimination between signature profiles representing normal variation within the complex milieu of environmental factors that trigger biological response in a given sentinel species and permit a greater understanding of normal versus anthropogenic-associated modulation of biological pathways, which prove detrimental to marine fauna. It is anticipated that incorporation of contaminant-specific molecular signatures into current risk assessment paradigms will lead to enhanced wildlife management strategies that minimize the impacts of our industrialized society on marine ecosystems.
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Affiliation(s)
- Nik Veldhoen
- Department of Biochemistry and Microbiology, University of Victoria, P.O. Box 3055 Stn CSC, Victoria, B.C., Canada
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7
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Sic1 plays a role in timing and oscillatory behaviour of B-type cyclins. Biotechnol Adv 2012; 30:108-30. [DOI: 10.1016/j.biotechadv.2011.09.004] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2011] [Revised: 08/18/2011] [Accepted: 09/12/2011] [Indexed: 12/23/2022]
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8
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MELNIK RODERICKVN, WEI XILIN, MORENO–HAGELSIEB GABRIEL. NONLINEAR DYNAMICS OF CELL CYCLES WITH STOCHASTIC MATHEMATICAL MODELS. J BIOL SYST 2011. [DOI: 10.1142/s0218339009002879] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Cell cycles are fundamental components of all living organisms and their systematic studies extend our knowledge about the interconnection between regulatory, metabolic, and signaling networks, and therefore open new opportunities for our ultimate efficient control of cellular processes for disease treatments, as well as for a wide variety of biomedical and biotechnological applications. In the study of cell cycles, nonlinear phenomena play a paramount role, in particular in those cases where the cellular dynamics is in the focus of attention. Quantification of this dynamics is a challenging task due to a wide range of parameters that require estimations and the presence of many stochastic effects. Based on the originally deterministic model, in this paper we develop a hierarchy of models that allow us to describe the nonlinear dynamics accounting for special events of cell cycles. First, we develop a model that takes into account fluctuations of relative concentrations of proteins during special events of cell cycles. Such fluctuations are induced by varying rates of relative concentrations of proteins and/or by relative concentrations of proteins themselves. As such fluctuations may be responsible for qualitative changes in the cell, we develop a new model that accounts for the effect of cellular dynamics on the cell cycle. Finally, we analyze numerically nonlinear effects in the cell cycle by constructing phase portraits based on the newly developed model and carry out a parametric sensitivity analysis in order to identify parameters for an efficient cell cycle control. The results of computational experiments demonstrate that the metabolic events in gene regulatory networks can qualitatively influence the dynamics of the cell cycle.
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Affiliation(s)
- RODERICK V. N. MELNIK
- M2NeT Lab and Department of Mathematics, Wilfrid Laurier University, 75 University Avenue West, Waterloo, Ontario, N2L 3C5, Canada
| | - XILIN WEI
- M2NeT Lab and Department of Mathematics, Wilfrid Laurier University, 75 University Avenue West, Waterloo, Ontario, N2L 3C5, Canada
| | - GABRIEL MORENO–HAGELSIEB
- Department of Biology, Wilfrid Laurier University, 75 University Avenue West, Waterloo, Ontario, N2L 3C5, Canada
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9
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Alfieri R, Bartocci E, Merelli E, Milanesi L. Modeling the cell cycle: From deterministic models to hybrid systems. Biosystems 2011; 105:34-40. [DOI: 10.1016/j.biosystems.2011.03.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2010] [Revised: 03/03/2011] [Accepted: 03/05/2011] [Indexed: 10/18/2022]
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10
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PKPD and Disease Modeling: Concepts and Applications to Oncology. CLINICAL TRIAL SIMULATIONS 2011. [DOI: 10.1007/978-1-4419-7415-0_13] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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11
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Mosca E, Alfieri R, Merelli I, Viti F, Calabria A, Milanesi L. A multilevel data integration resource for breast cancer study. BMC SYSTEMS BIOLOGY 2010; 4:76. [PMID: 20525248 PMCID: PMC2900226 DOI: 10.1186/1752-0509-4-76] [Citation(s) in RCA: 75] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2009] [Accepted: 06/03/2010] [Indexed: 02/02/2023]
Abstract
BACKGROUND Breast cancer is one of the most common cancer types. Due to the complexity of this disease, it is important to face its study with an integrated and multilevel approach, from genes, transcripts and proteins to molecular networks, cell populations and tissues. According to the systems biology perspective, the biological functions arise from complex networks: in this context, concepts like molecular pathways, protein-protein interactions (PPIs), mathematical models and ontologies play an important role for dissecting such complexity. RESULTS In this work we present the Genes-to-Systems Breast Cancer (G2SBC) Database, a resource which integrates data about genes, transcripts and proteins reported in literature as altered in breast cancer cells. Beside the data integration, we provide an ontology based query system and analysis tools related to intracellular pathways, PPIs, protein structure and systems modelling, in order to facilitate the study of breast cancer using a multilevel perspective. The resource is available at the URL http://www.itb.cnr.it/breastcancer. CONCLUSIONS The G2SBC Database represents a systems biology oriented data integration approach devoted to breast cancer. By means of the analysis capabilities provided by the web interface, it is possible to overcome the limits of reductionist resources, enabling predictions that can lead to new experiments.
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Affiliation(s)
- Ettore Mosca
- Institute for Biomedical Technologies, National Research Council, Segrate (Milan), Italy.
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12
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Gatherer D. So what do we really mean when we say that systems biology is holistic? BMC SYSTEMS BIOLOGY 2010; 4:22. [PMID: 20226033 PMCID: PMC2850881 DOI: 10.1186/1752-0509-4-22] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 08/12/2009] [Accepted: 03/12/2010] [Indexed: 01/08/2023]
Abstract
BACKGROUND An old debate has undergone a resurgence in systems biology: that of reductionism versus holism. At least 35 articles in the systems biology literature since 2003 have touched on this issue. The histories of holism and reductionism in the philosophy of biology are reviewed, and the current debate in systems biology is placed in context. RESULTS Inter-theoretic reductionism in the strict sense envisaged by its creators from the 1930s to the 1960s is largely impractical in biology, and was effectively abandoned by the early 1970s in favour of a more piecemeal approach using individual reductive explanations. Classical holism was a stillborn theory of the 1920s, but the term survived in several fields as a loose umbrella designation for various kinds of anti-reductionism which often differ markedly. Several of these different anti-reductionisms are on display in the holistic rhetoric of the recent systems biology literature. This debate also coincides with a time when interesting arguments are being proposed within the philosophy of biology for a new kind of reductionism. CONCLUSIONS Engaging more deeply with these issues should sharpen our ideas concerning the philosophy of systems biology and its future best methodology. As with previous decisive moments in the history of biology, only those theories that immediately suggest relatively easy experiments will be winners.
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Affiliation(s)
- Derek Gatherer
- MRC Virology Unit, Institute of Virology, University of Glasgow, Church Street, Glasgow G11 5JR, UK.
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13
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Milanesi L, Romano P, Castellani G, Remondini D, Liò P. Trends in modeling Biomedical Complex Systems. BMC Bioinformatics 2009; 10 Suppl 12:I1. [PMID: 19828068 PMCID: PMC2762057 DOI: 10.1186/1471-2105-10-s12-i1] [Citation(s) in RCA: 14] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
In this paper we provide an introduction to the techniques for multi-scale complex biological systems, from the single bio-molecule to the cell, combining theoretical modeling, experiments, informatics tools and technologies suitable for biological and biomedical research, which are becoming increasingly multidisciplinary, multidimensional and information-driven. The most important concepts on mathematical modeling methodologies and statistical inference, bioinformatics and standards tools to investigate complex biomedical systems are discussed and the prominent literature useful to both the practitioner and the theoretician are presented.
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Affiliation(s)
- Luciano Milanesi
- Institute of Biomedical Technology, National Research Council, Milan, Italy
| | - Paolo Romano
- Bioinformatics, National Cancer Research Institute, Genoa, Italy
| | - Gastone Castellani
- Physics Department of Bologna University, Galvani Center for Biocomplexity and INFN Italy
| | - Daniel Remondini
- Physics Department of Bologna University, Galvani Center for Biocomplexity and INFN Italy
| | - Petro Liò
- Computer Laboratory, University of Cambridge, Cambridge, UK
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14
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Fauré A, Thieffry D. Logical modelling of cell cycle control in eukaryotes: a comparative study. MOLECULAR BIOSYSTEMS 2009; 5:1569-81. [PMID: 19763341 DOI: 10.1039/b907562n] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Dynamical modelling is at the core of the systems biology paradigm. However, the development of comprehensive quantitative models is complicated by the daunting complexity of regulatory networks controlling crucial biological processes such as cell division, the paucity of currently available quantitative data, as well as the limited reproducibility of large-scale experiments. In this context, qualitative modelling approaches offer a useful alternative or complementary framework to build and analyse simplified, but still rigorous dynamical models. This point is illustrated here by analysing recent logical models of the molecular network controlling mitosis in different organisms, from yeasts to mammals. After a short introduction covering cell cycle and logical modelling, we compare the assumptions and properties underlying these different models. Next, leaning on their transposition into a common logical framework, we compare their functional structure in terms of regulatory circuits. Finally, we discuss assets and prospects of qualitative approaches for the modelling of the cell cycle.
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Affiliation(s)
- Adrien Fauré
- Aix-Marseille University & INSERM U928-TAGC, Marseille, France.
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15
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Qutub AA, Mac Gabhann F, Karagiannis ED, Vempati P, Popel AS. Multiscale models of angiogenesis. ACTA ACUST UNITED AC 2009; 28:14-31. [PMID: 19349248 DOI: 10.1109/memb.2009.931791] [Citation(s) in RCA: 102] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Vascular disease, cancer, stroke, neurodegeneration, diabetes, inflammation, asthma, obesity, arthritis--the list of conditions that involve angiogenesis reads like main chapters in a book on pathology. Angiogenesis, the growth of capillaries from preexisting vessels, also occurs in normal physiology, in response to exercise or in the process of wound healing.Why and when is angiogenesis prevalent? What controls the process? How can we intelligently control it? These are the key questions driving researchers in fields as diverse as cell biology, oncology, cardiology, neurology, biomathematics, systems biology, and biomedical engineering. As bioengineers, we approach angiogenesis as a complex, interconnected system of events occurring in sequence and in parallel, on multiple levels, triggered by a main stimulus, e.g., hypoxia.
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Affiliation(s)
- Amina A Qutub
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA.
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16
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Antezana E, Egaña M, Blondé W, Illarramendi A, Bilbao I, De Baets B, Stevens R, Mironov V, Kuiper M. The Cell Cycle Ontology: an application ontology for the representation and integrated analysis of the cell cycle process. Genome Biol 2009; 10:R58. [PMID: 19480664 PMCID: PMC2718524 DOI: 10.1186/gb-2009-10-5-r58] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2008] [Revised: 04/17/2009] [Accepted: 05/29/2009] [Indexed: 01/26/2023] Open
Abstract
A software resource for the analysis of cell cycle related molecular networks. The Cell Cycle Ontology ( is an application ontology that automatically captures and integrates detailed knowledge on the cell cycle process. Cell Cycle Ontology is enabled by semantic web technologies, and is accessible via the web for browsing, visualizing, advanced querying, and computational reasoning. Cell Cycle Ontology facilitates a detailed analysis of cell cycle-related molecular network components. Through querying and automated reasoning, it may provide new hypotheses to help steer a systems biology approach to biological network building.
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Affiliation(s)
- Erick Antezana
- Department of Plant Systems Biology, VIB, Technologiepark 927, B-9052 Gent, Belgium.
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17
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Abstract
One of the early success stories of computational systems biology was the work done on cell-cycle regulation. The earliest mathematical descriptions of cell-cycle control evolved into very complex, detailed computational models that describe the regulation of cell division in many different cell types. On the way these models predicted several dynamical properties and unknown components of the system that were later experimentally verified/identified. Still, research on this field is far from over. We need to understand how the core cell-cycle machinery is controlled by internal and external signals, also in yeast cells and in the more complex regulatory networks of higher eukaryotes. Furthermore, there are many computational challenges what we face as new types of data appear thanks to continuing advances in experimental techniques. We have to deal with cell-to-cell variations, revealed by single cell measurements, as well as the tremendous amount of data flowing from high throughput machines. We need new computational concepts and tools to handle these data and develop more detailed, more precise models of cell-cycle regulation in various organisms. Here we review past and present of computational modeling of cell-cycle regulation, and discuss possible future directions of the field.
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Affiliation(s)
- Attila Csikász-Nagy
- The Microsoft Research - University of Trento Centre for Computational and Systems Biology, Piazza Manci 17, Povo-Trento I-38100, Italy.
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18
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Frutos R, Viari A, Vachiéry N, Boyer F, Lefrançois T, Martinez D. Emergence and potential of high-throughput and integrative approaches in pathology. Ann N Y Acad Sci 2009; 1149:62-5. [PMID: 19120175 DOI: 10.1196/annals.1428.060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
In recent years a major revolution has occurred in the analysis and understanding of pathogenesis and host-pathogens/parasite interactions. This revolution has been achieved through the emergence of the high-throughput integrative approaches used in the "omics" fields-such as genomics, transcriptomics, proteomics, interactomics, and metabolomics. The novelty of these approaches has resulted from the development of high-throughput apparatus, assisted by the increasing power and software of computers that allow for high-speed, multifactorial simultaneous analysis of numerous samples. This level of integration allows for in-depth analysis of mechanisms, pace, and patterns of the evolution and adaptation of pathogens. This evolution from linear to multifactorial approaches has opened new ways of creating and characterizing new vaccines, diagnostic candidates, and drug targets.
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Affiliation(s)
- Roger Frutos
- Cirad, TA A-15/G, Campus International de Baillarguet, Montpellier, France
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19
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Abstract
Systems biology aims at a quantitative understanding of systemic behaviour as a function of its components and their interactions. In systems biology studies computer models play an important role: (i) to integrate the components' behaviour and (ii) to analyse experimental data sets. With the growing number of kinetic models that are being constructed for parts of biological systems, it has become important to store these models and make them available in a standard form, such that these models can be combined, eventually leading to a model of a complete system. In the present chapter we describe database initiatives that contain kinetic models for biological systems, together with a number of other systems biology resources related to kinetic modelling.
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20
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Alves R, Vilaprinyo E, Hernández-Bermejo B, Sorribas A. Mathematical formalisms based on approximated kinetic representations for modeling genetic and metabolic pathways. Biotechnol Genet Eng Rev 2008; 25:1-40. [DOI: 10.5661/bger-25-1] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
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