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Carvalho NRG, He Y, Smadbeck P, Flannick J, Mercader JM, Udler M, Manrai AK, Moreno J, Patel CJ. Assessing the genetic contribution of cumulative behavioral factors associated with longitudinal type 2 diabetes risk highlights adiposity and the brain-metabolic axis. medRxiv 2024:2024.01.30.24302019. [PMID: 38352440 PMCID: PMC10863013 DOI: 10.1101/2024.01.30.24302019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/19/2024]
Abstract
While genetic factors, behavior, and environmental exposures form a complex web of interrelated associations in type 2 diabetes (T2D), their interaction is poorly understood. Here, using data from ~500K participants of the UK Biobank, we identify the genetic determinants of a "polyexposure risk score" (PXS) a new risk factor that consists of an accumulation of 25 associated individual-level behaviors and environmental risk factors that predict longitudinal T2D incidence. PXS-T2D had a non-zero heritability (h2 = 0.18) extensive shared genetic architecture with established clinical and biological determinants of T2D, most prominently with body mass index (genetic correlation [rg] = 0.57) and Homeostatic Model Assessment for Insulin Resistance (rg = 0.51). Genetic loci associated with PXS-T2D were enriched for expression in the brain. Biobank scale data with genetic information illuminates how complex and cumulative exposures and behaviors as a whole impact T2D risk but whose biology have been elusive in genome-wide studies of T2D.
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Affiliation(s)
- Nuno R. G. Carvalho
- School of Biological Sciences; Georgia Institute of Technology; Atlanta, GA, 30332, USA
| | - Yixuan He
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT, Boston, MA, 02142, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, 02114, USA
| | - Patrick Smadbeck
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT, Boston, MA, 02142, USA
| | - Jason Flannick
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT, Boston, MA, 02142, USA
- Division of Genetics and Genomics, Boston Children’s Hospital, Boston, MA, 02115, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, 02115, USA
| | - Josep M. Mercader
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT, Boston, MA, 02142, USA
| | - Miriam Udler
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT, Boston, MA, 02142, USA
| | - Arjun K Manrai
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, 02115, USA
| | - Jordi Moreno
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT, Boston, MA, 02142, USA
- Department of Medicine, Harvard Medical School, Boston, MA, 02115, USA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA
| | - Chirag J. Patel
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, 02115, USA
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2
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Costanzo MC, Roselli C, Brandes M, Duby M, Hoang Q, Jang D, Koesterer R, Kudtarkar P, Moriondo A, Nguyen T, Ruebenacker O, Smadbeck P, Sun Y, Butterworth AS, Aragam KG, Lumbers RT, Khera AV, Lubitz SA, Ellinor PT, Gaulton KJ, Flannick J, Burtt NP. Cardiovascular Disease Knowledge Portal: A Community Resource for Cardiovascular Disease Research. Circ Genom Precis Med 2023; 16:e004181. [PMID: 37814896 PMCID: PMC10843166 DOI: 10.1161/circgen.123.004181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/11/2023]
Affiliation(s)
- Maria C. Costanzo
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Carolina Roselli
- Precision Cardiology Laboratory, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Cardiology, University of Groningen, University Medical Center Groningen, The Netherlands
| | - MacKenzie Brandes
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Marc Duby
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Quy Hoang
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Dongkeun Jang
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Ryan Koesterer
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Parul Kudtarkar
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - Annie Moriondo
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Trang Nguyen
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Oliver Ruebenacker
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Patrick Smadbeck
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Ying Sun
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - Adam S. Butterworth
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- NIHR Blood and Transplant Research Unit in Donor Health and Behaviour, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Cambridge Centre of Excellence, Division of Cardiovascular Medicine, Addenbrooke’s Hospital, Cambridge, UK
- Victor Phillip Dahdaleh Heart & Lung Research Institute, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, University of Cambridge, Cambridge, UK
| | - Krishna G. Aragam
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - R. Thomas Lumbers
- British Heart Foundation Research Accelerator, University College London, London, UK
- Institute of Health Informatics, University College London, London, UK
- Health Data Research UK London, University College London, London, UK
- Bart’s Heart Centre, St. Bartholomew’s Hospital, London, UK
| | - Amit V. Khera
- Verve Therapeutics, Boston, MA, USA
- Division of Cardiology, Brigham and Women’s Hospital, Boston, MA, USA
| | - Steven A. Lubitz
- Cardiovascular Disease Initiative, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Demoulas Center for Cardiac Arrhythmias, Massachusetts General Hospital, Boston, MA, USA
| | - Patrick T. Ellinor
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Demoulas Center for Cardiac Arrhythmias, Massachusetts General Hospital, Boston, MA, USA
| | - Kyle J. Gaulton
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - Jason Flannick
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Pediatrics, Boston Children’s Hospital, Boston, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Noël P. Burtt
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
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Costanzo MC, von Grotthuss M, Massung J, Jang D, Caulkins L, Koesterer R, Gilbert C, Welch RP, Kudtarkar P, Hoang Q, Boughton AP, Singh P, Sun Y, Duby M, Moriondo A, Nguyen T, Smadbeck P, Alexander BR, Brandes M, Carmichael M, Dornbos P, Green T, Huellas-Bruskiewicz KC, Ji Y, Kluge A, McMahon AC, Mercader JM, Ruebenacker O, Sengupta S, Spalding D, Taliun D, Smith P, Thomas MK, Akolkar B, Brosnan MJ, Cherkas A, Chu AY, Fauman EB, Fox CS, Kamphaus TN, Miller MR, Nguyen L, Parsa A, Reilly DF, Ruetten H, Wholley D, Zaghloul NA, Abecasis GR, Altshuler D, Keane TM, McCarthy MI, Gaulton KJ, Florez JC, Boehnke M, Burtt NP, Flannick J. The Type 2 Diabetes Knowledge Portal: An open access genetic resource dedicated to type 2 diabetes and related traits. Cell Metab 2023; 35:695-710.e6. [PMID: 36963395 PMCID: PMC10231654 DOI: 10.1016/j.cmet.2023.03.001] [Citation(s) in RCA: 27] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 10/23/2022] [Accepted: 02/28/2023] [Indexed: 03/26/2023]
Abstract
Associations between human genetic variation and clinical phenotypes have become a foundation of biomedical research. Most repositories of these data seek to be disease-agnostic and therefore lack disease-focused views. The Type 2 Diabetes Knowledge Portal (T2DKP) is a public resource of genetic datasets and genomic annotations dedicated to type 2 diabetes (T2D) and related traits. Here, we seek to make the T2DKP more accessible to prospective users and more useful to existing users. First, we evaluate the T2DKP's comprehensiveness by comparing its datasets with those of other repositories. Second, we describe how researchers unfamiliar with human genetic data can begin using and correctly interpreting them via the T2DKP. Third, we describe how existing users can extend their current workflows to use the full suite of tools offered by the T2DKP. We finally discuss the lessons offered by the T2DKP toward the goal of democratizing access to complex disease genetic results.
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Affiliation(s)
- Maria C Costanzo
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02132, USA
| | - Marcin von Grotthuss
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02132, USA
| | - Jeffrey Massung
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02132, USA
| | - Dongkeun Jang
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02132, USA
| | - Lizz Caulkins
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02132, USA
| | - Ryan Koesterer
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02132, USA
| | - Clint Gilbert
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02132, USA
| | - Ryan P Welch
- Department of Biostatistics and The Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Parul Kudtarkar
- Department of Pediatrics, University of California San Diego, La Jolla, CA 92161, USA
| | - Quy Hoang
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02132, USA
| | - Andrew P Boughton
- Department of Biostatistics and The Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Preeti Singh
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02132, USA
| | - Ying Sun
- Department of Pediatrics, University of California San Diego, La Jolla, CA 92161, USA
| | - Marc Duby
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02132, USA
| | - Annie Moriondo
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02132, USA
| | - Trang Nguyen
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02132, USA
| | - Patrick Smadbeck
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02132, USA
| | - Benjamin R Alexander
- Simulation and Modeling Sciences, Pfizer Worldwide Research, Development and Medical, Cambridge, MA 02139, USA
| | - MacKenzie Brandes
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02132, USA
| | - Mary Carmichael
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02132, USA
| | - Peter Dornbos
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02132, USA; Department of Pediatrics, Boston Children's Hospital, Boston, MA 02115, USA; Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA
| | - Todd Green
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02132, USA
| | - Kenneth C Huellas-Bruskiewicz
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02132, USA
| | - Yue Ji
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Alexandria Kluge
- Genomics Platform, The Broad Institute of MIT and Harvard, Cambridge, MA 02132, USA
| | - Aoife C McMahon
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Josep M Mercader
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02132, USA; Department of Medicine, Harvard Medical School, Boston, MA 02115, USA; Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Oliver Ruebenacker
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02132, USA
| | - Sebanti Sengupta
- Department of Biostatistics and The Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Dylan Spalding
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Daniel Taliun
- Department of Biostatistics and The Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Philip Smith
- National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD 20892, USA
| | - Melissa K Thomas
- Tailored Therapeutics-Diabetes, Eli Lilly and Company, Lilly Corporate Center DC 0545, Indianapolis, IN 46285, USA
| | - Beena Akolkar
- National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD 20892, USA
| | - M Julia Brosnan
- Internal Medicine Research Unit, Pfizer Worldwide Research, Development and Medical, Cambridge, MA 02139, USA
| | - Andriy Cherkas
- Team Early Projects Type 1 Diabetes, Therapeutic Area Diabetes and Cardiovascular Medicine, Research & Development, Sanofi, Industriepark Höchst-H831, Frankfurt am Main 65926, Germany
| | - Audrey Y Chu
- Merck Research Laboratories, Boston, MA 02115, USA
| | - Eric B Fauman
- Integrative Biology, Internal Medicine Research Unit, Pfizer Worldwide Research, Development and Medical, Cambridge, MA 02139, USA
| | | | | | - Melissa R Miller
- Internal Medicine Research Unit, Pfizer Worldwide Research, Development and Medical, Cambridge, MA 02139, USA
| | - Lynette Nguyen
- Foundation for the National Institutes of Health, North Bethesda, MD 20852, USA
| | - Afshin Parsa
- National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD 20892, USA
| | | | - Hartmut Ruetten
- CardioMetabolism & Respiratory Medicine, Boehringer Ingelheim International GmbH, 55216 Ingelheim/Rhein, Germany
| | - David Wholley
- Foundation for the National Institutes of Health, North Bethesda, MD 20852, USA
| | - Norann A Zaghloul
- National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD 20892, USA
| | - Gonçalo R Abecasis
- Department of Biostatistics and The Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA; Regeneron Pharmaceuticals, Tarrytown, NY 10591, USA
| | - David Altshuler
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02132, USA
| | - Thomas M Keane
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Mark I McCarthy
- Wellcome Centre for Human Genetics, University of Oxford, Oxford OX3 9DU, UK; Oxford Centre for Diabetes Endocrinology & Metabolism, University of Oxford, Oxford OX3 7BN, UK
| | - Kyle J Gaulton
- Department of Pediatrics, University of California San Diego, La Jolla, CA 92161, USA
| | - Jose C Florez
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02132, USA; Department of Medicine, Harvard Medical School, Boston, MA 02115, USA; Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Michael Boehnke
- Department of Biostatistics and The Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Noël P Burtt
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02132, USA.
| | - Jason Flannick
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02132, USA; Department of Pediatrics, Boston Children's Hospital, Boston, MA 02115, USA; Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA.
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Smadbeck P, Stumpf MPH. Coalescent models for developmental biology and the spatio-temporal dynamics of growing tissues. J R Soc Interface 2016; 13:rsif.2016.0112. [PMID: 27053656 PMCID: PMC4874433 DOI: 10.1098/rsif.2016.0112] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2016] [Accepted: 03/11/2016] [Indexed: 01/01/2023] Open
Abstract
Development is a process that needs to be tightly coordinated in both space and time. Cell tracking and lineage tracing have become important experimental techniques in developmental biology and allow us to map the fate of cells and their progeny. A generic feature of developing and homeostatic tissues that these analyses have revealed is that relatively few cells give rise to the bulk of the cells in a tissue; the lineages of most cells come to an end quickly. Computational and theoretical biologists/physicists have, in response, developed a range of modelling approaches, most notably agent-based modelling. These models seem to capture features observed in experiments, but can also become computationally expensive. Here, we develop complementary genealogical models of tissue development that trace the ancestry of cells in a tissue back to their most recent common ancestors. We show that with both bounded and unbounded growth simple, but universal scaling relationships allow us to connect coalescent theory with the fractal growth models extensively used in developmental biology. Using our genealogical perspective, it is possible to study bulk statistical properties of the processes that give rise to tissues of cells, without the need for large-scale simulations.
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Affiliation(s)
- Patrick Smadbeck
- Centre for Integrative Systems Biology, Imperial College London, London SW7 2AZ, UK
| | - Michael P H Stumpf
- Centre for Integrative Systems Biology, Imperial College London, London SW7 2AZ, UK
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Abstract
We present elements of a stability theory for small, stochastic, nonlinear chemical reaction networks. Steady state probability distributions are computed with zero-information (ZI) closure, a closure algorithm that solves chemical master equations of small arbitrary nonlinear reactions. Stochastic models can be linearized around the steady state with ZI-closure, and the eigenvalues of the Jacobian matrix can be readily computed. Eigenvalues govern the relaxation of fluctuation autocorrelation functions at steady state. Autocorrelation functions reveal the time scales of phenomena underlying the dynamics of nonlinear reaction networks. In accord with the fluctuation-dissipation theorem, these functions are found to be congruent to response functions to small perturbations. Significant differences are observed in the stability of nonlinear reacting systems between deterministic and stochastic modeling formalisms.
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Affiliation(s)
- Patrick Smadbeck
- Department of Chemical Engineering and Materials Science, University of Minnesota, 421 Washington Ave. SE, Minneapolis, Minnesota 55455, USA
| | - Yiannis N Kaznessis
- Department of Chemical Engineering and Materials Science, University of Minnesota, 421 Washington Ave. SE, Minneapolis, Minnesota 55455, USA
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Abstract
Stochasticity in the dynamics of small reacting systems requires discrete-probabilistic models of reaction kinetics instead of traditional continuous-deterministic ones. The master probability equation is a complete model of randomly evolving molecular populations. Because of its ambitious character, the master equation remained unsolved for all but the simplest of molecular interaction networks. With the first solution of chemical master equations, a wide range of experimental observations of small-system interactions may be mathematically conceptualized.
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Affiliation(s)
- Patrick Smadbeck
- Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, MN 55455, USA
| | - Yiannis N Kaznessis
- Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, MN 55455, USA
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Volzing K, Biliouris K, Smadbeck P, Kaznessis Y. Computer-Aided Design of Synthetic Biological Constructs with the Synthetic Biology Software Suite. Synth Biol (Oxf) 2013. [DOI: 10.1016/b978-0-12-394430-6.00007-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022] Open
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Arkin AP, Baker D, Biliouris K, Bokinsky G, Bundy BC, Carrara P, Church GM, Cirino PC, Cobb RE, Eriksen DT, Freestone T, Fussenegger M, Groff D, Guimaraes JC, Heng BC, Huang S, Itaya M, Jewett MC, Kaznessis Y, Keasling J, Kim JE, Kim TY, Kim YB, Kuruma Y, Lee SY, Li S, Liu CC, Luisi PL, Luo Y, Ma S, Mee MT, de Souza TP, Qian S, Ranji A, Richter F, Schmidt-Dannert C, Shin JH, Smadbeck P, Smith RP, Sohn SB, Stano P, Tang N, Tanouchi Y, Tian J, Tikh I, Volzing K, Wang HH, Wu JC, You L, Zhao H, Zhou JX. Contributors. Synth Biol (Oxf) 2013. [DOI: 10.1016/b978-0-12-394430-6.00022-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
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9
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Abstract
As stochastic simulations become increasingly common in biological research, tools for analysis of such systems are in demand. The deterministic analogue to stochastic models, a set of probability moment equations equivalent to the Chemical Master Equation (CME), offers the possibility of a priori analysis of systems without the need for computationally costly Monte Carlo simulations. Despite the drawbacks of the method, in particular non-linearity in even the simplest of cases, the use of moment equations combined with moment-closure techniques has been used effectively in many fields. The techniques currently available to generate moment equations rely upon analytical expressions that are not efficient upon scaling. Additionally, the resulting moment-dependent matrix is lower diagonal and demands massive memory allocation in extreme cases. Here it is demonstrated that by utilizing factorial moments and the probability generating function (the Z-transform of the probability distribution) a recursive algorithm is produced. The resulting method is scalable and particularly efficient when high-order moments are required. The matrix produced is banded and often demands substantially less memory resources.
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Affiliation(s)
- P. Smadbeck
- Department of Chemical Engineering and Materials Science, University of Minnesota, 421 Washington Ave SE, Minneapolis, MN 55455, USA
| | - Y. N. Kaznessis
- Department of Chemical Engineering and Materials Science, University of Minnesota, 421 Washington Ave SE, Minneapolis, MN 55455, USA
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10
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Abstract
In the present work, we address a major challenge facing the modeling of biochemical reaction networks: when using stochastic simulations, the computational load and number of unknown parameters may dramatically increase with system size and complexity. A proposed solution to this challenge is the reduction of models by utilizing nonlinear reaction rate laws in place of a complex multi-reaction mechanism. This type of model reduction in stochastic systems often fails when applied outside of the context in which it was initially conceived. We hypothesize that the use of nonlinear rate laws fails because a single reaction is inherently Poisson distributed and cannot match higher order statistics. In this study we explore the use of Hill-type rate laws as an approximation for gene regulation, specifically transcription repression. We matched output data for several simple gene networks to determine Hill-type parameters. We show that the models exhibit inaccuracies when placed into a simple feedback repression model. By adding an additional abstract reaction to the models we account for second-order statistics. This split Hill rate law matches higher order statistics and demonstrates that the new model is able to more accurately describe the mean protein output. Finally, the modified Hill model is shown to be modular and models retain accuracy when placed into a larger multi-gene network. The work as presented may be used in gene regulatory or cell-signaling networks, where multiple binding events can be captured by Hill kinetics. The added benefit of the proposed split-Hill kinetics is the improved accuracy in modeling stochastic effects. We demonstrate these benefits with a few specific reaction network examples.
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Affiliation(s)
- Patrick Smadbeck
- Department of Chemical Engineering and Materials Science, University of Minnesota, 421 Washington Ave SE, Minneapolis, Minnesota 55455, USA
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