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Konur S, Gheorghe M, Krasnogor N. Verifiable biology. J R Soc Interface 2023; 20:20230019. [PMID: 37160165 PMCID: PMC10169095 DOI: 10.1098/rsif.2023.0019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/11/2023] Open
Abstract
The formalization of biological systems using computational modelling approaches as an alternative to mathematical-based methods has recently received much interest because computational models provide a deeper mechanistic understanding of biological systems. In particular, formal verification, complementary approach to standard computational techniques such as simulation, is used to validate the system correctness and obtain critical information about system behaviour. In this study, we survey the most frequently used computational modelling approaches and formal verification techniques for computational biology. We compare a number of verification tools and software suites used to analyse biological systems and biochemical networks, and to verify a wide range of biological properties. For users who have no expertise in formal verification, we present a novel methodology that allows them to easily apply formal verification techniques to analyse their biological or biochemical system of interest.
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Affiliation(s)
- Savas Konur
- Department of Computer Science, University of Bradford, Richmond Building, Bradford BD7 1DP, UK
| | - Marian Gheorghe
- Department of Computer Science, University of Bradford, Richmond Building, Bradford BD7 1DP, UK
| | - Natalio Krasnogor
- School of Computing Science, Newcastle University, Science Square, Newcastle upon Tyne NE4 5TG, UK
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Millar-Wilson A, Ward Ó, Duffy E, Hardiman G. Multiscale modeling in the framework of biological systems and its potential for spaceflight biology studies. iScience 2022; 25:105421. [DOI: 10.1016/j.isci.2022.105421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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3
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Tamm K, Peets T, Engelbrecht J. Mechanical waves in myelinated axons. Biomech Model Mechanobiol 2022; 21:1285-1297. [PMID: 35704223 DOI: 10.1007/s10237-022-01591-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 05/01/2022] [Indexed: 11/29/2022]
Abstract
The propagation of an action potential in nerves is accompanied by mechanical and thermal effects. Several mathematical models explain the deformation of the unmyelinated axon wall (a mechanical wave). In this paper, the deformation of the myelinated axon wall is studied. The mathematical model is inspired by the mechanics of microstructured materials with multiple scales. The model involves a Boussinesq-type equation together with a modification that describes the process in the myelin sheath. The dispersion analysis of such a model explains the behaviour of group and phase velocities. In addition, it is shown how dissipative effects may influence the process. Numerical calculations demonstrate the changes in velocities and wave profiles in the myelinated axon wall.
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Affiliation(s)
- Kert Tamm
- Department of Cybernetics, Tallinn University of Technology, Akadeemia tee 21, 12618, Tallinn, Harjumaa, Estonia.
| | - Tanel Peets
- Department of Cybernetics, Tallinn University of Technology, Akadeemia tee 21, 12618, Tallinn, Harjumaa, Estonia
| | - Jüri Engelbrecht
- Department of Cybernetics, Tallinn University of Technology, Akadeemia tee 21, 12618, Tallinn, Harjumaa, Estonia
- The Estonian Academy of Sciences, Kohtu 6, 10130, Tallinn, Harjumaa, Estonia
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Engelbrecht J, Tamm K, Peets T. Modelling of processes in nerve fibres at the interface of physiology and mathematics. Biomech Model Mechanobiol 2020; 19:2491-2498. [PMID: 32500424 DOI: 10.1007/s10237-020-01350-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Accepted: 05/23/2020] [Indexed: 01/03/2023]
Abstract
The in silico simulations are widely used in contemporary systems biology including the analysis of nerve pulse propagation. As known from numerous experiments, the propagation of an action potential is accompanied by mechanical and thermal effects. This calls for an analysis at the interface of physics, physiology and mathematics. In this paper, the background of the model equations governing the effects in nerve fibres is analysed from a physical viewpoint and then discussed how to unite them into a system by using the coupling forces. The leading hypothesis associates the coupling to the changes of variables, not to their values or amplitudes. This hypothesis models actually the physiological mechanisms of energy transductions in a fibre. The general assumptions in modelling the processes and the properties of the coupled system of equations are described. The dimensionless mathematical model which couples the action potential with mechanical waves together with temperature effects is presented in "Appendix". This model generates an ensemble of waves including the electrical signal and mechanical and thermal effects.
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Affiliation(s)
- Jüri Engelbrecht
- Department of Cybernetics, Tallinn University of Technology, Akadeemia tee 21, 12618, Tallinn, Estonia.
| | - Kert Tamm
- Department of Cybernetics, Tallinn University of Technology, Akadeemia tee 21, 12618, Tallinn, Estonia
| | - Tanel Peets
- Department of Cybernetics, Tallinn University of Technology, Akadeemia tee 21, 12618, Tallinn, Estonia
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Light and chemical oscillations: Review and perspectives. JOURNAL OF PHOTOCHEMISTRY AND PHOTOBIOLOGY C-PHOTOCHEMISTRY REVIEWS 2020. [DOI: 10.1016/j.jphotochemrev.2019.100321] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Zhang JD, Sach-Peltason L, Kramer C, Wang K, Ebeling M. Multiscale modelling of drug mechanism and safety. Drug Discov Today 2020; 25:519-534. [DOI: 10.1016/j.drudis.2019.12.009] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2019] [Revised: 12/06/2019] [Accepted: 12/23/2019] [Indexed: 12/19/2022]
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Abstract
In this chapter we consider in silico modeling of diseases starting from some simple to some complex (and mathematical) concepts. Examples and applications of in silico modeling for some important categories of diseases (such as for cancers, infectious diseases, and neuronal diseases) are also given.
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Machine Learning and Artificial Intelligence in Neurocritical Care: a Specialty-Wide Disruptive Transformation or a Strategy for Success. Curr Neurol Neurosci Rep 2019; 19:89. [PMID: 31720867 DOI: 10.1007/s11910-019-0998-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
PURPOSE OF REVIEW Neurocritical care combines the complexity of both medical and surgical disease states with the inherent limitations of assessing patients with neurologic injury. Artificial intelligence (AI) has garnered interest in the basic management of these complicated patients as data collection becomes increasingly automated. RECENT FINDINGS In this opinion article, we highlight the potential AI has in aiding the clinician in several aspects of neurocritical care, particularly in monitoring and managing intracranial pressure, seizures, hemodynamics, and ventilation. The model-based method and data-driven method are currently the two major AI methods for analyzing critical care data. Both are able to analyze the vast quantities of patient data that are accumulated in the neurocritical care unit. AI has the potential to reduce healthcare costs, minimize delays in patient management, and reduce medical errors. However, these systems are an aid to, not a replacement for, the clinician's judgment.
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Al-Mufti F, Dodson V, Lee J, Wajswol E, Gandhi C, Scurlock C, Cole C, Lee K, Mayer SA. Artificial intelligence in neurocritical care. J Neurol Sci 2019; 404:1-4. [PMID: 31302258 DOI: 10.1016/j.jns.2019.06.024] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2019] [Revised: 06/16/2019] [Accepted: 06/22/2019] [Indexed: 01/31/2023]
Abstract
BACKGROUND Neurocritical care combines the management of extremely complex disease states with the inherent limitations of clinically assessing patients with brain injury. As the management of neurocritical care patients can be immensely complicated, the automation of data-collection and basic management by artificial intelligence systems have garnered interest. METHODS In this opinion article, we highlight the potential artificial intelligence has in monitoring and managing several aspects of neurocritical care, specifically intracranial pressure, seizure monitoring, blood pressure, and ventilation. RESULTS The two major AI methods of analytical technique currently exist for analyzing critical care data: the model-based method and data driven method. Both of these methods have demonstrated an ability to analyze vast quantities of patient data, and we highlight the ways in which these modalities of artificial intelligence might one day play a role in neurocritical care. CONCLUSIONS While none of these artificial intelligence systems are meant to replace the clinician's judgment, these systems have the potential to reduce healthcare costs and errors or delays in medical management.
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Affiliation(s)
- Fawaz Al-Mufti
- Departments of Neurosurgery, Westchester Medical Center, New York Medical College, Valhalla, NY, United States of America; Departments of Neurology, Westchester Medical Center at New York Medical College, Valhalla, NY, United States of America.
| | - Vincent Dodson
- Department of Neurosurgery, Rutgers University, New Jersey Medical School, Newark, NJ, United States of America
| | - James Lee
- Department of Neurosurgery, Rutgers University, New Jersey Medical School, Newark, NJ, United States of America; Department of Neurology, Rutgers University, Robert Wood Johnson Medical School, New Brunswick, NJ, United States of America
| | - Ethan Wajswol
- Department of Neurosurgery, Rutgers University, New Jersey Medical School, Newark, NJ, United States of America
| | - Chirag Gandhi
- Departments of Neurosurgery, Westchester Medical Center, New York Medical College, Valhalla, NY, United States of America; Departments of Neurology, Westchester Medical Center at New York Medical College, Valhalla, NY, United States of America
| | - Corey Scurlock
- Departments of Anesthesiology, Westchester Medical Center at New York Medical College, Valhalla, NY, United States of America; Departments of Internal Medicine, Westchester Medical Center at New York Medical College, Valhalla, NY, United States of America
| | - Chad Cole
- Departments of Neurosurgery, Westchester Medical Center, New York Medical College, Valhalla, NY, United States of America
| | - Kiwon Lee
- Department of Neurosurgery, Rutgers University, New Jersey Medical School, Newark, NJ, United States of America; Department of Neurology, Rutgers University, Robert Wood Johnson Medical School, New Brunswick, NJ, United States of America
| | - Stephan A Mayer
- Department of Neurology, Henry Ford Health System, Detroit, MI, United States of America
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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: 1.2] [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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12
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Mukherjee S, Berghout P, Van den Akker HE. A lattice boltzmann approach to surfactant-laden emulsions. AIChE J 2018. [DOI: 10.1002/aic.16451] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Siddhartha Mukherjee
- Dept. of Chemical Engineering, Faculty of Applied Sciences Section of Transport Phenomena, Delft University of Technology; 2629 HZ, Delft The Netherlands
| | - Pieter Berghout
- Dept. of Mechanical, Aeronautical and Biomedical Engineering, Faculty of Science and Engineering, Bernal Institute; School of Engineering, University of Limerick Limerick; Ireland
| | - Harry E.A. Van den Akker
- Dept. of Chemical Engineering, Faculty of Applied Sciences Section of Transport Phenomena, Delft University of Technology; 2629 HZ, Delft The Netherlands
- Dept. of Mechanical, Aeronautical and Biomedical Engineering, Faculty of Science and Engineering, Bernal Institute; School of Engineering, University of Limerick Limerick; Ireland
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13
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Some Aspects of Nonlinearity and Self-Organization In Biosystems on Examples of Localized Excitations in the DNA Molecule and Generalized Fisher–KPP Model. Symmetry (Basel) 2018. [DOI: 10.3390/sym10030053] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
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14
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Getz WM, Marshall CR, Carlson CJ, Giuggioli L, Ryan SJ, Romañach SS, Boettiger C, Chamberlain SD, Larsen L, D'Odorico P, O'Sullivan D. Making ecological models adequate. Ecol Lett 2017; 21:153-166. [PMID: 29280332 DOI: 10.1111/ele.12893] [Citation(s) in RCA: 51] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2017] [Revised: 11/07/2017] [Accepted: 11/12/2017] [Indexed: 12/22/2022]
Abstract
Critical evaluation of the adequacy of ecological models is urgently needed to enhance their utility in developing theory and enabling environmental managers and policymakers to make informed decisions. Poorly supported management can have detrimental, costly or irreversible impacts on the environment and society. Here, we examine common issues in ecological modelling and suggest criteria for improving modelling frameworks. An appropriate level of process description is crucial to constructing the best possible model, given the available data and understanding of ecological structures. Model details unsupported by data typically lead to over parameterisation and poor model performance. Conversely, a lack of mechanistic details may limit a model's ability to predict ecological systems' responses to management. Ecological studies that employ models should follow a set of model adequacy assessment protocols that include: asking a series of critical questions regarding state and control variable selection, the determinacy of data, and the sensitivity and validity of analyses. We also need to improve model elaboration, refinement and coarse graining procedures to better understand the relevancy and adequacy of our models and the role they play in advancing theory, improving hind and forecasting, and enabling problem solving and management.
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Affiliation(s)
- Wayne M Getz
- Department of Environmental Science, Policy, and Management, University of California, Berkeley, CA, 94720, USA.,Schools of Mathematical Sciences and Life Sciences, University of KwaZulu, Natal, South Africa
| | - Charles R Marshall
- Museum of Paleontology and Department Integrative Biology, University of California, Berkeley, CA, 94720, USA
| | - Colin J Carlson
- Department of Environmental Science, Policy, and Management, University of California, Berkeley, CA, 94720, USA
| | - Luca Giuggioli
- Bristol Centre for Complexity Sciences, Department of Engineering Mathematics, and School of Biological Sciences, University of Bristol, Bristol, UK
| | - Sadie J Ryan
- Department of Geography, and Emerging Pathogens Institute, University of Florida, Gainesville, FL, 32601, USA.,Schools of Mathematical Sciences and Life Sciences, University of KwaZulu, Natal, South Africa
| | - Stephanie S Romañach
- Wetland and Aquatic Research Center, U.S. Geological Survey, Fort Lauderdale, FL, 33314, USA
| | - Carl Boettiger
- Department of Environmental Science, Policy, and Management, University of California, Berkeley, CA, 94720, USA
| | - Samuel D Chamberlain
- Department of Environmental Science, Policy, and Management, University of California, Berkeley, CA, 94720, USA
| | - Laurel Larsen
- Department of Geography, University of California, Berkeley, CA, 94720, USA
| | - Paolo D'Odorico
- Department of Environmental Science, Policy, and Management, University of California, Berkeley, CA, 94720, USA
| | - David O'Sullivan
- Department of Geography, University of California, Berkeley, CA, 94720, USA
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Materi W, Wishart DS. Computational Systems Biology in Cancer: Modeling Methods and Applications. GENE REGULATION AND SYSTEMS BIOLOGY 2017. [DOI: 10.1177/117762500700100010] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
In recent years it has become clear that carcinogenesis is a complex process, both at the molecular and cellular levels. Understanding the origins, growth and spread of cancer, therefore requires an integrated or system-wide approach. Computational systems biology is an emerging sub-discipline in systems biology that utilizes the wealth of data from genomic, proteomic and metabolomic studies to build computer simulations of intra and intercellular processes. Several useful descriptive and predictive models of the origin, growth and spread of cancers have been developed in an effort to better understand the disease and potential therapeutic approaches. In this review we describe and assess the practical and theoretical underpinnings of commonly-used modeling approaches, including ordinary and partial differential equations, petri nets, cellular automata, agent based models and hybrid systems. A number of computer-based formalisms have been implemented to improve the accessibility of the various approaches to researchers whose primary interest lies outside of model development. We discuss several of these and describe how they have led to novel insights into tumor genesis, growth, apoptosis, vascularization and therapy.
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Affiliation(s)
- Wayne Materi
- National Research Council, National Institute for Nanotechnology (NINT) Edmonton, Alberta, Canada
| | - David S. Wishart
- Departments of Biological Sciences and Computing Science, University of Alberta
- National Research Council, National Institute for Nanotechnology (NINT) Edmonton, Alberta, Canada
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Rodriguez RL, Albeck JG, Taha AY, Ori-McKenney KM, Recanzone GH, Stradleigh TW, Hernandez BC, Tang FYV, Chiang EPI, Cruz-Orengo L. Impact of diet-derived signaling molecules on human cognition: exploring the food-brain axis. NPJ Sci Food 2017; 1:2. [PMID: 31304244 PMCID: PMC6548416 DOI: 10.1038/s41538-017-0002-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2016] [Revised: 08/25/2017] [Accepted: 09/01/2017] [Indexed: 01/02/2023] Open
Abstract
The processes that define mammalian physiology evolved millions of years ago in response to ancient signaling molecules, most of which were acquired by ingestion and digestion. In this way, evolution inextricably linked diet to all major physiological systems including the nervous system. The importance of diet in neurological development is well documented, although the mechanisms by which diet-derived signaling molecules (DSMs) affect cognition are poorly understood. Studies on the positive impact of nutritive and non-nutritive bioactive molecules on brain function are encouraging but lack the statistical power needed to demonstrate strong positive associations. Establishing associations between DSMs and cognitive functions like mood, memory and learning are made even more difficult by the lack of robust phenotypic markers that can be used to accurately and reproducibly measure the effects of DSMs. Lastly, it is now apparent that processes like neurogenesis and neuroplasticity are embedded within layers of interlocked signaling pathways and gene regulatory networks. Within these interdependent pathways and networks, the various transducers of DSMs are used combinatorially to produce those emergent adaptive gene expression responses needed for stimulus-induced neurogenesis and neuroplasticity. Taken together, it appears that cognition is encoded genomically and modified by epigenetics and epitranscriptomics to produce complex transcriptional programs that are exquisitely sensitive to signaling molecules from the environment. Models for how DSMs mediate the interplay between the environment and various neuronal processes are discussed in the context of the food-brain axis.
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Affiliation(s)
- Raymond L. Rodriguez
- Department of Molecular and Cellular Biology, College of Biological Sciences, One Shields Avenue, University of California, Davis, Davis, CA 95616 USA
| | - John G. Albeck
- Department of Molecular and Cellular Biology, College of Biological Sciences, One Shields Avenue, University of California, Davis, Davis, CA 95616 USA
| | - Ameer Y. Taha
- Department of Food Science and Technology, College of Agriculture and Environmental Sciences, One Shields Avenue, University of California, Davis, Davis, CA 95616 USA
| | - Kassandra M. Ori-McKenney
- Department of Molecular and Cellular Biology, College of Biological Sciences, One Shields Avenue, University of California, Davis, Davis, CA 95616 USA
| | - Gregg H. Recanzone
- Department of Neurobiology, Physiology and Behavior, College of Biological Sciences, One Shields Avenue, University of California, Davis, Davis, CA 95616 USA
- Center for Neuroscience, College of Biological Sciences, University of California, Davis, Davis, CA 95616 USA
| | - Tyler W. Stradleigh
- Department of Neurobiology, Physiology and Behavior, College of Biological Sciences, One Shields Avenue, University of California, Davis, Davis, CA 95616 USA
- Center for Neuroscience, College of Biological Sciences, University of California, Davis, Davis, CA 95616 USA
- Department of Psychiatry and Behavioral Sciences, School of Medicine, University of California, Davis, Davis, CA 95616 USA
| | - Bronte C. Hernandez
- Department of Molecular and Cellular Biology, College of Biological Sciences, One Shields Avenue, University of California, Davis, Davis, CA 95616 USA
| | | | - En-Pei Isabel Chiang
- Department of Food Science and Biotechnology, National Chung Hsing University, Taichung, Taiwan
- Agricultural Biotechnology Center, National Chung Hsing University, Taichung, Taiwan
| | - Lillian Cruz-Orengo
- Department of Anatomy, Physiology & Cell Biology, School of Veterinary Medicine, University of California, Davis, Davis, CA 95616 USA
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Berlin R, Gruen R, Best J. Systems Medicine-Complexity Within, Simplicity Without. JOURNAL OF HEALTHCARE INFORMATICS RESEARCH 2017; 1:119-137. [PMID: 28713872 PMCID: PMC5491616 DOI: 10.1007/s41666-017-0002-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2016] [Revised: 04/12/2017] [Accepted: 04/25/2017] [Indexed: 12/14/2022]
Abstract
This paper presents a brief history of Systems Theory, progresses to Systems Biology, and its relation to the more traditional investigative method of reductionism. The emergence of Systems Medicine represents the application of Systems Biology to disease and clinical issues. The challenges faced by this transition from Systems Biology to Systems Medicine are explained; the requirements of physicians at the bedside, caring for patients, as well as the place of human-human interaction and the needs of the patients are addressed. An organ-focused transition to Systems Medicine, rather than a genomic-, molecular-, or cell-based effort is emphasized. Organ focus represents a middle-out approach to ease this transition and to maximize the benefits of scientific discovery and clinical application. This method manages the perceptions of time and space, the massive amounts of human- and patient-related data, and the ensuing complexity of information.
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Affiliation(s)
- Richard Berlin
- Department of Computer Science, University of Illinois, Urbana, IL USA
| | - Russell Gruen
- Nanyang Institute of Technology in Health and Medicine, Department of Surgery, Lee Kong Chian School of Medicine, Singapore, Singapore
| | - James Best
- Lee Kong Chian School of Medicine, Singapore, Singapore
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18
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Jeanquartier F, Jean-Quartier C, Cemernek D, Holzinger A. In silico modeling for tumor growth visualization. BMC SYSTEMS BIOLOGY 2016; 10:59. [PMID: 27503052 PMCID: PMC4977902 DOI: 10.1186/s12918-016-0318-8] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/10/2016] [Accepted: 07/12/2016] [Indexed: 12/18/2022]
Abstract
BACKGROUND Cancer is a complex disease. Fundamental cellular based studies as well as modeling provides insight into cancer biology and strategies to treatment of the disease. In silico models complement in vivo models. Research on tumor growth involves a plethora of models each emphasizing isolated aspects of benign and malignant neoplasms. Biologists and clinical scientists are often overwhelmed by the mathematical background knowledge necessary to grasp and to apply a model to their own research. RESULTS We aim to provide a comprehensive and expandable simulation tool to visualizing tumor growth. This novel Web-based application offers the advantage of a user-friendly graphical interface with several manipulable input variables to correlate different aspects of tumor growth. By refining model parameters we highlight the significance of heterogeneous intercellular interactions on tumor progression. Within this paper we present the implementation of the Cellular Potts Model graphically presented through Cytoscape.js within a Web application. The tool is available under the MIT license at https://github.com/davcem/cpm-cytoscape and http://styx.cgv.tugraz.at:8080/cpm-cytoscape/ . CONCLUSION In-silico methods overcome the lack of wet experimental possibilities and as dry method succeed in terms of reduction, refinement and replacement of animal experimentation, also known as the 3R principles. Our visualization approach to simulation allows for more flexible usage and easy extension to facilitate understanding and gain novel insight. We believe that biomedical research in general and research on tumor growth in particular will benefit from the systems biology perspective.
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Affiliation(s)
- Fleur Jeanquartier
- Holzinger Group, Research Unit HCI-KDD, Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Auenbruggerplatz 2/V, 8036, AT, Graz, Austria
| | - Claire Jean-Quartier
- Holzinger Group, Research Unit HCI-KDD, Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Auenbruggerplatz 2/V, 8036, AT, Graz, Austria
| | - David Cemernek
- Holzinger Group, Research Unit HCI-KDD, Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Auenbruggerplatz 2/V, 8036, AT, Graz, Austria
| | - Andreas Holzinger
- Holzinger Group, Research Unit HCI-KDD, Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Auenbruggerplatz 2/V, 8036, AT, Graz, Austria. .,Institute of Information Systems and Computer Media, Graz University of Technology, Inffeldgasse 16c, Graz, 8010, AT, Austria.
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19
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Schmidt JM, De Georgia M. Multimodality monitoring: informatics, integration data display and analysis. Neurocrit Care 2015; 21 Suppl 2:S229-38. [PMID: 25208675 DOI: 10.1007/s12028-014-0037-1] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
The goal of multimodality neuromonitoring is to provide continuous, real-time assessment of brain physiology to prevent, detect, and attenuate secondary brain injury. Clinical informatics deals with biomedical data, information, and knowledge including their acquisition, storage, retrieval, and optimal use for clinical decision-making. An electronic literature search was conducted for English language articles describing the use of informatics in the intensive care unit setting from January 1990 to August 2013. A total of 64 studies were included in this review. Clinical informatics infrastructure should be adopted that enables a wide range of linear and nonlinear analytical methods be applied to patient data. Specific time epochs of clinical interest should be reviewable. Analysis strategies of monitor alarms may help address alarm fatigue. Ergonomic data display that present results from analyses with clinical information in a sensible uncomplicated manner improve clinical decision-making. Collecting and archiving the highest resolution physiologic and phenotypic data in a comprehensive open format data warehouse is a crucial first step toward information management and two-way translational research for multimodality monitoring. The infrastructure required is largely the same as that needed for telemedicine intensive care applications, which under the right circumstances improves care quality while reducing cost.
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Affiliation(s)
- J Michael Schmidt
- Division of Critical Care Neurology, Neurological Institute, Columbia University College of Physicians and Surgeons, 177 Fort Washington Avenue, MHB Suite 8-300, New York, NY, 10032, USA,
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Cilfone NA, Kirschner DE, Linderman JJ. Strategies for efficient numerical implementation of hybrid multi-scale agent-based models to describe biological systems. Cell Mol Bioeng 2015; 8:119-136. [PMID: 26366228 PMCID: PMC4564133 DOI: 10.1007/s12195-014-0363-6] [Citation(s) in RCA: 61] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Biologically related processes operate across multiple spatiotemporal scales. For computational modeling methodologies to mimic this biological complexity, individual scale models must be linked in ways that allow for dynamic exchange of information across scales. A powerful methodology is to combine a discrete modeling approach, agent-based models (ABMs), with continuum models to form hybrid models. Hybrid multi-scale ABMs have been used to simulate emergent responses of biological systems. Here, we review two aspects of hybrid multi-scale ABMs: linking individual scale models and efficiently solving the resulting model. We discuss the computational choices associated with aspects of linking individual scale models while simultaneously maintaining model tractability. We demonstrate implementations of existing numerical methods in the context of hybrid multi-scale ABMs. Using an example model describing Mycobacterium tuberculosis infection, we show relative computational speeds of various combinations of numerical methods. Efficient linking and solution of hybrid multi-scale ABMs is key to model portability, modularity, and their use in understanding biological phenomena at a systems level.
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Affiliation(s)
- Nicholas A. Cilfone
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Denise E. Kirschner
- Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI, USA
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21
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Drug release through liposome pores. Colloids Surf B Biointerfaces 2015; 126:80-6. [DOI: 10.1016/j.colsurfb.2014.11.042] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2014] [Revised: 11/23/2014] [Accepted: 11/25/2014] [Indexed: 11/19/2022]
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Gray A, Harlen OG, Harris SA, Khalid S, Leung YM, Lonsdale R, Mulholland AJ, Pearson AR, Read DJ, Richardson RA. In pursuit of an accurate spatial and temporal model of biomolecules at the atomistic level: a perspective on computer simulation. ACTA CRYSTALLOGRAPHICA SECTION D: BIOLOGICAL CRYSTALLOGRAPHY 2015; 71:162-72. [PMID: 25615870 PMCID: PMC4304696 DOI: 10.1107/s1399004714026777] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/07/2014] [Accepted: 12/05/2014] [Indexed: 11/15/2022]
Abstract
Despite huge advances in the computational techniques available for simulating biomolecules at the quantum-mechanical, atomistic and coarse-grained levels, there is still a widespread perception amongst the experimental community that these calculations are highly specialist and are not generally applicable by researchers outside the theoretical community. In this article, the successes and limitations of biomolecular simulation and the further developments that are likely in the near future are discussed. A brief overview is also provided of the experimental biophysical methods that are commonly used to probe biomolecular structure and dynamics, and the accuracy of the information that can be obtained from each is compared with that from modelling. It is concluded that progress towards an accurate spatial and temporal model of biomacromolecules requires a combination of all of these biophysical techniques, both experimental and computational.
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Affiliation(s)
- Alan Gray
- The Edinburgh Parallel Computing Centre, The University of Edinburgh, Edinburgh EH9 3JZ, Scotland
| | - Oliver G. Harlen
- School of Mathematics, University of Leeds, Leeds LS2 9JT, England
| | - Sarah A. Harris
- School of Physics and Astronomy, University of Leeds, Leeds LS2 9JT, England
- Astbury Centre for Structural Molecular Biology, University of Leeds, Leeds LS2 9JT, England
- Correspondence e-mail:
| | - Syma Khalid
- Faculty of Natural and Environmental Sciences, University of Southampton, Southampton SO17 1BJ, England
| | - Yuk Ming Leung
- Faculty of Natural and Environmental Sciences, University of Southampton, Southampton SO17 1BJ, England
| | - Richard Lonsdale
- Max-Planck-Institut für Kohlenforschung, Kaiser-Wilhelm-Platz 1, 45470 Mülheim an der Ruhr, Germany
- Department of Chemistry, Philipps-Universität Marburg, Hans-Meerwein Strasse, 35032 Marburg, Germany
| | - Adrian J. Mulholland
- Centre for Computational Chemistry, University of Bristol, Bristol BS8 1TS, England
| | - Arwen R. Pearson
- Astbury Centre for Structural Molecular Biology, University of Leeds, Leeds LS2 9JT, England
- Hamburg Centre for Ultrafast Imaging, University of Hamburg, Hamburg, Germany
| | - Daniel J. Read
- School of Mathematics, University of Leeds, Leeds LS2 9JT, England
| | - Robin A. Richardson
- School of Physics and Astronomy, University of Leeds, Leeds LS2 9JT, England
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Castiglione F, Pappalardo F, Bianca C, Russo G, Motta S. Modeling biology spanning different scales: an open challenge. BIOMED RESEARCH INTERNATIONAL 2014; 2014:902545. [PMID: 25143952 PMCID: PMC4124842 DOI: 10.1155/2014/902545] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/16/2014] [Accepted: 06/25/2014] [Indexed: 02/03/2023]
Abstract
It is coming nowadays more clear that in order to obtain a unified description of the different mechanisms governing the behavior and causality relations among the various parts of a living system, the development of comprehensive computational and mathematical models at different space and time scales is required. This is one of the most formidable challenges of modern biology characterized by the availability of huge amount of high throughput measurements. In this paper we draw attention to the importance of multiscale modeling in the framework of studies of biological systems in general and of the immune system in particular.
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Affiliation(s)
- Filippo Castiglione
- Institute for Applied Mathematics, National Research Council of Italy, Rome, Italy
| | | | - Carlo Bianca
- Theoretical Physics of Condensed Matter, Sorbonne Universities, UPMC Univ Paris 6, 75252 Paris Cedex 05, France
- UMR 7600 LPTMC, CNRS, 75252 Paris Cedex 05, France
| | - Giulia Russo
- Department of Pharmaceutical Sciences, University of Catania, Catania, Italy
| | - Santo Motta
- Department of Mathematics and Computer Science, University of Catania, 95125 Catania, Italy
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Kirschner DE, Hunt CA, Marino S, Fallahi-Sichani M, Linderman JJ. Tuneable resolution as a systems biology approach for multi-scale, multi-compartment computational models. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2014; 6:289-309. [PMID: 24810243 PMCID: PMC4102180 DOI: 10.1002/wsbm.1270] [Citation(s) in RCA: 46] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/04/2013] [Revised: 03/14/2014] [Accepted: 03/19/2014] [Indexed: 01/19/2023]
Abstract
The use of multi-scale mathematical and computational models to study complex biological processes is becoming increasingly productive. Multi-scale models span a range of spatial and/or temporal scales and can encompass multi-compartment (e.g., multi-organ) models. Modeling advances are enabling virtual experiments to explore and answer questions that are problematic to address in the wet-lab. Wet-lab experimental technologies now allow scientists to observe, measure, record, and analyze experiments focusing on different system aspects at a variety of biological scales. We need the technical ability to mirror that same flexibility in virtual experiments using multi-scale models. Here we present a new approach, tuneable resolution, which can begin providing that flexibility. Tuneable resolution involves fine- or coarse-graining existing multi-scale models at the user's discretion, allowing adjustment of the level of resolution specific to a question, an experiment, or a scale of interest. Tuneable resolution expands options for revising and validating mechanistic multi-scale models, can extend the longevity of multi-scale models, and may increase computational efficiency. The tuneable resolution approach can be applied to many model types, including differential equation, agent-based, and hybrid models. We demonstrate our tuneable resolution ideas with examples relevant to infectious disease modeling, illustrating key principles at work. WIREs Syst Biol Med 2014, 6:225–245. doi:10.1002/wsbm.1270 How to cite this article:WIREs Syst Biol Med 2014, 6:289–309. doi:10.1002/wsbm.1270
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Affiliation(s)
- Denise E Kirschner
- Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI, USA
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Villaverde AF, Banga JR. Reverse engineering and identification in systems biology: strategies, perspectives and challenges. J R Soc Interface 2014; 11:20130505. [PMID: 24307566 PMCID: PMC3869153 DOI: 10.1098/rsif.2013.0505] [Citation(s) in RCA: 163] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2013] [Accepted: 11/12/2013] [Indexed: 12/17/2022] Open
Abstract
The interplay of mathematical modelling with experiments is one of the central elements in systems biology. The aim of reverse engineering is to infer, analyse and understand, through this interplay, the functional and regulatory mechanisms of biological systems. Reverse engineering is not exclusive of systems biology and has been studied in different areas, such as inverse problem theory, machine learning, nonlinear physics, (bio)chemical kinetics, control theory and optimization, among others. However, it seems that many of these areas have been relatively closed to outsiders. In this contribution, we aim to compare and highlight the different perspectives and contributions from these fields, with emphasis on two key questions: (i) why are reverse engineering problems so hard to solve, and (ii) what methods are available for the particular problems arising from systems biology?
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Affiliation(s)
| | - Julio R. Banga
- BioProcess Engineering Group, IIM-CSIC, Spanish National Research Council, Vigo 36208, Spain
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莫 有. Cellular Automata Modeling of HIV-Immune System. Biophysics (Nagoya-shi) 2014. [DOI: 10.12677/biphy.2014.21001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
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Abstract
Complex biological systems operate under non-equilibrium conditions and exhibit emergent properties associated with correlated spatial and temporal structures. These properties may be individually unpredictable, but tend to be governed by power-law probability distributions and/or correlation. This article reviews the concepts that are invoked in the treatment of complex systems through a wide range of respiratory-related examples. Following a brief historical overview, some of the tools to characterize structural variabilities and temporal fluctuations associated with complex systems are introduced. By invoking the concept of percolation, the notion of multiscale behavior and related modeling issues are discussed. Spatial complexity is then examined in the airway and parenchymal structures with implications for gas exchange followed by a short glimpse of complexity at the cellular and subcellular network levels. Variability and complexity in the time domain are then reviewed in relation to temporal fluctuations in airway function. Next, an attempt is given to link spatial and temporal complexities through examples of airway opening and lung tissue viscoelasticity. Specific examples of possible and more direct clinical implications are also offered through examples of optimal future treatment of fibrosis, exacerbation risk prediction in asthma, and a novel method in mechanical ventilation. Finally, the potential role of the science of complexity in the future of physiology, biology, and medicine is discussed.
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Affiliation(s)
- Béla Suki
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA.
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Shublaq N, Sansom C, Coveney PV. Patient-specific modelling in drug design, development and selection including its role in clinical decision-making. Chem Biol Drug Des 2013; 81:5-12. [PMID: 22765044 DOI: 10.1111/j.1747-0285.2012.01444.x] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Genomics has made enormous progress in the twelve years since the publication of the first draft human genome sequence, but it has not yet been translated into the clinic. Despite spiralling development costs, the number of new drug registrations is not increasing. One reason for this lies in the genetic complexity of disease. Most diseases involve dysregulation in pathways that involve many genes, and many (including most cancers) are themselves genetically heterogeneous. Systems biology involves the multi-level simulation of physiology, cell biology and biochemistry using complex computational techniques. We show here using case studies in cancer and HIV how such computational models, and particularly models based on individual patient data, can be used for drug design and development, and in the selection of the appropriate treatment for a given patient in the face of resistance mutations. If these techniques are to be adopted in routine clinical practice, clinicians will need better training in modern approaches to the integrated analysis of large-scale heterogeneous data and multi-scale models, while developers will need to provide much more usable tools. Investment in computational infrastructure is needed so that results can be returned on clinically relevant timescales and data warehouses designed with data protection as well as accessibility in mind.
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Affiliation(s)
- Nour Shublaq
- Centre for Computational Science & Computational Life & Medical Sciences Network, University College London, London, UK
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Karagiannis GS, Berk A, Dimitromanolakis A, Diamandis EP. Enrichment map profiling of the cancer invasion front suggests regulation of colorectal cancer progression by the bone morphogenetic protein antagonist, gremlin-1. Mol Oncol 2013; 7:826-39. [PMID: 23659962 DOI: 10.1016/j.molonc.2013.04.002] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2013] [Revised: 03/20/2013] [Accepted: 04/05/2013] [Indexed: 12/21/2022] Open
Abstract
The cancer invasion front (CIF), a spatially-recognized area due to the frequent presence of peritumoral desmoplastic reaction, represents a cancer site where many hallmarks of cancer metastasis occur. It is now strongly suggested that the desmoplastic microenvironment holds crucial information for determining tumor development and progression. Despite extensive research on tumor-host cell interactions at CIFs, the exact paracrine molecular network that is hardwired into the proteome of the stromal and cancer subpopulations remains partially understood. Here, we interrogated the signaling pathways and the molecular functional signatures across the proteome of a desmoplastic coculture model system of colorectal cancer progression. We discovered a group of bone morphogenetic protein (BMP) antagonists that coordinates major biological programs in CIFs, including cell proliferation, invasion, migration and differentiation processes. Using a mathematical model of cancer cell progression, coupled to in vitro cell migration assays, we demonstrated that the prominent BMP antagonist gremlin-1 (GREM1) may trigger motility of cancer cell cohorts. Our data collectively demonstrate that the desmoplastic CIFs deploy a microenvironmental signature, based on BMP antagonism, in order to regulate the motogenic fates of cancer cell cohorts invading the adjacent stroma.
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Affiliation(s)
- George S Karagiannis
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada
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Abstract
BACKGROUND The concept of conserved processes presents unique opportunities for using nonhuman animal models in biomedical research. However, the concept must be examined in the context that humans and nonhuman animals are evolved, complex, adaptive systems. Given that nonhuman animals are examples of living systems that are differently complex from humans, what does the existence of a conserved gene or process imply for inter-species extrapolation? METHODS We surveyed the literature including philosophy of science, biological complexity, conserved processes, evolutionary biology, comparative medicine, anti-neoplastic agents, inhalational anesthetics, and drug development journals in order to determine the value of nonhuman animal models when studying conserved processes. RESULTS Evolution through natural selection has employed components and processes both to produce the same outcomes among species but also to generate different functions and traits. Many genes and processes are conserved, but new combinations of these processes or different regulation of the genes involved in these processes have resulted in unique organisms. Further, there is a hierarchy of organization in complex living systems. At some levels, the components are simple systems that can be analyzed by mathematics or the physical sciences, while at other levels the system cannot be fully analyzed by reducing it to a physical system. The study of complex living systems must alternate between focusing on the parts and examining the intact whole organism while taking into account the connections between the two. Systems biology aims for this holism. We examined the actions of inhalational anesthetic agents and anti-neoplastic agents in order to address what the characteristics of complex living systems imply for inter-species extrapolation of traits and responses related to conserved processes. CONCLUSION We conclude that even the presence of conserved processes is insufficient for inter-species extrapolation when the trait or response being studied is located at higher levels of organization, is in a different module, or is influenced by other modules. However, when the examination of the conserved process occurs at the same level of organization or in the same module, and hence is subject to study solely by reductionism, then extrapolation is possible.
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Affiliation(s)
- Ray Greek
- Americans For Medical Advancement (www.AFMA-curedisease.org), 2251 Refugio Rd, Goleta, CA, 93117, USA
| | - Mark J Rice
- Department of Anesthesiology, University of Florida College of Medicine, PO Box 100254, Gainesville, FL, 32610-0254, USA
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Helikar T, Kowal B, McClenathan S, Bruckner M, Rowley T, Madrahimov A, Wicks B, Shrestha M, Limbu K, Rogers JA. The Cell Collective: toward an open and collaborative approach to systems biology. BMC SYSTEMS BIOLOGY 2012; 6:96. [PMID: 22871178 PMCID: PMC3443426 DOI: 10.1186/1752-0509-6-96] [Citation(s) in RCA: 134] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/27/2012] [Accepted: 07/16/2012] [Indexed: 03/04/2023]
Abstract
BACKGROUND Despite decades of new discoveries in biomedical research, the overwhelming complexity of cells has been a significant barrier to a fundamental understanding of how cells work as a whole. As such, the holistic study of biochemical pathways requires computer modeling. Due to the complexity of cells, it is not feasible for one person or group to model the cell in its entirety. RESULTS The Cell Collective is a platform that allows the world-wide scientific community to create these models collectively. Its interface enables users to build and use models without specifying any mathematical equations or computer code - addressing one of the major hurdles with computational research. In addition, this platform allows scientists to simulate and analyze the models in real-time on the web, including the ability to simulate loss/gain of function and test what-if scenarios in real time. CONCLUSIONS The Cell Collective is a web-based platform that enables laboratory scientists from across the globe to collaboratively build large-scale models of various biological processes, and simulate/analyze them in real time. In this manuscript, we show examples of its application to a large-scale model of signal transduction.
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Affiliation(s)
- Tomáš Helikar
- Department of Mathematics, University of Nebraska at Omaha, Omaha, NE, USA.
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Coveney PV, Swadling JB, Wattis JAD, Greenwell HC. Theory, modelling and simulation in origins of life studies. Chem Soc Rev 2012; 41:5430-46. [PMID: 22677708 DOI: 10.1039/c2cs35018a] [Citation(s) in RCA: 53] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Origins of life studies represent an exciting and highly multidisciplinary research field. In this review we focus on the contributions made by theory, modelling and simulation to addressing fundamental issues in the domain and the advances these approaches have helped to make in the field. Theoretical approaches will continue to make a major impact at the "systems chemistry" level based on the analysis of the remarkable properties of nonlinear catalytic chemical reaction networks, which arise due to the auto-catalytic and cross-catalytic nature of so many of the putative processes associated with self-replication and self-reproduction. In this way, we describe inter alia nonlinear kinetic models of RNA replication within a primordial Darwinian soup, the origins of homochirality and homochiral polymerization. We then discuss state-of-the-art computationally-based molecular modelling techniques that are currently being deployed to investigate various scenarios relevant to the origins of life.
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Affiliation(s)
- Peter V Coveney
- Centre for Computational Science, Department of Chemistry, UCL, 20 Gordon Street, London, WC1H 0AJ, UK.
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Li X, Qian L, Bittner ML, Dougherty ER. A Systems Biology Approach in Therapeutic Response Study for Different Dosing Regimens-a Modeling Study of Drug Effects on Tumor Growth using Hybrid Systems. Cancer Inform 2012; 11:41-60. [PMID: 22442626 PMCID: PMC3298374 DOI: 10.4137/cin.s8185] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Motivated by the frustration of translation of research advances in the molecular and cellular biology of cancer into treatment, this study calls for cross-disciplinary efforts and proposes a methodology of incorporating drug pharmacology information into drug therapeutic response modeling using a computational systems biology approach. The objectives are two fold. The first one is to involve effective mathematical modeling in the drug development stage to incorporate preclinical and clinical data in order to decrease costs of drug development and increase pipeline productivity, since it is extremely expensive and difficult to get the optimal compromise of dosage and schedule through empirical testing. The second objective is to provide valuable suggestions to adjust individual drug dosing regimens to improve therapeutic effects considering most anticancer agents have wide inter-individual pharmacokinetic variability and a narrow therapeutic index. A dynamic hybrid systems model is proposed to study drug antitumor effect from the perspective of tumor growth dynamics, specifically the dosing and schedule of the periodic drug intake, and a drug’s pharmacokinetics and pharmacodynamics information are linked together in the proposed model using a state-space approach. It is proved analytically that there exists an optimal drug dosage and interval administration point, and demonstrated through simulation study.
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Affiliation(s)
- Xiangfang Li
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA
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Banerji A, Ghosh I. Fractal symmetry of protein interior: what have we learned? Cell Mol Life Sci 2011; 68:2711-37. [PMID: 21614471 PMCID: PMC11114926 DOI: 10.1007/s00018-011-0722-6] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2011] [Revised: 04/21/2011] [Accepted: 05/03/2011] [Indexed: 10/18/2022]
Abstract
The application of fractal dimension-based constructs to probe the protein interior dates back to the development of the concept of fractal dimension itself. Numerous approaches have been tried and tested over a course of (almost) 30 years with the aim of elucidating the various facets of symmetry of self-similarity prevalent in the protein interior. In the last 5 years especially, there has been a startling upsurge of research that innovatively stretches the limits of fractal-based studies to present an array of unexpected results on the biophysical properties of protein interior. In this article, we introduce readers to the fundamentals of fractals, reviewing the commonality (and the lack of it) between these approaches before exploring the patterns in the results that they produced. Clustering the approaches in major schools of protein self-similarity studies, we describe the evolution of fractal dimension-based methodologies. The genealogy of approaches (and results) presented here portrays a clear picture of the contemporary state of fractal-based studies in the context of the protein interior. To underline the utility of fractal dimension-based measures further, we have performed a correlation dimension analysis on all of the available non-redundant protein structures, both at the level of an individual protein and at the level of structural domains. In this investigation, we were able to separately quantify the self-similar symmetries in spatial correlation patterns amongst peptide-dipole units, charged amino acids, residues with the π-electron cloud and hydrophobic amino acids. The results revealed that electrostatic environments in the interiors of proteins belonging to 'α/α toroid' (all-α class) and 'PLP-dependent transferase-like' domains (α/β class) are highly conducive. In contrast, the interiors of 'zinc finger design' ('designed proteins') and 'knottins' ('small proteins') were identified as folds with the least conducive electrostatic environments. The fold 'conotoxins' (peptides) could be unambiguously identified as one type with the least stability. The same analyses revealed that peptide-dipoles in the α/β class of proteins, in general, are more correlated to each other than are the peptide-dipoles in proteins belonging to the all-α class. Highly favorable electrostatic milieu in the interiors of TIM-barrel, α/β-hydrolase structures could explain their remarkably conserved (evolutionary) stability from a new light. Finally, we point out certain inherent limitations of fractal constructs before attempting to identify the areas and problems where the implementation of fractal dimension-based constructs can be of paramount help to unearth latent information on protein structural properties.
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Affiliation(s)
- Anirban Banerji
- Bioinformatics Centre, University of Pune, Maharashtra, India.
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Qu Z, Garfinkel A, Weiss JN, Nivala M. Multi-scale modeling in biology: how to bridge the gaps between scales? PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2011; 107:21-31. [PMID: 21704063 DOI: 10.1016/j.pbiomolbio.2011.06.004] [Citation(s) in RCA: 95] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2011] [Accepted: 06/11/2011] [Indexed: 11/25/2022]
Abstract
Human physiological functions are regulated across many orders of magnitude in space and time. Integrating the information and dynamics from one scale to another is critical for the understanding of human physiology and the treatment of diseases. Multi-scale modeling, as a computational approach, has been widely adopted by researchers in computational and systems biology. A key unsolved issue is how to represent appropriately the dynamical behaviors of a high-dimensional model of a lower scale by a low-dimensional model of a higher scale, so that it can be used to investigate complex dynamical behaviors at even higher scales of integration. In the article, we first review the widely-used different modeling methodologies and their applications at different scales. We then discuss the gaps between different modeling methodologies and between scales, and discuss potential methods for bridging the gaps between scales.
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Affiliation(s)
- Zhilin Qu
- Department of Medicine (Cardiology), David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA.
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Abstract
Dramatic advances in molecular biology dominated twentieth century biomedical science and delineated the function of individual genes and molecules in exquisite detail. However, biological processes cannot be fully understood based on the properties of individual genes and molecules alone, since these elements act in concert to enable the specific functions that make for living cells and organisms. The discipline of systems biology provides a novel conceptual framework for understanding biological phenomenon. Systems biology synthesizes information concerning the interactions of genes and molecules and allows characterization of the supramolecular networks and functional modules that represent the most essential aspects of cell organization and physiology.
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Abstract
The Virtual Physiological Human is synonymous with a programme in computational biomedicine that aims to develop a framework of methods and technologies to investigate the human body as a whole. It is predicated on the transformational character of information technology, brought to bear on that most crucial of human concerns, our own health and well-being.
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Affiliation(s)
- Peter V. Coveney
- Centre for Computational Science, University College London, 20 Gordon Street, London WC1H 0AJ, UK
| | - Vanessa Diaz
- Department of Mechanical Engineering, University College London, Torrington Place, London WC1E 7JE, UK
| | - Peter Hunter
- Auckland Bioengineering Institute, The University of Auckland, Auckland 1142, New Zealand
| | - Peter Kohl
- Heart Science Centre, Imperial College, Harefield Hospital, Hill End Road, Harefield UB9 6JH, UK
| | - Marco Viceconti
- Medical Technology Laboratory, Instituto Orthopedico Rizzoli, via di Barbiano 1/10, 40136 Bologna, Italy
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Abstract
Systems biology is all about networks. A recent trend has been to associate systems biology exclusively with the study of gene regulatory or protein-interaction networks. However, systems biology approaches can be applied at many other scales, from the subatomic to the ecosystem scales. In this review, we describe studies at the sub-cellular, tissue, whole plant and crop scales and highlight how these studies can be related to systems biology. We discuss the properties of system approaches at each scale as well as their current limits, and pinpoint in each case advances unique to the considered scale but representing potential for the other scales. We conclude by examining plant models bridging different scales and considering the future prospects of plant systems biology.
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Affiliation(s)
- Mikaël Lucas
- Centre for Plant Integrative Biology, University of Nottingham, Nottingham, UK.
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Wan S, Coveney PV. Rapid and accurate ranking of binding affinities of epidermal growth factor receptor sequences with selected lung cancer drugs. J R Soc Interface 2011; 8:1114-27. [PMID: 21227963 DOI: 10.1098/rsif.2010.0609] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023] Open
Abstract
The epidermal growth factor receptor (EGFR) is a major target for drugs in treating lung carcinoma. Mutations in the tyrosine kinase domain of EGFR commonly arise in human cancers, which can cause drug sensitivity or resistance by influencing the relative strengths of drug and ATP-binding. In this study, we investigate the binding affinities of two tyrosine kinase inhibitors--AEE788 and Gefitinib--to EGFR using molecular dynamics simulation. The interactions between these inhibitors and the EGFR kinase domain are analysed using multiple short (ensemble) simulations and the molecular mechanics/Poisson-Boltzmann solvent area (MM/PBSA) method. Here, we show that ensemble simulations correctly rank the binding affinities for these systems: we report the successful ranking of each drug binding to a variety of EGFR sequences and of the two drugs binding to a given sequence, using petascale computing resources, within a few days.
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Affiliation(s)
- Shunzhou Wan
- Centre for Computational Science, Chemistry Department, University College London, 20 Gordon Street, London WC1H 0AJ, UK
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Kell DB. Towards a unifying, systems biology understanding of large-scale cellular death and destruction caused by poorly liganded iron: Parkinson's, Huntington's, Alzheimer's, prions, bactericides, chemical toxicology and others as examples. Arch Toxicol 2010; 84:825-89. [PMID: 20967426 PMCID: PMC2988997 DOI: 10.1007/s00204-010-0577-x] [Citation(s) in RCA: 286] [Impact Index Per Article: 20.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2010] [Accepted: 07/14/2010] [Indexed: 12/11/2022]
Abstract
Exposure to a variety of toxins and/or infectious agents leads to disease, degeneration and death, often characterised by circumstances in which cells or tissues do not merely die and cease to function but may be more or less entirely obliterated. It is then legitimate to ask the question as to whether, despite the many kinds of agent involved, there may be at least some unifying mechanisms of such cell death and destruction. I summarise the evidence that in a great many cases, one underlying mechanism, providing major stresses of this type, entails continuing and autocatalytic production (based on positive feedback mechanisms) of hydroxyl radicals via Fenton chemistry involving poorly liganded iron, leading to cell death via apoptosis (probably including via pathways induced by changes in the NF-κB system). While every pathway is in some sense connected to every other one, I highlight the literature evidence suggesting that the degenerative effects of many diseases and toxicological insults converge on iron dysregulation. This highlights specifically the role of iron metabolism, and the detailed speciation of iron, in chemical and other toxicology, and has significant implications for the use of iron chelating substances (probably in partnership with appropriate anti-oxidants) as nutritional or therapeutic agents in inhibiting both the progression of these mainly degenerative diseases and the sequelae of both chronic and acute toxin exposure. The complexity of biochemical networks, especially those involving autocatalytic behaviour and positive feedbacks, means that multiple interventions (e.g. of iron chelators plus antioxidants) are likely to prove most effective. A variety of systems biology approaches, that I summarise, can predict both the mechanisms involved in these cell death pathways and the optimal sites of action for nutritional or pharmacological interventions.
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Affiliation(s)
- Douglas B Kell
- School of Chemistry and the Manchester Interdisciplinary Biocentre, The University of Manchester, Manchester M1 7DN, UK.
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Terentiev AA, Moldogazieva NT, Shaitan KV. Dynamic proteomics in modeling of the living cell. Protein-protein interactions. BIOCHEMISTRY (MOSCOW) 2010; 74:1586-607. [DOI: 10.1134/s0006297909130112] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Degracia DJ. Towards a dynamical network view of brain ischemia and reperfusion. Part I: background and preliminaries. ACTA ACUST UNITED AC 2010; 3:59-71. [PMID: 21528102 DOI: 10.6030/1939-067x-3.1.59] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
The general failure of neuroprotectants in clinical trials of ischemic stroke points to the possibility of a fundamental blind spot in the current conception of ischemic brain injury, the "ischemic cascade". This is the first in a series of four papers whose purpose is to work towards a revision of the concept of brain ischemia by applying network concepts to develop a bistable model of brain ischemia. This first paper sets the stage for developing the bistable model of brain ischemia. Necessary background in network theory is introduced using examples from developmental biology which, perhaps surprisingly, can be adapted to brain ischemia with only minor modification. Then, to move towards a network model, we extract two core generalizations about brain ischemia from the mass of empirical data. First, we conclude that all changes induced in the brain by ischemia can be classified as either damage mechanisms that contribute to cell death, or stress responses that contribute to cell survival. Second, we move towards formalizing the idea of the "amount of ischemia", I, as a continuous, nonnegative, monotonically increasing quantity. These two generalizations are necessary precursors to reformulating brain ischemia as a bistable network.
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Affiliation(s)
- Donald J Degracia
- Department of Physiology, Wayne State University, Detroit, MI 48201, U.S.A
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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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A systems biology network model for genetic association studies of nicotine addiction and treatment. Pharmacogenet Genomics 2009; 19:538-51. [PMID: 19525886 DOI: 10.1097/fpc.0b013e32832e2ced] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
OBJECTIVE Interpreting genome-scale genetic association data, particularly for complex diseases and phenotypes, requires extensive use of prior knowledge across a broad range of potential biological and environmental influences, spanning many scientific subdisciplines. We suggest that known or hypothesized disease risk factors, and causal mechanisms, can be represented using an ontology, a computational specification of a set of concepts and the relations between them. METHODS We have integrated the expertise of multiple investigators in nicotine pharmacokinetics and pharmacodynamics, nicotine dependence, and clinical smoking cessation outcomes, and represented this knowledge in an ontology-based network model. Our model spans multiple scales, from molecules, genes and cellular pathways, to complex behavioral phenotypes and even environmental factors. To leverage previous and ongoing work in the field of ontology development, we adopt, expand upon and relate elements from existing ontologies whenever possible. RESULTS We discuss several applications of our ontology: to support interdisciplinary research by graphically representing a complex scientific theory, to facilitate meta-analysis across different studies, to highlight potential interactions, and to support statistical analysis and causal modeling. We demonstrate that our ontology can focus hypothesis testing on areas supported by current theory. CONCLUSION We describe how an ontology-based computational representation can be applied to disease risk factors and mechanisms, enabling the use of prior knowledge in large-scale genetic association studies in general. In specific, we have developed an initial Smoking Behavior Risk Ontology to support studies related to the pharmacogenetics of nicotine addiction and treatment.
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Abstract
Recent years have witnessed an explosive growth in available biological data pertaining to autoimmunity research. This includes a tremendous quantity of sequence data (biological structures, genetic and physical maps, pathways, etc.) generated by genome and proteome projects plus extensive clinical and epidemiological data. Autoimmunity research stands to greatly benefit from this data so long as appropriate strategies are available to enable full access to and utilization of this data. The quantity and complexity of this biological data necessitates use of advanced bioinformatics strategies for its efficient retrieval, analysis and interpretation. Major progress has been made in development of specialized tools for storage, analysis and modeling of immunological data, and this has led to development of a whole new field know as immunoinformatics. With advances in novel high-throughput immunology technologies immunoinformatics is transforming understanding of how the immune system functions. This paper reviews advances in the field of immunoinformatics pertinent to autoimmunity research including databases, tools in genomics and proteomics, tools for study of B- and T-cell epitopes, integrative approaches, and web servers.
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Affiliation(s)
- Nikolai Petrovsky
- Flinders Medical Centre/Flinders University, Bedford Park, Adelaide, SA, 5042, Australia
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Lacroix D, Planell JA, Prendergast PJ. Computer-aided design and finite-element modelling of biomaterial scaffolds for bone tissue engineering. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2009; 367:1993-2009. [PMID: 19380322 DOI: 10.1098/rsta.2009.0024] [Citation(s) in RCA: 49] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
Scaffold biomaterials for tissue engineering can be produced in many different ways depending on the applications and the materials used. Most research into new biomaterials is based on an experimental trial-and-error approach that limits the possibility of making many variations to a single material and studying its interaction with its surroundings. Instead, computer simulation applied to tissue engineering can offer a more exhaustive approach to test and screen out biomaterials. In this paper, a review of the current approach in biomaterials designed through computer-aided design (CAD) and through finite-element modelling is given. First we review the approach used in tissue engineering in the development of scaffolds and the interactions existing between biomaterials, cells and mechanical stimuli. Then, scaffold fabrication through CAD is presented and characterization of existing scaffolds through computed images is reviewed. Several case studies of finite-element studies in tissue engineering show the usefulness of computer simulations in determining the mechanical environment of cells when seeded into a scaffold and the proper design of the geometry and stiffness of the scaffold. This creates a need for more advanced studies that include aspects of mechanobiology in tissue engineering in order to be able to predict over time the growth and differentiation of tissues within scaffolds. Finally, current perspectives indicate that more efforts need to be put into the development of such advanced studies, with the removal of technical limitations such as computer power and the inclusion of more accurate biological and genetic processes into the developed algorithms.
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Affiliation(s)
- Damien Lacroix
- Institute for Bioengineering of Catalonia, 08028 Barcelona, Spain.
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Voss A, Schulz S, Schroeder R, Baumert M, Caminal P. Methods derived from nonlinear dynamics for analysing heart rate variability. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2009; 367:277-96. [PMID: 18977726 DOI: 10.1098/rsta.2008.0232] [Citation(s) in RCA: 299] [Impact Index Per Article: 19.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
Methods from nonlinear dynamics (NLD) have shown new insights into heart rate (HR) variability changes under various physiological and pathological conditions, providing additional prognostic information and complementing traditional time- and frequency-domain analyses. In this review, some of the most prominent indices of nonlinear and fractal dynamics are summarized and their algorithmic implementations and applications in clinical trials are discussed. Several of those indices have been proven to be of diagnostic relevance or have contributed to risk stratification. In particular, techniques based on mono- and multifractal analyses and symbolic dynamics have been successfully applied to clinical studies. Further advances in HR variability analysis are expected through multidimensional and multivariate assessments. Today, the question is no longer about whether or not methods from NLD should be applied; however, it is relevant to ask which of the methods should be selected and under which basic and standardized conditions should they be applied.
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Affiliation(s)
- Andreas Voss
- Department of Medical Engineering and Biotechnology, University of Applied Sciences Jena, 07745 Jena, Germany.
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Sadiq SK, Mazzeo MD, Zasada SJ, Manos S, Stoica I, Gale CV, Watson SJ, Kellam P, Brew S, Coveney PV. Patient-specific simulation as a basis for clinical decision-making. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2008; 366:3199-219. [PMID: 18573758 DOI: 10.1098/rsta.2008.0100] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
Patient-specific medical simulation holds the promise of determining tailored medical treatment based on the characteristics of an individual patient (for example, using a genotypic assay of a sequence of DNA). Decision-support systems based on patient-specific simulation can potentially revolutionize the way that clinicians plan courses of treatment for various conditions, ranging from viral infections to arterial abnormalities. Basing medical decisions on the results of simulations that use models derived from data specific to the patient in question means that the effectiveness of a range of potential treatments can be assessed before they are actually administered, preventing the patient from experiencing unnecessary or ineffective treatments. We illustrate the potential for patient-specific simulation by first discussing the scale of the evolving international grid infrastructure that is now available to underpin such applications. We then consider two case studies, one concerned with the treatment of patients with HIV/AIDS and the other addressing neuropathologies associated with the intracranial vasculature. Such patient-specific medical simulations require access to both appropriate patient data and the computational resources on which to perform potentially very large simulations. Computational infrastructure providers need to furnish access to a wide range of different types of resource, typically made available through heterogeneous computational grids, and to institute policies that facilitate the performance of patient-specific simulations on those resources. To support these kinds of simulations, where life and death decisions are being made, computational resource providers must give urgent priority to such jobs, for example by allowing them to pre-empt the queue on a machine and run straight away. We describe systems that enable such priority computing.
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Affiliation(s)
- S Kashif Sadiq
- Centre for Computational Science, Department of Chemistry, University College London, London WC1H 0AJ, UK
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Scott DJ, Manos S, Coveney PV. Design of Electroceramic Materials Using Artificial Neural Networks and Multiobjective Evolutionary Algorithms. J Chem Inf Model 2008; 48:262-73. [DOI: 10.1021/ci700269r] [Citation(s) in RCA: 14] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- D. J. Scott
- Centre for Computational Science, Department of Chemistry, University College London, Christopher Ingold Laboratories, 20 Gordon Street, London WC1H 0AJ, U.K
| | - S. Manos
- Centre for Computational Science, Department of Chemistry, University College London, Christopher Ingold Laboratories, 20 Gordon Street, London WC1H 0AJ, U.K
| | - P. V. Coveney
- Centre for Computational Science, Department of Chemistry, University College London, Christopher Ingold Laboratories, 20 Gordon Street, London WC1H 0AJ, U.K
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