Joseph W. Thornton and Jamie T. Bridgham
- Published in print:
- 2007
- Published Online:
- September 2008
- ISBN:
- 9780199299188
- eISBN:
- 9780191714979
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199299188.003.0016
- Subject:
- Biology, Evolutionary Biology / Genetics
This chapter reviews the use of ancestral gene resurrection to understand how the members of a biologically crucial gene family, the steroid hormone receptors, evolved their diverse and highly ...
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This chapter reviews the use of ancestral gene resurrection to understand how the members of a biologically crucial gene family, the steroid hormone receptors, evolved their diverse and highly specific functions. It also discusses some methodological questions and concerns — particularly related to uncertainty in the reconstruction of ancestral sequences — and point to potential future directions for the budding field of ancestral gene resurrection. Topics covered include the evolution of molecular interactions, steroid hormones and their receptors, evolution of corticoid receptor specificity, and evolution of the MR-aldosterone interaction.Less
This chapter reviews the use of ancestral gene resurrection to understand how the members of a biologically crucial gene family, the steroid hormone receptors, evolved their diverse and highly specific functions. It also discusses some methodological questions and concerns — particularly related to uncertainty in the reconstruction of ancestral sequences — and point to potential future directions for the budding field of ancestral gene resurrection. Topics covered include the evolution of molecular interactions, steroid hormones and their receptors, evolution of corticoid receptor specificity, and evolution of the MR-aldosterone interaction.
Raymond Brun
- Published in print:
- 2009
- Published Online:
- May 2009
- ISBN:
- 9780199552689
- eISBN:
- 9780191720277
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199552689.003.0002
- Subject:
- Physics, Theoretical, Computational, and Statistical Physics
This chapter begins by presenting a statistical definition of state and transport parameters of non-equilibrium flows, starting from the concept of distribution function. It then describes the ...
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This chapter begins by presenting a statistical definition of state and transport parameters of non-equilibrium flows, starting from the concept of distribution function. It then describes the evolution of the system with the Boltzmann equation and with the macroscopic conservation equations. The various types of intermolecular collisions, elastic, inelastic, reactive are taken into account in the collisional balance of the Boltzmann equation and expressions of the corresponding collision frequencies are given. In the appendices, some results on molecular interaction potentials and details on collisional processes are presented, as well as a few elements of tensorial algebra.Less
This chapter begins by presenting a statistical definition of state and transport parameters of non-equilibrium flows, starting from the concept of distribution function. It then describes the evolution of the system with the Boltzmann equation and with the macroscopic conservation equations. The various types of intermolecular collisions, elastic, inelastic, reactive are taken into account in the collisional balance of the Boltzmann equation and expressions of the corresponding collision frequencies are given. In the appendices, some results on molecular interaction potentials and details on collisional processes are presented, as well as a few elements of tensorial algebra.
Emery D. Conrad and John J. Tyson
- Published in print:
- 2006
- Published Online:
- August 2013
- ISBN:
- 9780262195485
- eISBN:
- 9780262257060
- Item type:
- chapter
- Publisher:
- The MIT Press
- DOI:
- 10.7551/mitpress/9780262195485.003.0006
- Subject:
- Mathematics, Mathematical Biology
This chapter explains how cell biologists can make reliable connections between molecular interaction networks and cell behaviors, when intuition fails in all but the simplest cases. It proposes to ...
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This chapter explains how cell biologists can make reliable connections between molecular interaction networks and cell behaviors, when intuition fails in all but the simplest cases. It proposes to make the connection by translating the reaction network into a set of nonlinear differential equations that describe how all the interacting species are changing with time. Differential equations define a vector field in the state space of the network. The vector field points to certain stable attractors, which can be correlated with long-term, stable behavior of the network and of the cell it governs. Transitions from one stable attractor to another represent the responses of the cell to specific perturbations (signals). A natural way to describe the signal-response properties of a regulatory network is in terms of a one-parameter bifurcation diagram, which efficiently displays the stable attractors (steady states and oscillators) and transitions between attractors as signal strength (the “parameter”) varies. These ideas are illustrated with simple examples of linear, hyperbolic, and sigmoidal signal-response curves; bistable switches based on positive feedback or mutual inhibition; and limit cycle oscillators based on substrate depletion, activator-inhibitor interactions, or time-delayed negative feedback.Less
This chapter explains how cell biologists can make reliable connections between molecular interaction networks and cell behaviors, when intuition fails in all but the simplest cases. It proposes to make the connection by translating the reaction network into a set of nonlinear differential equations that describe how all the interacting species are changing with time. Differential equations define a vector field in the state space of the network. The vector field points to certain stable attractors, which can be correlated with long-term, stable behavior of the network and of the cell it governs. Transitions from one stable attractor to another represent the responses of the cell to specific perturbations (signals). A natural way to describe the signal-response properties of a regulatory network is in terms of a one-parameter bifurcation diagram, which efficiently displays the stable attractors (steady states and oscillators) and transitions between attractors as signal strength (the “parameter”) varies. These ideas are illustrated with simple examples of linear, hyperbolic, and sigmoidal signal-response curves; bistable switches based on positive feedback or mutual inhibition; and limit cycle oscillators based on substrate depletion, activator-inhibitor interactions, or time-delayed negative feedback.
Enric Canadell, Marie-Liesse Doublet, and Christophe Iung
- Published in print:
- 2012
- Published Online:
- December 2013
- ISBN:
- 9780199534937
- eISBN:
- 9780191774935
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199534937.003.0010
- Subject:
- Physics, Condensed Matter Physics / Materials
This chapter considers ways to condense the information of the full Brillouin zone in simple chemical terms. The simplest analytical tool devised for this purpose is the density of states (DOS). The ...
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This chapter considers ways to condense the information of the full Brillouin zone in simple chemical terms. The simplest analytical tool devised for this purpose is the density of states (DOS). The discussions cover the calculation and analysis of the DOS; the combined use of DOS and crystal orbital overlap population (COOP); the step-by-step determination of the DOS (the (Pt(NH3)4Cl)2+ chain); density of states and fragment molecular orbital interaction analysis (application to the [(C5H5)M] chains); and transition metal diborides with the AlB2 structure type (a 3D case study).Less
This chapter considers ways to condense the information of the full Brillouin zone in simple chemical terms. The simplest analytical tool devised for this purpose is the density of states (DOS). The discussions cover the calculation and analysis of the DOS; the combined use of DOS and crystal orbital overlap population (COOP); the step-by-step determination of the DOS (the (Pt(NH3)4Cl)2+ chain); density of states and fragment molecular orbital interaction analysis (application to the [(C5H5)M] chains); and transition metal diborides with the AlB2 structure type (a 3D case study).
Avelino Corma and Adolfo Plasencia
- Published in print:
- 2017
- Published Online:
- January 2018
- ISBN:
- 9780262036016
- eISBN:
- 9780262339308
- Item type:
- chapter
- Publisher:
- The MIT Press
- DOI:
- 10.7551/mitpress/9780262036016.003.0007
- Subject:
- Society and Culture, Technology and Society
Avelino Corma, the distinguished research chemist explains why scientific discovery is difficult. He then explains how ‘molecular recognition’ is achieved in nanochemistry, how molecular design and ...
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Avelino Corma, the distinguished research chemist explains why scientific discovery is difficult. He then explains how ‘molecular recognition’ is achieved in nanochemistry, how molecular design and creating nanoreactors with zeolites is carried out in the laboratory to trap nanoparticles and make them react selectively, and what is meant by the ‘sociology of nanoparticles’. The relationship of chemistry with brain function or genome evolution is also considered. He then reflects on the role of chemistry from ancient times, when the discovery and synthesis of ammonia enabled the development of agriculture and societies, to the world as we know it today. The reason why chemistry is a fundamental discipline for balancing our ‘energy basket’ is also discussed, particularly with regard to achieving sustainable development of our planet.Less
Avelino Corma, the distinguished research chemist explains why scientific discovery is difficult. He then explains how ‘molecular recognition’ is achieved in nanochemistry, how molecular design and creating nanoreactors with zeolites is carried out in the laboratory to trap nanoparticles and make them react selectively, and what is meant by the ‘sociology of nanoparticles’. The relationship of chemistry with brain function or genome evolution is also considered. He then reflects on the role of chemistry from ancient times, when the discovery and synthesis of ammonia enabled the development of agriculture and societies, to the world as we know it today. The reason why chemistry is a fundamental discipline for balancing our ‘energy basket’ is also discussed, particularly with regard to achieving sustainable development of our planet.
Erik De Schutter (ed.)
- Published in print:
- 2009
- Published Online:
- August 2013
- ISBN:
- 9780262013277
- eISBN:
- 9780262258722
- Item type:
- book
- Publisher:
- The MIT Press
- DOI:
- 10.7551/mitpress/9780262013277.001.0001
- Subject:
- Neuroscience, Techniques
This book offers an introduction to current methods in computational modeling in neuroscience, and describes realistic modeling methods at levels of complexity ranging from molecular interactions to ...
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This book offers an introduction to current methods in computational modeling in neuroscience, and describes realistic modeling methods at levels of complexity ranging from molecular interactions to large neural networks. A “how to” book rather than an analytical account, it focuses on the presentation of methodological approaches, including the selection of the appropriate method and its potential pitfalls. The book is intended for experimental neuroscientists and graduate students who have little formal training in mathematical methods, but will also be useful for scientists with theoretical backgrounds who want to start using data-driven modeling methods. The mathematics needed are kept to an introductory level; the first chapter explains the mathematical methods the reader needs to master to understand the rest of the book. The chapters are written by scientists who have successfully integrated data-driven modeling with experimental work, so all of the material is accessible to experimentalists and offers comprehensive coverage with little overlap, and extensive cross-references moving from basic building blocks to more complex applications.Less
This book offers an introduction to current methods in computational modeling in neuroscience, and describes realistic modeling methods at levels of complexity ranging from molecular interactions to large neural networks. A “how to” book rather than an analytical account, it focuses on the presentation of methodological approaches, including the selection of the appropriate method and its potential pitfalls. The book is intended for experimental neuroscientists and graduate students who have little formal training in mathematical methods, but will also be useful for scientists with theoretical backgrounds who want to start using data-driven modeling methods. The mathematics needed are kept to an introductory level; the first chapter explains the mathematical methods the reader needs to master to understand the rest of the book. The chapters are written by scientists who have successfully integrated data-driven modeling with experimental work, so all of the material is accessible to experimentalists and offers comprehensive coverage with little overlap, and extensive cross-references moving from basic building blocks to more complex applications.
