This is a graduate-level introduction to quantitative concepts and methods in the science of living systems. It relies on a systems approach for understanding the physical principles operating in biology. Physical phenomena are treated at the appropriate spatio-temporal scale and phenomenological equations are used in order to reflect the system of interest. Biological details enter to the degree necessary for understanding specific processes, but in many cases the approach is not reductionist. This is in line with the approach taken by physics to many other complex systems.
The book bridges the gap between graduate students’ general physics courses and research papers published in professional journals. It gives students the foundations needed for independent research in biological physics and for working in collaborations aimed at quantitative biology and biomedical research. Also included are modern mathematical and theoretical physics methods, giving the student a broad knowledge of tools that can shed light on the sophisticated mechanisms brought forth by evolution in biological systems.
The content covers many aspects that have been the focus of active research over the past twenty years, reflecting the authors' experience as leading researchers and teachers in this field.
Author(s): Krastan B. Blagoev, Herbert Levine
Series: Graduate Texts in Physics
Publisher: Springer
Year: 2022
Language: English
Pages: 264
City: Cham
Preface
Introduction
Contents
Contributors
1 Nonequilibrium Physics of Molecules and Cells
1.1 Thermodynamics
1.1.1 Phase Transitions
1.2 Foundations of Statistical Physics
1.2.1 Liouville Theorem for Hamiltonian Systems
1.2.2 Stability of Nonlinear Dynamical Systems
1.2.3 Phase Space Dynamics of Dynamical Systems
1.2.4 Canonical Ensemble
1.2.5 Correlation and Response Functions
1.2.6 Linear Response Theory for Hamiltonian Systems
1.2.7 Fluctuation–Dissipation Theorem
1.2.8 Diffusion
1.3 Phase Separation in Living Cells
1.3.1 The Szilard Model
1.3.2 Nucleation, Growth, Coarsening, and Coalescence in Oversaturated Solutions
1.4 A Biophysical Example: Telomere Homeostasis
1.4.1 Telomerase Control of Telomere Length
1.4.2 Telomere Sister Chromatid Exchange and Biased Diffusion
References
2 Probing the Energy Landscapes of Biomolecular Folding and Function
2.1 Energy Landscape Theory: The Interface of Physics and Molecular Biology
2.2 The Landscapes of Protein Folding
2.2.1 Principle of Minimal Frustration
2.2.2 Landscape-Inspired Models for the Study of Folding
2.2.3 All-Atom Explicit-Solvent Models
2.3 Models for Studying Biomolecular Functional Dynamics
2.3.1 Normal Mode Analysis
2.3.2 Multi-basin Effective Potential Energy Models
2.3.3 Simulations with Semi-empirical All-Atom Models
2.4 How Disorder Guides Biomolecular Function
2.4.1 Partial Unfolding During Function: Cracking
2.4.2 Biomolecular Association: Fly-Casting
2.4.3 Molecular Machines: Entropically Guided Rearrangements
2.5 Concluding Remarks
References
3 Energetic and Structural Properties of Macromolecular Assemblies
3.1 Chemical Composition of Macromolecular Assemblies
3.2 The Ribosome
3.2.1 Biological Role and Mechanistic Characteristics
3.2.2 Physical Considerations
3.2.3 Methods for Probing Ribosome Energetics
3.3 Viruses
3.3.1 Physical Considerations and Questions
3.3.2 Methods for Probing Packaging in Viruses
3.4 Concluding Remarks
References
4 Organization of Intracellular Transport
4.1 Introduction
4.2 Why Intracellular Transport Requires Active Processes?
4.3 Components of Intracellular Transport
4.4 Current Understanding of Mechanisms of Intracellular Transport
4.5 Open Questions and Future Directions
References
5 Introduction to Stochastic Kinetic Models for Molecular Motors
5.1 Introduction
5.2 Stochastic Kinetic Models
5.3 One-State Model
5.4 Two-State Model
5.5 Solution for an Arbitrary Network
5.5.1 Master Equation and Average Run Time
5.5.2 Distributions
5.5.3 Average Properties
5.5.4 Simple Examples
5.6 Experiments Performed Under Constant External Load
5.7 Advantages and Limitations of Stochastic Kinetic Models
5.8 Appendix A: Mathematical Functions
5.9 Appendix B: The Distribution of Run Length
5.10 Appendix C: Derivation of the Run Time Distribution
5.11 Appendix D: Velocity Distribution
5.11.1 One-State Model
5.11.2 Two-State Model
5.12 Appendix E: Averages in the N-state Model
References
6 Physics of the Cell Membrane
6.1 The Phospholipid Bilayer
6.2 Membrane Proteins
6.2.1 Integral Proteins
6.2.2 Peripheral Proteins
6.2.3 Receptors
6.3 Membrane Fusion
6.3.1 Intermediate Structures
6.3.2 Membrane Tension as a Driving Force
6.3.3 Fusion Proteins
6.3.4 Electrostatic Forces
6.4 Energy Required to Bend a Membrane
6.4.1 Fluid Properties of the Plasma Membrane
6.4.2 Bending Energies and the Helfrich Hamiltonian
6.4.3 Free Energy and Shape of a Bent Membrane
References
7 Introduction to Models of Cell Motility
7.1 Introduction
7.2 Random-Walk Models
7.3 Looking Under the Hood
7.3.1 Dicty
7.3.2 E. Coli
7.4 Shapes
7.4.1 Cellular Potts Model
7.4.2 Phase Field Model
7.5 Models of Collective Motility
7.5.1 Agent-Based Approaches
7.5.2 Subcellular Elements
7.5.3 Vertex/Voronoi Models
7.5.4 Shapes, Revisited
7.6 Continuum Models
References
8 Modeling Biological Information Processing Networks
8.1 Introduction
8.2 Representing Biological Networks and Analyzing their Topology
8.3 Dynamic Modeling
8.3.1 Modeling T Cell Survival
8.3.2 Modeling Epithelial to Mesenchymal Transition (EMT)
8.4 Integration of the Interaction Network and Regulatory Rules
8.5 Conclusions
References
9 Introduction to Evolutionary Dynamics
9.1 Birth-Death Processes
9.2 The Kimura Problem
9.3 Selection-Mutation Equilibrium
9.4 Clonal Interference
9.5 The Luria-Delbrück Process
References
