Wishlisted Wishlist

Please confirm that you want to add A mathematical way to think about biology to your Wishlist.

Add to Wishlist

A mathematical way to think about biology

Why "is" biology log-normal? Why do some circuits oscillate? See biology from a physical sciences perspective.
3.6 (57 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
9,552 students enrolled
Created by Dr. David Liao
Last updated 9/2015
  • 15.5 hours on-demand video
  • 2 Articles
  • 1 Supplemental Resource
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I Learn?
Apply physical sciences perspectives to biological research
Be able to teach yourself quantitative biology
Be able to communicate with mathematical and physical scientists
View Curriculum
  • Algebra
  • Exposure to calculus (there is an appendix for students interested in review)

A mathematical way to think about biology comes to life in this lavishly illustrated video book. After completing these videos, students will be better prepared to collaborate in physical sciences-biology research. These lessons demonstrate a physical sciences perspective: training intuition by deriving equations from graphical illustrations.

"Excellent site for both basic and advanced lessons on applying mathematics to biology."
-Tweeted by the U.S. National Cancer Institute's Office of Physical Sciences Oncology

Who is the target audience?
  • Undergraduate students
  • Graduate students
  • Postdoctoral scholars
  • Lab managers
  • Funding agency program staff
  • Principal investigators and grant writers
  • Citizen scientists
  • Patient advocates
  • Lifelong learners
  • Integrative Cancer Biology Program members
  • Physical Sciences Oncology Network members
  • National Centers for Systems Biology members
Students Who Viewed This Course Also Viewed
Curriculum For This Course
Expand All 134 Lectures Collapse All 134 Lectures 15:24:36
Welcome to mathematics for insightful biology
1 Lecture 01:48

This video outlines contents from the course

Welcome to mathematics for insightful biology
Using deterministic models to study aspects of stochastic systems
15 Lectures 02:23:31

Concepts of stochasticity underlie many of the models of dynamic systems explored in quantitative biology. We describe some of these ideas in this and the following three videos. In this video, we state that systems exhibiting deterministic dynamics can sample a messy variety of waiting times between chemical reaction events even when the motions of component parts are periodic. Particularly, this can happen when the periods of motion of individual parts are incommensurate (pairs of periods form ratios that are irrational).

Stochasticity a: Incommensurate periods

In a deterministic system with complicated interactions, small differences in initial conditions can quickly avalanche into qualitative differences in dynamics. Since initial conditions can only be measured with finite certainty, the dynamics of such systems are, for practical purposes, unpredictable after short times.

Stochasticity b: Practically unpredictable deterministic dynamics

In the previous two videos, deterministic systems displayed dynamics with aspects associated with stochasticity. In contrast, some systems not only mimic some aspects associated with stochasticity, but, instead, display indeterminism at a fundamental level. For example, when a collection of completely identical systems later displays heterogeneous outcomes, the systems are fundamentally indeterministic. They have no initial properties that can be used to discern which individual system will display which particular outcome.

Stochasticity c: Fundamentally indeterministic processes

Markov models are often used when developing mathematical models of systems which partially or more fully display aspects associated with stochasticity (depending on how fully a system displays aspects associated with stochasticity, the use of a Markov model might need to be recognized as a conceptual approximation). Icons that can represent the use of such models include spinning wheels of fortune and rolling dice.

Stochasticity d: Memory-free (Markov) processes and their visual representations

In this and the following three videos, we present a canonical worked problem that is presented in introductory systems biology coursework. For an example of this mathematical lesson, see Alon, Ch. 2.4, pp. 18-21. In this video, we animate a time sequence of translation and degradation events that cause the number of copies of a protein of interest in a cell to change over time.

Canonical protein dynamics a: Translation and degradation events occur over time

We derive a differential equation approximating the time-rate of change of the number of copies of protein in the cell modeled by the animation in the previous video. This differential equation reads, dx/dt = β - αx. We depict aspects of this differential equation with a flowchart. It is important to remember that this differential equation does not represent all aspects of the stochastic dynamics in the toy model presented in the previous video.

Canonical protein dynamics b: Differential equation and flowchart

We sketch a slope field corresponding to the differential equation derived in the previous video. We use this slope field to draw a qualitative curve describing how the number of copies of protein is expected to rise over time, when starting from an initial value of zero.

Canonical protein dynamics c: Qualitative graphical solution

We obtain an analytic solution for the relationship between the number of copies of protein and time for the differential equation qualitatively investigated in the previous video. We find that the rise time, T1/2, is ln(2) divided by the degradation rate coefficient, α. The fact that the rise time is independent of the translation rate β is sometimes used as a pedagogical example of the importance of quantitative reasoning for gaining insights into biological dynamics that would be difficult to develop through natural-language and vaguely-structured notional reasoning alone.

Canonical protein dynamics d: Analytic solution and rise time

Using a collision picture to understand why reaction rates look like polynomials of reactant concentrations

Mass action 1a: Law of mass action

Cooperativity of a simple (oversimplified) kind

Mass action 1b: Cooperativity and Hill functions

How Hill functions, considered in combination with linear degradation, can support bistability

Mass action 1c: Bistability

This video introduces collisional population dynamics and tabular game theory (comparative statics). The particular game in this example is the prisoner's dilemma. In this game survival of the relatively most fit occurs simultaneously with decrease in overall fitness. For a printable tutorial explaining how evolutionary game theoretic differential equations can be applied to analyze population dynamics, please refer to doi:10.1098/rsfs.2014.0037.

Evolutionary game theory Ia: Population dynamics

  • Brief introduction to tabular game theory
  • An outcome of the prisoner's dilemma is simultaneous stability of D with, as a consequence, lower than maximum possible payoff for D
  • We give a taste of the idea that tabular game theory and the population dynamics from the preceding video are connected deeply.  We state that (1) that payoffs from tabular game theory can be associated with rate coefficients from the population dynamics in part 1a, and (2) that part 1a should be referred to as evolutionary game theory.  
  • The purpose is to inspire the audience to read in textbooks how this conceptual connection can be established.  
Evolutionary game theory 1b: Preview comparison with tabular game theory

In the previous slide deck, we noted similarities between population dynamics and business transaction payoff pictures. In this and the next video, we provide deeper understanding of these connections. In this video, we derive the population dynamics equations in such a way that it is natural to say that cells being modeled repeatedly play games and are subject to game outcomes. For a printable tutorial describing interpretations that can be associated with evolutionary game theoretic differential equations, please see doi:10.1098/rsfs.2014.0038.

Evolutionary game theory IIa: Cells repeatedly playing games

Repeated simple interactions in a population of robotic replicators can achieve results seemingly related to results obtained from sophisticated computations. The use of population dynamics and business transaction payoff matrix analyses from the previous slide deck to obtain this understanding is an example of quantitative reasoning.
Evolutionary game theory IIb: Relationship between time and sophisticated comput
Probability and statistics
14 Lectures 01:38:06

The first of five videos on introductory statistics, this module introduces probability distributions and averages. The average (also called "arithmetic mean") quantitatively expresses the notion of a central tendency among the results of an experiment.

Statistics a: Probability distributions and averages

The average of a sum is the sum of the averages. The average of a constant multiplied against a function is the constant multiplied by the average of the function. The average of a constant is the constant itself.

Statistics b: Identities involving averages

The variance of a function is the average of the square of the function. For the purposes of theoretic calculations, it might be useful to express the variance using the "inside-out" computation formula described in this video.

Statistics c: Dispersion and variance

Two variables are said to be statistically independent if the outcome of an experiment tracked by one variable does not affect the relative likelihoods of different outcomes of the experiment tracked by the other variable. The two-variable probability distribution factorizes into two probability distribution functions.

Statistics d: Statistical independence

The covariance of statistically independent variables is zero. The variance of a sum of statistically independent variables equals the sum of the variances of the variables. This identity is often used to derive uncertainty propagation formulas.

Statistics e: Identities following from statistical independence

This slide deck provides examples of how hypotheses about probabilistic processes can be used to discuss probability distributions and obtain theoretical values for averages and variances. In this first video, we describe the Bernoulli trial, which corresponds to the experiment in which a coin is flipped to determine on which of two sides it lands.

Probability a: Bernoulli trial

In this second video in this slide deck, we discuss the binomial distribution. This distribution describes the probability of getting x heads out of N coin tosses (Bernoulli trials), each individually having probability p of success.

Probability b: Binomial distribution

In the Poisson limit, we take a series of [independent] Bernoulli trials (giving rise to a binomial distribution) and allow the number of coin flips N to increase without bound while allowing the chance p of success on a particular coin flip to decrease without bound in such a compensatory fashion that the average number of successes ("heads") is unchanged. Because the likelihood of "heads" on any given toss decreases without bound, this limit is called the limit of rare events.

Probability c: Poisson distribution

To study the combinatorics involved in an example where the central limit theorem applies, we will need to work with the factorials of large numbers. Stirling's approximation is an approximation for n! for large n. In this video, we motivate this approximation by comparing the expression for ln(n!) with an integral of the natural log function.

Preparation for central limit theorem: Stirling's approximation

The central limit theorem states that a Gaussian probability distribution arises when describing an overall variable that is a sum of a large number of independently randomly fluctuating variables, no small number of which dominate the fluctuations of the overall variable.

Central limit theorem a: Statement of theorem

In some situations, when the number of coin tosses is large, Stirling's approximation can be applied to factorials that appear in the expression for the binomial distribution. The resulting expression is basically an exponential function of a quadratic function with a negative leading coefficient. This is the hallmark of a Gaussian distribution.

Central limit theorem b: Optional derivation (special case)

For a Gaussian distribution, roughly two-thirds of the probability is found within the first standard deviation.

Central limit theorem c: Properties of Gaussian distributions

Because equipment in physics experiments is highly-engineered, individual device contributions to measurement fluctuations might be "small." The overall fluctuations in the final measured quantity might be well approximated using a first-order Taylor expansion in terms of individual device fluctuations. Fluctuations in measurements are thus sums over random variables, and thus, potentially Gaussian distributed.

Prevalence of Gaussians a: Noise in physics labs is allegedly often Gaussian

The levels of molecules in biological systems can approximate "temporary" steady-state values that equal products of rate coefficients and reactant concentrations. Since logarithms convert products into sums, the logarithms of the levels of some biological molecules can be normally distributed. Hence, the levels of the biological molecules are log-normally distributed.

Prevalence of Gaussians b: Noise in biology is allegedly often log-normal
Uncertainty propagation
9 Lectures 59:12
Quadrature formula is a result of Taylor expanding functions of multiple fluctuating variables, assuming that fluctuations are independent, and then applying the identity "variances of sums are sums of variances"
Uncertainty propagation a: Quadrature

Standard deviation vs. sample standard deviation

Mean vs. sample mean

Standard deviation of the mean vs. standard error of the mean

Uncertainty propagation b: Sample estimates

Origin of the famous factor of sqrt(n), which is the ratio by which the standard deviation of the distribution of the sample means is smaller than the standard deviation of the distribution of the measurements (parent distribution)
Uncertainty propagation c: Square-root of sample size (sqrt(n)) factor

Are error bars non-overlapping, barely touching, or tightly overlapping?  What p-value do people associate with the situation in which error bars barely touch?
Uncertainty propagation d: Comparing error bars visually

"I quantitated staining intensity for 1 million cells from 5 patients, everything I measure is statistically significant!" It is quite possible that you need to use n = 5, instead of 5 million, for the √ n factor in the standard error.

Uncertainty propagation e: Illusory sample size

In order to identify theoretical curves that closely imitate a set of experimental data, it is necessary to be able to quantify to what extent a set of data and a curve look similar. To address this need, we present the definition of the quantity chi-squared. For a given number of measurements, a smaller chi-squared indicates a closer match between the data and the curve of interest. In other words, a smaller chi-squared corresponds to a situation in which it looks more as though the data "came from" Gaussian distributions centered on the curve. The average chi-squared value across a number of experiments, each involving M measurements, is M.

Sample variance curve fitting a: Chi-squared

We slightly modify the definition of chi-squared developed in the previous video for the situation in which a "correct" curve has not been theoretically determined beforehand. We choose a "best guess" curve with corresponding best guess values of fitting parameters by minimizing chi-squared, which corresponds to maximizing likelihood.

Sample variance curve fitting b: Minimizing chi-squared

Using the concepts developed in the preceding two videos, we present a checklist of steps necessary for performing fitting of mathematical curves to data with error bars. These steps include checking whether the reduced chi-squared value is in the neighborhood of unity and inspecting a plot of normalized residuals to check for systematic patterns. This algorithm is appropriate for general education undergraduate "teaching laboratory" courses.

Sample variance curve fitting c: Checklist for undergraduate curve fitting

The purposes of this exercise are (1) to practice sample variance curve fitting in MatLab and (2) to understand that the time-sequence according to which data are acquired can affect the apparent size of error bars. This exercise is a PDF, instead of a video, so that you can refer to a print-out next to your computer as you work through the commands described.
Sample variance curve fitting exercise for MatLab
5 pages
Computation of stochastic dynamics
6 Lectures 47:27

Dynamics of population probability distributions can be described by using differential equations.

Master equation

In this and the following two videos, we present the stochastic simulation algorithm. To apply this algorithm, we need to specify the kinds of reactions that a system can undergo, we need to determine waiting times that elapse between consecutive reactions, and we need to determine the identities of the reactions that occur. In this first video, we illustrate how a systems' possible reactions are specified by specifying reaction rates and stoichiometries.

Stochastic simulation algorithm a: Specifying reaction types and stoichiometries

Use a pseudo-random number to specify a duration of time, drawn from an exponential distribution, that elapses until the next reaction.

Stochastic simulation algorithm b: Time until next event

Use a pseudo-random number to choose, with probability proportional to average probability rate (propensity), a particular reaction type for the next chemical reaction.

Stochastic simulation algorithm c: Determining type of next event

Model: RNA polymerase makes many (usually unsuccessful) independent attempts to initiate transcription and mRNA strands degrade after a precise lifetime
Outcome: mRNA copy numbers are Poisson distributed
Poissonian copy numbers a: Stochastic transcription and deterministic degradation

Model: RNA polymerase makes many (usually unsuccessful) independent attempts to initiate transcription.  Once a mRNA strand is produced, it begins to make independent (usually many unsuccessful) attempts to be degraded.  

Outcome: As in part a, mRNA copy numbers are Poisson distributed

Poissonian copy numbers b: Stochastic transcription and stochastic degradation
Linear algebra
10 Lectures 01:27:46
Motivating example: Modeling dynamics of web start-up company customer base
Linear algebra Ia: Teaser

Vectors, vector spaces, and coordinate systems
Linear algebra Ib: Vectors

Linear operators, matrix representation, matrix multiplication
Linear algebra Ic: Operators

Using eigenvalue-eigenvector analysis to solve for the dynamics of the demographics of the web-startup customer base. First, we qualitatively describe the long-term behavior of the system in paying/non-paying customer population space.

Linear algebra Id: Solution of teaser (part 1)

Now that we have qualitatively described the long-term behavior of the paying and non-paying customer populations in this model, we obtain mathematical descriptions using eigenvector-eigenvalue analysis.

Linear algebra Id: Solution of teaser (continued)

Simple quasispecies eigendemographics and eigenrates based on Bull, Meyers, and Lachmann, "Quasispecies made simple," PLoS Comp Biol, 1(6):e61 (2005)

In this first video, we obtain discrete-time-step population dynamics equations by considering proliferation and mutation events at the level of the single cell.

Intro quasispecies a: Population dynamics from single-cell mechanisms

We use eigenvalue-eigenvector analysis to describe the long-term steady-state population composition. We find that relative dominance in a population is determined, not merely by "fitness" alone, but also depends on the degree to which individuals "breed true."

Intro quasispecies b: Eigenvalue-eigenvector analysis

Euler's formula: Expanding the exponential function in terms of sine and cosine
Complex exponentials in the complex plane Euler's identity exp() = -1
Euler II: Complex exponentials

In this and the next video, we develop a familiarity with the representation of vector rotations using rotation matrices. This understanding is helpful for identify dynamical systems that support oscillations in physics, engineering, and biology. A rotation operator rotates a vector by an angle without changing the length of the vector. A rotation matrix represents the action of a rotation operator on a vector.

Linear algebra II: Rotation a: Rotation matrix

How can we determine whether a dynamical system can be represented using something that looks like a rotation matrix? Rotation matrices have complex eigenvalues. We can determine whether a dynamical system supports rotational motion by determining whether the matrix representing the system's dynamics has complex eigenvalues.

Linear algebra II: Rotation b: Complex eigenvalues
Differential equations
13 Lectures 01:39:14
  • Direction fields, quiver plots, and integral curves
  • Numerical integration of systems of differential equations.  

CAUTION: I'm not familiar enough with numerical integration to know whether the particular example of the method for step-size adaptation in the video is used generally (or at all) in commonly available software packages.  The purpose of the example was to show that it is possible to generate an error estimate (a) without knowledge of the actual solution and (b) by comparing the solutions from two numerical integration algorithms.  

Numerical integration of differential equations

In this and the following three videos, we present a canonical introduction to mRNA-protein system from systems biology 101. In the fourth video in this slide deck, we summarize the process of linear stability analysis that can be applied to systems of differential equations that can be expressed in the form of 2x2 matrix equations.

In this first video, we obtain the system of differential equations describing this model by presenting assumptions that mRNA molecules are transcribed and degraded and that copies of protein are translated and degraded.

Linear stability analysis a: Transcription-translation model

We identify particularly simple, one-dimensional, trajectories of the transcription-translation model through mRNA level-protein level state space.

Linear stability analysis b: Nullclines and critical point

Some of the trajectories in mRNA-protein level state space are one-dimensional (unbending). This insight allows us to learn that the dynamics of the vector in mRNA-protein state space are described by a linear combination of eigenvectors with weighting coefficients that are exponential functions of time with coefficients equal to the corresponding eigenvalues.

Linear stability analysis c: Eigenvalue-eigenvector analysis

Eigenvalues and eigenvectors of a linear system can be used to classify a critical point as a source node, sink node, saddle, source star, sink star, source degenerate node, sink degenerate node, source spiral, sink, spiral, or center.

Linear stability analysis d: Cribsheet

Adaptation is not absence of change; instead it is the presence of eventually compensatory changes See also: Read Ma, Trusina, El-Samad, Lim, and Tang, "Defining network topologies that can achieve biochemical adaptation," Cell 138: 760-773 (2009).

In this video, we describe an example of an incoherent feed-forward loop molecular circuit topology, which, as we learn in the following two videos, supports adaptation. In the fourth video in this slide deck, we summarize the method of almost linear stability analysis that can be used to study systems in which the differential equations cannot be expressed in the form of a matrix equation with constant coefficients.

Almost linear stability analysis a: Incoherent feed-forward loop

Adaptation is the eventual restoration of the level despite the lasting presence of a change in a stimulus that temporarily caused a change in the read out. The incoherent feed-forward loop is one way to use three nodes to produce this effect. After the level of input A rises, activation of read out C rises, but inhibition of C through B also rises. The final steady-state level of read out C is unchanged. However, since the level of inhibitor B takes some time to rise, inhibition of C is temporarily insufficient to compensate for increased activation of C by A. Thus, the level of C is temporarily higher before it approaches its original value.

We visualize nullclines and critical points in the BC phase portrait before and after a step change in A.

Almost linear stability analysis b: Adaptation

The system of differential equations describing the incoherent feed-forward loop in this example cannot be directly expressed in the form of a 2x2 matrix equation with constant coefficients. A power series expansion is used to identify higher-order terms that are neglected in the vicinity of the critical point. The remaining portion of the system of differential equations is linear and can be analyzed using eigenvalue-eigenvector methods. The dynamics obtained are consistent with the dynamics described more qualitatively in the previous video.

Almost linear stability analysis c: Eigenvalue-eigenvector analysis

Even though an almost linear system is not exactly a linear system, the portions of the system that are not linear vanish with decreasing distance from the critical point of interest faster than the linear portion vanishes. The linear portion (which can be expressed using a matrix equation with constant coefficients) dominates near the critical point. The cribsheet of linear stability analysis can be used to classify a critical point of an almost linear system with two modifications. If application of linear stability analysis suggests a star or a degenerate node, the shapes of the trajectories should be checked by carefully graphing by hand. If application of linear stability analysis suggests a center, actual trajectories will circulate, but they need to be carefully graphed by hand to determine whether they sink inward, expand outward, or are closed.

Almost linear stability analysis d: Cribsheet

In this and the following four videos, we present some concepts that can be used to design and recognize mathematical models that support oscillatory behavior. In this first video, we show that oscillations can be viewed as cyclic loops in a 2-dimensional plane. One way to arrange for a pair of variables R and J to perform oscillations is to let the time-derivative of each variable be proportional to the value of the other variable, with a negative sign in the coefficient of one of these differential equations.

Oscillations a: Romeo and Juliet

The angles at which nullclines pass through the phase plane (e.g. steep vs. shallow) determine the relative arrangement of regions in which quivers point in the top-left, bottom-left, bottom-right, and top-right directions. By modifying the slopes of nullclines, and thus the relatively positions of these regions, the qualitative dynamics of a dynamical system might be modified to support a stable star, a stable spiral, a closed loop, or even an unstable spiral. One way to understand how parameters affect trajectories is to understand how parameters affect the slopes that nullclines make when drawn in the phase plane.

Oscillations b: Twisting nullclines

A spiral sink can be modified to support a closed-loop trajectory if the system is modified so as to perform motion in the present that would, in the original dynamical system, have, instead, been performed at a previous time.

Oscillations c: Time delays

A deterministc spiral sink that is highly skewed can support repeated oscillations when stochastic fluctuations kick the system out of the sink and onto a nearby region of rapid flow.

Oscillations d: Stochastic excitation
Physical oncology
4 Lectures 21:21

Interfaces between the physical sciences and oncology have become especially active in recent years owing, in part, to the Physical Sciences-Oncology Centers (PSOC) Network funded by the U.S. National Cancer Institute. While physical and mathematical scientists have historically contributed to instrumentation and technology development in the medical sciences, the PSOC network also promotes the application of physical sciences ways of thinking to understanding basic cancer biology and cancer therapy.

Introduction to physical oncology

The abstract organized into this and the following two videos highlights two recent papers from authors at the University of California, San Francisco working within the Princeton Physical Sciences Oncology Center. In this video, we review examples of ways that the timings of biochemical reactions can appear to be random.
  1. Liao D, Estévez-Salmerón L, and Tlsty T D 2012 Conceptualizing a tool to optimize therapy based on dynamic heterogeneity Phys. Biol.9(6):065005 (doi:10.1088/1478-3975/9/6/065005) (open-access online)
  2. Liao D, Estévez-Salmerón L, and Tlsty T D 2012 Generalized principles of stochasticity can be used to control dynamic heterogeneity Phys. Biol.9(6):065006 (doi:10.1088/1478-3975/9/6/065006) (open-access online)

The authors dedicate this paper to Dr Barton Kamen who inspired its initiation and enthusiastically supported its pursuit.

The research described in these articles was supported by award U54CA143803 from the US National Cancer Institute. The content is solely the responsibility of the authors and does not necessarily represent the official views of the US National Cancer Institute or the US National Institutes of Health.

(C) 2012-2013 David Liao (lookatphysics.com) CC-BY-SA (license updated 2013 March 27). When distributing this set of three videos under the Creative Commons license, please cite the full journal references above (including authors and dois) as well as the citation information for this video collection:

Title: Dynamic heterogeneity for the physical oncologist
Author of work: David Liao
The full citation of the papers (at least the first paper) is necessary because the journal Phys. Biol. has released these works under a CC-BY-NC-SA license. These papers are copyrighted and not public domain.

Dynamic heterogeneity a: Stochastic biochemistry

Stochastic fluctuations in the levels of intracellular molecules can lead to transitions between phenotypic states in individual cells.

Dynamic heterogeneity b: Phenotypic interconversion

In the previous video, we asked whether phenotypic interconversion was a source of therapeutic failure or a therapeutic opportunity. In this video, we develop a graphical device, called a metronomogram, to understand that the dynamics of a phenotypically interconverting population (eventual reduction, expansion, or maintenance of population size) can depend on whether therapy is administered with sufficient time frequency.

Dynamic heterogeneity c: Metronomogram
Spatially-resolved systems
1 Lecture 05:46

We use a simple lattice model of synchronous reproduction of annual plants to give an example of a kind of spatially-resolved modeling that is easy to program into personal computers for routine study. This example happens to use a "winner takes all" replacement rule. See Nowak and May, Nature (1992) for an article describing spatial patterns that can arise when using a "winner takes all" model. In this video, we see that heterogeneous coexistence (as distinguished from homogeneous dominance by a single subpopulation) can sometimes be promoted by spatial localization.

Cellular automata a: Deterministic cellular automata
Statistical physics
11 Lectures 01:34:59
Systems have states and energy levels
Energy can be exchanged between parts of a world If the Hamiltonian of the world is time-independent, the overall energy of the world is conserved
Fundamental postulate of statistical mechanics: In an isolated system, all accessible microstates are accessed equally
Statistical physics 101a: Fundamental postulate of statistical mechanics

Notating the configurations of a world consisting of multiple parts

Cartesian product

Statistical physics 101b: Cartesian product

Bath: many parts
Number of ways to find the bath configured exponentially decays with increasing system energy
Boltzmann factor
Statistical physics 101c: Distribution of energy between a small system and a large bath

The system energy most typically observed is the one that corresponds to the greatest number, W, of configurations of the world 
Ways (W), entropy (sigma), free energy (F), probability (P), partition function (Z), taking derivative of Z 
Maximizing ways of the world 
Maximizing entropy of the world 
Minimizing free energy of the system
Statistical physics 101d: Expressions for calculating average properties of systems connected to baths

Introduction to ideal chain, exploring world configurations
Purpose: Calculate average elongation of chain
Ideal chain a: Introduction to model

Writing the Hamiltonian for a series of independent links
Ideal chain b: Hamiltonian and partition function

The average of the elongation of the chain (averaged by exploring states of the chain while the world explores accessible states equally) saturates for large weights
Ideal chain c: Expectation of energy and elongation

In this unit, we provide intuitional background for studying Jeremy England's recent paper, "Statistical physics of self-replication," at a level mostly appropriate for algebra-based high school physics courses. To understand the irreversibility of a macroscopic state change, it is important to compare the volumes of the portions of phase space corresponding to two macrostates within the volume of phase space that is kinetically accessible. In this video, we provide probabilistic language for describing the dynamic exploration of microstates of a universe.

Macroscopic irreversibility a: Microstates of universe are explored over time

In the models we will consider, the conditional probability of a transition from a microstate of the universe, i, to a microstate universe, j, is equal to the conditional probability of a transition from microstate j to microstate i. We refer to this assumption as an assumption of microscopic reversibility.

Macroscopic irreversibility b: Microscopic reversibility

Transitions from a cluster of microstates of the universe associated with one microstate of the system to another cluster of microstates of the universe associated with another microstate of the system can be probabilistically favored to proceed in the forward direction. This occurs when the number of microstates in the final cluster is greater than the number of microstates in the initial cluster. Irreversibility equals the ratio of the number of microstates of the universe in the final cluster to the number of microstates of the universe in the initial cluster. Irreversibility increases with increasing heat exhausted to the reservoir when paths are taken in the forward direction.

Macroscopic irreversibility c: Ratio of volumes in phase space

The irreversibility of a transition from a macroscopic state to another macroscopic state depends on the numbers of microstates of the universe in the two macrostates. The irreversibility of such a transition effectively equals the ratio of the number of kinetically accessible microstates of the universe belonging to the second macrostate of interest to the number of kinetically accessible microstates of the universe belonging to the first macrostate of interest.

Macroscopic irreversibility d: Kinetically accessible volumes of phase space
4 More Sections
About the Instructor
3.6 Average rating
57 Reviews
9,552 Students
1 Course
Physicist (PhD, Princeton 2010)

David's illustrations have been published in Science, Physical Review Letters, Molecular Pharmaceutics, Biosensors and Bioelectronics, and the Proceedings of the National Academy of Sciences.

University of California, San Francisco

Associate Professional Researcher 2015-Current

Analyst, 2012-2014

Postdoc, 2010-2012 Tlsty Lab

Princeton University (PhD, Physics, 2010 MA, Physics, 2007)

Advisor: Robert H. Austin

2006-2009 National Defense Science and Engineering Graduate Research Fellowship

2009-2010 National Science Foundation Graduate Research Fellowship

Harvey Mudd College BS, Physics, 2005

Advisor: Robert J. Cave

Report Abuse