This lecture by Prof. Jeff Gore covers models and experiments of predator-prey interactions and oscillations. He begins with the Lokta-Volterra model, which has been called both "bad" (mathematically speaking) and "profoundly important."

Interspecies interactions

In this lecture, Prof. Jeff Gore finishes the discussion of the Lotka-Volterra competition model. He then moves on to the topic of non-transitive interactions, what he calls rock-paper-scissor interactions.

Dynamics of populations in space

In this lecture, Prof. Jeff Gore asks why are some species abundant and others rare? Are there universal patterns at play? And what lead to these patterns?

The neutral theory of ecology

In this lecture, Prof. Jeff Gore discusses the stability, resilience, and diversity of populations at a systems level. He begins by considering a single population, and then moves on to a simple model of interactions between species.

Ecosystem stability, critical transitions, and biodiversity

This lecture by Prof. Jeff Gore covers two topics. The first is the evolution of virulence, and how to model host-parasite interactions. The second is the evolutionary benefit of sex.

Parasites, the evolution of virulence and sex

In this lecture, Prof. Jeff Gore discusses the regulation of genes in response to changing environments. He discusses a few theories for phenotypic heterogeneity and examples from biology.

Survival in fluctuating environments

In this lecture, Prof. Jeff Gore covers the principles of the scientific article "An equivalence principle for the incorporation of favorable mutations in asexual populations."

Clonal interference and the distribution of beneficial mutations

In this lecture, Prof. Jeff Gore discusses the Nature article "Optimality and evolutionary tuning of the expression level of a protein," with emphasis on the connection between theory, prediction, and experiment.

Microbial evolution experiments and optimal gene circuit design

In this lecture, Prof. Jeff Gore continues his discussion of clonal interference (CI) and the equivalence principle. He discussed CI and the rate of evolution. And finally he thinks about evolution from the perspective of rugged fitness landscapes.

Fitness landscapes and sequence spaces

In this lecture, Prof. Jeff Gore begins with a review problem on rugged landscaped. He then moved on to the main subject: evolutionary game theory. This includes the Nash equilibrium, the prisoner's dilemma, and the hawk-dove game.

Evolutionary games

This lecture by Prof. Jeff Gore is on the topic of evolution in finite populations. Several aspects are covered, including the Moran process, neutral and non-neutral evolution, and stochastic extinction of beneficial mutants.

Evolution in finite populations

This lecture by Prof. Jeff Gore is about the mechanisms of biological pattern formation. One mechanism discussed is diffusion.

Robustness in development and pattern formation

In this lecture, Prof. Jeff Gore continues his discussion of bacterial chemotaxis, or how bacteria find food. The principle is a biased random walk of runs and tumbles, and is a shown to display perfect adaptation.

Robustness and bacterial chemotaxis

In this lecture, Prof. Jeff Gore asks, and answers, questions like how do bacteria find food? How do they know which direction to swim, and how do they swim? All of these questions relate to the low Reynold's number regime in which bacteria live.

Life at low Reynold’s number

In this lecture, Prof. Jeff Gore discusses the feed-forward loop (FFL) network motif. He covers coherent type 1 (C1) and the incoherent type 1 (I1) FFL motifs. This discussion of network motifs is extended to larger structures.

Feed-forward loop network motif

Prof. Jeff Gore discusses modeling stochastic systems. The discussion of the master equation continues. Then he talks about the Gillespie algorithm, an exact way to simulate stochastic systems. He then moves on to the Fokker-Planck equation.

Stochastic modeling

In this lecture, the class analyzes a simple model of gene expression, first to understand the deterministic behavior of the model, and then to look at the stochastic behavior of the model.

Causes and consequences of stochastic gene expression

This lecture by Prof. Jeff Gore centers on discussion of one of his favorite scientific papers: "Probing gene expression in live cells, one protein molecule at a time," by Yu et al. http://dx.doi.org/10.1126/science.1119623

This lecture by Prof. Jeff Gore centers on discussion of one of his favorite scientific papers: "Probing gene expression in live cells, one protein molecule at a time," by Yu et al. <a href="http://dx.doi.org/10.1126/science.1119623" rel="nofollow" target="_blank">http://dx.doi.org/10.1126/science.1119623</a>

Introduction to stochastic gene expression

Prof. Jeff Gore continues the discussion of oscillators, including alternative designs for oscillators. He then discusses the article Emergence of scaling in random networks, by  Barabási &amp; Albert. The final topic is network motifs.

Graph properties of transcription networks

In this lecture, Prof. Jeff Gore discusses the toggle switch, or two genes that repress each other. He then moves on to dimensionless equations and stability analysis.

Synthetic biology and stability analysis in the toggle switch

Very useful and accessible

Systems Biology (2014)

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