I was a young mathematics professor at MIT when I began reading Richard Dawkins’ 1976 book “The Selfish Gene” about the primacy of genes in Darwinian evolution. In the last chapter, Dawkins introduced what he called “memes” – analogues of genes that resided in peoples’ brains rather than in molecules of DNA. I recall saying to myself: Oh, that’s how it all works.

How what works? Pretty much everything. For example, you.

As a mathematician, I had long been aware of the work of logicians Alan Turing (the father of computer science) and Steven Kleene (the discoverer of the so called “recursion theorem”), and it had been apparent to me, and to others who cared about such things, that their results implied that the things stored in computers (which I later called cenes) were also analogues of genes.

I was really struck by all of this and ever since I have worked to put the pieces together.

It was clear from the start that genes, memes, and cenes must obey Darwin’s laws: survival of the fittest, mutation and natural selection. But with time it became clear that Darwin’s laws are often misunderstood, and that there are other laws, not previously described, that also apply. It also became clear that these laws did not just apply to genes, memes, and cenes, but to a larger class of things I came to call “prenes”

Genes have become central to the study of biological evolution. I believe that memes, cenes and other prenes have the potential to occupy a similar position with respect to the study of societal and computer evolution.

Note: The latest draft of Professor Adleman’s book Genes, Memes and Cenes can be downloaded from his webpage.

Proteins mediate the critical processes of life and beautifully solve the challenges faced during the evolution of modern organisms. Our goal is to design a new generation of proteins that address current day problems not faced during evolution. In contrast to traditional protein engineering efforts, which have focused on modifying naturally occurring proteins, we design new proteins from scratch based on Anfinsen’s principle.

Circuits of interacting proteins can perform a variety of “computational” functions in living cells. They allow cells to encode and decode signals, store information, and compute responses to complex stimuli. What kinds of design principles allow natural protein circuits to perform these functions effectively? How can we design synthetic protein circuits that provide similar or totally new functionality? This talk will explore emerging paradigms of natural and synthetic protein circuit design in mammalian cells. A major focus will be on core intercellular communication pathways such as BMP and Notch which use multiple promiscuously interacting ligands and receptors, as well as new approaches for programming fully synthetic mammalian circuits.

Markov chain Monte Carlo methods have become ubiquitous across science and engineering to model dynamics and explore large combinatorial sets. One of the most striking discoveries has been the realization that many natural Markov chains undergo phase transitions whereby they abruptly change from being efficient to inefficient as some parameter of the system is modified, also revealing interesting properties of the underlying stationary distributions. We will explore valuable insights that phase transitions provide in three settings. First, they allow us to understand the limitations of certain classes of sampling algorithms, potentially leading to faster alternative approaches. Second, they reveal statistical properties of stationary distributions, giving insight into various models interacting agents. Third, they predict emergent phenomena that can be harnessed to design distributed algorithms for certain asynchronous models of programmable active matter and swarm robotics. We will see how these three research threads are closely interrelated and inform one another.

How do signaling networks within cells process and encode information?

This talk investigate the theory side of this question, through the dynamical systems arising from biochemical networks. We focus specifically on two dynamical properties – bistability and oscillations – which may indicate that the underlying biochemical mechanism acts as a biological switch or clock. Our main mathematical tool is to harness a rational parametrization (that is, a map with rational-function coordinates) of the set of steady states of a system. We describe how such a parametrization allows us to analyze the dynamics of a large class of signaling networks involving phosphorylation and other forms of post-translational modification. Taken together, our results elucidate how bistability and oscillations emerge in important biological networks, and point to design principles for achieving such dynamics.

I was a graduate student floundering around for a thesis topic when Len Adleman published his remarkable paper on “Molecular computation of solutions to combinatorial problems” in 1994. That paper brought me together with Paul Rothemund, who had just received his undergraduate degree, and he introduced me to Ned Seeman’s incredible (even at the time!) body of work on DNA nanotechnology. One thing led to another, and Paul and I presented our ideas at the first workshop on DNA Based Computers at Princeton in April of 1995, where we met Len and Ned. Heady times, as they say. In this talk I will revisit some of the deeper ideas that were explored in those days, and trace their development from DNA computing through DNA nanotechnology and molecular programming and in some sense “back” to DNA data storage. I will emphasize “thinking like a physicist” and “thinking like a computer scientist”, as well as the interplay of theory and experiment. I will conclude with some probably way-off-the-mark conjectures about the future.