The natural sciences are a single intellectual enquiry into the universe of objects that surround us. They are linked by a common method: inducing hypotheses from a mixture of data and intuition, deducing predictions, and testing them by experiment and observation. The sciences depend on mathematics, from the simple act of counting to sophisticated methods required for computational chemistry and theoretical physics.
The Integrated Science curriculum will introduce motivated freshmen to the concepts and methods needed to attack the life sciences in the 21st century. For both semesters, students will take the equivalent of two courses, meeting for formal instruction every day, performing hands-on, original research, and using modern computer methods to simulate scenarios and analyze data.
Darwin and Wallace's theory of evolution revealed that living things have a purpose: their structure, function, and behavior are integrated to leave as many progeny as possible. For much of the 20th century, this difference, and the astonishing diversity of form and function, tended to separate biology from the other natural sciences: biology's complexity made it unappealing to many mathematicians, physicists, and chemists, and the "assume a spherical cow" flavor of theorists' simplifying assumptions made biologists skeptical about how useful theory was for understanding biology. Two advances have pushed biologists towards theorists and computer scientists: the need to test our understanding of biological processes by making explicit, mathematical models and the need to convert large datasets into information and, ultimately, knowledge.
We will teach students that the answer to 'How will you solve this problem?' is 'By any means necessary!' Our goal is to teach them how to find interesting problems, the means to solve them, and above all, the knowledge and courage to invent the new methods that make previously insoluble problems soluble. Coupling concepts and methods to problems that excite students and making them use these tools in their own research will embed the concepts in their working memory.
We will teach through iterated cycles of experiment and analysis, making use of experimental computation to simulate a system of interacting entities and explore the effect of parameter variation on the system's properties. Our goal is to complement the formal derivation of theorems, show the productive interplay between theory, simulation, and experiment, and show that computer systems and programs, like biological objects, have purposes.
Mastering a restricted syntax to write algorithms will help students think about how biological systems use the restricted syntax of chemistry and genetics to accomplish tasks. Concepts like modularity, exploration with selection, error detection and correction, and recycling previous inventions are important in the function and evolution of both organisms and code. Six faculty will teach the course, working as three pairs of one life scientist and one physical scientist.
The students will use their knowledge to conduct original scientific research. To ensure that all the students start with abilities in laboratory experiments and computer simulations, we will have two, two week-long boot camps, one in computation and one in experimentation. After the boot camps, students will perform original research in project laboratories. The project labs will be based on the research of and run by the Bauer Fellows, independent scientists who spend five years at Harvard after their PhDs and run small research groups.
We will measure the success of our program in three ways: the extent to which they continue in and succeed at original research during their sophomore, junior, and senior years; the knowledge that students retain when they leave Harvard, three years after completing the IS curriculum; and the extent to which they pursue careers that depend on scientific training.
We try to understand the “rules of the game” that explain how cells function and evolve. We study budding yeast, using experimental evolution, genetic analysis, synthetic biology, and cell biology. We try to make quantitative measurements that discriminate amongst different classes of models. Members of the lab come from both biology and physics backgrounds.
How does biological novelty evolve? Because we lack time travel, this process is difficult to study in nature, and we therefore apply selective pressure in the laboratory. We have evolved multicellularity, altered mating preferences, circadian oscillators, genetic instability, and new connections between signaling pathways and have developed methods to find the mutations that cause these new phenotypes. We are interested both in general questions about what determines evolutionary trajectories and the specific mechanisms that organisms invent to produce novel traits.
How do cells accomplish specific tasks and how did these solutions evolve? We follow the Feynman principle of “What I cannot create, I cannot understand” by engineering and analyzing the behavior of new yeast strains. As examples, we have used synthetic biology to support the notions that the efficient use of secreted public goods drove the evolution of multicellularity, that multicellularity arose before cellular differentiation, and that novel symbioses could arise without requiring previous evolutionary co-adaptation.
How do cells respond and adapt to their environment to maximize the chance that they survive and reproduce? Achieving these aims requires the coordination of thousands of reactions under a wide range of inter- and extracellular conditions. We are exploring how yeast cells respond to sudden starvation and have discovered that they can rapidly halt their cell cycles, at any stage, and then, later, slowly resume cell division. We are asking how they arrest, whether the arrest destabilizes the genome, and how cells adapt to start dividing again.
Finally, we collaborate with David Nelson (Physics) to combine theory and experiment to investigate population dynamics and evolution in space and time.
As of March 10, 2015