I am a theorist and computational scientist working on problems in systems biology.

What is systems biology? The field of systems biology emerged in response to the swelling bounty of high-throughput data about molecular aspects of cell biology. These data intensified the need for understanding how coherent system behavior arises (or is lost) from a staggering diversity of molecular assemblies that interact with one another asynchronously and autonomously. Systems biology, thus, aims at developing and integrating new experimental and mathematical techniques in the pursuit of principles that would make the nature of cellular phenotypes more intelligible and their control more deliberate. This pursuit is driven by the practical need to cure disease. However, it also reflects a want for a theoretical perspective needed to think the complexity of the cell and the organism, and, by extension, evolution itself.

In the "pre systems biology" phase of my research, I made practical contributions to computational RNA folding, which started the “Vienna RNA package” (an open-source software tool kit for the computational study of RNA secondary structure). I studied the relation between plasticity and evolvability in the context of RNA folding, which I viewed as a proxy for a genotype-phenotype relation. With my colleagues, I coined the concept of a “neutral network”: a mutationally connected network of sequences that share the same phenotype. The concept found experimental corroboration, and spilled over into other areas of biological research. It can be used to formalize a notion of mutational accessibility between phenotypes, giving rise to a topological concept of neighborhood in phenotye space, which allows for a sound definition of continuity and discontinuity in evolution. I took this as a basis for reasoning about evolutionary innovation.

Another thread of early work aimed at establishing a formal notion of (functional) organization. Biologist Leo Buss and I defined a model in which "molecules" were programs that act on each other (one program using the other as input data) yielding new programs according to a specific calculus (known as \(\lambda\)-calculus). A flow-reactor of many such particles evolves towards steady-states that are kinetically self-maintaining algebras, i.e. organizations. This journey connected me to the theoretical foundations of computer science.

My current computational work is focused on developing and applying novel methods for making abstract mechanistic models of processes based on protein-protein interactions as they occur in signaling and assembly. The techniques we develop and deploy are guided by formal methods in computer science, especially concurrency theory, and produced a rule-based modeling language with a graph-rewrite semantics, known as Kappa. Kappa is best viewed as a symbolic language like chemistry but adapted to molecular biology. This research is motivated by the belief that model-based reasoning must become an integrated aspect of bioinformatics and by the belief that capturing the combinatorial complexity of signaling systems is necessary for understanding their plasticity and evolvability in phylogenesis, ontogenesis, and disease.

When joining the Department of Systems Biology at Harvard Medical School in 2004, I developed an interest in experimental research. I chose to study the phenomenon of aging in the model organism C. elegans. Some of our work aimed at longitudinal measurements of molecular physiology, such as redox potential, using fluorescent sensors in single cells of individual organisms throughout their lifespan. Our main approach to aging was based on endpoint statistics, i.e. the change in the statistical properties of lifespan distributions under chemical, physical, and genetic perturbations. We discovered that the mortality statistics of C. elegans responds to an overwhelming number of physical, chemical and genetic interventions simply by a rescaling of time, leaving the shape of lifespan distributions invariant. This observation suggests the existence of an effective, or phenomenological, description of aging: a simple equation describing how a macroscopic state variable characterizing the resilience of an organism changes stochastically with time governed by a single rate constant. With a substantive milestone wrapped up, I decided in 2016 to simplify and focus my research life by phasing out our exciting 10-year journey into experimental research.

My lab brings together scientists with a broad diversity of backgrounds and skills including statistics, biophysics, genetics, computer science and molecular biology. The Department of Systems Biology provides us with an intellectually powerful and engaging environment.