I am interested in the interplay of evolvability and robustness: how organisms can simultaneously generate phenotypic novelty, yet be buffered against the forces of genetic and environmental change. I work in the emerging discipline of evolutionary systems biology using a combination of empirical genomics and bioinformatics, collaborations with wet-lab experimentalists as well as mathematical modeling and computer simulation. I have applied these research strategies to examine the molecular mechanisms and evolution of systems as diverse as the human major histocompatibility complex (MHC), yeast transcriptional networks and prion biology. One major focus of my research is understanding the basic principles of how genotype “maps” to phenotype via cellular networks and processes and how variation in these networks is manifest across both individual lifespans and at evolutionary timescales.  A second focus is applying these principles to identify the mechanistic underpinnings of human diseases.

Evolvability and robustness One mechanism of evolvability is the accumulation of hidden genetic variation and subsequent release of the variation when it might be adaptive. In yeast, variation accumulates in the untranslated regions of genes and can be released through translational readthrough caused by prion aggregates of the translation termination factor Sup35, known as [PSI+].  Previous experimental evidence suggests that such readthrough can produce new, genetically complex, and often adaptive phenotypes when the organism is stressed.  My mathematical modeling, coupled with data from experimental colleagues suggest that prion-forming ability may be a consequence of selection for evolvability (Lancaster & Masel 2009, Lancaster et al. 2010).  Another prion I have investigated, [MOT3+], transforms the transcriptional networks inside yeast to survive stress by inducing a multicellular states such as biofilm formation (Holmes et al. 2013).  I am currently extending this work to examine other prions and processes that can rewire metabolic and signal transduction networks  (Jarosz* & Lancaster* et al. 2004).

Comparative network genomics for human health In a second research focus, I search for causative variants of different phenotypes, including disease, using an evolutionary systems biology approach in both human and yeast datasets (Tardiff et al. 2013; Vincent et al. 2013).  For example, using both in-vitro evolution experiments and whole-genome deep-sequencing, my colleagues and I have discovered that resistance to certain antifungal agents can come at the cost of an inability to respond to stress (Vincent et al. 2013). This has significant implications for human health as creating antibiotics with evolutionary constraints is a potential strategy for limiting the emergence of drug resistance.

Computational and systems biology methodologies and tools In a third research area, I develop methodologies, algorithms, software and tools focusing on sequence analysis and computational modeling for both evolutionary and translational biology. I was  part of a team at the CBMI working with the Wall lab and LPM to develop a cloud-based infrastructure, COSMOS (Gafni et al., 2014) for performing next-generation whole-genome analysis in clinical timeframes.  I have also recently created an open-source online tool for searching for prion-like domains in protein sequences (Lancaster et al., 2014). I previously developed a bioinformatics pipeline for population genomics, PyPop, which operates on multi-locus genotype data, and performs many different analyses including tests for different modes of selection (Lancaster et al. 2003; Lancaster et al. 2007).  I was also a primary developer of the agent-based modeling software package, Swarm at the Santa Fe Institute in New Mexico, and I worked on the Madonna differential equation solver package developed at University of California, Berkeley.