Columbia University in the City of New York

Tavazoie Lab

Laboratory of Systems Biology

Predictive dynamic understanding of cellular behavior remains one of the grand challenges of modern biology. Work in our group is laying the technological, computational, and conceptual foundation to help make this vision a reality. In particular, we are:

(1)  Developing technologies to make routine systems-level observations of the abundance, activity, and interactions of hundreds of thousands of molecular species.

(2)  Developing computational methods to discover biologically meaningful structure in these observations and to build predictive models of interaction and regulation at the level of DNA, RNA, and protein sequences.

(3)  Searching for organizing principles that govern the function and evolution of molecular networks.

The main focus of our work is to understand how cells adapt to changes in their external environment. We study this inherently systems-level phenomenon across a range of timescales, from rapid transcriptional responses, to multi-generational epigenetic reprogramming, to long-term rewiring of signaling and regulatory networks over evolutionary timescales.

A distinguishing feature of our approach is to study systems-level cellular behavior with minimal prior assumptions. For example, we apply machine learning to large-scale global observations, such as gene expression across thousands of conditions, to identify the critical regulatory components at the level of DNA, RNA, and protein, and to determine how they are organized into higher-level regulatory and genetic networks (Beer Cell, 2004; Elemento Molecular Cell, 2007; Goodarzi Nature, 2012). This unbiased strategy has been essential to our ability to decode genomic elements that drive transcriptional and post-transcriptional responses across diverse physiological, developmental, and pathological processes, including cancer initiation and progression (Goodarzi Molecular Cell, 2009; Goodarzi Nature, 2014).

The agnostic nature of our approach (global observations and minimally biased machine learning) allows the system, itself, to reveal the essential governing principles. This has been crucial to uncovering surprising new phenomena such as the ability of microbial regulatory networks to predict changes in their external environment (Tagkopoulos Science, 2008). Often these higher-level principles are obscured by approaches that focus only on a narrow slice of the cell's response.

We also utilize laboratory experimental evolution to probe the innate capacity of molecular networks to re-wire and adapt to novel challenging environments (Goodarzi MSB 2010). In addition to revealing general principles by which cells adapt to extreme environments (Hottes PLoS Genetics, 2013), these studies are identifying new mechanisms by which individual bacteria and populations develop clinically significant levels of antibiotic resistance and persistence (Girgis PNAS, 2012).

Our systems-level approach often requires global observations that are beyond the scale and resolution of existing methods. We thus develop new enabling technologies with substantially higher throughput and resolution, for example: global in vivo protein-DNA interaction profiling (Vora, Molecular Cell, 2009), transposon-based fitness and epistasis profiling (Girgis, PLoS Genetics, 2007), global adaptive mutation mapping (Goodarzi Nature Methods, 2009), and functional surveys to discover post-transcriptional regulatory elements (Oikonomou Cell Reports, 2014).

We make use of diverse experimental systems from bacteria to mammalian cell-lines in order to study general principles that operate across organismal taxa and complexity.

Active areas of research include:

Noise, phenotypic heterogeneity and adaptation to extreme environments

Adaptation through epigenetic reprogramming of gene expression

Antibiotic resistance and persistence

Predictive models of gene expression integrating transcriptional and post-transcriptional regulation

Post-transcriptional regulation by RNA-structural elements and RNA-binding proteins

Transcriptional and post-transcriptional perturbations contributing to cancer initiation and progression

Decoding regulation of gene expression in the mammalian brain

Next-generation technologies for mapping protein-DNA, protein-RNA, and protein-protein interactions

Open Positions:

We are looking for creative and ambitious graduate students and postdocs to join our highly interdisciplinary and interactive group. Please send inquiries to: st2744 [at]

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