Kalliopi Trachana

Research Scientist

Send Email

(206) 732-1463

The principles of critical state transitions, early warning signs, and more generally, complex systems dynamics, offer a theoretical framework and analysis tools for understanding and PREDICTING major transitions in human physiology.

With this philosophy deeply rooted in my heart, I am part of a team that aims to transform the way we study cell differentiation from induced pluripotent stem cells (iPSCs).

Using cutting-edge technologies, we have managed to identify universal metrics for critical transitions during cell differentiation and dissect basic principles on how cell populations take decisions.

Next step is to demonstrate how we can translate such information into action, and try to optimize and automate the development of stem cell differentiation protocols.


single cell analysis, induced pluripotent stem cells, developmental biology, bioinformatics, data integration

Collaborating with Huang lab, here at ISB, we aim to show how the theory of attractors in gene network dynamics provides a solid framework to solve real-world problems, such as optimizing the efficiency of directed differentiation and deriving new metrics for small molecules (drug) screening.

A. Overview of the conceptual and theoretical framework

The process of cellular differentiation is central to our understanding of the nature of multicellular living systems, their stability in a changing environment, and how such systems fail in disease. Since a cell’s phenotype (i.e. pluripotency) are largely determined by the activity of genes, it follows that the rules of molecular interactions encoded by the Gene Regulatory Network (GRN) translate directly into rules of cellular interactions; for instance, mutually inhibiting transcription factors can organize the segregation of sister lineages.

In other words, the gene expression profiles, we measure in our experiments, is a manifestation of GRN constraints, and represent steady states (attractors) of the dynamical system. Stu Kauffman suggested that these attractor states correspond to cell types in multicellular organisms, and the process of differentiation corresponds to the trajectory in the state space leading into one of the attractors (Kauffman SA: The Origins of Order: Self-Organization and Selection in Evolution. 1993, Oxford University Press, New York).

[su_frame align=”center”]Framework(2)[/su_frame]

Fundamental principles of high-dimensional dynamical systems to explain the coordinated change of gene expression during cell fate commitment. A. Basic concepts. The cube represents a 3D state space (describing a three gene genome; A, B, and C) with their expression levels (Xa, Xb, and Xc) as axes. Each gene represents a dimension in state space, and discrete gene expression profiles occupy distinct points in the cell state space (i.e.  state S [Xa, Xb, Xc] is a point in cell state space; blue ball). When expression pattern changes, the cell moves along a trajectory. B. Application of state space and cell state concepts to a differentiating population. The interaction between genes (state space dimensions) prevents the random dispersion into the entire cell state space. Instead, it allows cells to occupy only distinct regions (attractors) by following the trajectories. The mutual inhibition of Xb and Xc, for instance, pushes cells away towards different attractors (sister lineages). For more a more comprehensive view: Huang_PloSBiol_2010

B. Project-specific details

Directed differentiation and efficient manipulation towards specific cell types from iPSCs are essential for disease modeling and other translational applications (i.e. drug screening). Towards this aim, researchers try to delineate decision-making mechanisms of multipotent progenitors and in particular, how external perturbations (small molecules, or other cell culture conditions) instruct the transitions towards specific cell fates.

We are using iPSC to cardiomyocytes (iCM) differentiation as a model system to design an integrative systems approach to characterize, predict, and manipulate state transitions in cell differentiation.

Interrogating the system at single-cell resolution provides as a new level of granularity to understand how we can “push” biological systems towards the desired state (here, cardiac lineage).


We are wrapping up the work on iCM differentiation! Stay tuned!

To read my previous work (in comparative genomics/orthology), please check:

Trachana K, Forslund K, Larsson T, Powell S, Doerks T, von Mering C, Bork P. A phylogeny-based benchmarking test for orthology inference reveals the limitations of function-based validation. PLoS One. 2014 Nov 4;9(11):e111122.

Powell S, Forslund K, Szklarczyk D, Trachana K, Roth A, Huerta-Cepas J, Gabaldón T, Rattei T, Creevey C, Kuhn M, Jensen LJ, von Mering C, Bork P. eggNOG v4.0: nested orthology inference across 3686 organisms. Nucleic Acids Res. 2014 Jan;42(Database issue):D231-9.

Chen WH, Trachana K, Lercher MJ, Bork P. Younger genes are less likely to be essential than older genes, and duplicates are less likely to be essential than singletons of the same age. Mol Biol Evol. 2012 Jul;29(7):1703-6.

Powell S, Szklarczyk D, Trachana K, Roth A, Kuhn M, Muller J, Arnold R, Rattei T, Letunic I, Doerks T, Jensen LJ, von Mering C, Bork P. eggNOG v3.0: orthologous groups covering 1133 organisms at 41 different taxonomic ranges. Nucleic Acids Res. 2012 Jan;40(Database issue):D284-9.

Trachana K, Larsson TA, Powell S, Chen WH, Doerks T, Muller J, Bork P. Orthology prediction methods: a quality assessment using curated protein families. Bioessays. 2011 Oct;33(10):769-80.

Trachana K, Jensen LJ, Bork P. Evolution and regulation of cellular periodic processes: a role for paralogues. EMBO Rep. 2010 Mar;11(3):233-8.

Christodoulou F, Raible F, Tomer R, Simakov O, Trachana K, Klaus S, Snyman H, Hannon GJ, Bork P, Arendt D. Ancient animal microRNAs and the evolution of tissue identity. Nature. 2010 Feb 25;463(7284):1084-8.

Papanikolaou N, Trachana K, Theodosiou T, Promponas VJ, Iliopoulos I. Gene socialization: gene order, GC content and gene silencing in Salmonella. BMC Genomics. 2009 Dec 11;10:597.

Urbonavicius J, Auxilien S, Walbott H, Trachana K, Golinelli-Pimpaneau B, Brochier-Armanet C, Grosjean H. Acquisition of a bacterial RumA-type tRNA(uracil-54, C5)-methyltransferase by Archaea through an ancient horizontal gene transfer. Mol Microbiol. 2008 Jan;67(2):323-35. Epub 2007 Dec 7. PubMed PMID: 18069966.

I care, and I act to:

  • build STEM awareness, advocate about women in STEM, teach K-12 students and educators about systems biology and P4 medicine
  • transfer my skills (lecture in EMBO &  ISB courses)

I recommend: