Education & Training
- Magistere (BSc and Masters equivalent), Biology/Biochemistry, Ecole Normale Superieure / University Paris 6, 2004
- Masters, Interdisciplinary Approaches to Life Sciences, University Paris 7, 2005
- Ph.D. Bioinformatics, University of Grenoble, 2011
Research Interest Summary
Systems biology is the study of biological networks. The information contained in the genome of every living cell encodes a specific set of biomolecules (eg. transcripts, proteins). These biomolecules interact with each other, with the genome and with the environment, forming intricate and dynamic networks that underlie all cellular processes. Biological networks define how organisms look and behave, whether they will die or thrive in different environments. Ultimately, biological networks influence the probability that genomic information will be propagated to the next generation. Thus studying networks will transform how we think about evolution.
Evolution is the process through which populations and species change over successive generations. We know a lot about how natural selection and random drift together govern the inheritance of genetic material. However, the mechanisms underpinning evolutionary innovation remain obscure. How do new genes appear? How do organisms adapt to changing environments? If biological networks performed their functions in the manner of predictable machines, they could not evolve. There must be organizational principles that make biological networks plastic and robust for evolutionary innovation to take place. We seek to discover what these principles are. Through this quest we hope to expand knowledge of how cells work and of how evolution works.
Wang T, Ma J, Hogan AN, Fong S, Licon K, Tsui B, Kreisberg JF, Adams PD, Carvunis AR, Bannasch DL, Ostrander EA, Ideker T. Quantitative Translation of Dog-to-Human Aging by Conserved Remodeling of the DNA Methylome. Cell Systems (2020).
Vakirlis N, Carvunis AR*, McLysaght A*. *=corresponding authors. Synteny-based analyses indicate that sequence divergence is not the main source of orphan genes eLIFE (2020). Link to spotlight in eLIFE.
Vakirlis N, Acar O, Hsu B, Castilho Coelho N, Van Oss SB, Wacholder A, Medetgul-Ernar K, Bowman II RW, Hines CP, Iannotta J, Parikh SB, McLysaght A, Camacho CJ, O’Donnell AF*, Ideker T*, Carvunis AR*. *=corresponding authors. De novo emergence of adaptive membrane proteins from thymine-rich genomic sequences. Nature Communications (2020). Recommended on F1000.
Keeling DM, Garza P, Nartey CM, Carvunis AR. The Recalcitrance and Resilience of Scientific Function. Poroi (2020).
Keeling DM, Garza P, Nartey CM*, Carvunis AR* *=corresponding authors. The meanings of ‘function’ in biology and the problematic case of de novo gene emergence. eLife (2019). Recommended on F1000.
Van Oss SB., Carvunis AR. De novo gene birth..PLOS Genetics. Wikipedia (2019)
Ernst P., Carvunis AR. Of mice, men and immunity: a case for evolutionary systems biology. Nature Immunology (2018)
Domazet-Lošo T*, Carvunis AR*,#, Albà MM, Šestak MS, Bakarić R, Neme R, Tautz D#: No evidence for phylostratigraphic bias impacting inferences on patterns of gene emergence and evolution. MBE (2017). *=co-first authors. #=co-corresponding authors.
Carvunis AR, Wang T*, Skola D*, Yu A, Chen J, Kreisberg J, Ideker T: Evidence for a common evolutionary rate in metazoan transcriptional networks. eL i fe (2015). *=co-first authors.
Carvunis AR, Ideker T: Siri of the Cell – what biology could learn from the iPhone. Cell (2014).
Mitra K*,Carvunis AR*,Ramesh SK, IdekerT:Integrative approaches for finding modular structure in biological networks.Nature Reviews Genetics (2013). *=co-first authors.
Carvunis AR, Rolland T, Wapinski I, Calderwood MA, Yildirim MA, Simonis N, Charloteaux B, Hidalgo CA, Barbette J, Santhanam B, Brar GA, Weissman JS, Regev A, Thierry-Mieg N, Cusick ME, Vidal M: Proto-genes and de novo gene birth. Nature (2012).
Carvunis AR, Roth FP, Calderwood MA, Cusick ME, Superti-Furga G, Vidal M: Interactome networks, in Handbook of Systems Biology (2012).