Systems Biology


Design of Biological Systems

Luis Serrano (ICREA Research Professor)
Christina Kiel, Maria Lluch
Eva Yus, Javier Delgado, Besray Unal (Interpod program with Yogi Jaeger), Jae-Seong Yang, Martin Schaeffer
Kiana Toufighi (shared with Ben Lehner), Marie Jeanne Trussart, Veronica Llorens, Carolina Gallo
Sira Martinez, Tony Ferrar, Violeta Beltran, Hannah Benisty
Lenore Cowan


Our group is interested in the quantitative understanding and rational engineering of living systems (ranging from gene networks to organisms). For this purpose we use a combination of tools that involve software for protein design and simulations of networks and experimental approaches. Our approach is based on first understanding a system and then engineering it to obtain the properties we want. Our philosophy is also whenever possible identifying the possible practical applications for human health and biotechnology of our work.

Figure 1

Figure 1. Genes affected in RASopathies (Kiel & Serrrano., 2014). RASopathies are a group of germline developmental disorders of the Ras-MAPK pathway, such as Noonan, cardio-facio-cutaneous (CFC), Costello, and LEOPARD syndromes. Most mutants described result in up-regulating the RAS-RAF-ERK-MAPK-kinase cascade. These 15 genes form a connected network with no isolated members (Fig 1A). It is intriguing that mutations in the same 15 genes are also frequently identified in different types of human cancers (Fig 1B ). Using the protein design algorithm FoldX, we predict that most of the missense mutations with destabilizing energies are in structural regions that control the activation of proteins, and only a few are predicted to compromise protein folding. We find a trend in which energy changes are higher for cancer compared to RASopathy mutations. When energy changes in RASopathies are high, we find a higher proportion of compensatory changes that by network modelling result in only minor downstream pathway deregulation. In summary, we suggest that quantitative rather than qualitative network differences determine the phenotypic outcome of RASopathy compared to cancer mutations.

  • A. Network diagram showing affected genes in RASopathies. Proteins are displayed in white boxes and arranged in a network with their respective genes in grey. RASopathy diseases are indicated in blue.
  • B. Diseasome of RASopathies and cancer. Each node corresponds to a distinct disorder or cancer type. The size of the node corresponds to the total number of genes (among the 15 genes) that are involved in a particular disease. Abbreviations: NS, Noonan syndrome; NF1, neurofibromatosis type 1; CFC, cardiofaciocutaneous; LS, LEOPARD syndrome; HGF, hereditary gingival fibromatosis; CM-AVM, capillary malfunction – arteriovenous malfunction; ALPS, autoimmune lymphoproliferative syndrome. Suffixes in NS (NS1, NS3, NS4, NS5, NS6, and NS7) and LS (LS1 to LS3) are different forms of the respective disease according to the classification in the OMIM database.

Research Projects

  • Quantitative understanding and whole-cell modelling of a whole organism: M. pneumoniae
  • Engineering of M. pneumoniae to treat human diseases.
  • Quantitative understanding of Signal Transduction in humans and its role in disease

Selected Publications

Kiel C, Ebhardt HA et al.
“Quantification of ErbB Network Proteins in Three Cell Types Using Complementary Approaches Identifies Cell-General and Cell-Type-Specific Signaling Proteins.”
J Proteome Res, 13(1): 300-313 (2014).

Kiel C and Serrano L.
“Structure-energy-based predictions and network modelling of RASopathy and cancer missense mutations.”
Mol Syst Biol, 10: 727 (2014).

Llorens-Rico V, Serrano L et al.
“Assessing the hodgepodge of non-mapped reads in bacterial transcriptomes: real or artifactual RNA chimeras?”
BMC Genomics, 15: 633 (2014).

Schaefer MH, Yang JS et al.
“Protein conservation and variation suggest mechanisms of cell type-specific modulation of signaling pathways.”
PLoS Comput Biol, 10(6): e1003659 (2014).

Yang JS, Sabido E et al.
“TAPAS: tools to assist the targeted protein quantification of human alternative splice variants.”
Bioinformatics, 30(20): 2989-2990 (2014).