Logic of Genomics Systems Laboratory

Driving research interests

Regulation and minimal control motifs. The study of the regulation of function at the molecular level has experienced a drastic transition in recent years. While not so long ago the focus was on identifying the molecules relevant for regulation, an effort that still is underway, we are now asking how these molecules work in combination as circuits. This approach brought to cellular regulation concepts from control engineering that can now not only be theoretically examined by also tested experimentally. These results emphasize the importance of nonlinearity in regulation and also the limits of engineering in synthetic biology. We have examined how minimal regulatory motifs control two of the main dynamical processes in the cell (oscillations and differentiation) and also characterized experimentally recurrent motifs in bacterial stress-response systems. You can find some references for this work here.

The architecture of large biochemical systems. Confronting the complexity of biochemical networks, or procaryotic and eucaryotic genomes, one wonders whether it will be ever possible to recognize some structural order in these systems. Not so long ago, we expected a lack of order. For instance, genes were considered to be placed at random in genomes, and the null model for complex networks was that of a random graph. This has changed. We know now that both systems do have a characteristic architecture. Some biochemical networks show a modular organization, and we wonder why. Genes' position is far from being random, and we also want to explain why. We are also interested in understanding the type of evolutionary processes that originated this organization. Some references of our work on these topics can be found here.

Regulation and dysregulation of cell collectives. One of the most fundamental evolutionary innovations implies the transition from biological entities that work independently to biological entities that work as a collective. At the cellular level, we immediately think of the transformation from unicellular to multicellular organisms. But we know of many instances in which unicellular organisms work as a collective, e.g., the microbiome. We have addressed this topic in many complementary ways, ranging from synthetic microbial consortia to stem cell niches. It is also the motivation for our interest in cancer and aging, as two basic situations where understanding the dysregulation of cell collectives is of primary importance. Please find some of our recent work here.

Learning in natural and artificial systems. Many questions in biology are connected with the intrinsic capacity to learn of biological systems. Learning can be at an evolutionary scale, with the genome as hardware that stores what an organism learned from past environments. But learning is also related to everyday processes. The genome learns: epigenetics. The cell learns: metabolism. The organism learns: neural processes. Collective of organisms also learns: ecological networks. It is also interesting to understand how we have implemented learning in synthetic systems, which takes us to many open problems in the sciences of the artificial. See some of our recent work here.

What can, and cannot, be predicted. The study of evolutionary and physiological processes commonly meets the issue of predictability. When is evolution predictable? We would also like to understand which cellular signatures anticipate dysfunction or which core processes are more constrained, and thus more predictable than others. Do we need to think differently? Coarse descriptions of molecular processes could help. There are many interesting open questions. Some have biomedical implications: can we trust artificial systems that learn the biology of a problem and make decisions? We did some work that you can find here.