Luís A. Nunes Amaral
Integrative approach to modeling cellular signaling pathways
The number of organisms whose complete genome has been sequenced has grown exponentially in the last few years. For each of these organisms, we know with remarkable accuracy the exact sequence of nucleotides in their DNA—in other words, we know their biochemical “blueprint.” Unfortunately, our understanding of life processes has not grown proportionally. For example, experimental evidence demonstrates that only 20% of the genes in yeast are essential for survival, but the reasons why these genes are essential are not known.
At a totally different scale, the scale of macro socio-economic systems, the increase in computational capabilities is also enabling us to acquire and process unprecedented amounts of information. As for processes at the molecular scale, our predictive power has not increased at the same pace. For example, nowadays it is relatively straightforward to obtain the number of passengers flying between any pair of cities in the world. Still, when a virus outbreak occurs, such as the SARS outbreak in 2003, little is known about which countries will be affected.
Cells and the air transportation system are examples of complex systems. In complex systems, individual components interact with each other—usually in nonlinear ways—giving rise to complex networks of interactions that are neither totally regular nor totally random. Partly because of the interactions themselves and partly because of the interaction topology, complex systems cannot be properly understood by just analyzing their constituent parts—as Phil Anderson already pointed out in 1974, more is different.
Our group conducts research on complex systems across a wide variety of disciplines and develops models that provide insight into the emergence, evolution, and stability of these systems. The study of these problems requires an approach that emphasizes a holistic view of the system instead of using reductionist principles and solely focusing on the details or individual parts. The advantage of this approach is that we are not confined to a single discipline or field when choosing problems to investigate, giving us the opportunity to choose ones that are both scientifically interesting and possessing a broad impact to the community at large. This allows us to study problems as disparate as the growth of Escherichia coli in a bioreactor and what makes a good mentor.
To this end we develop and validate models that can be studied by means of computational experiments and verified using experimental or empirical data where available. We focus on the identification of the mechanisms determining the dynamics of a system and translate these mechanisms into a parsimonious set of rules that can be implemented and investigated computationally.
Dynamics and heterogeneity of a fate determinant during transition towards cell differentiation. Peláez N, Gavalda-Miralles A, Wang B, Tejedor Navarro H, Gudjonson H, Rebay I, Dinner AR, Katsaggelos AK, Amaral LAN, and Carthew RW. eLife. 2015;4:e08924.
Scaling and optimal synergy: Two principles determining microbial growth in complex media. Massucci FA, Guimerà R, Amaral LAN, and Sales-Pardo M. Physical Review E. 2015 June 8;91(6):062703.
Cross-evaluation of metrics to estimate the significance of creative works. Wasserman M, Zeng XHT, and Amaral LAN. PNAS. 2015 February 3;112(5):1281-1286.
Impact of heterogeneity and socioeconomic factors on individual behavior in decentralized sharing ecosystems. Gavaldà-Miralles A, Choffnes DR, Otto JS, Sánchez MA, Bustamante FE, Amaral LAN, Duch J, and Guimerà R. PNAS. 2014 October 28;111(43):15322-15327.
Adoption of a High-Impact Innovation in a Homogeneous Population. Weiss CH, Poncela-Casasnovas J, Glaser JI, Pah AR, Persell SD, Baker DW, Wunderink RG, and Amaral LAN. Physical Review X. 2014 October 15;4(4):041008.
Quantifying Position-Dependent Codon Usage Bias. Hockenberry AJ, Sirer MI, Amaral LAN, and Jewett MC. Molecular Biology and Evolution. 2014 July;31(7):1880-1893.
Use of a global metabolic network to curate organismal metabolic networks. Pah AR, Guimerà R, Mustoe AM, and Amaral LAN. Scientific Reports. 2013 April 22;3:1695.
The Possible Role of Resource Requirements and Academic Career-Choice Risk on Gender Differences in Publication Rate and Impact. Duch J, Zeng XHT, Sales-Pardo M, Radicchi F, Otis S, Woodruff TK, and Amaral LAN. PLoS ONE. 2012 December 12;7(12):e51332.
Phenomenological Model for Predicting the Catabolic Potential of an Arbitrary Nutrient. Seaver SMD, Sales-Pardo M, Guimerà R, and Amaral LAN. PLoS Computational Biology. 2012 November 1;8(11):e1002762.
Macro-level Modeling of the Response of C. elegans Reproduction to Chronic Heat Stress. McMullen PD, Aprison EZ, Winter PB, Amaral LAN, Morimoto RI, and Ruvinsky I. PLoS Computational Biology. 2012 January 26;8(1):e1002338.
Prompting Physicians to Address a Daily Checklist and Process of Care and Clinical Outcomes: A Single-Site Study. Weiss CH, Moazed F, McEvoy CA, Singer BD, Szleifer I, Amaral LAN, Kwasny M, Watts CM, Persell SD, Baker DW, Sznajder JI, and Wunderink RG. American Journal of Respiratory and Critical Care Medicine. 2011 September 15;184(6):680-686.
Duality between Time Series and Networks. Campanharo ASLO, Sirer MI, Malmgren RD, Ramos FM, and Amaral LAN. PLoS ONE. 2011 August 11;6(8):e23378.
View all publications by Luís A. Nunes Amaral listed in the National Library of Medicine (PubMed). Current and former IBiS students in blue.