Esteban Lanzarotti’s research while affiliated with University of Buenos Aires and other places

En este trabajo presentamos un modelo de simulación basado en agentes que combina la dinámica biológi- ca de una infección viral de SARS-Cov-2 en los huéspedes con la transmisión del virus entre individuos de una pobla- ción mediante un ambiente compartido, produciendo ası́ un modelo multiescala. Se muestra que el modelo es capaz de reproducir evoluciones tı́picas de la propagación de una epidemia en una población y permite estudiar el impacto de parámetros biológicos y comportamientos sociales en las curvas de contagio. Desde el punto de vista computacional, mostramos la conveniencia de utilizar métodos de integración por cuantificación.

In this paper, we developed an innovative and plural methodology for a socio-cultural assessment of ecosystem services (ES). This methodology was performed using diverse and interdependent tools applied within the framework of ethnoecology and post-normal science, with the aim of identifying ES from the perspective of local communities that inhabit different socio-ecosystems, highlighting the relevance of Indigenous and Local Knowledge (ILK). As examples of how this methodology works, we analyzed a multiple case study performed in three peasant communities of the Dry Chaco eco-region, Argentina. We identified ES in all the categories and their fundamental contributions to the particular way of life in this area. The method is flexible enough to be used in other socio-ecosystems with different environmental and social features.

Modelling and simulation of complex systems is key to exploring and understanding social processes, benefiting from formal mechanisms to derive global-level properties from local-level interactions. In this paper we extend the body of knowledge on formal methods in complex systems by applying EB-DEVS, a novel formalism tailored for the modelling, simulation and live identification of emergent properties. We guide the reader through the implementation of different classical models for varied social systems to introduce good modelling practices and showcase the advantages and limitations of modelling emergence with EB-DEVS, in particular through its live emergence detection capability. This work provides case study-driven evidence for the neatness and compactness of the approach to modelling communication structures that can be explicit or implicit, static or dynamic, with or without multilevel interactions, and with weak or strong emergent behaviour. Throughout examples we show that EB-DEVS permits conceptualising the analysed societies by incorporating emergent behaviour when required, namely by integrating as a macro-level aggregate the Gini index in the Sugarscape model, Fads and Fashion in the Dissemination of Culture model, size-biased degree distribution in a Preferential Attachment model, happiness index in the Segregation model and quarantines in the SIR epidemic model. In each example we discuss the role of communication structures in the development of multilevel simulation models, and illustrate how micro-macro feedback loops enable the modelling of macro-level properties. Our results stress the relevance of multilevel features to support a robust approach in the modelling and simulation of complex systems.

With the arrival of the pandemic in Argentina in March 2020, a working group of scientists from two institutes belonging to the Faculty of Exact and Natural Sciences of the University of Buenos Aires and CONICET, together with colleagues from different academic institutions in the country, decided to put forth our experience and knowledge in data science and associated disciplines, towards helping with decision-making in the context of COVID-19. Data analysis within Argentina and other countries, scenario simulation, as well as rapid response projects- mainly in the province of Buenos Aires- were all within the scope of our aim. This review article outlines some of the activities carried out by our team throughout these pandemic months.

New anthropic potentially harmful compounds are released into the environment everyday. In this context, broad range bioassays have emerged providing economically viable and widely applicable alternatives due to their ability to detect the cumulative toxicity of mixtures of both known and unknown chemicals in a sample, thus allowing direct information about water quality. Here we present a low-cost, wide-range algae-based biosensor that is easy to assemble and operate by untrained users and provides direct readings. It was developed as a request of a peasant social movement organization to assess the toxicity of drinking water in rural communities affected by pesticide spraying. Two fresh water algae strains, Scenedesmus acutus and Pseudokirchneriella subcapitata, were immobilized in alginate beads and tested as bioindicators. After incubation with different pollutants for five days, naked eye analysis by several observers proved to be a successful method to survey algae’s growth and establish the detection limits. Best detection limits were 10 ppm for technical-grade acid glyphosate, 15 ppm for glyphosate-based formulation, 50 ppb for atrazine formulation, 7.5 ppm for copper and 250 ppb for chromium. Absorbance measurements upon algae resuspension validated these results.The developed device was successfully tested in participatory workshops conducted at rural communities. Children, adults and elders with no scientific training were able to build the sensor and interpret the results, thus evaluating the quality of rain and well water used in their communities.

HIV-1 determinants of coreceptor usage within the gp120 V3 loop have been broadly studied over the past years. This information has led to the development of state-of the-art bioinformatic tools that are useful to predict co-receptor usage based on the V3 loop sequence mainly of subtypes B, C and A. However, these methods show a poor performance for subtype F V3 loops, which are found in an increasing number of HIV-1 strains worldwide. In the present work we investigated determinants of viral tropisms in the understudied subtype F by looking at genotypic and structural information of coreceptor:V3 loop interactions in a novel group of 40 subtype F V3 loops obtained from HIV-1 strains phenotypically characterized either as syncytium inducing or non-syncytium inducing by the MT-2 assay. We provide novel information about estimated interactions energies between a set of V3 loops with known tropism in subtype F, that allowed us to improve predictions of the coreceptor usage for this subtype. Understanding genetic and structural features underlying HIV coreceptor usage across different subtypes is relevant for the rational design of preventive and therapeutic strategies aimed at limiting the HIV-1 epidemic worldwide.

Abstract Aromatic rings are important residues for biological interactions and appear to a large extent as part of protein–drug and protein–protein interactions. They are relevant for both protein stability and molecular recognition processes due to their natural occurrence in aromatic aminoacids (Trp, Phe, Tyr and His) as well as in designed drugs since they are believed to contribute to optimizing both affinity and specificity of drug-like molecules. Despite the mentioned relevance, the impact of aromatic clusters on protein–protein and protein–drug complexes is still poorly characterized, especially in those that go beyond a dimer. In this work, we studied protein–drug and protein–protein complexes and systematically analyzed the presence and structure of their aromatic clusters. Our results show that aromatic clusters are highly prevalent in both protein–protein and protein–drug complexes, and suggest that protein–protein aromatic clusters have idealized interactions, probably because they were optimized by evolution, as compared to protein–drug clusters that were manually designed. Interestingly, the configuration, solvent accessibility and secondary structure of aromatic residues in protein–drug complexes shed light on the relation between these properties and compound affinity, allowing researchers to better design new molecules.

The interaction between the class I major histocompatibility complex (MHC), the peptide presented by the MHC and the T-cell receptor (TCR) is a key determinant of the cellular immune response. Here, we present TCRpMHCmodels, a method for accurate structural modelling of the TCR-peptide-MHC (TCR-pMHC) complex. This TCR-pMHC modelling pipeline takes as input the amino acid sequence and generates models of the TCR-pMHC complex, with a median Cα RMSD of 2.31 Å. TCRpMHCmodels significantly outperforms TCRFlexDock, a specialised method for docking pMHC and TCR structures. TCRpMHCmodels is simple to use and the modelling pipeline takes, on average, only two minutes. Thanks to its ease of use and high modelling accuracy, we expect TCRpMHCmodels to provide insights into the underlying mechanisms of TCR and pMHC interactions and aid in the development of advanced T-cell-based immunotherapies and rational design of vaccines. The TCRpMHCmodels tool is available at http://www.cbs.dtu.dk/services/TCRpMHCmodels/.