ISCB Student Council Symposium 2021, a virtual global venue : challenges and lessons learned

Since 2004, the ISCB Student Council has been organizing different symposia worldwide, gathering together the community of young computational biologists. Due to the coronavirus disease 2019 (COVID-19) pandemic situation, the world scientific community was forced to cancel in-person meetings for almost two years, imposing the adoption of virtual formats instead. After the successful editions of our continental symposia in 2020 in the USA, Latin America, and Europe, we organized our flagship global event, the Student Council Symposium (SCS) 2021, trying to apply all previous lessons learned and to exploit the advantages that virtuality has to offer

Global network of computational biology communities: ISCB's regional student groups breaking barriers [version 1; peer review: Not peer reviewed]

Regional Student Groups (RSGs) of the International Society for Computational Biology Student Council (ISCB-SC) have been instrumental to connect computational biologists globally and to create more awareness about bioinformatics education. This article highlights the initiatives carried out by the RSGs both nationally and internationally to strengthen the present and future of the bioinformatics community. Moreover, we discuss the future directions the organization will take and the challenges to advance further in the ISCB-SC main mission: “Nurture the new generation of computational biologists”.Fil: Shome, Sayane. University of Iowa; Estados UnidosFil: Parra, Rodrigo Gonzalo. European Molecular Biology Laboratory; Alemania. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Fatima, Nazeefa. Uppsala Universitet; SueciaFil: Monzon, Alexander Miguel. Università di Padova; ItaliaFil: Cuypers, Bart. Universiteit Antwerp; BélgicaFil: Moosa, Yumna. University of KwaZulu Natal; SudáfricaFil: Da Rocha Coimbra, Nilson. Universidade Federal de Minas Gerais; BrasilFil: Assis, Juliana. Universidade Federal de Minas Gerais; BrasilFil: Giner Delgado, Carla. Universitat Autònoma de Barcelona; EspañaFil: Dönertaş, Handan Melike. European Molecular Biology Laboratory. European Bioinformatics Institute; Reino UnidoFil: Cuesta Astroz, Yesid. Universidad de Antioquia; Colombia. Universidad Ces. Facultad de Medicina.; ColombiaFil: Saarunya, Geetha. University of South Carolina; Estados UnidosFil: Allali, Imane. Universite Mohammed V. Rabat; Otros paises de África. University of Cape Town; SudáfricaFil: Gupta, Shruti. Jawaharlal Nehru University; IndiaFil: Srivastava, Ambuj. Indian Institute of Technology Madras; IndiaFil: Kalsan, Manisha. Jawaharlal Nehru University; IndiaFil: Valdivia, Catalina. Universidad Andrés Bello; ChileFil: Olguín Orellana, Gabriel José. Universidad de Talca; ChileFil: Papadimitriou, Sofia. Vrije Unviversiteit Brussel; Bélgica. Université Libre de Bruxelles; BélgicaFil: Parisi, Daniele. Katholikie Universiteit Leuven; BélgicaFil: Kristensen, Nikolaj Pagh. Technical University of Denmark; DinamarcaFil: Rib, Leonor. Universidad de Copenhagen; DinamarcaFil: Guebila, Marouen Ben. University of Luxembourg; LuxemburgoFil: Bauer, Eugen. University of Luxembourg; LuxemburgoFil: Zaffaroni, Gaia. University of Luxembourg; LuxemburgoFil: Bekkar, Amel. Universite de Lausanne; SuizaFil: Ashano, Efejiro. APIN Public Health Initiatives; NigeriaFil: Paladin, Lisanna. Università di Padova; ItaliaFil: Necci, Marco. Università di Padova; ItaliaFil: Moreyra, Nicolás Nahuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Ecología, Genética y Evolución de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Ecología, Genética y Evolución de Buenos Aires; Argentin

Revealing the impact of lifestyle stressors on the risk of adverse pregnancy outcomes with multitask machine learning

Psychosocial and stress-related factors (PSFs), defined as internal or external stimuli that induce biological changes, are potentially modifiable factors and accessible targets for interventions that are associated with adverse pregnancy outcomes (APOs). Although individual APOs have been shown to be connected to PSFs, they are biologically interconnected, relatively infrequent, and therefore challenging to model. In this context, multi-task machine learning (MML) is an ideal tool for exploring the interconnectedness of APOs on the one hand and building on joint combinatorial outcomes to increase predictive power on the other hand. Additionally, by integrating single cell immunological profiling of underlying biological processes, the effects of stress-based therapeutics may be measurable, facilitating the development of precision medicine approaches.ObjectivesThe primary objectives were to jointly model multiple APOs and their connection to stress early in pregnancy, and to explore the underlying biology to guide development of accessible and measurable interventions.Materials and MethodsIn a prospective cohort study, PSFs were assessed during the first trimester with an extensive self-filled questionnaire for 200 women. We used MML to simultaneously model, and predict APOs (severe preeclampsia, superimposed preeclampsia, gestational diabetes and early gestational age) as well as several risk factors (BMI, diabetes, hypertension) for these patients based on PSFs. Strongly interrelated stressors were categorized to identify potential therapeutic targets. Furthermore, for a subset of 14 women, we modeled the connection of PSFs to the maternal immune system to APOs by building corresponding ML models based on an extensive single cell immune dataset generated by mass cytometry time of flight (CyTOF).ResultsJointly modeling APOs in a MML setting significantly increased modeling capabilities and yielded a highly predictive integrated model of APOs underscoring their interconnectedness. Most APOs were associated with mental health, life stress, and perceived health risks. Biologically, stressors were associated with specific immune characteristics revolving around CD4/CD8 T cells. Immune characteristics predicted based on stress were in turn found to be associated with APOs.ConclusionsElucidating connections among stress, multiple APOs simultaneously, and immune characteristics has the potential to facilitate the implementation of ML-based, individualized, integrative models of pregnancy in clinical decision making. The modifiable nature of stressors may enable the development of accessible interventions, with success tracked through immune characteristics

Exploring molecular mechanisms with simulations and data analyses

Proteins are the active players in the cells and carry out most of the significant functions throughout biology such as metabolism, immunity, and maintaining structural integrity. Protein characteristic behaviors such as dynamics and residue level co-evolution play a critical role in determining the detailed behaviors of any given protein. We have employed a range of computational methods: molecular dynamics simulations, principal component analysis of conformations, molecular modeling, and co-evolution analysis of sequences to attempt to understand the molecular mechanisms in membrane transporters and transmembrane proteins. The systems considered here include the Multi-Drug-Resistant efflux pumps present in gram-negative bacteria and the classical cadherins observed in epithelial cells. We gain critical insights into mechanisms from these studies regarding functional dynamics and identifying structural features that play key roles in the efflux of cyclic lipids in HpnN transporters. Further, our studies newly identify a novel cis interface between classical cadherin. This originates from a strong correlation between co-evolving residues, suggesting that this interaction plays an important role in cell-cell adhesion. Based on this work, we have been able to devise a workflow incorporating these methods that provide new insights for selection of sites to target to inhibit function

Exploring molecular mechanisms with simulations and data analyses

Proteins are the active players in the cells and carry out most of the significant functions throughout biology such as metabolism, immunity, and maintaining structural integrity. Protein characteristic behaviors such as dynamics and residue level co-evolution play a critical role in determining the detailed behaviors of any given protein. We have employed a range of computational methods: molecular dynamics simulations, principal component analysis of conformations, molecular modeling, and co-evolution analysis of sequences to attempt to understand the molecular mechanisms in membrane transporters and transmembrane proteins. The systems considered here include the Multi-Drug-Resistant efflux pumps present in gram-negative bacteria and the classical cadherins observed in epithelial cells. We gain critical insights into mechanisms from these studies regarding functional dynamics and identifying structural features that play key roles in the efflux of cyclic lipids in HpnN transporters. Further, our studies newly identify a novel cis interface between classical cadherin. This originates from a strong correlation between co-evolving residues, suggesting that this interaction plays an important role in cell-cell adhesion. Based on this work, we have been able to devise a workflow incorporating these methods that provide new insights for selection of sites to target to inhibit function

Exploring molecular mechanisms with simulations and data analyses

Proteins are the active players in the cells and carry out most of the significant functions throughout biology such as metabolism, immunity, and maintaining structural integrity. Protein characteristic behaviors such as dynamics and residue level co-evolution play a critical role in determining the detailed behaviors of any given protein. We have employed a range of computational methods: molecular dynamics simulations, principal component analysis of conformations, molecular modeling, and co-evolution analysis of sequences to attempt to understand the molecular mechanisms in membrane transporters and transmembrane proteins. The systems considered here include the Multi-Drug-Resistant efflux pumps present in gram-negative bacteria and the classical cadherins observed in epithelial cells. We gain critical insights into mechanisms from these studies regarding functional dynamics and identifying structural features that play key roles in the efflux of cyclic lipids in HpnN transporters. Further, our studies newly identify a novel cis interface between classical cadherin. This originates from a strong correlation between co-evolving residues, suggesting that this interaction plays an important role in cell-cell adhesion. Based on this work, we have been able to devise a workflow incorporating these methods that provide new insights for selection of sites to target to inhibit function