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Institute for Computational and Experimental Research in Mathematics (ICERM)

This semester program brings together mathematicians, statisticians, computer scientists, and experimental biologists to address the challenges involved in genome-scale phylogenetic inference. An overarching goal is to unite biologically-sound modeling with algorithms that can be implemented in a computationally tractable manner and that scale as both the number of species and the amount of genetic data increase. Specific topics to be investigated include: theory and practice of model-based inference for trees and networks, combinatorial and algebraic approaches for inferring phylogenies, software and algorithm development for inference and visualization, Bayesian approaches to phylogenetic inference, models for phylogenetic diversity and node dating, and methods for assessing model fit.

ICERM;     Email: info@icerm.brown.edu

biology, computation, genomic data, phylogenomics, software development, algorithm development

Institute for Computational and Experimental Research in Mathematics (ICERM)

In this workshop, we will consider challenges and best practices in translating theory to software for phylogenetic inference. We will discuss strategies adopted by the tool development community, their relative advantages, and lessons learned through software development. We will discuss a variety of objective functions (parametric and non-parametric) and algorithmic techniques used by inference methods. We will cover parallelization and how it interacts with the choice of objective function and algorithmic technique. Finally, we will discuss best practices for evaluating new and existing software tools, not just to validate the implementation but also to study accuracy and scalability in relation to theory and usage in realistic bioinformatics pipelines. In addition to discussing how theory influences tools, we will examine how tool development can inform the theoretical questions asked.

ICERM;     Email: info@icerm.brown.edu

bioinformatics, phylogenetic inference, software development

The International Centre for Mathematical Sciences (ICMS)

This Winter School is a part of an initiative aimed at advancing mathematical education and collaboration, with a specific focus on fostering ties between diverse groups and promoting mathematical modeling as a tool to address biomedical challenges, especially within the African context. Participants will gain valuable skills and experience, contributing to the global effort to enhance mathematical competence and diversity.

Angewandte Mathematik (allgem.),    Kurse und Veranstaltungen für Studenten der Mathematik

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

The progress in understanding genes and genomes has given a boost to the use of synthetic DNA for biological and technological applications. Synthetic nucleic acids play a central role in synthetic biology and in emerging therapeutic paradigms, e.g., genome editing and nucleic acid vaccines. DNA-based data storage is making significant progress, and thanks to its extreme data-density, its high durability and its timelessness, it is promising to be the next standard for data archival systems. Synthetic biology and the use of synthetic DNA for information storage applications bring important algorithmic and data analysis challenges. In synthetic biology, reagent and assay design are often driven by algorithmic approaches. Novel synthesis technologies offer cost-reduction by several orders of magnitude at the cost of increased error-rate, raising new coding-theoretic questions. This Dagstuhl Seminar aims to bring together leading biologists, chemists, engineers, computer/data scientists, and theoreticians working on synthetic biology and on DNA-based data storage, to enable joint work in small groups leading to discussing and exploring recent advancements and current challenges.

Molekularbiologie,    Genomforschung und Bioinformatik

European Molecular Biology Organization (EMBO)

European Molecular Biology Organization (EMBO)

Molekularbiologie,    Wissenschaftliches Rechnen und Datenwissenschaft