Publications related to Transcriptional Regulatory Networks across Species : Evolution, Inference, and Refinement

The microbial genomics of glacier-fed streams: adaptations to an extreme ecosystem

Glacier-fed streams are the cold, ultra-oligotrophic, and unstable streams that are fed by glacial meltwater. Despite these extreme conditions, they harbour a diverse and abundant microbial diversity that develops into biofilms, covering the boulders and s ...

EPFL2024

Meeting our Makers: Uncovering the cis-regulatory activity of transposable elements using statistical learning

Cyril David Son-Tuyên Pulver

The adaptation of organisms to their environment depends on the innovative potential inherent to genetic variation. In complex organisms such as mammals, processes like development and immunity require tight gene regulation. Complex forms emerge more often ...

EPFL2024

Quantitative characterization of the inorganic phosphate gene regulatory networks in S. cerevisiae and bottom up engineering an orthogonal gene network

Shiyu Cheng

Gene regulatory networks (GRNs) play a crucial role in an organism's response to changing environmental conditions. Cellular behaviors typically result from the integration of multiple gene outputs, and their regulation often demands precise control of num ...

EPFL2024

The dynamic genetic determinants of increased transcriptional divergence in spermatids

Christina Ernst

Cis-genetic effects are key determinants of transcriptional divergence in discrete tissues and cell types. However, how cis- and trans-effects act across continuous trajectories of cellular differentiation in vivo is poorly understood. Here, we quantify al ...

Nature Portfolio2024

A ride time-oriented scheduling algorithm for dial-a-ride problems

Nikolaos Geroliminis, Claudia Bongiovanni, Mor Kaspi

This paper offers a new algorithm to efficiently optimize scheduling decisions for dial-a-ride problems (DARPs), including problem variants considering electric and autonomous vehicles (e-ADARPs). The scheduling heuristic, based on linear programming theor ...

Pergamon-Elsevier Science Ltd2024

Reach For the Arcs: Reconstructing Surfaces from SDFs via Tangent Points

Yingying Ren

We introduce an algorithm to reconstruct a mesh from discrete samples of a shape's Signed Distance Function (SDF). A simple geometric reinterpretation of the SDF lets us formulate the problem through a point cloud, from which a surface can be extracted wit ...

Association for Computing Machinery2024

Gibbs sampling the posterior of neural networks

Giovanni Piccioli, Lenka Zdeborová, Emanuele Troiani

In this paper, we study sampling from a posterior derived from a neural network. We propose a new probabilistic model consisting of adding noise at every pre- and post-activation in the network, arguing that the resulting posterior can be sampled using an ...

Iop Publishing Ltd2024

Single-cell epigenomic reconstruction of developmental trajectories from pluripotency in human neural organoid systems

Fides Zenk

Cell fate progression of pluripotent progenitors is strictly regulated, resulting in high human cell diversity. Epigenetic modifications also orchestrate cell fate restriction. Unveiling the epigenetic mechanisms underlying human cell diversity has been di ...

Nature Portfolio2024

Decoding Chromatin Ubiquitylation: A Chemical Biology Perspective

Beat Fierz, Pauline Franz

Since Strahl and Allis proposed the "language of covalent histone modifications", a host of experimental studies have shed light on the different facets of chromatin regulation by epigenetic mechanisms. Initially proposed as a concept for controlling gene ...

Academic Press Ltd- Elsevier Science Ltd2024

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Mattia Atzeni

The ability to reason, plan and solve highly abstract problems is a hallmark of human intelligence. Recent advancements in artificial intelligence, propelled by deep neural networks, have revolutionized disciplines like computer vision and natural language ...

EPFL2024

Adaptive projected variational quantum dynamics

Giuseppe Carleo, Stefano Barison, David Linteau

We propose an adaptive quantum algorithm to prepare accurate variational time evolved wave functions. The method is based on the projected variational quantum dynamics (pVQD) algorithm, that performs a global optimization with linear scaling in the number ...

Amer Physical Soc2024

Molecular hypergraph neural networks

Philippe Schwaller, Junwu Chen

Graph neural networks (GNNs) have demonstrated promising performance across various chemistry-related tasks. However, conventional graphs only model the pairwise connectivity in molecules, failing to adequately represent higher order connections, such as m ...

Aip Publishing2024

Bacterial diversity dominates variable macrophage responses of tuberculosis patients in Tanzania

Jacques Fellay, Zhi Ming Xu

The Mycobacterium tuberculosis complex (MTBC) comprises nine human-adapted lineages that differ in their geographical distribution. Local adaptation of specific MTBC genotypes to the respective human host population has been invoked in this context. We aim ...

Nature Portfolio2024

Multi-well plate lid for single-step pooling of 96 samples for high-throughput barcode-based sequencing

Bart Deplancke, Daniel Migliozzi, Hüseyin Baris Atakan, Mustafa Demir, Riccardo Dainese, Daniel Alpern, Dariia Gudkova, Gilles Weder

High-throughput transcriptomics is of increasing fundamental biological and clinical interest. The generation of molecular data from large collections of samples, such as biobanks and drug libraries, is boosting the development of new biomarkers and treatm ...

Springer2024

Towards an Understanding of Hydraulic Sensitivity: Graph Theory Contributions to Water Distribution Analysis

Jérôme Chenal

Water distribution systems (WDSs) are complex networks with numerous interconnected junctions and pipes. The robustness and reliability of these systems are critically dependent on their network structure, necessitating detailed analysis for proactive leak ...

MDPI2024

Ancestral genome reconstruction enhances transposable element annotation by identifying degenerate integrants

Didier Trono, Evaristo Jose Planet Letschert, Wayo Matsushima

Growing evidence indicates that transposable elements (TEs) play important roles in evolution by providing genomes with coding and non-coding sequences. Identification of TE-derived functional elements, however, has relied on TE annotations in individual s ...

2024

Environment-dependent epistasis increases phenotypic diversity in gene regulatory networks

Florence Gauye, Florian Baier

Mutations to gene regulatory networks can be maladaptive or a source of evolutionary novelty. Epistasis con-founds our understanding of how mutations affect the expression patterns of gene regulatory networks, a chal-lenge exacerbated by the dependence of ...

AMER ASSOC ADVANCEMENT SCIENCE2023

Impact of phylogeny on structural contact inference from protein sequence data

Nicola Dietler, Umberto Lupo, Anne-Florence Raphaëlle Bitbol

Local and global inference methods have been developed to infer structural contacts from multiple sequence alignments of homologous proteins. They rely on correlations in amino acid usage at contacting sites. Because homologous proteins share a common ance ...

ROYAL SOC2023

Statistical learning quantifies transposable element-mediated cis-regulation

Didier Trono, Julien Léonard Duc, Alexandre Coudray, Cyril David Son-Tuyên Pulver, Shaoline Sheppard, Evaristo Jose Planet Letschert, Raphaël Gérard Théodore Michel Marie de Deloÿe et Fourcade de Fondeville, Julien Paul André Pontis, Delphine Yvette L Grun

Background: Transposable elements (TEs) have colonized the genomes of most metazoans, and many TE-embedded sequences function as cis-regulatory elements (CREs) for genes involved in a wide range of biological processes from early embryo- genesis to innate ...

2023

Byzantine-Resilient Learning Beyond Gradients: Distributing Evolutionary Search

Rachid Guerraoui, Andrei Kucharavy, Matteo Monti

Modern machine learning (ML) models are capable of impressive performances. However, their prowess is not due only to the improvements in their architecture and training algorithms but also to a drastic increase in computational power used to train them.|S ...

Assoc Computing Machinery2023