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BEGIN:VEVENT
DTSTAMP:20260601T091421Z
DTSTART:20260608T130000Z
DTEND:20260608T140000Z
SUMMARY:Benjamin Maier -- From Multi-Omics Data to Executable Mechanistic
  Models of Cell Signalling [IN PERSON]
UID:{http://www.columbasystems.com/customers/uom/gpp/eventid/}j1q9-mm3dxl
 xn-q5zhti
DESCRIPTION:Join us for this seminar by Benji Maier (EMBL-EBI) as part of
  the Maths in the Life Sciences seminar series (and the online North Wes
 t Seminar Series in Mathematical Biology and Data Sciences in collaborat
 ion with Liverpool Universities). \n\nTitle: From Multi-Omics Data to Ex
 ecutable Mechanistic Models of Cell Signalling: Network-based approaches
  to study context-specific cell signalling\n\nAbstract: Cellular signall
 ing allows organisms to communicate and adapt to environmental changes w
 ith deregulation leading to many diseases. Yet our current view\, often 
 summarized by annotated consensus pathways\, fails to capture context-sp
 ecific signal transduction rewiring and is biased towards well-studied p
 rocesses. Multi-omics technologies now allow hypothesis-free\, data-driv
 en exploration of biological systems\, phenotypes\, perturbations\, and 
 disease states. However\, the scale and noise of these data\, together w
 ith the need for manual curation\, make it difficult to systematically g
 enerate mathematical models.\nIn this seminar\, I will present a modular
  workflow for deriving context-specific\, executable mathematical signal
 ling models from multi-omics and phenotype data. The workflow builds on 
 tools previously developed in the Petsalaki group: SELPHI2.0 and PhosX f
 or kinase-substrate relationships and kinase-activity inference\, phuEGO
  for active signalling module reconstruction from phosphoproteomics (v1)
  as well as spatial/single-cell transcriptomics data (v2)\, and CHARLIE 
 for phenotype-specific network extraction.\nA key bottleneck in converti
 ng data-driven signalling networks into mathematical models is the lack 
 of edge directionality and regulatory sign. FlowSign addresses this by p
 redicting edge direction and regulation from prior knowledge\, omics dat
 a\, user annotations\, and network topology. The resulting networks can 
 be transformed into Boolean or ODE-based models to simulate context-spec
 ific signalling dynamics\, test perturbation effects\, and prioritize ex
 perimentally testable hypotheses. I will demonstrate this pipeline in on
 going projects on melanoma drug resistance\, cardiac organoid design\, a
 nd digital twins for rare-disease patients.\n\nSpeaker Information: Benj
 i is a final-year Systems Biology PhD candidate in Evangelia Petsalaki’s
  Whole Cell Signalling group at EMBL-EBI and the University of Cambridge
 \, as part of the EMBL International PhD Programme. His research focuses
  on data-driven and mechanistic modelling of cell signalling\, including
  multi-omics network reconstruction\, prediction of regulatory interacti
 on signs\, and building executable models for cardiac organoid design\, 
 melanoma drug resistance and rare-disease digital twins. He holds a B.Sc
 . in Biological Sciences from Heidelberg University and completed a join
 t M.Sc. programme in Molecular Techniques in Life Science at SciLifeLab 
 (KTH Royal Institute of Technology\, Stockholm University & Karolinska I
 nstitutet) in Stockholm.\n\nPetsalaki Group: Evangelia Petsalaki’s resea
 rch group studies human cell signalling in healthy and disease condition
 s. The group uses interdisciplinary approaches\, including data-driven n
 etwork inference\, modelling of cell processes and data integration\, to
  understand how different environmental or genetic conditions affect cel
 l signalling responses leading to diverse cell phenotypes. Their long-te
 rm aim is to create whole-cell signalling models\, to better understand 
 cell functions and disease. Evangelia moved to GSK in April 2026 and ret
 ains a 20% position at EMBL-EBI.\n\nSelected publications / Useful Readi
 ng:\nSELPHI2: Data-Driven Extraction of Human Kinase-Substrate Relations
 hips From Omics Datasets (https://doi.org/10.1016/j.mcpro.2025.100994)\n
 PhosX: data-driven kinase activity inference from phosphoproteomics expe
 riments (https://doi.org/10.1093/bioinformatics/btae697)\nphuEGO: A Netw
 ork-Based Method to Reconstruct Active Signaling Pathways From Phosphopr
 oteomics Datasets (https://doi.org/10.1016/j.mcpro.2024.100771)\nIdentif
 ication of phenotype-specific networks from paired gene expression-cell 
 shape imaging data (https://doi.org/10.1101/gr.276059.121)\nIdentificati
 on of phenotype-specific networks from paired gene expression-cell shape
  imaging data (https://doi.org/10.1038/s44320-025-00183-5)\nDecoding MAS
 LD Progression: A Molecular Trajectory-Based Framework for Modelling Dis
 ease Dynamics (https://doi.org/10.1101/2025.01.14.632908)\n\nThe talk wi
 ll be also be streamed via Teams\, please contact carl.whitfield@manches
 ter.ac.uk or igor.chernyavsky@manchester.ac.uk for the link\, or sign up
  to the mailing list.\n\nTo subscribe to the mailing list for this event
  series\, please send an e-mail with the phrase “subscribe math-lifesci-
 seminar” in the message body to listserv@listserv.manchester.ac.uk
STATUS:TENTATIVE
TRANSP:TRANSPARENT
CLASS:PUBLIC
LOCATION:4.04\, Simon Building\, Manchester
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