Mathematics has provided the language to describe, explain, and predict biological phenomena. Today, with the explosive growth of data and artificial intelligence, biology is witnessing a profound shift: traditional mechanistic models are being complemented by powerful learning approaches capable of uncovering hidden patterns and structures in complex datasets. This mini workshop highlights how geometry, topology, and modern algorithms can extend classical approaches. The goal is to explore how the synthesis of theory-driven and data-driven tools is shaping the next generation of biological discovery.

•Registration: Free but mandatory (see details below).
•Venue (Day 1): Class D, Institute for Research in Fundamental Sciences (IPM), No. 70, Lavasani St., Tehran, Iran (Address).
•Venue (Day 2): The virtual access link will be sent to registered participants in due time.

Poster
Hide To download a high quality version click on the photo
Poster

Speakers

Abstract:
What can mathematics contribute to biology? It can elucidate principles, develop models and provide tools for the analysis of biological data. In turn, this can inspire new mathematical research. The examples presented will include fundamentals of life, neurobiological models and biological (and other) networks. The new mathematical tools will include stochastic dynamical systems, combinatorial schemes, hypergraph analysis and geometric principles of machine learning.

Abstract:
Artificial intelligence (AI) based Molecular Sciences have begun to gain momentum due to the great advancement in experimental data, computational power and learning models. However, a major issue that remains for all these AI-based learning models is the efficient molecular representations and featurization. Here we propose advanced mathematics-based molecular representations and featurization. Molecular structures and their interactions are represented by high-order topological and algebraic models (including Rips complex, Alpha complex, Neighborhood complex, Dowker complex, Hom-complex, Tor-algebra, Rhombille tiling, etc). Mathematical invariants (from persistent homology, Ricci curvature, persistent spectral, Analytic torsion, algebraic variety, etc) are used as molecular descriptors for learning models. Further, we develop geometric and topological deep learning models that can systematically incorporate molecular high-order, multiscale, and periodic information, and use them for analysing molecular data from chemistry, biology, and materials.

Abstract:
TBA

Abstract:
Mathematical modeling has served as a cornerstone of biology over the past century, transforming it from a largely descriptive science into a predictive one. From Mendelian genetics and population dynamics to epidemic spreading, neural excitability, and pattern formation, mathematical frameworks have provided mechanistic insight and predictive power. Today, however, the rapid rise of artificial intelligence and data-driven approaches raises the question: is mathematical modeling still relevant? This talk argues that the future lies in combining modeling and learning, where hypotheses about data structure complement predictive algorithms, leading to deeper insights into biological systems.

Schedule

Download PDF version


Registration

Registration: Free but mandatory.
Registration deadline: Sptember 16, 2025.




Registration Form


CAPTCHA image

Organizing Committee:

  • Dr. Marzieh Eidi SCaDS. AI and Max Planck Institute (MPI MIS), Leipzig, Germany
  • Dr. Zahra Eidi School of Biological Sciences, IPM, Tehran, Iran
  • Dr. Maryam Shahdoost School of Biological Sciences, IPM, Tehran, Iran
  • Dr. Mojtaba Madadi Asl School of Biological Sciences, IPM, Tehran, Iran
  • Mr. Arash Farjami School of Biological Sciences, IPM, Tehran, Iran

SCHOOL OF BIOLOGICAL SCIENCE
SCHOOL OF BIOLOGICAL SCIENCE
IPM
Institute for Research in Fundamental Sciences (IPM)

Useful Information

Travel to Tehran

Practical Information

 

 

 

IPM Institute for Research in Fundamental Sciences

Farmaniyeh

School of Biological Sciences,

No. 70, Corner of Shahid Farbin Alley,
Shahid Lavasani St, Tehran - Iran

  • Tel: +98 21 24509640, Fax: +98 21 22825352
  • bsinfo@ipm.ir
Top