Upcoming events

Talk: Using AI for more efficient prevention of infectious diseases in a warmer and globalized world

Prof. Joacim Rocklöv, Department of Public Health and Clinical Medicine, Umeå University, Sweden
12th February 2020, 16:15 - Mathematikon Conference Room (5th floor)

Climate change, increasing human mobility and trade, pathogen evolution and resistance, urbanization, and ecological range shifts - all these global factors destabilize the current pattern of infectious diseases, notably those transmitted by vectors. There is general agreement that this will lead to the emergence and re-emergence of a wide range of infectious diseases. In Europe, water and vector-borne diseases such as Vibriosis, Dengue, Chikungunya, West Nile Virus, Lyme disease and Tick-borne encephalitis are proliferating and emerging among previously immunologically naive populations. This may result in severe disease outbreaks, morbidity, mortality, long-term disability and increasing burdens of disease. In Low-and-Middle-Income Countries (LMICs), Ebola has not yet been contained, malaria, Dengue and cholera are still associated with massive disease burdens, and arboviruses are likewise on the rise. Globally, resistance to antibiotics and insecticides is a growing concern. New genotypes and new pathogens are threatening to unleash pandemics with potential to have major societal impact, if not effectively monitored and controlled. The recent Corona virus situation is a good example of how sensitive the global population is to local emergence of viruses with epidemic potential. Achieving the sustainable development goals 2030 requires new methods for monitoring, surveillance and analysis, all of which are key for the deployment of more efficient, timely and strategic prevention.

To address these unprecedented global challenges, the public health professions are called on to develop new approaches and innovative techniques and solutions. We are now entering a world, where increasing availability of high quality, high-dimensional data and advanced computation techniques allow for previously unimaginable levels of precision and granularity with respect to monitoring and forecasting of disease outbreaks, their associated burdens and intervention demands. Digitalization, machine learning and artificial intelligence are still in its infancy in terms of public health applications, but hold great promise for revolutionizing public health decision-making, and for sustaining and safeguarding the global population.

This talk provides a few examples on how data from many different disciplines and domains, including climate, human mobility and social media, can be integrated in machine learning, and help timely risk assessment, better forecasts and support the development of more effective prevention strategies. In the talk I will discuss the methods, findings and give examples of tangible decision tools in the making in collaboration with public health policy makers.

European Conference on Mathematical and Theoretical Biology 2020

Heidelberg, 31st August-4th September 2020

The 12th European Conference on Mathematical and Theoretical Biology (ECMTB2020) will take place between 31st August-4th September in Heidelberg, Germany, and is a joint event organized by the European Society for Mathematical and Theoretical Biology (ESMTB) and the Society for Mathematical Biology (SMB). The meeting will bring together researchers and students interested in mathematical modelling with applications to life sciences. Join us at this exciting scientific event to learn about cutting-edge research from our prestigious speakers from all around the world, and present your work to receive feedback and take part in insightful discussions.

The conference will comprise plenary presentations, contributed talks, mini-symposia and posters grouped thematically (scientific groups to be announced). The event will be hosted in the New University building (Neue Universität) located in the charming Heidelberg old town (Altstadt).

Follow us on facebook @ecmtb2020 and on twitter @ecmtb2020.

MathBio PhD Networking Event

Date, time, location: TBA

Past events

Colloquium: Cancer Modeling through Evolutionary Game Theory

Prof. David Basanta and Dr. Jeffrey West,
Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center, Tampa, US
5th February 2018, 2pm - Mathematikon Conference Room (5th Floor)

The colloquium will feature an introductory course by Dr. Jeffrey West on "Modeling the evolution of cancer from a game theoretic perspective" and a lecture by Prof. David Basanta with title "Evolutionary Game Theory to define cancer ecology and evolution".

For more details, abstracts and schedule please go here.

HGS Compact Course: Computational Methods and Strategies in Structure-based Drug Design

Dr. Joanna Panecka-Hofman, Dr. Daria Kokh, Dr. Neil Bruce (HITS)
4-5th December 2017, 9am - Mathematikon SR12 (5th Floor)

The course will present principles of simulation methods applicable in structure-based drug design and practical aspects of structure-based drug design strategies. We will focus on such techniques as ligand docking, molecular dynamics and binding free energy estimation methods. The course will cover the following TOPICS: 

  • Lead design strategies with examples
  • Force fields and interaction fields
  • Binding site identification, mapping and comparison
  • Ligand docking methods with examples
  • Thermodynamics vs. kinetics
  • Molecular dynamics and enhanced sampling in drug design

For more details, abstract and registration procedure please visit:   http://www.mathcomp.uni-heidelberg.de/curriculum/

Talk: Fixation Probabilities under Demographic Fluctuations

Dr. Peter Czuppon, Max Planck Institute for Evolutionary Biology, Plön
9th November 2017, 4pm - BioQuant SR043

We consider a population consisting of two species. Each type gives birth and dies independently of the other one. Population size is regulated by intra- and interspecific competition events letting the model follow generalized Lotka-Volterra dynamics. A quantity of interest in finite populations is the probability of fixation/extinction of one type. While it has been studied broadly in the context of fixed or deterministically varying population sizes we approximate the fixation probability in populations with stochastically fluctuating sizes. In order to do so we will take the limit of weak selection, i.e. the "fitness" benefit of one type over the other is very small.

Compact Course: "Virtual Screening of Drug Discovery"

Prof. Markus Lill, Purdue University
26-29th June 2017

Virtual screening has become an essential element of the drug discovery process. Virtual screening is used to search a large library of small molecules for binding to a target protein and select a small subset of compounds for subsequent experimental validation and optimization. In this block course we will discuss the methodological basis and practical applications of structure-based and ligand-based virtual screening methods such as docking, shape-based, pharmacophore and fingerprint concepts.

The course alternates lectures and practical sections in the computer lab.

Talk: "Modelling glioma growth with fully anisotropic diffusion"

Prof. Thomas Hillen, University of Alberta
6th June 2017

The human brain has a complex geometric structure consisting of white and gray matter, blood vessels, ventricles, skull etc. It forms a highly anisotropic medium. Glioma in the brain are known to invade along white matter tracks and along other brain structures. Using diffusion tensor imaging (DTI) it is now possible to obtain directional information of the brain geometry. In my talk I will show how this DTI information can be used to parametrize a fully anisotropic diffusion equation for glioma spread. We validate the model on clinical data of glioma patients and discuss the future use in treatment design. (joint work with A. Swan, K.J. Painter, C. Surulescu, C. Engwer, M. Knappitsch, A. Murtha).

Talk: "Analytical approximations for spatial stochastic gene expression in single cells and tissues"

Dr. Ramon Grima, Edinburgh University
8th March 2017

Gene expression occurs in an environment in which both stochastic and diffusive effects are significant. Spatial stochastic simulations are computationally expensive compared to their deterministic counterparts and hence little is currently known of the significance of intrinsic noise in a spatial setting. I will show how starting from the reaction-diffusion master equation (RDME) describing stochastic reaction-diffusion processes, we can derive closed-form expressions for the approximate steady-state mean concentrations which are explicit functions of the dimensionality of space, rate constants and diffusion coefficients. These are generally different from those given by the deterministic theory of reaction-diffusion processes, thus highlighting the importance of intrinsic noise. Our theory is confirmed by comparison with stochastic simulations, using the RDME and Brownian dynamics, of two models of stochastic and spatial gene expression in single cells and tissues. Lastly, time permitting, I will discuss how one can extend these results to stochastic spatial simulations of intracellular processes which take into account macromolecular crowding, namely the volume exclusion due to the finite size of molecules. 


Dr. Diana-Patricia Danciu

Applied Analysis and Modelling in Biosciences
Institute of Applied Mathematics
Heidelberg University
Mathematikon, INF 205, Office 2.233
+49 (0)6221 5414138



Dr. Ana Victoria Ponce Bobadilla

Numerical Methods for Multiscale Models
Institute of Applied Mathematics
Mathematikon, INF 205, Office 1.315
+49 (0)6221 5414113



Dr. Verena Körber

Division of Theoretical Systems Biology
German Cancer Research Center (DKFZ)
Im Neuenheimer Feld 280
+49 (0)6221 5451384