Global Challenges for Women in Math Science

"We investigate the development of resistance to a DNA damaging anti-cancer drug in a population of tumor cells. Therefore, we use a hybrid mathematical model - this means, a model which is a combination of two different modelling approaches, namely an agent-based model and partial differential equations. The model is implemented numerically and simulations are performed to explore the conditions leading to resistance.

Most importantly, given that this approach is single-cell based, it allows to track in both, time and space, the history of each cell. This led us to investigate resistance at both the single and the population level. Given that the model is spatially explicit, we are able to unveiled the key role that the tumour microenvironment plays in resistance development.

"With the grant form the Entrepreneurial Award I will visit the Moffitt Cancer Research Center in Tampa Florida to further my collaboration and hopefully bring my project to publication."

Dr. Judith Cerit, TUM Department of Mathematics

"In my research, I develop a mathematical modeling framework which facilitates to account for variability between individual cells. The framework combines flexible statistical models with a priori information about cellular signaling pathways. It was already successfully applied to study pain sensitization in primary sensory neurons.

With the Entrepreneurial Award 2018, I was able to stay some weeks in the Sander lab at the cBio Center, Harvard Medical School and Dana-Farber Cancer Institute in Boston. My future research will benefit a lot from the expertise I gained during that time and from the network I was able to establish. Furthermore, I participated in the 26th Conference on Intelligent Systems for Molecular Biology (ISMB) in Chicago where I gave two presentations."

Carolin Loos, Helmholtz Center Munich

"Due to a higher life quality in developed and developing countries, the life expectation of human population has increased over the past decades. Therefore, the insurance sector needs to adjust business plans by studying the human mortality and, therefore, analyzing the risk that individuals might live longer than expected. In this project we - Pierre Devolder, Lexuri Fernández und Donation Hainaut - aim at mathematically modelling the mortality rate.

I presented our preceding research project „Stochastic Mortality in a Lévy Process Framework and Application to Longevity Products“ at the 31st International Congress of Actuaries (ICA CIA) that took place in Berlin. Therefore, I used the funding provided by the Entrepreneurial Award 2018."

Dr. Lexuri Fernandez, TUM Department of Mathematics

"Fine particulate matter is a key driver of global health and therefore it is vital to accurately model and estimate the exposure to this airborne contaminant, especially in areas of severe and persistent air pollution such as China and its biggest cities like Beijing. 

The aim of our project is to develop flexible multivariate methodology, which is able to accurately model and predict the levels of fine particulate matter and human exposure to air pollution in Beijing.  

I used the grant to fund a research visit at the TUM Center for Mathematics, to collaborate with Professor Claudia Czado. The work has been fundamental for a working paper entitled "A Bayesian Nonlinear State Space Copula Model to Predict Air Pollution in Beijing". The paper will be submitted to a top international statistical journal."

Luciana Dalla Valle, University of Plymouth

"In my project, I study dynamic properties and the long-term behavior of stochastic differential equations.This modern research topic makes it possible to model complex systems from the natural sciences, engineering and economicsand to predict whether a particular phenomenon will occur.

Therefore, stochastic equations are used to describe the transitions of the states of a physical system - for example, the change between ice ages and interglacial periods on Earth, currents and waves in an ocean, or the development of a stock price. In order to make predictions, randomness is appropriately quantified and calculated using mathematical methods.

We consider random dynamic systems that are driven by Gaussian processes with temporal memory. An example of such a process is the fractal Brownian motion. We published the first results in the preprint "Rough center manifolds" and I will introduce them in May 2019 at the SIAM Conference on Applications of Dynamical Systems in Snowbird, USA. I will use the money from the award for the journey."

Dr. Alexandra Neamtu, TUM Department for Mathematics

"In my PhD project, I develop stochastic dynamic models for heterogeneous molecular biological systems and work on the efficient parameter estimation for such models. The aim is to gain a better understanding of dynamic intracellular processes, e.g. the translation kinetics after mRNA transfection.

I used the money from the Entrepreneurial Award in 2018 to attend the DYNSTOCH Meeting in Porto, Portugal, where I gave a talk titled "Bayesian Inference for Diffusion: Using Higher-Order Approximations for Transition Densities". Moreover, I presented a poster titled "Modelling mRNA Transfection Using Diffusion Processes" at the ISMB and the ISCB Student Council Symposium in Chicago, USA, where I received an "Outstanding Poster" Award."

Susanne Pieschner, Helmholtz Center Munich

"In weather prediction, ensemble prediction systems (EPS) typically generate probabilistic forecast ensembles. Each ensemble member is the output of a numerical weather prediction (NWP) model. Statistical post-processing models improve the reliability of the prediction.

In our research project we develop new models that explore the dependence structure between weather variables and their ensemble forecasts with so-called Vine Copulas in a data driven way. We have already developed a model for improved temperature forecasts and are now planning to predict wind speed and precipitation under spatial or temporal dependencies or extreme weather events.

We spent the money provided by the Entrepreneurial Award of the "Global Challenges for Women in Math Science" program for two research visits for collaboration with Claudia Czado's working group at TUM. In addition, in 2018, I gave talks on our project at two conferences."

Dr. Anette Moeller, Technical University Clausthal

"The main subject of my project are geometric inequalities for convex compact sets. Convexity naturally appears in many areas of mathematics, such as Linear Programming, Probability Theory, Functional Analysis, Partial Differential Equations, Information Theory and the Geometry of Numbers. For instance, density functions of the important probability measures (like Gaussian, Exponential or Uniform) are logarithmic concave functions.

Although convexity is a simple to formulate property, convex bodies possess a surprisingly rich structure. The research on geometric inequalities takes not only place deep in the center of Convex Geometry, but it also spreads its branches into a variety of surrounding areas, such as Computational Geometry, Banach Space Theory or Asymptotic Geometric Analysis. Fields where computations are done with high-dimensional data sets, such as biology, physics, astronomy and meteorology, can benefit from applying techniques derived from this result."

Katherina von Dichter, TUM Department of Mathematics

"Many cancer risk models are constructed with data from prior clinical trials and used to provide the probability of cancer for future samples. The ability to provide effective predictions for various new samples is a key measure of model performance that can only be assessed in external validation. Therefore, external validation is an essential step in cancer risk model development.

To better understand what would happen in validation experiments if the training and test samples are similar, we introduce a validation method that incorporates the quantification of similarity across samples into the analysis, which is inspired by the casual inference theory. Such approach can remedy the deficiencies of current fashions in this field that ignore the heterogeneity between data sets and its results can be better interpreted.

The Entrepreneurial Award will be used to attend the 62nd International Statistical Institute (ISI) World Statistics Congress, in which I would like to have an opportunity to present and discuss my project."

Yiyao Chen, Clinic and Polyclinic for Nuclear Medicine

"I am interested in identifying clonal heterogeneity in cancer tissues mainly in Acute Myeloid Leukemia (AML). For this, I focus on the statistical analysis and parameterization of transcriptional heterogeneity from mRNA-Seq counts taken from small groups of cells.

I received the Entrepreneurial Award in 2018 and used the support for participating at the “Single Cell Biology” Conference in Hinxton, UK. There, biologists, method developers and computational modelers presented their work on single-cell biology. During the poster sessions I got the chance to show my poster on "Probability distributions for scRNA-Seq data" and discuss it with many interested colleagues.

I will use the remaining part of the money for the Symposia "Single Cells: Technology to Biology" in February 2019 in Singapore. This time I will present a poster on "Stochastic profiling for mRNA sequencing data"." 

Lisa Amrhein, Helmholtz Center Munich

"Melanoma is a cancer of the pigment producing skin cells. Around 80 percent of melanoma patients relapse soon after an initially successful therapy - a situation in which some cells are able to survive and acquire drug resistance leading to their uncontrolled growth. Using mathematical models, we aim to understand the mechanisms that lead to therapy resistance in melanoma cells. 

The 'Global Challenges for Women in Math Science Award 2019' enables me to visit my collaboration partners at the University of Pennsylvania, Philadelphia, USA, to further discuss this project."

Lea Schuh, TUM Department of Mathematics

"In biological models, there are usually several parameters, which need to be estimated. In most cased, there parameters are different for different cells, meaning that they are not constants, but distributions. Therefore, I used a so called mixed effect modelling method to capture these variability in my project.

In addition, further methods are required to propagate these parameters through the model (mostly nonlinear). I implemented sigma point methods as well as sampling based methods to approximate the distributions. The accuracy and computation time of these methods are compared using several toy models and one biological pathway model, the JAK/STAT model."

Dantong Wang, Helmholtz Center Munich

"During my studies, I received several Achievement Awards from the Women in Math Science program, which helped me, among other things, to finance two study visits abroad and to obtain specialized literature. Above all, the award honors and rewards someones own academic achievements. That strengthened my self-confidence and spurred me to embark on my own doctorate after Master's graduation." 

Sarah Braun, Master's graduate of the Department of Mathematics