I am a junior group leader interested in theoretical and computational biophysics applied to various biological questions. We develop stochastic simulations to study the dynamics of coarse-grained mesoscopic systems.

Supervision
Master students from Physics, Computer Sciences, and Quantitative Biology that are interested in a Master thesis are encouraged to contact me.

Teaching
Head of the module "Stochastic Models of Biological Systems" for Computer Sciences Master students. Students from Physics that are interested in the module are also invited to contact me.

Tin did his PhD dissertation studying the bacterial genomes, analysing how horizontal transfer of DNA segments between bacteria facilitates their evolution and shapes their scaling laws. Thereafter, Tin continues his work on bacterial genomics, develops model to describe how DNA transfer shapes nucleotide substitution, and applies this model on the phylogenetic tree reconstruction of closely related bacterial strains that experience frequent DNA transfer.

In addition, Tin also studies metabolism in bacteria, exploring how adaptive evolution through DNA transfer constrains the network properties of metabolism, and how the limited space in the cytoplasm constrains the resource allocation among enzymes, ribosomes, and other molecular machineries in a bacterial cell.

Knowledge about the Michaelis constant K_M and the turnover number k_cat for enzyme-catalyzed reactions is required for advanced models of cellular metabolis. However, since the experimental measurement of k_cat and K_M is difficult and time consuming, no experimental data exists for many relevant enzymes. To approach this problem, I am trying to predict these values using methods from Machine and Deep Learning. To train different models, I use information about enzyme structures, substrates, assay conditions, and metabolic network properties.

I was involved in organization and exercise groups for students in the following lectures:
- "Algorithmen und Datenstrukturen" (winter Semester 2019/2020)
- "Rechnerarchitektur" (sommer semester 2019)

CAM (Crassulacean Acid Metabolism) is a major photosynthetic pathway that has evolved convergently in many plant families facing water-restricted environments. This pathway is able to combine high production rates with extreme water efficiency, attracting interest from basic and applied researchers. In my project I use modeling and computer simulations to better understand its central mechanisms and the paths followed in its evolution.

Prokaryotic genomes are fluid and change rapidly over evolutionary time, because of the prevalence of horizontal gene transfer (HGT) among even distantly related lineages. I am working on quantitative and stochastic modeling of HGT in the broader context of how prokaryotes have contributed to the origin of eukaryotes. This also involves gaining a better understanding of what characteristics of genes and genomes boost or hinder HGT.

I plan on constructing large-scale kinetic models and use them to determine metabolic states, including metabolite/protein concentrations, with higher accuracy compared to existing modeling approaches. Starting from available reconstructed genome-scale models (FBA-models), we would add enzymes, kinetics and protein synthesis. Models would be simulated using concepts of balanced growth theory. The work follows community standards of systems biology to improve quality and reusability.