Lehrstuhl für Mechatronik in Maschinenbau und Fahrzeugtechnik (MEC)

Systems Biology

Computational systems biology addresses the dynamical behavior of complex biological phenomena by incorporating experimental observations and theoretical research. It displays indefinitely many applications, some of them being traced in our group, mainly related to cancer and virus research. These include the research on qualitative modeling, signal and regulation pathways, cell cycle dynamics, genomic instability, multiscale modeling, etc. To this end, we apply advanced control, mathematical and ML techniques. The studies under the umbrella of CSB help explore the dynamics and the control therapies against the biological systems.

Multiscale Modeling of Cancer Dynamics

This research is focused on multiscale modeling of cancer via coupling the macroscale (tissue level) dynamics to the microscale (sub-cellular level) molecular interactions. We have developed a macroscale model governed by a partial differential equations (PDEs). We consider the co-evolution of healthy and mutated cell lineages distributed into three compartments with stem, progenitor and mature cells, under homeostatic regulation.

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Computational Systems Biology

Computational Systems Biology is a multidisciplinary domain which focuses on the holistic study of complex biological systems by using and developing efficient data structures, algorithms, visualization and modelling tools to understand the dynamics of system. In this regard, machine learning is to be used to develop pipelines and work flows for efficient preprocessing and analysis of omics data to identify cancer biomarkers which can yield important pathways crucial to cancer progression.

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SARS-CoV-2

The SARS-CoV-2 outbreak has become one of the biggest threats to the global economy and financial market since World War II. The high infectious rate and complex behaviour mechanisms of the SARS-CoV-2 have caused a serious challenge to the modern civilization and technological achievements. One of the distinctive characteristics of SARS-CoV-2 is its high inclination to mutations with 25.2 substitutions per month as suggested by the stats from Gisaid. Emerging deep learning techniques promise accurate mutation predictions which in conjunction with appropriate vaccine designs can turn to an efficient toolkit to combat the pandemic of the SARS-CoV-2 virus (or other). Machine learning has already proven successful in mutation predictions of other viruses (e.g. Influenza). The underlying project focuses on developing deep learning models whose task is prediction of the mutations in the genome of SARS-CoV-2.

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