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“We are bridging a gap that cannot yet be closed in the lab.” – Zoran Nikoloski about the world of bioinformatics

Prof. Zoran Nikolski
Photo : Thomas Roese
Prof. Zoran Nikolski

Biology is the scientific research of life, and computer science is the art of automatic information processing. Does that go together? Absolutely! Bioinformatics has been in the limelight since at least the first extensive sequencing of the human genome in 2003. After all, it played a major role in the “decoding” of human DNA. As a science at the interface between biology and computer science, it combines the principles and technological possibilities of both worlds to get to the bottom of topical issues. How does the highly complex metabolism of plants, animals, and humans work? How can crops be optimized, for example to make them more resistant to heat? And can our knowledge of the metabolism of bacteria be used to turn them into tiny biological factories? Matthias Zimmermann talked to bioinformatics specialist Prof. Dr. Zoran Nikoloski about old and new scientific questions, complex networks and their modelling as well as research results that are suitable for practical implementations.

You are Professor of Bioinformatics. What do you research?

Traditional computer science tries to solve general problems in society with the help of computers. Transferring this principle to biology, I would say that we deal with biological problems that we tackle with the help of computers. And there are many such problems, for example understanding the structure and function of protein complexes or modeling the processes in metabolic networks. In fact, the field offers endless possibilities, with which it has continued to grow. Classical bioinformatics began with the retrospectively simple task of deciphering the DNA code. The starting point was basically the project of identifying genes in DNA strands, which is nothing more than a sequence of letters.

The emergence of so-called OMICS technologies and other technological possibilities to analyze organisms at the molecular level then provided us with an enormous flood of data – terabytes of data on various levels of organisms! This led to the question of how we could use this data in a meaningful way, for example to understand which mechanisms underlie cellular processes. And, subsequently, how can the data help us to recognize things that have not been measured themselves or are not measurable at all? To a certain extent, the latter describes the second wave of bioinformatics, combined with the paradigm of systems biology.

Do people who pursue bioinformatics tend to come from the field of computer science or biology?

Those who started out in bioinformatics 40 years ago definitely came from computer science because the questions they were dealing with were very much driven by computer science. But the more closely the problems were connected with biology, the more biologists joined. At the moment it’s quite balanced. However, I believe that with the further development of AI tools that help break down technical barriers, more and more ‘pure’ biologists will switch to bioinformatics.

Which specialist area do you come from?

Computer science and applied mathematics, particularly graph / network theory. I came to biology through my interest in the spread of diseases via networks. I investigated how the structures of a network enable or prevent, accelerate or slow down the spreading of agents. It doesn’t matter what the agent actually is: a pathogen or a messenger substance, a neurotransmitter, or an animal. I realized that research into networks is important for many areas of science and that the results can be widely applied. Since then, I have intensively worked on analyzing cellular networks and the question of how they interact with each other.

So biology benefits from the concepts of computer science here. What about the other way around?

About 20 years ago, there was a major shift towards biologically inspired design, which aims to imitate or transfer biological principles to improve computer science. A typical example are algorithms based on the concept of mutation and evolution. We are currently seeing that the knowledge we have gained about networks, especially from biological networks, is being transferred to the synthetic field. Indeed, in synthetic biology, new systems are being developed that are based on fundamental biological principles. Like a bridge in the other direction.

Your research ranges from computational biology and bioinformatics to systems biology. Can you explain the differences?

In computational biology, we try to describe and simulate biological systems mechanistically in order to replicate and predict certain phenotypes. Bioinformatics is about relating genotypes to phenotypes, understanding how DNA modifications influence certain characteristics, how proteins are structured or function. And in systems biology, we look at the organism as a combination of many different systems, for example gene regulation networks, protein-protein interaction networks, and metabolic networks. We try to understand how these networks influence certain traits, processes, and dynamics of the system, especially those that we cannot see and measure. So we can say that the three areas build on each other: from mechanistic simulation to mapping genotypes to phenotypes to understanding how these different systems interact and generate higher parts of the system that are not necessarily observed.

Your work focuses on so-called metabolic networks. How can you research these highly complex systems at all?

All research into metabolic networks begins with the genome, i.e., the sequenced genetic material of an organism. Thanks to 100 years of biochemistry, genes and their effects are relatively well researched for some plants and microorganisms. We know what these genes do, what kind of proteins they form and what reactions these proteins catalyze. This knowledge, the entire catalog of known enzymatic reactions, is available in large biochemical databases. For our research, we take this information from the databases and combine it into networks – not as a pretty drawing on a whiteboard but as a matrix. With this matrix, a representation of the networks in our studied organism, we can calculate what is happening in it and make predictions because we can link data that has been measured for individual processes and simulate what cannot be measured. In this way, we bridge a gap in the model – between structure and function – that cannot yet be closed in the laboratory.

This also enables predictions about more complex traits such as growth, yield, disease progression, and reactions to changes in the environment. The long-term goal is, of course, to know where and how to intervene in the metabolic network in order to obtain an organism with the desired trait.

Which organisms do you work with?

We work with microorganisms, such as yeast, model algae and plants, such as corn, rice, and tomatoes. We also research human metabolism and different types of cancer cells.

Why so many different organisms?

By working with very different organisms, we are able to identify universal design principles that are then widely applicable in synthetic biology. Our models show that metabolic networks are organized similarly across a wide variety of organisms. Accordingly, our approaches to developing genotypes that perform certain tasks better can ultimately be used in a variety of ways.

Would you describe your research as basic or applied research?

That depends on the project. Some things, such as the analysis and description of metabolic networks, requires years of theoretical work before concrete applications can even be considered. Research on the development of cell factories can also have immediate practical applications, with results that can be used in industry within a year. Theoretical research on the robustness and plasticity of organisms might not be directly transferable, but it creates knowledge whose value may only become apparent later.

How is that?

I think the most interesting question that we have pursued so far is, for instance, how we can use the knowledge about the natural metabolic variability in order to predict the behavior of genotypes and populations. Why is this important? Because when I have models for the natural adaptability of organisms, I can use the designs available in nature to create a new genotype that will perform better at a certain task. It might be more efficient at photosynthesis, for example. For this purpose, we compared the natural variability of a typical C3 plant like barley with a photosynthetically more efficient C4 plant like corn. For both, we created comprehensive models on their photosynthetic activity. This enabled us to identify the actual differences in the DNA of both plants, which are responsible for the differences in their photosynthetic efficiency. This knowledge can now be used in a variety of ways. For example, the properties of the responsible enzyme can be improved in the lab because we know what needs to be changed. In fact, we just started a follow-up project with the University of Cambridge in December 2023, based on this basic research, in which we repeat this analysis with even more organisms and take first steps in bioengineering.

What else are you currently researching?

The project “RESIST”, for example, brings together partners from South Africa, Israel, Ireland, Bulgaria, and Germany. We are looking at so-called resurrection plants and their unique abilities to withstand droughts and continue to be able to survive even after months or years without any water. If we find out how this variability of their metabolic networks comes about, we can transfer it to other plants and develop drought resistant cultures.

A very application-oriented project …

Just like our project “ALFAFUELS”, which we only launched on January 1, 2024, and in which we try to obtain aviation fuel from cyanobacteria. During the first year, my group will be involved in the design of strategies to create precursor substances for jet fuel, which will then be developed and tested in the second year. In the third year, everything will be optimized for industrial application. So the path from the lab to practice is quite short in this case.

Omics Technologies

Omics technologies are derived from the suffix ‘omic’, which marks subfields of modern biology that engage in the analysis of collectivities of similar individual elements. This includes, for example, genomics, which looks at the genes of an organism, proteomics, which researches proteins, and metabolomics, which examines metabolic networks.

Metabolic networks

Metabolism describes all chemical transformations of substances in the bodies of living beings into intermediary products (metabolites) and end products. Metabolism, after all, is a complex network of individual reactions that determine and influence each other.

C3 and C4 plants

During the photosynthesis of C3 plants, carbon dioxide is being absorbed into chemical components with three carbon atoms. During hot or dry, their photosynthetic performance is reduced as the stomata close to prevent excessive evaporation of water. They are most efficient under moderate light conditions and temperatures between 15 and 25 degrees Celsius. C4 plants, on the other hand, reach their optimal photosynthetic performance between 30 and 47 degrees Celsius and are more resistant to heat and drought. C3 crop plants include, for example, wheat, rye, barley, oat, potatoes, soybeans, hemp, and rice as well as all tree species worldwide. C4 plants include, for example, corn, sugar cane, and millet.

The Researcher

Prof. Dr. Zoran Nikoloski is group leader of the research group “Mathematical Modelling and Systems Biology” at the Max Planck Institute for Molecular Plant Physiology and, since 2017, Professor of Bioinformatics at the University of Potsdam.
Email: znikouni-potsdamde

The Projects

The overall objective of PlantaSYST is to establish a Center for Plant Systems Biology and Biotechnology (CPSBB) in Plovdiv, Bulgaria.
Participants: Center of Plant Systems Biology and Biotechnology (CPSBB); Maritsa Vegetable Crops Research Institute (MVCRI); Institute of Microbiology, Laboratory of Metabolomics, all Bulgaria; Max Planck Institute for Molecular Plant Physiology; University of Potsdam
Funding: European Union / HORIZON 2020
Duration: 2017–2025

The main objective of „RESIST“ is to decipher the genetic determinants of desiccation tolerance in resurrection plants and to identify similarities and differences to model and crop species. The knowledge will then be transferred to economically important species.
Participants: Center of Plant Systems Biology and Biotechnology (CPSBB), Bulgaria (coordination); Max  Planck Institute for Molecular Plant Physiology; University of Potsdam; Ben-Gurion University of the Negev (BGU), Israel; BioAtlantis Ltd., Ireland; University of Cape Town (UCT), South Africa
Funding: European Union / HORIZON 2020
Duration: 2020–2024

Funding: European Union / Horizon Europe
Duration: 2024–2027


This text was published in the university magazine Portal Wissen - Eins 2024 „Bildung:digital“ (PDF).