Maaike and Pranas participated in the third summer school on Economic Principles in Cell Biology that took place on the 8-11th July, 2024, in Paris and online. Lab PhD students Francesco and Luis also attended the lectures online. Maaike was one of some 30 participants selected for in-person attendance, and Pranas gave one of the introductory lectures, “An inventory of cell components” (together with Diana Széliová, University of Vienna). A small size of this course came to our advantage for the social part: we enjoyed both the science and the beers at the Seine (and watching EURO2024!) with colleagues coming from different parts of the continent and beyond.

The scientific part of the summer school was accompanied by two more soft-skills oriented activities: first, the workshop on the creative process in science, Night Science by Martin Lercher. You might have heard of the editorials- and podcast series of Martin and Itai Yanai under the same name – give it a listen if you haven’t! The last day was dedicated for the Atelier SEnS, a workshop on exploring the relationship between one’s personal values and the research they conduct.

The summer school is an activity that emerged from the community initiative of the same name that aimed to bring the colleagues working under similar philosophy together. It all started as a monthly seminar via Zoom in early 2020, organized by a handful of professors (including our own Frank!), and the community expanded over time. Eventually, an idea to write an open-source textbook started gaining momentum. The book project is led by Wolfram Liebermeister at INRAE (France), and Pranas is one of the coordinators of the initiative since recently. The summer school is one of the ways to promote the textbook project, and to test its didactical value in practice.

Overall, it was a very nice experience, and quite a relaxed one (in both best and worst ways of it). The 2025 summer school, if all things turn as planned, will be held in Vienna, with a lot of improvements/changes planned, so we are quite excited to join it if it materializes!

Since cells have finite biosynthetic resources for protein synthesis, a rise in one protein concentration is generally at the expense of that of others. A logical consequence is then that phenotypic traits trade-off: cells cannot excel at everything. They cannot grow fast and be very stress tolerant and adaptive to new conditions at the same time. Another consequence is that protein under- and over-expression without a long-term fitness benefit is likely selected against. 

How do cells then decide on the expression level of proteins? Can they even tune protein concentrations optimally — to prevent wasteful over-expression and suboptimal under-expression? What do cells try to achieve by changing protein concentrations? How can they decide that tuning is finished and that protein concentrations are optimal?

In this new paper (https://doi.org/10.1042/EBC20230045), we can gave an overview of how cell can achieve growth-rate-maximising tuning of metabolic protein concentrations, via optimal gene expression of metabolic genes. We pioneered this method in Berkhout et al.(https://doi.org/10.1038/srep01417) and generalised it in Planqué et al. (https://doi.org/10.1371/journal.pcbi.1006412), and applied its way of thinking to understand the regulation of ribosomal gene expression in E. coli in Bosdriesz et al. (https://doi.org/10.1111/febs.13258). Here we give an elementary overview of this theoretical method. We apply it to understand the gene-regulatory feedback regulation of amino-acid metabolism. 

Some more background information on this way of thinking can also be found in some teaching material I wrote for a course on enzyme kinetics (https://teusinkbruggemanlab.nl/course-information-from-enzyme-kinetics-to-models-of-metabolism/).

We hope that we have inspired you to think also about how cellular objectives can be achieved by gene-regulatory circuits.

Some time ago, we started an initiative in the lab to collect all the best (and worst) practices on how to reconstruct, curate, and simulate genome-scale metabolic models (GEMs). A total of 7 colleagues, including a visiting PhD student Gioele Lazzari from the University of Verona, and a MSc rotation student Steven Wijnen, have put their forces together with Pranas to reflect on their experience, share tips, tricks, and caveats on the art of genome-scale modeling. After months of discussions, writing, and polishing, we would like to share the first public version of the handbook as an early Valentine’s present to the community :).

We cover topics from the very basics on data collection to make GEMs to rather advanced material, such as creating context-specific or community metabolic models. The handbook is accompanied by two Jupyter notebooks that can be used for training colleagues new to GEMs, or in your teaching. All materials are licensed under CC-BY-NC, which permits unrestricted non-commercial use of the materials as long as attribution is given.

It has been a great experience, and the initiative has been received very well by the lab members outside of this writing group. We invite fellow colleagues to contribute with ideas, thinking, and writing, if they see value in a community effort to share their best practices. You are always welcome to drop a line to Pranas on these matters.

Happy modeling!

Microbiologists are searching for a universal theory of how bacteria form communities based not on their species but on the roles they play.

A new article in the popular science magazine Quanta has highlighted our work on metabolic preferences and their genomic markers. How can we identify rules of microbial communities? What are the traits that determine success in a community? What level of abstraction is useful when thinking about the many species of bacteria living in a given community? How can we design microbial consortia? These are some of the questions that the research (including our own) described in this article is trying to unravel. Exciting times for microbial ecology!

Read the article here: https://www.quantamagazine.org/the-quest-for-simple-rules-to-build-a-microbial-community-20240117/

The Netherlands Organisation for Scientific Research (NWO) granted the FERMI Perspective proposal led by Herwig Bachmann from the Systems Biology Lab. The project aims to accelerate the protein transition by improving the taste of plant-based products through fermentation by microbes. With colleagues from the Wageningen University and TU Delft and 10 industrial partners, the project will combine experimental and computational methods to unravel the biochemistry of the underlying conversions: which chemistry, enzymes and pathways turn beany and grassy flavours into meaty or neutral ones? This knowledge should accelerate a shift towards a more plant-based diet and will contribute to a more sustainable food production chain.

For more info, see the infographic below (click to enlarge).

Which metabolic enzymes should a cell change in concentration to give rise to a large change in steady-state metabolic flux? Which enzymes should an experimentalist inhibit to reduce the flux the most?
Why are some kinase-phosphatase couples in cellular signal transduction ultra-sensitive to changes in signals, while others are not? What is the function of negative feedback in metabolic pathways? How can you design metabolic pathways that are insensitive to particular environmental changes and highly sensitive to others?

Answering such questions requires consideration of enzymes active in networks, as their activity and network influence depend on their reactant concentrations, which are co-determined by all enzymes in the network. Answering such questions therefore needs a systems perspective. One that is quantitative also; as all enzymes influence the network’s functions but to different degrees. The concept of a single rate-limiting step is therefore generally overly simplistic.

Now about 50 years ago, two papers were published, one by Burns & Kacser and another by Heinrich & Rapoport, which dealt with a sensitivity analysis of metabolic pathways to change in enzyme activities and concentrations. They also managed to derive theorems relating sensitivity coefficients. Those coefficients come in two forms: elasticity coefficients and control coefficients, and their products are called response coefficients. We invite you to check out those papers, they have not aged. The entire framework is called Metabolic Control Analysis (MCA).

Bas (Teusink) and myself grew up as young scientists in the scientific community of MCA due to our PhD supervisor, Prof Dr Hans Westerhoff, who was one of the pioneers and advocates of MCA. MCA was therefore part of our training and thinking, and we have published papers about this theory.

What always attracted me (Frank) as how Christine Reder‘s formulation of MCA makes the relation between reaction stoichiometry and enzyme kinetics so clear, and in completely general terms. By using concepts from linear algebra and enzyme kinetics, the control of enzymes on steady-state properties of enzyme networks could be written down in completely general terms. This gave me confidence that a general theory about cellular metabolism and growth can be derived, applicable across all domains of life. Aiming for this has been a research theme throughout my career.

One aspect of Reder’s theory is that the null space of the stoichiometric matrix is used to derive the summation theorems of MCA. But the null space is not unique, which always bothered me when I was a PhD student. This was changed when David Fell, Stefan Schuster and Thomas Dandekar generalised the definition of metabolic pathways in a unique manner, using a concept that is related to the null space of the stoichiometric matrix. They showed that any steady-state flux solution of a stoichiometric matrix is convex combination of a set of unique flux vectors, called elementary flux modes (EFMs). This solved the problem of the entire set of summation theorems (although I do not know whether any one ever published this). EFMs are (beautiful) mathematical objects with extremely appealing mathematical properties for biotechnology and evolutionary biology.

We worked for quite some time on EFMs in the context of the optimisation of stoichiometric models and of dynamic models (containing stoichiometry and enzyme kinetics). It turns that EFMs are the solution of optimisations of enzyme networks, given enzyme kinetics, where one aims to maximise a steady-state flux by optimising enzyme concentrations that sum to a fixed total. They are also the elementary solutions of flux balance analysis computations, considering only reaction stoichiometry and not enzyme kinetics.

If evolution maximises the growth rate of cells then what would be the flux control coefficients of all the optimally expressed enzymes? MCA suggests that you cannot answer this question, because you do not know the enzyme kinetics, and therefore you do not know the elasticity coefficients. But it turns out that you can! This was realised by Klipp & Heinrich, and even earlier by Burn & Kacser in a more simplified setting (in Burn’s thesis). The context of Klipp & Heinrich is regrettably also too simplified to consider the entire metabolic network of a cell that is growing at its maximal rate by having expressed all its enzymes optimally.

The solution to this problem we offer in our paper to the special issue of Biosystems celebrating the 50th anniversary of MCA. You can find the paper here. It is quite a read, we know, but it contains all the main ideas from start to end. We hope that it inspires you to become familiar with MCA, enzyme kinetics, and stoichiometric modelling concepts.

Bacteria grow in communities of many co-occurring species in , e.g., in your gut, in soil, or in the ocean. A fundamental process in these communities (more specifically, communities of heterotrophic bacteria, i.e., bacteria that utilize organic carbon sources) is that bacteria take up substrates (basically, food) like sugars and amino acids from the environment and turn them into biomass or convert them into something else they then excrete. For this new paper, what we wanted to know was: which substrates can different bacteria use (we were focused on substrates they can use as a carbon source)? Can we identify patterns of substrate utilization, e.g., are similar compounds consumed by similar bacteria? Can we predict these patterns by looking at which genes different bacteria encode? Our work touches on several important questions in microbiology, from microbial ecology (how do microbial communities work?) to biochemistry (how does the structure of metabolic pathways shape substrate utilization patterns?) to genomics & evolution (how are capabilities of substrate utilization encoded in the genome, and how did evolution shape these genomic patterns?).

By analyzing the growth of 182 different strains of marine bacteria on 135 different potential carbon sources, we found that we can describe the substrate preferences of our bacteria to a first approximation in terms of their preference for sugars (e.g., glucose or polysaccharides like starch) relative to acids (e.g., amino acids or organic acids which are important intermediate during the chemical conversion of substrates into biomass). This preference is encoded in the genomes of bacteria, which tells us about the evolution of these preferences, but also makes the preferences predictable from genomes.

Our work reveals a way to simplify how we think about the metabolic capabilities of bacteria: we can describe a given heterotrophic bacterium by its degree of specialization along the axis of sugar to acid specialists. This is very useful because it allows us to describe communities of bacteria in a simple way (e.g., by their collective degree of specialization). More fundamentally, our work also shows how the evolution of bacterial genomes is structured by biochemical constraints which drives bacteria to specialize along this axis of sugar to acid specialists. Since the metabolic preferences are encoded in genomes, we can estimate the metabolic capabilities of species that we have not (yet) cultured, but for which we have genomic information (e.g., by sequencing entire communities and piecing together the genomes of the constituent species, a process called metagenomics). This allows us to begin to understand the metabolic processes in many microbial communities in the environment in a simplified manner.

Out now: Genome content predicts the carbon catabolic preferences of heterotrophic bacteria

A custom of our lab is to start the academic year with a state-of-the-union day at the Hortus Botanicus of Amsterdam.

During this day, the PIs give an research overview of the last year, an outlook on the coming year, current duties, and their long-term research vision in the presence of the research (support) staff and all the researchers working on funded projects (PhDs and postdocs).

Bas gave an overview of the Holomicrobiome “groeifonds” project proposal that he is involved in, as an organising member. This is a 240 million euro project that aims to link Dutch microbiome research and company demands: to together drive innovations that will improve the quality of our soils, water, crops and human health by improved methods and technologies. The outlook is that this should give an input to the Dutch economy in an environmentally friendly and sustainable manner. The proposal needs to be reshaped in the coming months, resubmitted, and reevaluated by an independent committee on behalf of the Dutch government for approval (or rejection). Exciting times!

Matti chaired a session on the reconsideration of the logo of our lab, which now has effectively turned this into competition between groups of colleagues. Let’s see what they come up with!

Together with Ralf Steuer (Humboldt-University of Berlin, Germany) we recently wrote a review for Bioessays, see https://doi.org/10.1002/bies.202300015. It addresses how we currently view the consequences of finite biosynthetic resources (for protein expression) for cellular tasks such as stress tolerance, growth and adaptation to new conditions.

We focus on Escherichia coli and Saccharomyces cerevisiae. We acknowledge that the advanced understanding we have of their physiology may not be extrapolatable to microorganisms with a qualitatively different evolutionary history. We argue that these two microorganisms optimise the expression levels of needed metabolic enzymes, given that some fraction of biosynthetic resources is allocated to proteins needed to adapt to new conditions. That latter fraction of non-growth associated protein decreases with cellular growth rate (nutrient quality) and therefore makes fast growing E. coli and S. cerevisiae cells less stress tolerant and adaptive to new nutrients. This is an example of a trade off between growth and adaptation due to finite biosynthetic resources.

If you are interested in such ideas, how they emerge from computational models, and what their precise experimental evidence is then the review we wrote might be of interest to you.

In primate species, a change in lifestyle leads to adjustments in their microbiome. What does this mean? 

Over the last years, ARTIS Micropia Professor Remco Kort and his Bioinformatics & Systems Biology student Isabel Houtkamp studied the faeces of the western lowland gorilla. They did this by comparing the composition of the microbiome of the ARTIS gorillas with that of their wild counterparts – and also with that of humans. They were assisted by Walter Pirovano and Mark Bessem of the company BaseClear, experts in the field of microbial DNA analyses. This research revealed interesting correspondences. In both primate species, a change in lifestyle led to adjustments in their microbiome. So what does this mean? 

To find out, check out their new paper here and a behind-the-scenes story Remco wrote for Micropia.