Matthias Heinemann

Synthetic biology---putting engineering into biology

Synthetic biology is interpreted as the engineering-driven building of increasingly complex biological entities for novel applications. Encouraged by progress in the design of artificial gene networks, de novo DNA synthesis and protein engineering, we review the case for this emerging discipline. Key aspects of an engineering approach are purpose-orientation, deep insight into the underlying scientific principles, a hierarchy of abstraction including suitable interfaces between and within the levels of the hierarchy, standardization and the separation of design and fabrication. Synthetic biology investigates possibilities to implement these requirements into the process of engineering biological systems. This is illustrated on the DNA level by the implementation of engineering-inspired artificial operations such as toggle switching, oscillating or production of spatial patterns. On the protein level, the functionally self-contained domain structure of a number of proteins suggests possibilities for essentially Lego-like recombination which can be exploited for reprogramming DNA binding domain specificities or signaling pathways. Alternatively, computational design emerges to rationally reprogram enzyme function. Finally, the increasing facility of de novo DNA synthesis-synthetic biologys system fabrication process-supplies the possibility to implement novel designs for ever more complex systems. Some of these elements have merged to realize the first tangible synthetic biology applications in the area of manufacturing of pharmaceutical compounds.

From good old biochemical analyses to high-throughput omics measurements and back

Matthias Heinemann obtained a PhD in biochemical engineering from the RWTH Aachen University (Germany); did a postdoc with Sven Panke in the bioprocess lab of the ETH Zurich (Switzerland) followed by a group leader position at the Institute of Molecular Systems Biology at ETH Zurich (research unit of Uwe Sauer); since August 2009 he is professor for molecular systems biology at the University of Groningen (The Netherlands) leading a research program aiming at generating a system-level understanding of (microbial) metabolism.

Multidimensional optimality of microbial metabolism

Metabolic Networking Understanding complex biological networks, such as those underlying cellular metabolism, requires evaluation not only of the network connections but also the flux through the various biochemical pathways. Schuetz et al. (p. 601) explored the evolutionary constraints that appear to be most critical for the metabolic network in the bacteria Escherichia coli using a combination of experimental tests of reaction flux under various conditions along with mathematical modeling. As a pathway evolves, there are likely to be competing objectives that must be satisfied. Key objectives for the bacterium were strong performance under a given environmental condition, balanced by a requirement for adaptability—minimizing the adjustments required to respond to changed conditions. A key design principle of bacterial metabolic networks is optimal performance, but not at the expense of adaptability. Although the network topology of metabolism is well known, understanding the principles that govern the distribution of fluxes through metabolism lags behind. Experimentally, these fluxes can be measured by 13C-flux analysis, and there has been a long-standing interest in understanding this functional network operation from an evolutionary perspective. On the basis of 13C-determined fluxes from nine bacteria and multi-objective optimization theory, we show that metabolism operates close to the Pareto-optimal surface of a three-dimensional space defined by competing objectives. Consistent with flux data from evolved Escherichia coli, we propose that flux states evolve under the trade-off between two principles: optimality under one given condition and minimal adjustment between conditions. These principles form the forces by which evolution shapes metabolic fluxes in microorganisms’ environmental context.

Bacterial adaptation through distributed sensing of metabolic fluxes

The recognition of carbon sources and the regulatory adjustments to recognized changes are of particular importance for bacterial survival in fluctuating environments. Despite a thorough knowledge base of Escherichia colis central metabolism and its regulation, fundamental aspects of the employed sensing and regulatory adjustment mechanisms remain unclear. In this paper, using a differential equation model that couples enzymatic and transcriptional regulation of E. colis central metabolism, we show that the interplay of known interactions explains in molecular‐level detail the system‐wide adjustments of metabolic operation between glycolytic and gluconeogenic carbon sources. We show that these adaptations are enabled by an indirect recognition of carbon sources through a mechanism we termed distributed sensing of intracellular metabolic fluxes. This mechanism uses two general motifs to establish flux‐signaling metabolites, whose bindings to transcription factors form flux sensors. As these sensors are embedded in global feedback loop architectures, closed‐loop self‐regulation can emerge within metabolism itself and therefore, metabolic operation may adapt itself autonomously (not requiring upstream sensing and signaling) to fluctuating carbon sources.

Systematic assignment of thermodynamic constraints in metabolic network models

BackgroundThe availability of genome sequences for many organisms enabled the reconstruction of several genome-scale metabolic network models. Currently, significant efforts are put into the automated reconstruction of such models. For this, several computational tools have been developed that particularly assist in identifying and compiling the organism-specific lists of metabolic reactions. In contrast, the last step of the model reconstruction process, which is the definition of the thermodynamic constraints in terms of reaction directionalities, still needs to be done manually. No computational method exists that allows for an automated and systematic assignment of reaction directions in genome-scale models.ResultsWe present an algorithm that – based on thermodynamics, network topology and heuristic rules – automatically assigns reaction directions in metabolic models such that the reaction network is thermodynamically feasible with respect to the production of energy equivalents. It first exploits all available experimentally derived Gibbs energies of formation to identify irreversible reactions. As these thermodynamic data are not available for all metabolites, in a next step, further reaction directions are assigned on the basis of network topology considerations and thermodynamics-based heuristic rules. Briefly, the algorithm identifies reaction subsets from the metabolic network that are able to convert low-energy co-substrates into their high-energy counterparts and thus net produce energy. Our algorithm aims at disabling such thermodynamically infeasible cyclic operation of reaction subnetworks by assigning reaction directions based on a set of thermodynamics-derived heuristic rules. We demonstrate our algorithm on a genome-scale metabolic model of E. coli. The introduced systematic direction assignment yielded 130 irreversible reactions (out of 920 total reactions), which corresponds to about 70% of all irreversible reactions that are required to disable thermodynamically infeasible energy production.ConclusionAlthough not being fully comprehensive, our algorithm for systematic reaction direction assignment could define a significant number of irreversible reactions automatically with low computational effort. We envision that the presented algorithm is a valuable part of a computational framework that assists the automated reconstruction of genome-scale metabolic models.

The quantitative and condition-dependent Escherichia coli proteome

Measuring precise concentrations of proteins can provide insights into biological processes. Here we use efficient protein extraction and sample fractionation, as well as state-of-the-art quantitative mass spectrometry techniques to generate a comprehensive, condition-dependent protein-abundance map for Escherichia coli. We measure cellular protein concentrations for 55% of predicted E. coli genes (>2,300 proteins) under 22 different experimental conditions and identify methylation and N-terminal protein acetylations previously not known to be prevalent in bacteria. We uncover system-wide proteome allocation, expression regulation and post-translational adaptations. These data provide a valuable resource for the systems biology and broader E. coli research communities.

The Cost of Virulence: Retarded Growth of Salmonella Typhimurium Cells Expressing Type III Secretion System 1

Virulence factors generally enhance a pathogens fitness and thereby foster transmission. However, most studies of pathogen fitness have been performed by averaging the phenotypes over large populations. Here, we have analyzed the fitness costs of virulence factor expression by Salmonella enterica subspecies I serovar Typhimurium in simple culture experiments. The type III secretion system ttss-1, a cardinal virulence factor for eliciting Salmonella diarrhea, is expressed by just a fraction of the S. Typhimurium population, yielding a mixture of cells that either express ttss-1 (TTSS-1+ phenotype) or not (TTSS-1− phenotype). Here, we studied in vitro the TTSS-1+ phenotype at the single cell level using fluorescent protein reporters. The regulator hilA controlled the fraction of TTSS-1+ individuals and their ttss-1 expression level. Strikingly, cells of the TTSS-1+ phenotype grew slower than cells of the TTSS-1− phenotype. The growth retardation was at least partially attributable to the expression of TTSS-1 effector and/or translocon proteins. In spite of this growth penalty, the TTSS-1+ subpopulation increased from <10% to approx. 60% during the late logarithmic growth phase of an LB batch culture. This was attributable to an increasing initiation rate of ttss-1 expression, in response to environmental cues accumulating during this growth phase, as shown by experimental data and mathematical modeling. Finally, hilA and hilD mutants, which form only fast-growing TTSS-1− cells, outcompeted wild type S. Typhimurium in mixed cultures. Our data demonstrated that virulence factor expression imposes a growth penalty in a non-host environment. This raises important questions about compensating mechanisms during host infection which ensure successful propagation of the genotype.

Whole lifespan microscopic observation of budding yeast aging through a microfluidic dissection platform

Important insights into aging have been generated with the genetically tractable and short-lived budding yeast. However, it is still impossible today to continuously track cells by high-resolution microscopic imaging (e.g., fluorescent imaging) throughout their entire lifespan. Instead, the field still needs to rely on a 50-y-old laborious and time-consuming method to assess the lifespan of yeast cells and to isolate differentially aged cells for microscopic snapshots via manual dissection of daughter cells from the larger mother cell. Here, we are unique in achieving continuous and high-resolution microscopic imaging of the entire replicative lifespan of single yeast cells. Our microfluidic dissection platform features an optically prealigned single focal plane and an integrated array of soft elastomer-based micropads, used together to allow for trapping of mother cells, removal of daughter cells, monitoring gradual changes in aging, and unprecedented microscopic imaging of the whole aging process. Using the platform, we found remarkable age-associated changes in phenotypes (e.g., that cells can show strikingly differential cell and vacuole morphologies at the moment of their deaths), indicating substantial heterogeneity in cell aging and death. We envision the microfluidic dissection platform to become a major tool in aging research.

Systems biology of microbial metabolism

One current challenge in metabolic systems biology is to map out the regulation networks that control metabolism. From progress in this area, we conclude that non-transcriptional mechanisms (e.g. metabolite-protein interactions and protein phosphorylation) are highly relevant in actually controlling metabolic function. Furthermore, recent results highlight more functions of enzymes and metabolites than currently appreciated in genome-scale metabolic reconstructions, thereby adding another level of complexity. Combining experimental analyses and modeling efforts we are also beginning to understand how metabolic behavior emerges. Particularly, we recognize that metabolism is not simply a dull workhorse process but rather takes very active control of itself and other cellular processes, rendering true system-level understanding of metabolism possibly more difficult than for other cellular systems.

Functioning of a metabolic flux sensor in Escherichia coli

Regulation of metabolic operation in response to extracellular cues is crucial for cells’ survival. Next to the canonical nutrient sensors, which measure the concentration of nutrients, recently intracellular “metabolic flux” was proposed as a novel impetus for metabolic regulation. According to this concept, cells would have molecular systems (“flux sensors”) in place that regulate metabolism as a function of the actually occurring metabolic fluxes. Although this resembles an appealing concept, we have not had any experimental evidence for the existence of flux sensors and also we have not known how these flux sensors would work in detail. Here, we show experimental evidence that supports the hypothesis that Escherichia coli is indeed able to measure its glycolytic flux and uses this signal for metabolic regulation. Combining experiment and theory, we show how this flux-sensing function could emerge from an aggregate of several molecular mechanisms: First, the system of reactions of lower glycolysis and the feedforward activation of fructose-1,6-bisphosphate on pyruvate kinase translate flux information into the concentration of the metabolite fructose-1,6-bisphosphate. The interaction of this “flux-signaling metabolite” with the transcription factor Cra then leads to flux-dependent regulation. By responding to glycolytic flux, rather than to the concentration of individual carbon sources, the cell may minimize sensing and regulatory expenses.