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Research in the Louca lab
Microorganisms, notably Bacteria and Archaea, are the most ancient, the most widespread and the most ubiquitous form of life on Earth. Their metabolism drives biogeochemical cycles in virtually every ecosystem and has shaped Earth's surface chemistry over billions of years. Today, prokaryotes (Bacteria and Archaea) are able to utilize a myriad of metabolic pathways to gain energy, thereby occupying extraordinary niches that most other organisms cannot. Our lab is interested in how prokaryotes interact with their environment through their metabolism to drive biogeochemical fluxes and, reciprocally, how this interaction affects microbial diversity at ecological and geological time scales. To answer these questions we use experiments, field surveys, DNA sequencing, mathematical modeling and statistics. We also develop novel computational tools for analyzing massive biological data sets in novel and efficient ways.

Ongoing research topics and some potential future student theses are summarized below. Future students are encouraged to also develop their own research ideas!

The statistical properties of prokaryotic genomic diversity
In any given environment, pathways driving redox reactions for energy acquisition can often be shared by numerous coexisting genomes, and sequential pathways are often split across genomes in various alternative combinations. The processes determining pathway distribution patterns across genomes are largely unknown. Can prokaryotic genomic diversity, either within a single ecosystem or at global scales, be described by simple stochastic models of gene content shuffling? Can prokaryotic genomes be described as metacommunities of cooperating but selfishly replicating genes? Do environmental conditions influence the degree to which metabolic pathways are split across coexisting organisms? These questions may be investigated using the myriad of sequenced genomes available in public databases, or using genomes recovered from metagenomes from a single environment.

Gene-level and genome-level processes of prokaryotic macroevolution
What can gene-centric and genome-centric paradigms tell us about prokaryotic macroevolution? How does the invention or acquisition of new metabolic capabilities by a clade (e.g., via horizontal gene transfer) affect its overall diversification over geological time scales? For example, do Oxyphotobacteria (the only known bacterial clade capable of oxygenic photosynthesis) exhibit different speciation/extinction rates than other bacterial clades?

Development and validation of pathway-centric ecological models
Most metabolic pathways are found in a wide range of microbial taxa, each of which could potentially fill the same metabolic niche. This functional redundancy leads to a partial decoupling between a community's taxonomic composition and bulk metabolic activity. Pathway-centric models, whereby the distribution and activity of pathways are modeled regardless of the species that host them, could yield great insight into microbial metabolic network dynamics at ecosystem scales. Many questions remain along this endeavor. What is the proper format of pathway-centric models, and what are the limits of their applicability? Can pathway-centric models help us understand patterns of DNA, mRNA and protein distributions in nature? Reciprocally, how can metagenomic, metatranscriptomic and metaproteomic data be used to calibrate or validate pathway-centric models? The advent of environmental meta'omic sequencing data makes these questions more relevant than ever.

The role of genomic structure in microbial metabolic networks
What is the role of microbial genomes in microbial metabolic networks at ecosystem scales? How does the cooccurrence and interaction of different genes in genomes influence their metabolic activity and generate deviations from purely gene-centric predictions? These questions can be investigated using mechanistic models, time series monitoring of natural systems as well as microcosm experiments in the lab. Philosophically, this work is central to understanding the role of the various layers at which Life is organized, including individual genes, groups of genes (genomes) and groups of genomes (microbial communities). Practically, this work will help design more accurate biogeochemical models for natural as well as engineered ecosystems.

Development of multi-layered (gene-level + genome-level) geobiological models
Construction and evaluation of multi-layered (i.e. gene-level and genome-level) geobiological models, in which information on gene co-occurrences and pathway fragmentation is incorporated into gene-centric models. Such adjustments could substantially improve model accuracy, for example for industrial processes or marine ecosystems, where previous studies revealed that gene co-occurrences within genomes lead to deviations from purely gene-centric models. Mathematically frameworks derived from this work may be evaluated using sequencing and chemical data retrieved from microcosms in my lab or in collaboration with other labs.
Experimental and mathematical characterization of microbial system kinetics
Are there universal high-level principles governing the dynamics of microbial metabolic networks, for example in response to nutrient pulses or changing boundary conditions? Microcosm experiments in the lab may help unravel these principles. Metagenomic sequencing and stable isotope probing can be used to characterize the structural and functional responses of microbial systems. System identification techniques from engineering could be used to describe the response of entire microbial systems, just as standard "Monod" response curves are used for single strains. This work could mark the beginning of a transition in the field towards understanding complex microbial system kinetics, rather than the kinetics of single strains.

Equilibrium-reaction-transport models for geobiology
The vast majority of geobiological models, such as for nitrogen and sulfur cycling in oxygen minimum zones and sediments, take the form of reaction-transport differential equations in which biologically driven reaction rates are kinetically limited. Such models require extensive kinetic parameterization, which severely limits the applicability of these models to natural systems. Rapid microbial population growth and selection for efficient metabolism, however, can lead to reaction kinetics that are much faster than physical transport processes, thus essentially leading to local thermodynamic equilibrium conditions. Equilibrium-reactive-transport models exploit such local equilibrium conditions to eliminate kinetic parameters, and hold great potential for predicting biogeochemical processes solely based on thermodynamic principles and physics. This project will explore the applicability of equilibrium-reactive-transport models to geobiology, including diagenetic processes in sediments and elemental cycling in lakes and the ocean. The project will be strongly modeling based, but may also include experimental work.

Linking thermodynamics to microbial growth in natural systems
A powerful thermodynamic concept for understanding microbial systems is the Gibbs free energy (ΔG), which quantifies the energy available from specific metabolic reactions depending on chemical environmental conditions. While the sign of ΔG (negative or positive) is a strong indicator of the presence or absence of a metabolic reaction, to date a quantitative link between the magnitude of ΔG and microbial growth is restricted to a few isolates studied under laboratory conditions. This study will use novel mathematical modeling techniques to quantify the free energy flux through natural microbial systems and relate those fluxes to measured microbial productivity rates and biomass concentrations.

Development of scalable computational tools
The ongoing explosion of available microbial sequence data presents exceptional opportunities for reconstructing microbial diversification and extinction dynamics over geological time scales, and for unraveling mechanisms that shaped today's extant genomes. These massive datasets, for example including millions of 16S rRNA gene sequences, also present substantial computational challenges, because the majority of existing analytical tools were developed for much smaller datasets. In the Louca lab we develop novel efficient algorithms for handling millions of gene sequences or genomes, for example for constructing and dating trees, detecting horizontal gene transfer events, predicting phenotypes of uncultured organisms and reconstructing diversification dynamics from phylogenies. If you enjoy the intellectual challenge of developing algorithms for computational biology, then this could be the right topic for you!

Charting global microbial diversity
The bulk of microorganisms has never been, and probably will never be, cultured, and culturing is strongly biased towards organisms with specific metabolic traits. Hence our assessment of global microbial diversity is extremely limited and biased. Modern large-scale sequencing surveys can help fill this gap. A goal of ours is to combine existing and future datasets to chart the phenotypic, phylogenetic and geographical distribution of extant microbial diversity, to quantify discovery biases, and to identify relationships between phenotypic and phylogenetic diversity at global scales. This requires the development of new pipelines that can efficiently handle unprecedentedly large datasets. The insight gained from this work will be essential to reconstructing the processes shaping global microbial diversity over geological time scales, for example using Binary State Speciation and Extinction models.

Louca lab. Department of Biology, University of Oregon, Eugene, USA
© 2019 Stilianos Louca all rights reserved