Research

Laurence Loewe: General biological research interests

My research focuses on understanding the molecular, genetic and ecological basis for adaptive evolution and extinction in natural populations and its implications. This means that I am interested not only in understanding the long-term evolution of species, but also the multitude of factors that play crucial roles in the success of a species. On one end of the scale this includes spatial structure, local recombination and mutation rates, as well as life-history details and environmental changes. On the other end of the scale I am interested in very detailed intracellular molecular systems biological simulations that will hopefully contribute towards a better understanding of the frequency with which certain fitness changes occur.

Since the biological reality of natural populations is exceedingly complex, it is not possible to construct mathematically rigorous completely realistic 'true' models of evolution. Historically, much progress in evolutionary theory has derived from simple models that capture the essence of a few important features of evolution and allow a full understanding of this reduced universe. I am interested in bridging the gap between simple analytically understandable mathematical models and biological reality by building rigorous simulation models to understand the following big topics:

• Origin and extinction of species. I want to understand the various processes that can lead to extinction, prevent extinctions and lead to the origin of new species. This is important for designing effective strategies for long-term conservation of endangered species.
• How does evolution shape sequence patterns in genomes? This holds vital clues for understanding our own genome and is important for testing our understanding of evolution. A proper understanding of evolution is key for devising strategies to solve practical problems.
• Evolutionary consequences of human interferences with natural processes. We change our environment in more ways than most people realise. If we want to preserve this planet on the long term, we need to understand the evolutionary impact of our behaviour, from spreading mutagens over habitat destruction to the release of genetically modified organisms.
  
• Evolution of novel features. Such features can lead to multiple resistance and pathogenicity in microbes that can cause widespread dangerous infections. A thorough understanding of the evolution of these features is pivotal to finding the best strategies to reduce the corresponding threat. The generality of the theory of evolution often allows us to apply findings in one species to other cases as well.  
  
• The distribution of mutational effects and epistasis. Many DNA changes have some effect, even if it is very small. It can be difficult to estimate how many DNA changes have a particular effect and how these effects change if many mutations accumulate in a particular system. To address these questions I have started to develop intracellular molecular systems biological approaches. This involves simulating the chemical reactions that govern the dynamics of a particular molecular system. Detailed models might give us the ability to approximate the fitness consequences of arbitrary changes to the system of chemical reactions.
  

Systems biology research interests

My research in evolutionary genetics has repeatedly shown that the fitness effects of DNA changes are of key importance for determining the long-term outcome of many evolutionary processes. Thus my growing interest in quantifying these effects. Earlier work of mine has used very rough statistical approaches to estimate the distribution of deleterious mutational effects for the whole genome from the diversity values of a presumably unbiased random sample of genes. This approach can give a good bird's eye perspective overview, but it is always better to confirm results using an independent method. The recent rise of intracellular molecular systems biology may allow just that, and so I am interested in the following questions.

• Fitness correlates. Is it possible to define fitness correlates that allow us to predict the selection coefficients of a particular molecular system from the output of a systems biological simulation? Initial analyses provide some hope and I want to test this concept on the following three systems.
  
• Circadian clocks. These molecular clocks are virtually everywhere and help organisms to distinguish day and night. Quite a bit is known about their molecular mechanisms, so there is some hope that corresponding systems biological models are somewhat realistic. I am interested in exploring the parameter space of working clocks. For example, how difficult is it to improve or destroy a clock? Starting with a very simple clock (Goodwin oscillator) I plan to work my way up towards more complicated clocks like the one in the plant Arabidopsis thaliana.
  
• Signal transduction pathways. Using the interferon pathway as an example, I plan systems biological simulations that help to understand what molecular changes can make or break this important signalling system.
  
• RNA metabolism. This system's central role to genome function and gene regulation implies a correspondingly large potential for fitness relevant effects. I plan to build a mechanistic model that helps to explore the various potential effects of molecular changes in detailed models of RNA metabolism on fitness.
• Methods of simulation. Randomness and noise are prevalent features of molecular systems where frequently only a few components play vital roles. This seriously limits the predictive power of ordinary differential equations and thus I will employ stochastic simulations that track all the molecule counts over a time course for a particular run of the system. These time courses are then automatically analysed to compute the most interesting results. Since these simulations are very computing intensive, I will use evolution@home to compute the results.

Evolutionary genetics research interests

The bigger context for my systems biological work above are my interests in evolutionary genetics. More specifically, I am interested in the following questions:

• Extinctions caused by Muller's ratchet. Muller's ratchet is a population genetical process that can lead to deleterious mutation accumulation in a population. It has long been known to be capable of causing extinctions of species under various circumstances, but quantification of such circumstances have remained scarce. Using evolution@home, I built the largest existing database of Muller's ratchet simulation results. The accumulated results of many decades of CPU-time help to bring more precision to tests of the hypothesis that various species can or cannot be driven to extinction by Muller's ratchet.
• The strength of selection. While theory frequently uses selection coefficients to measure how strong selection will favour a particular genotype, too little is known on what these numbers actually are in nature. I am helping to put some numbers to the theory, as this is crucial for running meaningful simulations.
• The distribution of mutational effects (DME). It is well known that mutations can have widely varying effects on fitness. A DME quantifies the probability that a new mutation will have a particular strength of selection. I helped develop a new method for estimating DMEs that was first tested in two species of fruitfly (Drosophila miranda and D. pseudoobscura) and I am interested in expanding application and sophistication of this and similar methods.
• Effects of deleterious and adaptive mutations on patterns in genomes. We know that most mutations of any effect have harmful effects. I am interested in using theory and simulations to predict how observable patterns in genomes are shaped by these recurrent deleterious mutations. Theory also predicts that advantageous mutations leave clear signatures in genomes when they sweep to fixation. I am interested in helping to disentangle these from the various similar looking effects (e.g. of changes in population size). I hope this will improve our estimates of the frequency of advantageous mutations. To address these questions I am planing a massive simulation project that models many different mutations of different effects in different individuals with varying degrees of recombination and mutation. This will be implemented as a new simulator of evolution@home.

Global computing and IT research interests

Most of my biological questions are not easy to answer, as they involve many interacting entities on many different levels that can be very difficult to capture in analytical mathematical models. I use various mathematical tools, but when normal tools can no longer handle the complexity, I resolve to stochastic simulations run by evolution@home to do the heavy lifting. In 2001 I started evolution@home exactly for this purpose. It is the first global computing system for evolutionary biology and I am continually developing it, slowly but steadily. This fuels my interest in quite a number of IT topics like

• Grid computing. Global computing is just a special case of grid computing. It uses the Internet to distribute tasks to computers from the general public that volunteer for doing some work.
• Databases. Since computation of results can be extremely costly, it makes sense to store results for fast analyses. I am interested in efficient large-scale database organization to allow scalable handling of complex collections of results.
• Visualization. Complex results are often better understood if visualized appropriately. I am interested especially in visualization of multi-dimensional results.
• Statistical methods and machine learning. Each simulation produces much more data than can possibly be stored on any central server, no matter how powerful. Thus I design my simulators in such a way that they extract the key results of a simulation for central collection. I am interested in making this automated extraction of observations as powerful as possible to make the best use of the publicly contributed simulation time. This involves automating statistical analyses.
• Algorithm design, security, ... Complex simulations can benefit tremendously from clever algorithm design and any global computing network has to deal with security issues. So it is only natural that I am interested in these and other relevant topics as well.

To glue all these topics together with the analysis of actual sequence data I have a long-standing interest in bioinformatics.

For more details, you may want to have a look at some of my publications.