★ Bjørn Østman
Adami Lab
BEACON Center for the Study of Evolution in Action
Microbiology and Molecular Genetics
Michigan State University
Biomedical and Physical Sciences
567 Wilson Road, room 2228D.
East Lansing, MI 48824
Email: ostman@msu.edu
Phone: 517-355-8733


★ Research
I am interested in all many aspects of evolution. I work in computational biology, using various approaches to learn about fundamental processes of evolution. Bioinformatics is good for learning about real genes (data generously supplied by other researchers), and simulations are good for testing the mechanisms of evolution. I am particularly interested in how populations and organisms adapt to changing environments, both at the genetic and phenotypic level. Lately my research has focused on the evolutionary dynamics of populations evolving in rugged fitness landscapes.
Fitness landscape visualization | Specialization and trade-offs | Predicting evolution | Evolutionary metagenomics | Epistasis | Fitness landscape structure | Regulatory evolution

Fitness landscape visualization
NEW! Movies of evolutionary dynamics in fitness landscapes.

Resource specialization and trade-offs
How speciation occurs in nature is still a hot topic, with many biologists leaning towards allopatry being the most frequent mode. Empirical evidence and models of parapatric and sympatric speciation has made these modes plausible. That speciation occurs at all in asexual organisms is also a contentious issue.

In an agent-based model where fitness is given by efficiency of resource utilization and amount of available resources, asexual individuals compete for resources, and specialization and adaptive radiation occurs when there are trade-offs in resource use [6]. All resources are sufficient for growth, and fitness is derived from consumption according to a fitness function based on the Monod equation. No spatial parameters are included, making the model strictly sympatric. Mutations cause resource affinity to increase or decrease incrementally, or to completely disable an organism's ability to consume a resource. We observe that the degree of specialization, quantified by the number of ecotypes/species formed and the niche breadth of the population, is sensitive to the resource influx and the trade-offs involved in resource utilization, with stronger trade-offs leading to more specialization. The specialized organisms persist though negative frequency-dependent selection, which justifies calling then species by Van Valen's Ecologigal Species Concept. Allowing mutations that destroy resource affinity ("trait-lethal mutations") has a substantial positive effect on the degree of specialization.

Predicting evolution
The extent to which we can predict evolutionary outcomes depend on the regularity of the environment. The first two parameters to estimate are the population size and the mutation rate. Together with these values, the structure of the fitness landscape determines evolutionary dynamics [5]. The ruggedness of the fitness landscape, which is caused by epistasis [4], makes the structure less regular and therefore predictions more difficult. Additionally, if the fitness landscape is not static, but changes over time (as biological environments are wont to do), the dynamics may change, because populations may avoid getting stuck on local fitness peaks just as changes in the selection pressure changes the landscape quantitatively and possible qualitatively as well (i.e., changes in peak heights and the appearance/disappearance of peaks, respectively).

Evolutionary metagenomics
In the Schmidt lab I worked on evolutionary metagenomics. This involved elucidating evolutionary processes in microbial organisms using metagenomic data. Using dN/dS - the ratio of the number of nonsynonymous to synonymous substitutions - we can infer how selection has affected coding sequences (purifying, neutral, or positive selection). Using sequences from soils at Kellogg Biological Station, we estimated the differential selection pressure on a gene, nitrate reductase, in the denitrification pathway in denitrifying bacteria. Results show that this gene is under stronger purifying selection in soils used for traditional agriculture compared to soils that have always been native forest. For more about this research, go to BEACON's website.

Epistasis, pleiotropy, and adaptation

Fig. 1: Attained fitness as a function of mean epistasis.
The attained fitness, Ω, is the fitness of the most fit organism at the end of the simulation. In the NK landscape the attained fitness increases the more rugged/epistatic the landscape is. See [3] for how to calculate epistasis.
We used the NK model to study the role of epistasis and pleiotropy on adaptation by evolving populations of binary sequences. Evolution in rugged landscapes is highly influenced by the mutation-supply rate (product of population size and mutation rate), such that when the mutation rate is increased adaptation shifts from a mode of climbing a single peak to one where valleys between peaks can be traversed and much higher fitness gains are possible. When this transition happens, pleiotropic constraints are resolved, and pleiotropy instead becomes a factor beneficial to adaptation. We quantified epistasis between consecutive mutations, and found a positive relationship between adaptation and epistasis (fig. 1) [3, 4]. This result depends on the effect that in more rugged fitness landscapes the effect of muations vary more, with a greater range of selection coefficients. But it also emerges from a feature of the NK landscape that causes more rugged landscapes to contain higher peaks, which one might suspect is an artifact of this model. However, we propose that this is not coincidental, but rather is an effect one should also be able to observe in nature, namely that modules of epistatically interacting networks of genes should confer more fitness to the organism the larger the module is.

Fitness landscape structure
Evolutionary dynamics is shaped, constrained, and channeled by the fitness landscape. Much work has been expended to understand the evolutionary dynamics of adapting populations, but much less is known about the structure of the landscapes. We studied the global and local structure of complex fitness landscapes of interacting loci that describe protein folds or sets of interacting genes forming pathways or modules. In these landscapes high peaks are more likely to be found near other high peaks, corroborating Kauffman's "Massif Central" hypothesis [2]. Peaks cluster in such a way that they are more likely to be found near other peaks of similar height (fig. 3). This clustering allows peaks to form interconnected networks . These networks undergo a percolation phase transition as a function of minimum peak height, which indicates that evolutionary trajectories that take no more than two mutations to shift from peak to peak can span the entire genetic space. These networks allow adaptation to proceed in rigged fitness landscapes after a local fitness peak has been ascended.

Fig. 2: NK-model haplotypes for N=16 and K=2.
For these parameters, the fitness contribution of each locus is determined by interacting with 2 loci (adjacent in the representation shown here), giving rise to blocks of 2K+1 interacting genes. (A): Interactions between loci represented by lines. (B): Actual epistatic interactions on a particular high-fitness peaks, where the width of the lines indicates the strength of epistatic interactions. Three modules of strongly interacting loci are colored. The remaining interactions (dashed grey lines) are weak.
Fig. 3: Peaks cluster according to fitness.
Mean fitness of all peaks in a circular cluster with diameter d=2 (Hamming distance) as a function of fitness of the peak in the center of the cluster (black dots). Compare to the same peak heights but situated randomly in genotype space (gray dots).

Gene duplication and regulatory evolution

Fig. 4: Phylogeny of a Drosophila gene family.
Averaging over many gene families, we estimated the rate repression of gene expression to be about twice the rate of activation in recent Drosophila evolution.
Both gene duplication and change in gene regulatory regions are thought to be major sources of variation in evolution. When genes are duplicated with their regulatory regions intact, either the protein coding region is allowed to evolve a new function (neofunctionalization), or the gene expression pattern may change causing expression to be differentially lost (subfunctionalization). Losing expression can lead to evolutionary novelties, e.g., as in the formation of insect halteres. Using Drosophila gene expression data, sequence data, and phylogenetic analysis (fig. 4), we estimated the relative rates of loss and gain of gene expression in late fly embryogenesis. Confirming the hypothesis that loss of expression is more common than gain, we found that in recent evolution Drosophila genes have lost expression at approximately twice the rate that they have gained it [1].

[1] Repression and loss of gene expression outpaces activation and gain in recently duplicated fly genes, Oakley TH, Østman B, and Wilson ACV (2006), PNAS, 103, 11637.
[2] Critical properties of complex fitness landscapes, Østman B, Hintze A, Adami C (2011), in H. Fellermann, M. Dorr, M. M. Hanczyc, L. L. Laursen, S. Maurer, D. Merkle, P.-A. Monnard, K. Støy, and S. Rasmussen (eds), Proc. of the ALife XII Conference, pages 126-132. MIT Press.
[3] Impact of epistasis and pleiotropy on evolutionary adaptation, Østman B, Hintze A, Adami C (2011), Proc. Royal Soc. B.
[4] Effects of Epistasis and Pleiotropy on Fitness Landscapes Østman B (2013), in "Evolutionary Biology: Exobiology and Evolutionary Mechanisms" (P. Pontarotti, ed.). Evolutionary Biology: 16th Meeting 2012, Springer-Verlag.
[5] Predicting evolution in high-dimensional fitness landscapes, Østman B and Adami C (2013), in "Recent Advances in the Theory and Application of Fitness Landscapes" (A. Engelbrecht and H. Richter, eds.). Springer Series in Emergence, Complexity, and Computation.
[6] Trade-offs drive resource specialization and the gradual establishment of ecotypes, Østman B, Lin R, Adami C, in review.
[7] A genome wide dosage suppressor network reveals genetic robustness and a novel mechanism for Huntington's disease, Patra B, Kon Y, Sevold A M, Yadav G, Frumkin J P, Vallabhajosyula R R, Hintze A, Østman B, Schossau J, Bhan A, Marzolf B, Tamashiro J K, Kaur A, Baliga N S, Grayhack E J, Adami C, Galas D J, Raval A, Phizicky E M, Ray A, in review.


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MMG 892, Evolutionary dynamics and modeling Michigan State University, Fall 2011.

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Curriculum Vitae
PDF Nov 20, 2013


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Last updated November 20th, 2013.