Manfred Dietrich Laubichler

GENETIC MEASUREMENT THEORY OF EPISTATIC EFFECTS

Epistasis is defined as the influence of the genotype at one locus on the effect of a mutation at another locus. As such it plays a crucial role in a variety of evolutionary phenomena such as speciation, population bottle necks, and the evolution of genetic architecture (i.e., the evolution of dominance, canalization, and genetic correlations). In mathematical population genetics, however, epistasis is often represented as a mere noise term in an additive model of gene effects. In this paper it is argued that epistasis needs to be scaled in a way that is more directly related to the mechanisms of evolutionary change. A review of general measurement theory shows that the scaling of a quantitative concept has to reflect the empirical relationships among the objects. To apply these ideas to epistatic mutation effects, it is proposed to scale A x A epistatic effects as the change in the magnitude of the additive effect of a mutation at one locus due to a mutation at a second locus. It is shown that the absolute change in the additive effect at locus A due to a substitution at locus B is always identical to the absolute change in B due to the substitution at A. The absolute A x A epistatic effects of A on B and of B on A are identical, even if the relative effects can be different. The proposed scaling of A x A epistasis leads to particularly simple equations for the decomposition of genotypic variance. The Kacser Burns model of metabolic flux is analyzed for the presence of epistatic effects on flux. It is shown that the non-linearity of the Kacser Burns model is not sufficient to cause A x A epistasis among the genes coding for enzymes. It is concluded that non-linearity of the genotype-phenotype map is not sufficient to cause epistasis. Finally, it is shown that there exist correlations among the additive and epistatic effects among pairs of loci, caused by the inherent symmetries of Mendelian genetic systems. For instance, it is shown that a mutation that has a larger than average additive effect will tend to decrease the additive effect of a second mutation, i.e., it will tend to have a negative (canalizing) interaction with a subsequent gene substitution. This is confirmed in a preliminary analysis of QTL-data for adult body weight in mice.

The challenges and scope of theoretical biology

Scientific theories seek to provide simple explanations for significant empirical regularities based on fundamental physical and mechanistic constraints. Biological theories have rarely reached a level of generality and predictive power comparable to physical theories. This discrepancy is explained through a combination of frozen accidents, environmental heterogeneity, and widespread non-linearities observed in adaptive processes. At the same time, model building has proven to be very successful when it comes to explaining and predicting the behavior of particular biological systems. In this respect biology resembles alternative model-rich frameworks, such as economics and engineering. In this paper we explore the prospects for general theories in biology, and suggest that these take inspiration not only from physics, but also from the information sciences. Future theoretical biology is likely to represent a hybrid of parsimonious reasoning and algorithmic or rule-based explanation. An open question is whether these new frameworks will remain transparent to human reason. In this context, we discuss the role of machine learning in the early stages of scientific discovery. We argue that evolutionary history is not only a source of uncertainty, but also provides the basis, through conserved traits, for very general explanations for biological regularities, and the prospect of unified theories of life.

Development and evolution of caste dimorphism in honeybees – a modeling approach

The difference in phenotypes of queens and workers is a hallmark of the highly eusocial insects. The caste dimorphism is often described as a switch-controlled polyphenism, in which environmental conditions decide an individuals caste. Using theoretical modeling and empirical data from honeybees, we show that there is no discrete larval developmental switch. Instead, a combination of larval developmental plasticity and nurse worker feeding behavior make up a colony-level social and physiological system that regulates development and produces the caste dimorphism. Discrete queen and worker phenotypes are the result of discrete feeding regimes imposed by nurses, whereas a range of experimental feeding regimes produces a continuous range of phenotypes. Worker ovariole numbers are reduced through feeding-regime-mediated reduction in juvenile hormone titers, involving reduced sugar in the larval food. Based on the mechanisms identified in our analysis, we propose a scenario of the evolutionary history of honeybee development and feeding regimes.

How Molecular is Molecular Developmental Biology? A Reply to Alex Rosenberg's Reductionism Redux: Computing the Embryo

This paper argues in defense of theanti-reductionist consensus in the philosophy ofbiology. More specifically, it takes issues with AlexRosenbergs recent challenge of this position. Weargue that the results of modern developmentalgenetics rather than eliminating the need forfunctional kinds in explanations of developmentactually reinforce their importance.

Developmental Evolution in Social Insects: Regulatory Networks From Genes to Societies

The evolution and development of complex phenotypes in social insect colonies, such as queen-worker dimorphism or division of labor, can, in our opinion, only be fully understood within an expanded mechanistic framework of Developmental Evolution. Conversely, social insects offer a fertile research area in which fundamental questions of Developmental Evolution can be addressed empirically. We review the concept of gene regulatory networks (GRNs) that aims to fully describe the battery of interacting genomic modules that are differentially expressed during the development of individual organisms. We discuss how distinct types of network models have been used to study different levels of biological organization in social insects, from GRNs to social networks. We propose that these hierarchical networks spanning different organizational levels from genes to societies should be integrated and incorporated into full GRN models to elucidate the evolutionary and developmental mechanisms underlying social insect phenotypes. Finally, we discuss prospects and approaches to achieve such an integration.

Organism and character decomposition: Steps towards an integrative theory of biology

In this paper we argue that an operational organism concept can help to overcome the structural deficiency of mathematical models in biology. In our opinion, the structural deficiency of mathematical models lies mainly in our inability to identify functionally relevant biological characters in biological systems, and not so much in a lack of adequate mathematical representations of biological processes. We argue that the problem of character identification in biological systems is linked to the question of a properly formulated organism concept. Lastly, we demonstrate how a decomposition of an organism into independent characters in the context of a specific biological process--such as adaptation by means of natural selection--depends on the dynamical properties and invariance conditions of the equations that describe this process.

Decomposing multilocus linkage disequilibrium.

We present a mathematically precise formulation of total linkage disequilibrium between multiple loci as the deviation from probabilistic independence and provide explicit formulas for all higher-order terms of linkage disequilibrium, thereby combining J. Dausset et al.s 1978 definition of linkage disequilibrium with H. Geiringers 1944 approach. We recursively decompose higher-order linkage disequilibrium terms into lower-order ones. Our greatest simplification comes from defining linkage disequilibrium at a single locus as allele frequency at that locus. At each level, decomposition of linkage disequilibrium is mathematically equivalent to number theoretic compositions of positive integers; i.e., we have converted a genetic decomposition into a mathematical decomposition.

The varied lives of organisms: Variation in the historiography of the biological sciences

Abstract This paper emphasizes the crucial role of variation, at several different levels, for a detailed historical understanding of the development of the biomedical sciences. Going beyond valuable recent studies that focus on model organisms, experimental systems and instruments, we argue that all of these categories can be accommodated within our approach, which pays special attention to organismal and cultural variation. Our empirical examples are drawn in particular from recent historical studies of nineteenth- and early twentieth-century genetics and physiology. Based on the quasi-paradoxical conclusion that biological and cultural variation both constrains and enables innovation in the biomedical sciences, we argue that more attention should be paid to variation as an analytical category in the historiography of the life sciences.

The Organism is Dead. Long Live the Organism

On June 26, 2000 President Clinton announced the completion of a arst draft of the Human Genome in a White House ceremony.2 Attending the ceremony were Francis Collins, the head of the publicly funded Human Genome Project and J. Craig Venter, renegade scientiac entrepreneur and president of Celera Genomics of Rockville, Maryland. Prime Minister Tony Blair also attended, albeit virtually via satellite broadcast from London. By everybody’s account the impact of this carefully staged public announcement was mainly symbolical. President Clinton called the still rather preliminary map of the human genome the “most wondrous map ever produced by humankind” and an “epic-making triumph of science and reason.” The joint appearance of Collins and Venter was intended to symbolize the end of the animosities between the public and the private Genome Projects. This would be all the more important in the future because, as President Clinton suggested, “there is much hard work yet to be done.” Also mending the heated divisions between the two projects was Eric Lander, director of MIT’s Whitehead Institute, who—not for the arst

How Can History of Science Matter to Scientists

History of science has developed into a methodologically diverse discipline, adding greatly to our understanding of the interplay between science, society, and culture. Along the way, one original impetus for the then newly emerging discipline—what George Sarton called the perspective “from the point of view of the scientist”—dropped out of fashion. This essay shows, by means of several examples, that reclaiming this interaction between science and history of science yields interesting perspectives and new insights for both science and history of science. The authors consequently suggest that historians of science also adopt this perspective as part of their methodological repertoire.