Conference Agenda

Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).

 
Session Overview
Session
MS150, part 1: Fitness landscapes and epistasis
Time:
Thursday, 11/Jul/2019:
10:00am - 12:00pm

Location: Unitobler, F-112
30 seats, 54m^2

Presentations
10:00am - 12:00pm

Fitness landscapes and epistasis

Chair(s): Kristina Crona (American University, Washington, USA), Joachim Krug (Uni Koeln, Germany), Lisa Lamberti (ETHZ, Switzerland)

Studying relations, effects and properties of modified genes or organisms is an important topic in biology with implications in evolution, drug resistance and targeting, and much more. Biological data can many times be represented in digital form, a mutation has occurred or not, a species is present in an ecological system, or not. A fitness landscape is a function from such bit strings to some measured quality.
A property of fitness landscapes is epistasis, which is a phenomenon describing dependency relations among effects of combinations of modified genes. Polyhedral decompositions, such as cube triangulations induced by fitness landscapes, provide a systematic approach to epistasis.
In this session, we aim at bringing researches of various areas of science together to discuss contact points between applied polyhedral geometry, statistics and biology, and present recent developments in the field.

 

(25 minutes for each presentation, including questions, followed by a 5-minute break; in case of x<4 talks, the first x slots are used unless indicated otherwise)

 

Introduction to fitness landscapes and epistasis

Lisa Lamberti
ETHZ, Switzerland

How are fitness landscapes used in biology? What type of data is analyzed? Which methods are applicable to this theory?

These are some of the issues we will touch upon in this introductory talk and elaborate further throughout the session. The starting point of this presentation will be given by reviewing the geometric framework to study epistasis proposed by Beerenwinkel et al.(2007). In particular, we will define various forms of genetic interactions of current interest in evolutionary biology and demonstrate their utility. Computational limitations of these approaches will also be discussed.

 

Cluster partitions and fitness landscapes of the Drosophila fly microbiome

Holger Eble1, Michael Joswig1, Lisa Lamberti2, William Ludington3
1TU Berlin, Germany, 2ETHZ, Switzerland, 3Carnegie Institution for Science, Baltimore, USA

Beerenwinkel et al.(2007) suggested studying fitness landscapes via regular subdivisions of convex polytopes.Building on their approach we propose cluster partitions and cluster filtrations of fitness landscapes as a new mathematical tool. In this way, we provide a concise combinatorial way of processing metric information from epistatic interactions. Using existing Drosophila microbiome data, we demonstrate similarities with and differences to the previous approach. As one outcome we locate interesting epistatic information where the previous approach is less conclusive.

 

A mechanistic approach to understanding multi-way interactions between mutations

Michael Harms
University of Oregon, USA

An important goal for biologists is construction of quantitative, predictive models relating the genome sequence of an organism (its genotype) to its observed traits (its phenotype). This is a challenging problem. If mutations behave independently, the difference in phenotype between two genotypes that differ at L positions can be described as the sum of the individual effects of all L mutations. Mutations, however, rarely act independently: the effect of a mutation can change depending on the presence or absence of another mutation. We, and others, have even documented extensive three-way and even higher-ordered interactions between mutations. In my talk, I will discuss the evidence for these multi-way interactions, as well as how such interactions undermine models that sum the effects of mutations to predict phenotype from genotype. I will then discuss an alternative, mechanistically informed model, and describe experimental work done in my lab testing predictions of this model. Our work demonstrates a powerful, workable alternative to linear models and points to other classes of models that may be better suited for describing the map between genotype and phenotype.

 

Understanding the biophysics of molecules from large functional assays

Jakub Otwinowski
MPI for Dynamics and Self-Organization, Germany

Quantifying the relationship between a biomolecule's genetic sequence and its biological function is a fundamental problem that addresses how complex molecules can evolve. With statistical models inferred from large numbers of sequence-function pairs I show how a small number of intermediate molecular phenotypes can explain many aspects of sequence-function relationship. From a library of mutated promoter sequences and expression measurements in e.coli lac operon I infer detailed physical interactions between two regulatory proteins. Using a different heuristic approach, I infer fold stability from an thousands of mutated variants of green fluorescent protein. Finally, with a thermodynamic model I infer a detailed energy landscape of a small bacterial protein and separate the effects of mutations on binding and folding stability from 500k variants.