Evolutionary Systems Biology
Workshop on Evolutionary Systems Biology & Modeling Madison 2015 Bridging Disciplinary Boundaries |
Abstracts of the Workshop Tuesday, August 25th, 2015
Update 25 Aug 2015
Dr. Anne-Ruxandra Carvunis What Makes Us Different?
The molecular mechanisms underlying what makes each species unique remain largely unknown. A longstanding theory has been that species evolve new traits through the natural selection of mutations occurring within the functional protein-coding regions of genes. However, this view was significantly challenged by work in recent decades showing that the number of protein-coding genes in a genome of an organism is not correlated with organismal complexity, and that organisms as different as human and chimpanzee share a nearly identical set of protein-coding genes, among other findings. The resulting, more contemporary view, is that modifications of cellular networks made of intricate physical and functional interactions among genes and gene products may be the principal drivers of phenotypic diversity. I am exploring the evolution of cellular networks and how it relates to the evolution of genomes. I will describe how protein interactions are rewired under the action of natural selection during plant evolution. I will also present ongoing work comparing the dynamics of transcriptional network rewiring across mammals, birds and insects. My observations provide evidence for a molecular clock in transcriptional network evolution.
Prof. Laurence Loewe Is Evolutionary Systems Biology more narrow or broader than systems biology? Current Systems Biology grew out of the wish of molecular biologists to integrate the many diverse aspects of molecular systems that had been found in more reductionistic studies hunting for a single gene or protein. The simplest analysis is then to put them all as nodes into networks, resulting in the well known 'hairballs' of systems biology. I will present a perspective for Evolutionary Systems Biology that goes far beyond the comparison of hairballs and grew out of many discussions over many years. I will present a draft definition of EvoSysBio that is fundamentally mechanistic, integrates the molecular and population genetics aspects of evolution and provides fitness landscapes as a unifying perspective. The resulting research programme is explicitly broader than what is often interpreted as current systems biology. It necessarily draws from and contributes to almost every part of biology and many other quantitative disciplines.
Prof. Laurence Loewe Evolvix can in principle make it simple to describe massively parallel systems like the biochemical reaction networks of cells. After a brief introduction I will demonstrate how to make that work using a simple example. You can follow along using our laptop if you want.
Prof. John Yin The genome of every organism defines a process, and virus genomes are no exception. In an appropriate environment of a living host cell the release of a genome from an invading virus can take command, directing cellular material and energy resources toward the synthesis of components that are essential for virus growth: viral mRNA, viral proteins, and viral genomes. Assembly of these and other components yields progeny virus particles that, upon release by the cell, may then infect other susceptible cells. By performing quantitative experiments and building mathematical models of these processes we begin to link mechanistically how genomic information processing in limited host-resource environments can impact virus growth and infection spread. Such models may be of interest to evolutionary biologists who wish to explore mechanistically how mutations at the level of molecular functions -- or different resource environments -- can influence quantifiable measures of growth or fitness.
Claudia Solis-Lemus Bacteria and other organisms do not follow the paradigm of vertical inheritance of genetic material. Human beings, for example, inherit their DNA from their parents only (vertical transfer), but bacteria can share DNA between different species (horizontal transfer). Therefore, their evolution cannot be modeled by a tree. To incorporate these organisms to the tree of life, we need methods to infer phylogenetic networks. In this talk, I will present a statistical method to infer phylogenetic networks from DNA sequences. I will discuss the challenges and results on assessing the identifiability of the model. Our techniques to learn phylogenetic networks will enable scientists to incorporate organisms to the tree of life in parts that are more net-like than tree-like, and thus, complete a broader picture of evolution.
Jerdon Dresel, Kate Scheuer, Laurence Loewe Circadian clocks are a key biological function that controls our internal timekeeping. Over the past few decades, our understanding of the genetic functionality of these clocks has become clearer but there are still important questions that remain unanswered. A primary example of this is how the Jetlag (JET) F box protein interacts with the photoreceptor Cryptochrome (CRY) to degrade a central clock protein, Timeless (TIM), in our model organism Drosophila melanogastor. TIM is thought to control daily resetting of circadian clocks in a light-dependent environment via conformational changes in CRY that allows for the binding of JET to TIM to initiate degradation. In order to investigate this theory, we are integrating various alternative mechanisms for this process into systems biology models with the aim of exploring how simulated models can help determine which alternatives better explain observed data and the clock as a whole.
Kate Scheuer & Laurence Loewe Circadian clocks give rise to rhythmic behavior and are present in all living organisms, including the model species Drosophila melanogaster. Studying this clock can provide valuable information about circadian rhythms in other species. Computer models have been used to simulate Drosophila clocks for more that fifteen years. These simulations can collect large amounts of data and can be conducted more quickly than wet lab experiments. Some portions of the clock are well supported, while other portions of the clock are more contentious. This clock model builds on previous work by including more recently discovered proteins. The model could be used to explore controversial parts of the clock, including the influence of light, nuclear translocation of proteins, and the role of post-translational mechanisms in clock regulation.
Michael Veling Mitochondria are essential for cellular metabolism and signaling, and mitochondrial dysfunction is implicated over 100 diseases. Strikingly, 25% of mitochondrial proteins lack functional annotation. This lack of basic knowledge represents a biomedical bottleneck in understanding basic mitochondrial function and the pathophysiology of mitochondria-related diseases. To widen this bottleneck, we have developed a platform for functionally annotating disease-relevant Mitochondrial Orphan Proteins (MOPs). Our approach identifies physical interactors of these MOPs in varying conditions using affinity-enrichment mass spectrometry (AE-MS). Our data derive new connections between MOPs and established pathways and motivate new, testable hypotheses about their biochemical functions. |