Investigating biocomplexity through the agent-based paradigm.

Capturing the dynamism that pervades biological systems requires a computational approach that can accommodate both the continuous features of the system environment as well as the flexible and heterogeneous nature of component interactions. This presents a serious challenge for the more traditional mathematical approaches that assume component homogeneity to relate system observables using mathematical equations. While the homogeneity condition does not lead to loss of accuracy while simulating various continua, it fails to offer detailed solutions when applied to systems with dynamically interacting heterogeneous components. As the functionality and architecture of most biological systems is a product of multi-faceted individual interactions at the sub-system level, continuum models rarely offer much beyond qualitative similarity. Agent-based modelling is a class of algorithmic computational approaches that rely on interactions between Turing-complete finite-state machines--or agents--to simulate, from the bottom-up, macroscopic properties of a system. In recognizing the heterogeneity condition, they offer suitable ontologies to the system components being modelled, thereby succeeding where their continuum counterparts tend to struggle. Furthermore, being inherently hierarchical, they are quite amenable to coupling with other computational paradigms. The integration of any agent-based framework with continuum models is arguably the most elegant and precise way of representing biological systems. Although in its nascence, agent-based modelling has been utilized to model biological complexity across a broad range of biological scales (from cells to societies). In this article, we explore the reasons that make agent-based modelling the most precise approach to model biological systems that tend to be non-linear and complex

Decoding function through comparative genomics: from animal evolution to human disease

Deciphering the functionality encoded in the genome constitutes an essential first step to understanding the context through which mutations can cause human disease. In this dissertation, I present multiple studies based on the use or development of comparative genomics techniques to elucidate function (or lack of function) from the genomes of humans and other animal species. Collectively, these studies focus on two biological entities encoded in the human genome: genes related to human disease susceptibility and those that encode microRNAs - small RNAs that have important gene-regulatory roles in normal biological function and in human disease. Extending this work, I investigated the evolution of these biological entities within animals to shed light on how their underlying functions arose and how they can be modeled in non-human species. Additionally, I present a new tool that uses large-scale clinical genomic data to identify human mutations that may affect microRNA regulatory functions, thereby providing a method by which state-of-the-art genomic technologies can be fully utilized in the search for new disease mechanisms and potential drug targets. The scientific contributions made in this dissertation utilize current data sets generated using high-throughput sequencing technologies. For example, recent whole-genome sequencing studies of the most distant animal lineages have effectively restructured the animal tree of life as we understand it. The first two chapters utilize data from this new high-confidence animal phylogeny - in addition to data generated in the course of my work - to demonstrate that (1) certain classes of human disease have uncommonly large proportions of genes that evolved with the earliest animals and/or vertebrates, and (2) that canonical microRNA functionality - absent in at least two of the early branching animal lineages - likely evolved after the first animals. In the third chapter, I expand upon recent research in predicting microRNA target sites, describing a novel tool for predicting clinically significant microRNA target site variants and demonstrating its applicability to the analysis of clinical genomic data. Thus, the studies detailed in this dissertation represent significant advances in our understanding of the functions of disease genes and microRNAs from both an evolutionary and a clinical perspective

Comparative genomics of early animal evolution

The explosion of genomics permits investigations into the origin and early evolution of the Metazoa at the molecular level. In this thesis, I am particularly interested in investigating the molecular foundation of the animal senses (i.e. how animals perceive their world). To understand the directionality of evolutionary innovation a well-developed phylogenetic framework is necessary. On one hand, the combination of molecular and morphological data sets has revolutionized our views of metazoan relationships over the past decades, but on the other hand, a number of nodes on the metazoan tree remain uncertain. Uncertainty is particularly high with reference to the taxa generally named “early branching metazoans”. Unfortunately, understanding the relationships among these taxa is key to understanding the evolution of sensory perception (Nielsen 2008). In this thesis I will investigate both animal phylogenetics (to attempt to resolve the phylogeny among the early branching Metazoa) and the evolution of the metazoan sensory receptors. The G-protein coupled receptor superfamily (GPCR) superfamily is the main family of metazoan surface receptors. In this thesis, after an initial introduction (Chapter 1), I address and substantially clarify the relationship among the early branching animals (Chapter 2) using novel genomic data and publicly available expressed sequence tags (ESTs). I then move forward (Chapter 3) to use network-based methods to study the early evolution of the GPCR superfamily in Eukaryotes and animals. Finally (Chapter 4), I focus on the study of a specific subset of GPCRs (the a-group, Rhodopsin-like receptors). This GPCR group is particularly interesting as it includes the best studied and, arguably, one of the most interesting among the GPCR families: the Opsin family. Opsins are key proteins used in the process of light detection, and the origin and early evolution of this family are still substantially unknown. Chapter 4 addresses both these problems. The thesis is then concluded by a general discussion (Chapter 5) and a future directions (Chapter 6) section. Overall, this thesis provides new insights into the origin and early evolution of the Metazoa and their senses

The salmon louse genome: Copepod features and parasitic adaptations

Copepods encompass numerous ecological roles including parasites, detrivores and phytoplankton grazers. Nonetheless, copepod genome assemblies remain scarce. Lepeophtheirus salmonis is an economically and ecologically important ectoparasitic copepod found on salmonid fish. We present the 695.4 Mbp L. salmonis genome assembly containing ≈60% repetitive regions and 13,081 annotated protein-coding genes. The genome comprises 14 autosomes and a ZZ-ZW sex chromosome system. Assembly assessment identified 92.4% of the expected arthropod genes. Transcriptomics supported annotation and indicated a marked shift in gene expression after host attachment, including apparent downregulation of genes related to circadian rhythm coinciding with abandoning diurnal migration. The genome shows evolutionary signatures including loss of genes needed for peroxisome biogenesis, presence of numerous FNII domains, and an incomplete heme homeostasis pathway suggesting heme proteins to be obtained from the host. Despite repeated development of resistance against chemical treatments L. salmonis exhibits low numbers of many genes involved in detoxification.publishedVersio

The Evolution of Function in the Rab family of Small GTPases

Dissertation presented to obtain the PhD degree in Computational Biology.The question how protein function evolves is a fundamental problem with profound implications for both functional end evolutionary studies on proteins. Here, we review some of the work that has addressed or contributed to this question. We identify and comment on three different levels relevant for the evolution of protein function. First, biochemistry. This is the focus of our discussion, as protein function itself commonly receives least attention in studies on protein evolution.(...

Deploying Big Data To Crack The Genotype To Phenotype Code

Mechanistically connecting genotypes to phenotypes is a longstanding and central mission of biology. Deciphering these connections will unite questions and datasets across all scales from molecules to ecosystems. Although high-throughput sequencing has provided a rich platform on which to launch this effort, tools for deciphering mechanisms further along the genome to phenome pipeline remain limited. Machine learning approaches and other emerging computational tools hold the promise of augmenting human efforts to overcome these obstacles. This vision paper is the result of a Reintegrating Biology Workshop, bringing together the perspectives of integrative and comparative biologists to survey challenges and opportunities in cracking the genotype to phenotype code and thereby generating predictive frameworks across biological scales. Key recommendations include: promoting the development of minimum “best practices” for the experimental design and collection of data; fostering sustained and long-term data repositories; promoting programs that recruit, train, and retain a diversity of talent and providing funding to effectively support these highly cross-disciplinary efforts. We follow this discussion by highlighting a few specific transformative research opportunities that will be advanced by these efforts

Bioinformatics for comparative cell biology

For hundreds of years biologists have studied the naturally occurring diversity in plant and animal species. The invention of the electron microscope in the rst half of the 1900's reveled that cells also can be incredible complex (and often stunningly beautiful). However, despite the fact that the eld of cell biology has existed for over 100 years we still lack a formal understanding of how cells evolve: It is unclear what the extents are in cell and organelle morphology, if and how diversity might be constrained, and how organelles change morphologically over time.(...