From Genes to Ecosystems: Resource Availability and DNA Methylation Drive the Diversity and Abundance of Restriction Modification Systems in Prokaryotes

Together, prokaryotic hosts and their viruses numerically dominate the planet and are engaged in an eternal struggle of hosts evading viral predation and viruses overcoming defensive mechanisms employed by their hosts. Prokaryotic hosts have been found to carry several viral defense systems in recent years with Restriction Modification systems (RMs) were the first discovered in the 1950s. While we have biochemically elucidated many of these systems in the last 70 years, we still struggle to understand what drives their gain and loss in prokaryotic genomes. In this work, we take a computational approach to understand the underlying evolutionary drivers of RMs by assessing ‘big data’ signals of RMs in prokaryotic genomes and incorporating molecular data in trait-based mathematical models. Focusing on the Cyanobacteria, we found a large discrepancy in the frequency of RMs per genome in different environmental contexts, where Cyanobacteria that live in oligotrophic nutrient conditions have few to no RMs and those in nutrient-rich conditions consistently have many RMs. While our models agree with the observation that increased nutrient inputs make the selective pressure of RMs more intense, they were unable to reconcile the high numbers of RMs per genome with their potent defensive properties- a situation of apparent overkill. By incorporating viral methylation, an unavoidable effect of RMs, we were able to explain how organisms could carry over 15 RMs. With this discovery, we then tried and reassess the distribution of methyltransferases, an essential component of RMs that can also have alternate physiological rolls in the cell. We expand on conventional wisdom, that methyltransferases that are widely phylogenetically conserved are associated with global cellular regulation. However, we also find that organisms with high numbers of RMs also have a surprising amount of conservation in the methyltransferases that they carry. This data suggests caution should be used in associating phylogenic signals with functional rolls in methyltransferases as different functional rolls seem to overlap in their phylogenetic signal. Indeed, we suggest trait-based modeling may be the best tool in elucidating why organisms with a high selective pressure to maintain RMs appear to have conserved methyltransferase

Grouping promotes both partnership and rivalry with long memory in direct reciprocity

Biological and social scientists have long been interested in understanding how to reconcile individual and collective interests in iterated Prisoner's Dilemma. Many effective strategies have been proposed, and they are often categorized into one of two classes, `partners' and `rivals.' More recently, another class, `friendly rivals,' has been identified in longer-memory strategy spaces. Friendly rivals qualify as both partners and rivals: They fully cooperate with themselves, like partners, but never allow their co-players to earn higher payoffs, like rivals. Although they have appealing theoretical properties, it is unclear whether they would emerge in evolving population because most previous works focus on memory-one strategy space, where no friendly rival strategy exists. To investigate this issue, we have conducted large-scale evolutionary simulations in well-mixed and group-structured populations and compared the evolutionary dynamics between memory-one and memory-three strategy spaces. In a well-mixed population, the memory length does not make a major difference, and the key factors are the population size and the benefit of cooperation. Friendly rivals play a minor role because being a partner or a rival is often good enough in a given environment. It is in a group-structured population that memory length makes a stark difference: When memory-three strategies are available, friendly rivals become dominant, and the cooperation level nearly reaches a maximum, even when the benefit of cooperation is so low that cooperation would not be achieved in a well-mixed population. This result highlights the important interaction between group structure and memory lengths that drive the evolution of cooperation.Comment: 18 pages, 11 figure

Nature-inspired survivability: Prey-inspired survivability countermeasures for cloud computing security challenges

As cloud computing environments become complex, adversaries have become highly sophisticated and unpredictable. Moreover, they can easily increase attack power and persist longer before detection. Uncertain malicious actions, latent risks, Unobserved or Unobservable risks (UUURs) characterise this new threat domain. This thesis proposes prey-inspired survivability to address unpredictable security challenges borne out of UUURs. While survivability is a well-addressed phenomenon in non-extinct prey animals, applying prey survivability to cloud computing directly is challenging due to contradicting end goals. How to manage evolving survivability goals and requirements under contradicting environmental conditions adds to the challenges. To address these challenges, this thesis proposes a holistic taxonomy which integrate multiple and disparate perspectives of cloud security challenges. In addition, it proposes the TRIZ (Teorija Rezbenija Izobretatelskib Zadach) to derive prey-inspired solutions through resolving contradiction. First, it develops a 3-step process to facilitate interdomain transfer of concepts from nature to cloud. Moreover, TRIZ’s generic approach suggests specific solutions for cloud computing survivability. Then, the thesis presents the conceptual prey-inspired cloud computing survivability framework (Pi-CCSF), built upon TRIZ derived solutions. The framework run-time is pushed to the user-space to support evolving survivability design goals. Furthermore, a target-based decision-making technique (TBDM) is proposed to manage survivability decisions. To evaluate the prey-inspired survivability concept, Pi-CCSF simulator is developed and implemented. Evaluation results shows that escalating survivability actions improve the vitality of vulnerable and compromised virtual machines (VMs) by 5% and dramatically improve their overall survivability. Hypothesis testing conclusively supports the hypothesis that the escalation mechanisms can be applied to enhance the survivability of cloud computing systems. Numeric analysis of TBDM shows that by considering survivability preferences and attitudes (these directly impacts survivability actions), the TBDM method brings unpredictable survivability information closer to decision processes. This enables efficient execution of variable escalating survivability actions, which enables the Pi-CCSF’s decision system (DS) to focus upon decisions that achieve survivability outcomes under unpredictability imposed by UUUR

Modeling the Use of Nonrenewable Resources Using a Genetic Algorithm

This paper shows, how a genetic algorithm (GA) can be used to model an economic process: the interaction of profit-maximizing oil-exploration firms that compete with each other for a limited amount of oil. After a brief introduction to the concept of multi-agent-modeling in economics, a GA-based resource-economic model is developed. Several model runs based on different economic policy assumptions are presented and discussed in order to show how the GA-model can be used to gain insight into the dynamic properties of economic systems. The remainder outlines deficiencies of GA-based multi-agent approaches and sketches how the present model can be improved.

How might infant and paediatric immune responses influence malaria vaccine efficacy?

Naturally acquired immunity to malaria requires repeat infections yet does not engender sterile immunity or long-lasting protective immunologic memory. This renders infants and young children the most susceptible to malaria-induced morbidity and mortality, and the ultimate target for a malaria vaccine. The prevailing paradigm is that infants initially garner protection due to transplacentally transferred anti-malarial antibodies and other intrinsic factors such as foetal haemoglobin. As these wane infants have an insufficient immune repertoire to prevent genetically diverse Plasmodium infections and an inability to control malaria-induced immunopathology. This Review discusses humoral, cell-mediated and innate immune responses to malaria and how each contributes to protection – focusing on how deficiencies in infant and paediatric immune responses might influence malaria vaccine efficacy in this population. In addition, burgeoning evidence suggests a role for inhibitory receptors that limit immunopathology and guide the development of long-lived immunity. Precisely how age or malaria infections influence the function of these regulators is unknown. Therefore the possibility that infants may not have the immune-dexterity to balance effective parasite clearance with timely immune-regulation leading to protective immunologic memory is considered. And thus, malaria vaccines tested in adults and older children may not be predictive for trials conducted in infants