Evolving Boolean regulatory networks with epigenetic control

The significant role of epigenetic mechanisms within natural systems has become increasingly clear. This paper uses a recently presented abstract, tunable Boolean genetic regulatory network model to explore aspects of epigenetics. It is shown how dynamically controlling transcription via a DNA methylation-inspired mechanism can be selected for by simulated evolution under various single and multicellular scenarios. Further, it is shown that the effects of such control can be inherited without detriment to fitness

From Microbial Communities to Distributed Computing Systems

A distributed biological system can be defined as a system whose components are located in different subpopulations, which communicate and coordinate their actions through interpopulation messages and interactions. We see that distributed systems are pervasive in nature, performing computation across all scales, from microbial communities to a flock of birds. We often observe that information processing within communities exhibits a complexity far greater than any single organism. Synthetic biology is an area of research which aims to design and build synthetic biological machines from biological parts to perform a defined function, in a manner similar to the engineering disciplines. However, the field has reached a bottleneck in the complexity of the genetic networks that we can implement using monocultures, facing constraints from metabolic burden and genetic interference. This makes building distributed biological systems an attractive prospect for synthetic biology that would alleviate these constraints and allow us to expand the applications of our systems into areas including complex biosensing and diagnostic tools, bioprocess control and the monitoring of industrial processes. In this review we will discuss the fundamental limitations we face when engineering functionality with a monoculture, and the key areas where distributed systems can provide an advantage. We cite evidence from natural systems that support arguments in favor of distributed systems to overcome the limitations of monocultures. Following this we conduct a comprehensive overview of the synthetic communities that have been built to date, and the components that have been used. The potential computational capabilities of communities are discussed, along with some of the applications that these will be useful for. We discuss some of the challenges with building co-cultures, including the problem of competitive exclusion and maintenance of desired community composition. Finally, we assess computational frameworks currently available to aide in the design of microbial communities and identify areas where we lack the necessary tool

Reactions of different populations of Corylus avellana L. and Prunus spinosa L. to drought and frost stress under controlled conditions

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Using problem formulation for fit‐for‐purpose pre‐market environmental risk assessments of regulated stressors

Pre‐market/prospective environmental risk assessments (ERAs) contribute to risk analyses performed to facilitate decisions about the market introduction of regulated stressors. Robust ERAs begin with an explicit problem formulation, which involves among other steps: (1) formally devising plausible pathways to harm that describe how the deployment of a regulated stressor could be harmful; (2) formulating risk hypotheses about the likelihood and severity of such events; (3) identifying the information that will be useful to test the risk hypotheses; and (4) developing a plan to acquire new data for hypothesis testing should tests with existing information be insufficient for decision‐making. Here, we apply problem formulation to the assessment of possible adverse effects of RNA interference‐based insecticidal genetically modified (GM) plants, GM growth hormone coho salmon, gene drive‐modified mosquitoes and classical biological weed control agents on non‐target organisms in a prospective manner, and of neonicotinoid insecticides on bees in a retrospective manner. In addition, specific considerations for the problem formulation for the ERA of nanomaterials and for landscape‐scale population‐level ERAs are given. We argue that applying problem formulation to ERA maximises the usefulness of ERA studies for decision‐making, through an iterative process, because: (1) harm is defined explicitly from the start; (2) the construction of risk hypotheses is guided by policy rather than an exhaustive attempt to address any possible differences; (3) existing information is used effectively; (4) new data are collected with a clear purpose; (5) risk is characterised against well‐defined criteria of hypothesis corroboration or falsification; and (6) risk assessment conclusions can be communicated clearly. However, problem formulation is still often hindered by the absence of clear policy goals and decision‐making criteria (e.g. definition of protection goals and what constitutes harm) that are needed to guide the interpretation of scientific information. We therefore advocate further dialogue between risk assessors and risk managers to clarify how ERAs can address policy goals and decision‐making criteria. Ideally, this dialogue should take place for all classes of regulated stressors, as this can promote alignment and consistency on the desired level of protection and maximum tolerable impacts across regulated stressors

Investigating the role of cAMP in mycobacterial antimicrobial drug tolerance by the discovery of a novel cAMP-phosphodiesterase

M. tuberculosis, the causative agent of Tuberculosis (TB), is an ancient pathogen that has plagued mankind for over 70,000 years. In 2018, TB was responsible globally for 10 million new infections and 1.5 million deaths - more than any other infectious disease. M. tuberculosis bacilli have evolved to thrive in the harsh, nutrient limited environment within the host alveolar macrophage and evade the constant pressure of immune cell mediated killing. This evolution has led to the bacilli developing phenotypic adaptions that concurrently, drastically decrease their susceptibility to many antimicrobials. The basis for phenotypic adaptions is signalling to detect an environmental stimulus and to mediate an appropriate response. To this end, M. tuberculosis and other mycobacteria have evolved a robust cyclic AMP (cAMP) signalling system with multiple cAMP producing and cAMP binding effector proteins. Several of these proteins have already been shown to regulate virulence, carbon metabolism and essential gene expression. However, the link between cAMP signalling and antimicrobial susceptibility in mycobacteria has not previously been investigated. In this project, I identified a new cAMP degrading phosphodiesterase enzyme (Rv1339) and used it as a tool to significantly decrease intrabacterial levels of cAMP in mycobacteria. The effect of this in M. smegmatis mc2155 was to increase antimicrobial susceptibility. By using a combination of metabolomics, RNA-sequencing, antimicrobial susceptibility assays and bioenergetics analysis, I was able to characterise the potential mechanism behind this increased susceptibility. I was also able to begin preliminary work required to investigate this link in M. tuberculosis H37Rv. This work represents a proof-of-concept that targeting cAMP signalling is a promising new avenue for antimicrobial development, and expands our understanding of cAMP signalling in mycobacteria.Open Acces