Alternative Strategies for Microbial Remediation of Pollutants via Synthetic Biology

In order to understand the functioning of organisms on the molecular level, we need to know which genes are expressed, when and where in the organism, and to which extent. The regulation of gene expression is achieved through genetic regulatory systems structured by networks of interactions between DNA, RNA, proteins, and small molecules. As most genetic regulatory networks of interest involve many components connected through interlocking positive and negative feedback loops, an intuitive understanding of their dynamics is hard to obtain. As a consequence, formal methods and computer tools for the modeling and simulation of genetic regulatory networks will be indispensable. This paper reviews formalisms that have been employed in mathematical biology and bioinformatics to describe genetic regulatory systems, in particular directed graphs, Bayesian networks, Boolean networks and their generalizations, ordinary and partial differential equations, qualitative differential equations, stochastic equations, and rule-based formalisms. In addition, the paper discusses how these formalisms have been used in the simulation of the behavior of actual regulatory systems.

Microbial cells secrete numerous enzymes, scavenging molecules, and signals that can promote the growth and survival of other cells around them [1-4]. This observation is consistent with the evolution of cooperation within species [5], and there is now an increasing emphasis on the importance of cooperation between different microbial species [4, 6]. We lack, however, a systematic test of the importance of mutually positive interactions between different species, which is vital for assessing the commonness and importance of cooperative evolution in natural communities. Here, we study the extent of mutually positive interaction among bacterial strains isolated from a common aquatic environment. Using data collected from two independent experiments evaluating community productivity across diversity gradients, we show that (1) in pairwise species combinations, the great majority of interactions are net negative and (2) there is no evidence that strong higher-order positive effects arise when more than two species are mixed together. Our data do not exclude the possibility of positive effects in one direction where one species gains at the expense of another, i.e., predator-prey-like interactions. However, these do not constitute cooperation and our analysis suggests that the typical result of adaptation to other microbial species will be competitive, rather than cooperative, phenotypes. Copyright © 2012 Elsevier Ltd. All rights reserved.

Environmental pollution has been on the rise in the past few decades owing to increased human activities on energy reservoirs, unsafe agricultural practices and rapid industrialization. Amongst the pollutants that are of environmental and public health concerns due to their toxicities are: heavy metals, nuclear wastes, pesticides, green house gases, and hydrocarbons. Remediation of polluted sites using microbial process (bioremediation) has proven effective and reliable due to its eco-friendly features. Bioremediation can either be carried out ex situ or in situ, depending on several factors, which include but not limited to cost, site characteristics, type and concentration of pollutants. Generally, ex situ techniques apparently are more expensive compared to in situ techniques as a result of additional cost attributable to excavation. However, cost of on-site installation of equipment, and inability to effectively visualize and control the subsurface of polluted sites are of major concerns when carrying out in situ bioremediation. Therefore, choosing appropriate bioremediation technique, which will effectively reduce pollutant concentrations to an innocuous state, is crucial for a successful bioremediation project. Furthermore, the two major approaches to enhance bioremediation are biostimulation and bioaugmentation provided that environmental factors, which determine the success of bioremediation, are maintained at optimal range. This review provides more insight into the two major bioremediation techniques, their principles, advantages, limitations and prospects.