Literature Review on Systems Biology, Data Collection, and Soil Diversity
Mary SimsAs researchers develop new technologies, scientific exploration into some of the world’s most complex questions becomes increasingly feasible. Advanced drug treatment technologies enable individuals to survive previously fatal diseases, have more children, and live longer. This may initially sound great, but one must consider that an increasingly healthy population consumes more resources that must be sustained to support an even larger future population. In 2013, the United Nations projected that the world population would reach 9.6 billion by 2050; this raises the question: How on Earth are we going to feed these people (United Nations)? Put simply, we must grow more food, requiring that we understand the factors that improve soil health and sustainably yield more crops.
Unfortunately, this is not a simple process. Just as we cannot tweak the expression of one magical gene and cure obesity, we cannot introduce a single microbe in a soil system and dramatically improve crop yield. Similar to the gene interactions networks and environmental facts that regulate human physiological conditions such as obesity, complex microbial menageries within soil systems cultivate the abundance of certain crops. However, there is hope; just as technology got us into the food-deprived situation we may soon be facing, it can also get us out of this mess if it is fully exploited. Existing systems biologyand data collection technologies could help us understand the complex microbial interactions that improve soil health and sustainably increase crop output.
Unveiling the complex microbial interactions within soil systems first requires an understanding of systems biology. Gary Churchill, a well-known expert in this field, effectively describes systems biology as a means for researchers to gain an understanding of the interactions between numerous different variables in an inverted way. Instead of observing the effect that an independent variable has on a dependent variable, researchers invoke variation amongst the factors being analyzed, collect quantitative data, and infer relationships within the model. Churchill elaborates on the specific details of systems biology by explaining its applications in genomics research. In genomics, physiological systems are understood unraveled through massive datasets collected from genetically diverse, recombinant inbred mice (Churchill). Although systems biology research techniques are primarily linked to genomics research, they are also applicable to other biological systems in which significant variation exists within and amongst individual samples.
The microbes within soil systems are extremely complex. Their presence depends on an intricate combination of living and non-living factors such as plant roots, soil organic matter, Nitrogen based compounds, temperature, pH, geochemical molecules, water, and gases like carbon dioxide and oxygen. Since these conditions are likely to vary within every soil sample, one would expect that each sample has a different microbial population than the next. Bakker et al. investigated this idea by looking at the interactions between four plant species (Andropogon gerardii, Schizachyrium scoparium, Lespedeza capitata, and Lupinus perennis), the number of plants placed within a specific area, and bacterial populations, including Streptomyces. After collecting the operational taxonomic units of bacteria present in each soil population and running various statistical analyses on the data, they discovered that the detected bacterial taxa varied depending on the plant density, plant speciesand amount of Streptomyces antagonistic activity. This experiment confirms that varying surrounding factors can induce quantifiable microbial diversity (Bakker et al.). Current technologies that allow us to fly around the world to collect unique soil samples could permit the assembly of extremely diverse microbial datasets that could be analyzed using systems biology techniques.
The ability to use systems biology research techniques to understand soil systems poses the question: what systems biology techniques already exist? Some of the most applicable answers to the question exist within the field of system genomics. QTLnet, a statistical modeling approach, was created and described by Neto et al. to analyze the network of interactions between genetic loci called Quantitative Trait Loci (QTLs). Using a common statistical technique called Gaussian regression models, QTLnet compares a variable’s distribution to a random distribution. The results generated from this technique are then analyzed using the Markov chain Monte Carlo approach. This process determines the linear algebraic equations that best model the interactions between variables. QTLnet applies both of these statistical techniques to infer a causal relationship between QTLs and traits. Unlike previous studies, QTLnet does not assume that certain QTLs have an effect on specific phenotypes. This suggests that it gives a more comprehensive understanding of the phenotype networks existing between certain phenotypes and QTLs. Like other regression-based statistical approaches, however, QTLnet has a drawback; it requires large sample sizes in order to generate enough statistical power to make effective solutions (Neto et al.). In genomics, the solution to the problem involves breeding more mice, and in soil science, it involves measuring larger quantities of soil samples more efficiently. Despite its initial development as a tool for unraveling the human genome, the QTLnet model could be modified to increase the understanding of other systems, such as those amongst soil microbes, as long as the technology to efficiently collect enough samples exists.
Numerous recent advancements in soil data collection techniques have begun to allow researchers to collect more microbial data from soil samples and therefore increase efficiency of the data collection process. One of these developments includes the methodology described in Pascault et al.’s 2010 experiment. In this study, computational analysis techniques such as microbial fingerprinting and spectrometry permit the collection and analysis of data collected from large data samples throughout eleven consecutive months. Koressaar et al.’s MultiMPrimer3 method also suggests that data collection methods have improved. This approach identifies PCR primers within microbial species that can be used to determine whether or not microbes of a specific phylogeny are present within a soil sample (Koressaar et al.). Koressaar et al. makes a convincing argument that this new soil diagnostic is more effective than previous methods that did not have enough sensitivity to detect microbial DNA concentrations. When looked at together, Koressaar et al. and Pascualt et al. suggest that soil quantification methods are not only being further developed and implemented, but are also becoming more valuable in terms of how much output and precision you receive relative to the amount of time you put in. These new technologies allow for more productive data collection processes that permit the collection of larger datasets.
Research papers from a wide variety of scientific fields all point to the same idea; technology has advanced enough to understand the significant, quantifiable variation within soil systems through systems biology techniques. Bakker et al. confirmed that seemingly minute factors can have a large influence on the microbes present with soil, similar to how individual genes can influence entire traits, as described by Churchill. Koressaar et al. and Pascualt et al. explained new techniques that could efficiently generate enough statistical power in soil datasets to permit the application of Neto et al.’s approach to soil systems. All of these papers support the idea that employing systems biology techniques is the next logical step in soil science research. Extensive soil quantification and systems biology technology make collecting and analyzing large soil databases efficient and reasonable. With an increased understanding of soil systems and their manipulation, we could induce greater crop production and feed more people. As our population continues to grow exponentially, the pressure to understand soil and food production becomes palpable. Necessary varying technologies have already been developed to effectively progress this understanding, but they have never cohesively been implemented to accomplish feat. Technological advancement has always been the means for human abundance; integrating the various technologies within different scientific fields is the next intelligible step for our survival.
Works Cited
Bakker, Matthew G., et al. “Diffuse Symbioses: Roles of Plant-Plant, Plant-Microbe and Microbe-Microbe Interactions in Structuring the Soil Microbiome.” Molecular ecology 23.6 (2014): 1571-83. Print.
Churchill, Gary A. “Recombinant Inbred Strain Panels: A Tool for Systems Genetics.” Physiological genomics 31.2 (2007): 174-5. Print.
Koressaar, Triinu, et al. “Automatic Identification of Species-Specific Repetitive DNA Sequences and their Utilization for Detecting Microbial Organisms.” Bioinformatics 25.11 (2009): 1349-55. Print.
Neto, Elias C., et al. “Causal Graphical Models in Systems Genetics: A Unified Framework for Joint Inference of Causal Network and Genetic Architecture for Correlated Phenotypes.” The Annals of Applied Statistics 4.1 (2010): 320-39. Print.
Pascault, Noémie, et al. “In Situ Dynamics of Microbial Communities during Decomposition of Wheat, Rape, and Alfalfa Residues.” Microbial Ecology 60.4 (2010): 816-28. Print.
United Nations. “World Population Projected to Reach 9.6 Billion by 2050 with Most Growth in Developing Regions, Especially Africa – Says UN.” UN Press Release (2013): n. pag. 13 June 2013. Web. 15 Sept. 2014.