Constructing Microbial Consortia with Optimal Biomass Production Using a Genetic Algorithm
Frederik P.J. Vandecasteele
Thomas F. Hess
Ronald L. Crawford
Dept. of Biol. and Agric. Engineering University of Idaho Moscow, ID 83844-0904
[email protected]
Dept. of Biol. and Agric. Engineering University of Idaho Moscow, ID 83844-0904
Environmental Biotechnology Institute University of Idaho Moscow, ID 83844-1052
Abstract
Typical natural microbial ecosystems contain many different member organisms that all interact with each other in a highly nonlinear way. Although historically most work in microbiology has focused on pure cultures of single organisms, the study of groups of organisms (consortia) still forms a major challenge. While genetic algorithms are capable of optimising nonlinear systems and they should therefore be very well suited for studying microbial ecology, they have only rarely been used in this field. In this work, a genetic algorithm was used to construct a microbial consortium with optimal biomass production from separate isolated strains.
1
INTRODUCTION
Over the years, a vast amount of work in microbiology has been focused on the isolation and characterisation of pure cultures of microorganisms and this has advanced the field tremendously. In nature however, microorganisms virtually never occur as pure cultures. Rather, they live in complex ecological systems, with high levels of interaction. The actions of every member of an ecosystem can be advantageous, disadvantageous or neutral for one or more of the other members. These complex interactions make the study of ecosystems a particularly challenging one. As a new approach in microbial ecology, we propose the use of genetic algorithms to efficiently construct microbial consortia from sets of isolated strains. With this approach, a GA is used to search sets of isolated microbial strains for subsets of organisms that, when grown together, form ecosystems that optimally perform some particular ecological task. To our knowledge, this approach has never been described in literature, other than this current study and our work on constructing efficient
microbial consortia for azo dye degradation (Vandecasteele, 2003; Vandecasteele and Hess, 2003). Here we describe a GA approach to constructing microbial consortia with optimal biomass production from a set of different isolated strains. This work represents a novel way to addresses the fundamental ecological relationship between an ecosystem’s productivity and its species composition. A review of related work on this topic is given in Waide et al. (1999).
2 2.1
MATERIALS AND METHODS ISOLATION OF STRAINS
Microbial isolates with morphologically distinct colony features were obtained by incubating a dilution of a surface layer soil sample on R2A agar and incubating the plates at different temperatures. From these isolates, 20 different fast growing strains were selected that had grown well in an overnight Luria Bertani (LB) broth culture, as judged by the turbidity of the culture medium. An equal volume of 40% glycerol was added to the cultures (final concentration 20% glycerol), after which they were aliquoted in cyrovials and stored at -80 °C. 2.2
GA SETTINGS
The GA used here followed the generational model and had a population size of 20. Each solution was represented as a string of 20 bits, encoding the presence or absence of the corresponding microorganism. In this way, each solution encoded for a specific microbial consortium. The first generation was randomly generated. Fitness values were linearly rescaled, with µ'=µ and fmax'=2µ. If this yielded negative values, the fitness values were rescaled so that µ'=µ and f'min=0. Roulette Wheel selection was used and no elitism was applied. Single crossover was performed on each pair of selected individuals with a probability of 0.90. Mutation was performed by flipping bit values with a probability of 0.01 per bit. 20 generations in total were evaluated.
2003 Genetic and Evolutionary Computation Conference – Late-Breaking Papers, p. 299
FITNESS EVALUATION
3.2
The objective of this optimisation effort was obtaining a consortium with an optimal dry biomass production. To evaluate the fitness of the individuals in a generation, the corresponding microbial consortia were assembled and incubated in the lab. First, the separate strains were diluted from their stock vials by adding 50 µL of each vial to 3 mL of LB. The 20 consortia were then constructed in 20 standard glass test tubes by transferring 100 µL volumes of the right dilution tubes to the right consortium tubes. After this, LB was added to the consortium tubes to make up all volumes to 6 mL. Each consortium was now made in triplicate by vortexing its tube and transferring 2 mL to two new glass test tubes. 3 mL of LB was added to all tubes so the final volume of all test tubes was 5 mL. The 60 (3 x 20) tubes making up one generation were incubated at 37 °C and 200 rpm for 24 hours. After this, the cells in the broth were spun down and dried overnight at 55 °C. Dry biomass production was measured by determining the mass of the dry pellets. The fitness value of each individual in the GA was calculated as the average dry biomass production by the corresponding consortium for the three replicates. As a measure for the reproducibility of this experimental method, the standard deviation on the three replicates averaged over all 400 (20 individuals x 20 generations) evaluations was 0.000343 g.
3
RESULTS
GENE FREQUENCIES
For some strains, clear trends were visible in the average number of times they were present per consortium for each generation. Some strains (e.g. strain 12) were clearly positively selected by the algorithm while others (e.g. strain 3) were gradually eliminated from the population. This suggests that the respective positive or negative influence of the presence of these strains on the performances of the consortia they are a member of is very pronounced regardless of their possible interactions with the other members of each consortium. On the other hand, the frequencies of some strains (e.g. strain 2) did not show a clear upward or downward trend. This suggests that the influence of these strains on the consortia they are a member of was strongly dependent on the presence or absence of other strains. As an example, the trends of strains 2, 3 and 12 are given in Fig. 2. average presence in consortia
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strain 2 strain 3 strain 12
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3.1
TRENDS IN MAXIMUM AND AVERAGE FITNESS
Figure 2: Gene Frequencies of Three Strains
0.012 maximum
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3.3
AVERAGE NUMBER OF STRAINS PER CONSORTIUM
The trend in the average number of strains per consortium through the generations is depicted in Fig. 3. Apparently, the algorithm has been consistently selecting for a lower number of strains per consortium. This trend might be an indication of an underlying ecological principle governing this system. Also, the distinct stepwise character of this trend might indicate that the algorithm has been exploring different local optima in the fitness landscape.
0.008 0.006 0.004 0.002 0.000 0
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average number of strains per consortium
biomass production (g)
The maximum and average fitness values have significantly increased in generation 19 as compared to the first generation (p=0.015 and p
