Send Orders for Reprints to [email protected] 712 Current Genomics, 2018, 19, 712-722 REVIEW ARTICLE Metabolic Modeling of Microbial Community Interactions for Health, En- vironmental and Biotechnological Applications Kok Siong Ang1, Meiyappan Lakshmanan1, Na-Rae Lee2 and Dong-Yup Lee1,2,3,* 1Bioprocessing Technology Institute (BTI), A*STAR, Singapore 138668, Singapore; 2Department of Chemical and Bio- molecular Engineering, and NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), National Uni- versity of Singapore, Singapore 117585, Singapore; 3School of Chemical Engineering, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon, Gyeonggi-do 16419, Republic of Korea Abstract: In nature, microbes do not exist in isolation but co-exist in a variety of ecological and bio- logical environments and on various host organisms. Due to their close proximity, these microbes in- teract among themselves, and also with the hosts in both positive and negative manners. Moreover, these interactions may modulate dynamically upon external stimulus as well as internal community changes. This demands systematic techniques such as mathematical modeling to understand the intrin- sic community behavior. Here, we reviewed various approaches for metabolic modeling of microbial A R T I C L E H I S T O R Y communities. If detailed species-specific information is available, segregated models of individual or- Received: June 25, 2017 ganisms can be constructed and connected via metabolite exchanges; otherwise, the community may Revised: November 08, 2017 be represented as a lumped network of metabolic reactions. The constructed models can then be simu- Accepted: November 11, 2017 lated to help fill knowledge gaps, and generate testable hypotheses for designing new experiments. DOI: More importantly, such community models have been developed to study microbial interactions in 10.2174/1389202919666180911144055 various niches such as host microbiome, biogeochemical and bioremediation, waste water treatment and synthetic consortia. As such, the metabolic modeling efforts have allowed us to gain new insights into the natural and synthetic microbial communities, and design interventions to achieve specific goals. Finally, potential directions for future development in metabolic modeling of microbial com- munities were also discussed. Keywords: Microbial communities, Metabolism, Community modeling, Genome-scale metabolic models, Flux balance analy- sis, Kinetic models. 1. INTRODUCTION complex set of metabolite exchanges that no constituent spe- cies can accomplish on isolation. For example, metabolites Microbes are ubiquitous across Earth’s biosphere at typi- 9 6 transferred among microbes link disparate pathways from cal densities of 10 organisms per gram of soil [1] and 10 individual species to render novel metabolic functions [14, organisms per milliliter of sea water [2]. They take part in 15]. However, it is still difficult to understand microbial biogeochemical cycles, playing an essential role through communities and their interactions due to the current tech- their combined metabolic activities [3]. Microbes are also nical limitations in measuring possible metabolite exchange found in different hosts including insects, animals, and fluxes between different species in the community [16, 17]. plants, interacting among themselves as well as with their In this regard, metabolic modeling of microbial communities hosts [4-6]. In humans, they make up 1-3% of the human is very useful for addressing some of the questions and gaps body mass, and there is growing evidence of the importance left unfilled by the current state-of-the-art. Further integra- of human gut microbiome and relevance to human health [7, tion of mathematical modeling with omics profiling, espe- 8]. In addition, microbial communities have been harnessed cially metabolomics, is also a promising avenue to be pur- in diverse applications such as waste water treatment and sued [18]. Hypotheses can then be derived to design new food production [9, 10]. experiments for probing microbial communities, thereby enhancing our understanding in an iterative manner. In this Microbes can interact with each other and also with hosts review, we first summarize the existing methodologies to in various ways, e.g., sensing of chemical signals, and cross- model metabolic interactions within microbial communities. feeding of metabolites [11-13]. More specifically, microbial It is followed by a discussion on their various applications to communities can produce emergent capabilities through a date. Finally, we suggest potential directions for future work in metabolic modeling of microbial communities. *Address correspondence to this author at the School of Chemical Engi- neering, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon, 2. MODELING OF MICROBIAL COMMUNITIES Gyeonggi-do 16419, Republic of Korea; Tel/Fax: +82 31 2907253; +82 31 Modeling approaches for analyzing microbial communi- 290 7272; E-mail: [email protected] ties can be classified on the basis of temporal nature 1389-2029/18 $58.00+.00 ©2018 Bentham Science Publishers Metabolic Modeling of Microbial Community Interactions Current Genomics, 2018, Vol. 19, No. 8 713 Fig. (1). The approaches employed to model and study microbial communities can be divided into three major categories: compartmentalized and lumped. A) In a compartmentalized simulation model, each species or functional guild occupies its own distinct compartment. B) In a lumped reaction network simulation model, all reactions are included in a single network with no segregation into distinct subsets. C) The graph summarizes the number of published studies on modeling microbial communities (See supplementary materials for list of collected studies). There is a clear trend of an increasing number of studies published until 2 years ago, where the number of studies fell dramatically. (static vs. dynamic) and species segregation (compartmental- the need for detailed species or guild metabolic models, ized vs. lumped network) (Fig. 1). Large-scale static models compartmentalized models are usually used to model syn- can be built using the steady-state assumption, which avoids thetic consortia or natural communities with well-studied without kinetic parameters that are difficult to be obtained. dominant species. Static models are typically constrained using measured in- Generally, it is difficult to isolate and characterize most puts and outputs, and then solved by assuming a physiologi- microbes in natural communities [27]. Thus, most compart- cally relevant objective function such as growth maximiza- mentalized metabolic models of natural communities are tion [19, 20]. Such computed metabolic flux profile can be composed of models constructed from a small number of used to study the metabolic states in different parts of the well-studied individual members, especially dominant spe- community, and examine interactions between member spe- cies or taxa. On the other hand, large scale simulation mod- cies. Static metabolic networks can also be analyzed to gain els of complex communities adopt a lumped or “enzyme insight into the community’s collective metabolic functions. soup” approach when information on individual species is On the other hand, dynamic models allow us to capture the very limited and only meta-omics data is available to identi- dynamic behaviors such as time-dependent species abun- fy the community’s metabolic capabilities [28]. The lumped dance and metabolite concentrations. They are usually network can be constructed by mapping identified genes in smaller in size, due to the difficulty in obtaining associated the omics (such as metagenomic and metaproteomic) data to kinetic parameters. As few enzymatic parameters are availa- enzymatic reactions [29, 30]. Hence, the community is con- ble from enzyme databases, the majority need to be estimat- sidered as a single integrated network. The lack of species or ed using experimental data. guild boundaries often results in linking pathways from dif- ferent species, which may not reflect actual metabolite ex- 2.1. Steady-state Metabolic Models changes. This may lead to an overestimation of the commu- Steady-state metabolic models have been widely used to nity’s overall metabolic capabilities. Therefore, it should be characterize the metabolic behavior of various microbes [19, viewed as an upper bound that can be tightened as more in- 21]. Within the broad classification of steady-state modeling, formation is integrated into the model [28]. microbial community models can be compartmentalized or Tobalina et al. used metaproteomics data to build a static lumped on the basis of species segregation. Compartmental- and lumped metabolic network model based on enzyme ized network models segregate microbial species or func- function present in the community, following a modified tional guilds into separate compartments in a larger system, procedure of the ModelSEED pipeline [25, 31]. It consists of while lumped models consider all available enzyme func- three steps: The first uses expression data to construct a min- tions within the community as a single network. To construct imal network capable of producing biomass. The second and a compartmentalized community model, metabolic models third steps add alternate biomass production and non-growth of member species are first reconstructed individually, and essential reactions respectively, based on the detected pro- then integrated into a combined model.