Evolutionary causes and consequences of metabolic division of labour: why anaerobes do and aerobes don't

Regulating microbial redox reactions towards enhanced removal of refractory organic nitrogen from wastewater

Biotechnology for wastewater treatment is mainstream and effective depending upon microbial redox reactions to eliminate diverse contaminants and ensure aquatic ecological health. However, refractory organic nitrogen compounds (RONCs, e.g., nitro-, azo-, amide-, and N-heterocyclic compounds) with complex structures and high toxicity inhibit microbial metabolic activity and limit the transformation of organic nitrogen to inorganic nitrogen. This will eventually result in non-compliance with nitrogen discharge standards. Numerous efforts suggested that applying exogenous electron donors or acceptors, such as solid electrodes (electrostimulation) and limited oxygen (micro-aeration), could potentially regulate microbial redox reactions and catabolic pathways, and facilitate the biotransformation of RONCs. This review provides comprehensive insights into the microbial regulation mechanisms and applications of electrostimulation and micro-aeration strategies to accelerate the biotransformation of RONCs to organic amine (amination) and inorganic ammonia (ammonification), respectively. Furthermore, a promising approach involving in-situ hybrid anaerobic biological units, coupled with electrostimulation and micro-aeration, is proposed towards engineering applications. Finally, employing cutting-edge methods including multi-omics analysis, data science driven machine learning, technology-economic analysis, and life-cycle assessment would contribute to optimizing the process design and engineering implementation. This review offers a fundamental understanding and inspiration for novel research in the enhanced biotechnology towards RONCs elimination.

Microbiome-functionality in anaerobic digesters: A critical review

Microbially driven anaerobic digestion (AD) processes are of immense interest due to their role in the biovalorization of biowastes into renewable energy resources. The function-versatile microbiome, interspecies syntrophic interactions, and trophic-level metabolic pathways are important microbial components of AD. However, the lack of a comprehensive understanding of the process hampers efforts to improve AD efficiency. This study presents a holistic review of research on the microbial and metabolic "black box" of AD processes. Recent research on microbiology, functional traits, and metabolic pathways in AD, as well as the responses of functional microbiota and metabolic capabilities to optimization strategies are reviewed. The diverse ecophysiological traits and cooperation/competition interactions of the functional guilds and the biomanipulation of microbial ecology to generate valuable products other than methane during AD are outlined. The results show that AD communities prioritize cooperation to improve functional redundancy, and the dominance of specific microbes can be explained by thermodynamics, resource allocation models, and metabolic division of labor during cross-feeding. In addition, the multi-omics approaches used to decipher the ecological principles of AD consortia are summarized in detail. Lastly, future microbial research and engineering applications of AD are proposed. This review presents an in-depth understanding of microbiome-functionality mechanisms of AD and provides critical guidance for the directional and efficient bioconversion of biowastes into methane and other valuable products.

The trade-off between individual metabolic specialization and versatility determines the metabolic efficiency of microbial communities

In microbial systems, a metabolic pathway can be either completed by one autonomous population or distributed among a consortium performing metabolic division of labor (MDOL). MDOL facilitates the system's function by reducing the metabolic burden; however, it may hinder the function by reducing the exchange efficiency of metabolic intermediates among individuals. As a result, the function of a community is influenced by the trade-offs between the metabolic specialization and versatility of individuals. To experimentally test this hypothesis, we deconstructed the naphthalene degradation pathway into four steps and introduced them individually or combinatorically into different strains with varying levels of metabolic specialization. Using these strains, we engineered 1,456 synthetic consortia and found that 74 consortia exhibited higher degradation function than both the autonomous population and rigorous MDOL consortium. Quantitative modeling provides general strategies for identifying the most effective MDOL configuration. Our study provides critical insights into the engineering of high-performance microbial systems.

Why do lactic acid bacteria thrive in chain elongation microbiomes?

Efficient waste management is necessary to transition towards a more sustainable society. An emerging trend is to use mixed culture biotechnology to produce chemicals from organic waste. Insights into the metabolic interactions between community members and their growth characterization are needed to mediate knowledge-driven bioprocess development and optimization. Here, a granular sludge bioprocess for the production of caproic acid through sugar-based chain elongation metabolism was established. Lactic acid and chain-elongating bacteria were identified as the two main functional guilds in the granular community. The growth features of the main community representatives (isolate Limosilactobacillus musocae G03 for lactic acid bacteria and type strain Caproiciproducens lactatifermentans for chain-elongating bacteria) were characterized. The measured growth rates of lactic acid bacteria (0.051 ± 0.005 h-1) were two times higher than those of chain-elongating bacteria (0.026 ± 0.004 h-1), while the biomass yields of lactic acid bacteria (0.120 ± 0.005 g biomass/g glucose) were two times lower than that of chain-elongating bacteria (0.239 ± 0.007 g biomass/g glucose). This points towards differential growth strategies, with lactic acid bacteria resembling that of a r-strategist and chain-elongating bacteria resembling that of a K-strategist. Furthermore, the half-saturation constant of glucose for L. mucosae was determined to be 0.35 ± 0.05 g/L of glucose. A linear trend of caproic acid inhibition on the growth of L. mucosae was observed, and the growth inhibitory caproic acid concentration was predicted to be 13.6 ± 0.5 g/L, which is the highest reported so far. The pre-adjustment of L. mucosae to 4 g/L of caproic acid did not improve the overall resistance to it, but did restore the growth rates at low caproic acid concentrations (1-4 g/L) to the baseline values (i.e., growth rate at 0 g/L of caproic acid). High resistance to caproic acid enables lactic acid bacteria to persist and thrive in the systems intended for caproic acid production. Here, insights into the growth of two main functional guilds of sugar-based chain elongation systems are provided which allows for a better understanding of their interactions and promotes future bioprocess design and optimization.

Microbial redox cycling enhances ecosystem thermodynamic efficiency and productivity

Microbial life in low-energy ecosystems relies on individual energy conservation, optimizing energy use in response to interspecific competition and mutualistic interspecific syntrophy. Our study proposes a novel community-level strategy for increasing energy use efficiency. By utilizing an oxidation-reduction (redox) reaction network model that represents microbial redox metabolic interactions, we investigated multiple species-level competition and cooperation within the network. Our results suggest that microbial functional diversity allows for metabolic handoffs, which in turn leads to increased energy use efficiency. Furthermore, the mutualistic division of labour and the resulting complexity of redox pathways actively drive material cycling, further promoting energy exploitation. Our findings reveal the potential of self-organized ecological interactions to develop efficient energy utilization strategies, with important implications for microbial ecosystem functioning and the co-evolution of life and Earth.

Functional convergence in slow-growing microbial communities arises from thermodynamic constraints

The dynamics of microbial communities is complex, determined by competition for metabolic substrates and cross-feeding of byproducts. Species in the community grow by harvesting energy from chemical reactions that transform substrates to products. In many anoxic environments, these reactions are close to thermodynamic equilibrium and growth is slow. To understand the community structure in these energy-limited environments, we developed a microbial community consumer-resource model incorporating energetic and thermodynamic constraints on an interconnected metabolic network. The central element of the model is product inhibition, meaning that microbial growth may be limited not only by depletion of metabolic substrates but also by accumulation of products. We demonstrate that these additional constraints on microbial growth cause a convergence in the structure and function of the community metabolic network-independent of species composition and biochemical details-providing a possible explanation for convergence of community function despite taxonomic variation observed in many natural and industrial environments. Furthermore, we discovered that the structure of community metabolic network is governed by the thermodynamic principle of maximum free energy dissipation. Our results predict the decrease of functional convergence in faster growing communities, which we validate by analyzing experimental data from anaerobic digesters. Overall, the work demonstrates how universal thermodynamic principles may constrain community metabolism and explain observed functional convergence in microbial communities.

Multiscale models driving hypothesis and theory-based research in microbial ecology

Hypothesis and theory-based studies in microbial ecology have been neglected in favour of those that are descriptive and aim for data-gathering of uncultured microbial species. This tendency limits our capacity to create new mechanistic explanations of microbial community dynamics, hampering the improvement of current environmental biotechnologies. We propose that a multiscale modelling bottom-up approach (piecing together sub-systems to give rise to more complex systems) can be used as a framework to generate mechanistic hypotheses and theories (in-silico bottom-up methodology). To accomplish this, formal comprehension of the mathematical model design is required together with a systematic procedure for the application of the in-silico bottom-up methodology. Ruling out the belief that experimentation before modelling is indispensable, we propose that mathematical modelling can be used as a tool to direct experimentation by validating theoretical principles of microbial ecology. Our goal is to develop methodologies that effectively integrate experimentation and modelling efforts to achieve superior levels of predictive capacity.

Evolution of a cross-feeding interaction following a key innovation in a long-term evolution experiment with Escherichia coli

The evolution of a novel trait can profoundly change an organism's effects on its environment, which can in turn affect the further evolution of that organism and any coexisting organisms. We examine these effects and feedbacks following the evolution of a novel function in the Long-Term Evolution Experiment (LTEE) with Escherichia coli. A characteristic feature of E. coli is its inability to grow aerobically on citrate (Cit-). Nonetheless, a Cit+ variant with this capacity evolved in one LTEE population after 31 000 generations. The Cit+ clade then coexisted stably with another clade that retained the ancestral Cit- phenotype. This coexistence was shaped by the evolution of a cross-feeding relationship based on C4-dicarboxylic acids, particularly succinate, fumarate, and malate, that the Cit+ variants release into the medium. Both the Cit- and Cit+ cells evolved to grow on these excreted resources. The evolution of aerobic growth on citrate thus led to a transition from an ecosystem based on a single limiting resource, glucose, to one with at least five resources that were either shared or partitioned between the two coexisting clades. Our findings show that evolutionary novelties can change environmental conditions in ways that facilitate diversity by altering ecosystem structure and the evolutionary trajectories of coexisting lineages.

Thermophilic caproic acid production from grass juice by sugar-based chain elongation

Medium chain carboxylic acids (MCCA) such as caproic acid have a plethora of applications, ranging from food additives to bioplastics. MCCA can be produced via microbial chain elongation using waste and side-streams as substrates, a process that can be more sustainable than conventional production routes. Most chain elongation studies have focused on mesophilic conditions, with only two recent studies hinting at the possibility of thermophilic chain elongation, but a systematic study of its mechanisms is lacking. Here, we investigated thermophilic chain elongation from grass juice, to understand the effect of key operational parameters (pH, temperature, substrate) on the process performance and to establish the key microbial genera and their role in the system. The genus Caproiciproducens was identified as responsible for thermophilic chain elongation, and caproic acid production was most favorable at pH 6.0 and 50 °C among the conditions tested, reaching an average concentration of 3.4 g/L. Batch experiments showed that the substrate for caproic acid production were glucose and xylose, while lactic acid led to the production of only butyric acid. Fed-batch experiments showed that substrate availability and the presence of caproic acid in the system play a major role in shaping the profile of thermophilic chain elongation. The increase of the total sugar concentration by glucose addition (without changing the organic load) during continuous operation led to a microbial community dominated (75 %) by Caproiciproducens and increased by 76 % the final average caproic acid concentration to 6.0 g/L (13 g_COD/L) which represented 32 % (g/g) of the total carboxylic acids. The highest concentration achieved was 7.2 g/L (day 197) which is the highest concentration reported under thermophilic conditions thus far. The results of this work pave the way to the potential development of thermophilic systems for upgrading various underexplored abundant and cheap sugar-rich side-streams to caproic acid.

Guided by the principles of microbiome engineering: Accomplishments and perspectives for environmental use

Although the accomplishments of microbiome engineering highlight its significance for the targeted manipulation of microbial communities, knowledge and technical gaps still limit the applications of microbiome engineering in biotechnology, especially for environmental use. Addressing the environmental challenges of refractory pollutants and fluctuating environmental conditions requires an adequate understanding of the theoretical achievements and practical applications of microbiome engineering. Here, we review recent cutting-edge studies on microbiome engineering strategies and their classical applications in bioremediation. Moreover, a framework is summarized for combining both top-down and bottom-up approaches in microbiome engineering toward improved applications. A strategy to engineer microbiomes for environmental use, which avoids the build-up of toxic intermediates that pose a risk to human health, is suggested. We anticipate that the highlighted framework and strategy will be beneficial for engineering microbiomes to address difficult environmental challenges such as degrading multiple refractory pollutants and sustain the performance of engineered microbiomes in situ with indigenous microorganisms under fluctuating conditions.

Even allocation of benefits stabilizes microbial community engaged in metabolic division of labor

Microbial communities execute metabolic pathways to drive global nutrient cycles. Within a community, functionally specialized strains can perform different yet complementary steps within a linear pathway, a phenomenon termed metabolic division of labor (MDOL). However, little is known about how such metabolic behaviors shape microbial communities. Here, we derive a theoretical framework to define the assembly of a community that degrades an organic compound through MDOL. The framework indicates that to ensure community stability, the strains performing the initial steps should hold a growth advantage (m) over the "private benefit" (n) of the strain performing the last step. The steady-state frequency of the last strain is then determined by the quotient of n and m. Our experiments show that the framework accurately predicts the assembly of our synthetic consortia that degrade naphthalene through MDOL. Our results provide insights for designing and managing stable microbial systems for metabolic pathway optimization.

The r/K selection theory and its application in biological wastewater treatment processes

Understanding the characteristics of functional organisms is the key to managing and updating biological processes for wastewater treatment. This review, for the first time, systematically characterized two typical types of strategists in wastewater treatment ecosystems via the r/K selection theory and provided novel strategies for selectively enriching microbial community. Functional organisms involved in nitrification (e.g., Nitrosomonas and Nitrosococcus), anammox (Candidatus Brocadia), and methanogenesis (Methanosarcinaceae) are identified as r-strategists with fast growth capacities and low substrate affinities. These r-strategists can achieve high pollutant removal loading rates. On the other hand, other organisms such as Nitrosospira spp., Candidatus Kuenenia, and Methanosaetaceae, are characterized as K-strategists with slow growth rates but high substrate affinities, which can decrease the pollutant concentration to low levels. More importantly, K-strategists may play crucial roles in the biodegradation of recalcitrant organic pollutants. The food-to-microorganism ratio, mass transfer, cell size, and biomass morphology are the key factors determining the selection of r-/K-strategists. These factors can be related with operating parameters (e.g., solids and hydraulic retention time), biomass morphology (biofilm or granules), and operating modes (continuous-flow or sequencing batch), etc., to achieve the efficient acclimation of targeted r-/K-strategists. For practical applications, the concept of substrate flux was put forward to further benefit the selective enrichment of r-/K-strategists, fulfilling effective management and improvement of engineered pollution control bioprocesses. Finally, the future perspectives regarding the development of the r/K selection theory in wastewater treatment processes were discussed.

Substrate availability and toxicity shape the structure of microbial communities engaged in metabolic division of labor

Metabolic division of labor (MDOL) represents a widespread natural phenomenon, whereby a complex metabolic pathway is shared between different strains within a community in a mutually beneficial manner. However, little is known about how the composition of such a microbial community is regulated. We hypothesized that when degradation of an organic compound is carried out via MDOL, the concentration and toxicity of the substrate modulate the benefit allocation between the two microbial populations, thus affecting the structure of this community. We tested this hypothesis by combining modeling with experiments using a synthetic consortium. Our modeling analysis suggests that the proportion of the population executing the first metabolic step can be simply estimated by Monod-like formulas governed by substrate concentration and toxicity. Our model and the proposed formula were able to quantitatively predict the structure of our synthetic consortium. Further analysis demonstrates that our rule is also applicable in estimating community structures in spatially structured environments. Together, our work clearly demonstrates that the structure of MDOL communities can be quantitatively predicted using available information on environmental factors, thus providing novel insights into how to manage artificial microbial systems for the wide application of the bioindustry.

Noisy metabolism can promote microbial cross-feeding

Cross-feeding, the exchange of nutrients between organisms, is ubiquitous in microbial communities. Despite its importance in natural and engineered microbial systems, our understanding of how inter-species cross-feeding arises is incomplete, with existing theories limited to specific scenarios. Here, we introduce a novel theory for the emergence of such cross-feeding, which we term noise-averaging cooperation (NAC). NAC is based on the idea that, due to their small size, bacteria are prone to noisy regulation of metabolism which limits their growth rate. To compensate, related bacteria can share metabolites with each other to ‘average out’ noise and improve their collective growth. According to the Black Queen Hypothesis, this metabolite sharing among kin, a form of ‘leakage’, then allows for the evolution of metabolic interdependencies among species including de novo speciation via gene deletions. We first characterize NAC in a simple ecological model of cell metabolism, showing that metabolite leakage can in principle substantially increase growth rate in a community context. Next, we develop a generalized framework for estimating the potential benefits of NAC among real bacteria. Using single-cell protein abundance data, we predict that bacteria suffer from substantial noise-driven growth inefficiencies, and may therefore benefit from NAC. We then discuss potential evolutionary pathways for the emergence of NAC. Finally, we review existing evidence for NAC and outline potential experimental approaches to detect NAC in microbial communities.

Designing microbial communities to maximize the thermodynamic driving force for the production of chemicals

Microbial communities have become a major research focus due to their importance for biogeochemical cycles, biomedicine and biotechnological applications. While some biotechnological applications, such as anaerobic digestion, make use of naturally arising microbial communities, the rational design of microbial consortia for bio-based production processes has recently gained much interest. One class of synthetic microbial consortia is based on specifically designed strains of one species. A common design principle for these consortia is based on division of labor, where the entire production pathway is divided between the different strains to reduce the metabolic burden caused by product synthesis. We first show that classical division of labor does not automatically reduce the metabolic burden when metabolic flux per biomass is analyzed. We then present ASTHERISC (Algorithmic Search of THERmodynamic advantages in Single-species Communities), a new computational approach for designing multi-strain communities of a single-species with the aim to divide a production pathway between different strains such that the thermodynamic driving force for product synthesis is maximized. ASTHERISC exploits the fact that compartmentalization of segments of a product pathway in different strains can circumvent thermodynamic bottlenecks arising when operation of one reaction requires a metabolite with high and operation of another reaction the same metabolite with low concentration. We implemented the ASTHERISC algorithm in a dedicated program package and applied it on E. coli core and genome-scale models with different settings, for example, regarding number of strains or demanded product yield. These calculations showed that, for each scenario, many target metabolites (products) exist where a multi-strain community can provide a thermodynamic advantage compared to a single strain solution. In some cases, a production with sufficiently high yield is thermodynamically only feasible with a community. In summary, the developed ASTHERISC approach provides a promising new principle for designing microbial communities for the bio-based production of chemicals.

Communities of microbes are ubiquitous in nature and also of high relevance for industrial applications, e.g. for the production of biogas. The development and use of non-natural communities for biotechnological applications has become an important subject of research. In this work, we present a new computational method to design synthetic communities with improved capabilities for the synthesis of desired target metabolites. Our method takes a constraint-based metabolic model of an organism as input and searches for a suitable partitioning of the product pathway via different strains of the organism such that the thermodynamic driving force for product synthesis is maximized. Essentially, this approach exploits the fact that having multiple strains allows adjustment of different metabolite concentrations in the different strains by which the thermodynamic driving force for product synthesis can often be increased. We tested this approach with a core and with a genome-scale metabolic network model of Escherichia coli. We found that, for dozens of metabolites, there exist communities with specifically designed strains of E. coli where the maximal thermodynamic driving force can be increased compared to a single E. coli strain. In summary, our presented method provides a new approach, together with a new design principle, for the computational design of microbial communities.

Cyclic Conversions in the Nitrogen Cycle

The cyclic nature of specific conversions in the nitrogen cycle imposes strict limitations to the conversions observed in nature and explains for example why anaerobic ammonium oxidation (anammox) bacteria can only use nitrite – and not nitrate – as electron acceptor in catabolism, and why nitrite is required as additional electron donor for inorganic carbon fixation in anabolism. Furthermore, the biochemistry involved in nitrite-dependent anaerobic methane oxidation excludes the feasibility of using nitrate as electron acceptor. Based on the cyclic nature of these nitrogen conversions, we propose two scenarios that may explain the ecological role of recently discovered complete ammonia-oxidizing (comammox) Nitrospira spp., some of which were initially found in a strongly oxygen limited environment: (i) comammox Nitrospira spp. may actually catalyze an anammox-like metabolism using a biochemistry similar to intra-oxic nitrite-dependent methane oxidation, or (ii) scavenge all available oxygen for ammonia activation and use nitrate as terminal electron acceptor. Both scenarios require the presence of the biochemical machinery for ammonia oxidation to nitrate, potentially explaining a specific ecological niche for the occurrence of comammox bacteria in nature.

Microbial inefficient substrate use through the perspective of resource allocation models

Microorganisms extract energy from substrates following strategies that may seem suboptimal at first glance. Beyond the so-called yield-rate trade-off, resource allocation models, which focus on assigning different functional roles to the limited number of enzymes that a cell can support, offer a framework to interpret the inefficient substrate use by microorganisms. We review here relevant examples of substrate conversions where a significant part of the available energy is not utilised and how resource allocation models offer a mechanistic interpretation thereof, notably for open mixed cultures. Future developments are identified, in particular, the challenge of considering metabolic flexibility towards uncertain environmental changes instead of strict fixed optimality objectives, with the final goal of increasing the prediction capabilities of resource allocation models. Finally, we highlight the relevance of resource allocation to understand and enable a promising biorefinery platform revolving around lactate, which would increase the flexibility of waste-to-chemical biorefinery schemes.

A framework based on fundamental biochemical principles to engineer microbial community dynamics

Microbial communities are complex but there are basic principles we can apply to constrain the assumed stochasticity of their activity. By understanding the trade-offs behind the kinetic parameters that define microbial growth, we can explain how local interspecies dependencies arise and shape the emerging properties of a community. If we integrate these theoretical descriptions with experimental 'omics' data and bioenergetics analysis of specific environmental conditions, predictions on activity, assembly and spatial structure can be obtained reducing the a priori unpredictable complexity of microbial communities. This information can be used to define the appropriate selective pressures to engineer bioprocesses and propose new hypotheses which can drive experimental research to accelerate innovation in biotechnology.

Enabling rational gut microbiome manipulations by understanding gut ecology through experimentally-evidenced in silico models

The gut microbiome has emerged as a contributing factor in non-communicable disease, rendering it a target of health-promoting interventions. Yet current understanding of the host-microbiome dynamic is insufficient to predict the variation in intervention outcomes across individuals. We explore the mechanisms that underpin the gut bacterial ecosystem and highlight how a more complete understanding of this ecology will enable improved intervention outcomes. This ecology varies within the gut over space and time. Interventions disrupt these processes, with cascading consequences throughout the ecosystem. In vivo studies cannot isolate and probe these processes at the required spatiotemporal resolutions, and in vitro studies lack the representative complexity required. However, we highlight that, together, both approaches can inform in silico models that integrate cellular-level dynamics, can extrapolate to explain bacterial community outcomes, permit experimentation and observation over ecological processes at high spatiotemporal resolution, and can serve as predictive platforms on which to prototype interventions. Thus, it is a concerted integration of these techniques that will enable rational targeted manipulations of the gut ecosystem.

Resource allocation explains lactic acid production in mixed-culture anaerobic fermentations

Lactate production in anaerobic carbohydrate fermentations with mixed cultures of microorganisms is generally observed only in very specific conditions: the reactor should be run discontinuously and peptides and B vitamins must be present in the culture medium as lactic acid bacteria (LAB) are typically auxotrophic for amino acids. State-of-the-art anaerobic fermentation models assume that microorganisms optimise the adenosine triphosphate (ATP) yield on substrate and therefore they do not predict the less ATP efficient lactate production, which limits their application for designing lactate production in mixed-culture fermentations. In this study, a metabolic model taking into account cellular resource allocation and limitation is proposed to predict and analyse under which conditions lactate production from glucose can be beneficial for microorganisms. The model uses a flux balances analysis approach incorporating additional constraints from the resource allocation theory and simulates glucose fermentation in a continuous reactor. This approach predicts lactate production is predicted at high dilution rates, provided that amino acids are in the culture medium. In minimal medium and lower dilution rates, mostly butyrate and no lactate is predicted. Auxotrophy for amino acids of LAB is identified to provide a competitive advantage in rich media because less resources need to be allocated for anabolic machinery and higher specific growth rates can be achieved. The Matlab™ codes required for performing the simulations presented in this study are available at https://doi.org/10.5281/zenodo.4031144.

Toward Engineering Biosystems With Emergent Collective Functions

Many complex behaviors in biological systems emerge from large populations of interacting molecules or cells, generating functions that go beyond the capabilities of the individual parts. Such collective phenomena are of great interest to bioengineers due to their robustness and scalability. However, engineering emergent collective functions is difficult because they arise as a consequence of complex multi-level feedback, which often spans many length-scales. Here, we present a perspective on how some of these challenges could be overcome by using multi-agent modeling as a design framework within synthetic biology. Using case studies covering the construction of synthetic ecologies to biological computation and synthetic cellularity, we show how multi-agent modeling can capture the core features of complex multi-scale systems and provide novel insights into the underlying mechanisms which guide emergent functionalities across scales. The ability to unravel design rules underpinning these behaviors offers a means to take synthetic biology beyond single molecules or cells and toward the creation of systems with functions that can only emerge from collectives at multiple scales.