The genetic basis for adaptation of model-designed syntrophic co-cultures.

Understanding the fundamental characteristics of microbial communities could have far reaching implications for human health and applied biotechnology. Despite this, much is still unknown regarding the genetic basis and evolutionary strategies underlying the formation of viable synthetic communities. By pairing auxotrophic mutants in co-culture, it has been demonstrated that viable nascent E. coli communities can be established where the mutant strains are metabolically coupled. A novel algorithm, OptAux, was constructed to design 61 unique multi-knockout E. coli auxotrophic strains that require significant metabolite uptake to grow. These predicted knockouts included a diverse set of novel non-specific auxotrophs that result from inhibition of major biosynthetic subsystems. Three OptAux predicted non-specific auxotrophic strains-with diverse metabolic deficiencies-were co-cultured with an L-histidine auxotroph and optimized via adaptive laboratory evolution (ALE). Time-course sequencing revealed the genetic changes employed by each strain to achieve higher community growth rates and provided insight into mechanisms for adapting to the syntrophic niche. A community model of metabolism and gene expression was utilized to predict the relative community composition and fundamental characteristics of the evolved communities. This work presents new insight into the genetic strategies underlying viable nascent community formation and a cutting-edge computational method to elucidate metabolic changes that empower the creation of cooperative communities

A hybrid of differential search algorithm and flux balance analysis to: Identify knockout strategies for in silico optimization of metabolites production

An increasing demand of naturally producing metabolites has gained the attention of researchers to develop better algorithms for predicting the effects of reaction knockouts. With the success of genome sequencing, in silico metabolic engineering has aided the researchers in modifying the genome-scale metabolic network. However, the complexities of the metabolic networks, have led to difficulty in obtaining a set of knockout reactions, which eventually lead to increase in computational time. Hence, many computational algorithms have been developed. Nevertheless, most of these algorithms are hindered by the solution being trapped in the local optima. In this paper, we proposed a hybrid of Differential Search Algorithm (DSA) and Flux Balance Analysis (FBA), to identify knockout reactions for enhancing the production of desired metabolites. Two organisms namely Escherichia coli and Zymomonas mobilis were tested by targeting the production rate of succinic acid, acetic acid, and ethanol. From this experiment, we obtained the list of knockout reactions and production rate. The results show that our proposed hybrid algorithm is capable of identifying knockout reactions with above 70% of production rate from the wild-type

From a thin film model for passive suspensions towards the description of osmotic biofilm spreading

Biofilms are ubiquitous macro-colonies of bacteria that develop at various interfaces (solid-liquid, solid-gas or liquid-gas). The formation of biofilms starts with the attachment of individual bacteria to an interface, where they proliferate and produce a slimy polymeric matrix - two processes that result in colony growth and spreading. Recent experiments on the growth of biofilms on agar substrates under air have shown that for certain bacterial strains, the production of the extracellular matrix and the resulting osmotic influx of nutrient-rich water from the agar into the biofilm are more crucial for the spreading behaviour of a biofilm than the motility of individual bacteria. We present a model which describes the biofilm evolution and the advancing biofilm edge for this spreading mechanism. The model is based on a gradient dynamics formulation for thin films of biologically passive liquid mixtures and suspensions, supplemented by bioactive processes which play a decisive role in the osmotic spreading of biofilms. It explicitly includes the wetting properties of the biofilm on the agar substrate via a disjoining pressure and can therefore give insight into the interplay between passive surface forces and bioactive growth processes

NITROGEN CYCLING IN A FOREST STREAM DETERMINED BY A 15N TRACER ADDITION

Nitrogen uptake and cycling was examined using a six‐week tracer addition of 15N‐labeled ammonium in early spring in Walker Branch, a first‐order deciduous forest stream in eastern Tennessee. Prior to the 15N addition, standing stocks of N were determined for the major biomass compartments. During and after the addition, 15N was measured in water and in dominant biomass compartments upstream and at several locations downstream. Residence time of ammonium in stream water (5–6 min) and ammonium uptake lengths (23–27 m) were short and relatively constant during the addition. Uptake rates of NH4 were more variable, ranging from 22 to 37 μg N·m−2·min−1 and varying directly with changes in streamwater ammonium concentration (2.7–6.7 μg/L). The highest rates of ammonium uptake per unit area were by the liverwort Porella pinnata, decomposing leaves, and fine benthic organic matter (FBOM), although epilithon had the highest N uptake per unit biomass N. Nitrification rates and nitrate uptake lengths and rates were determined by fitting a nitrification/nitrate uptake model to the longitudinal profiles of 15N‐NO3 flux. Nitrification was an important sink for ammonium in stream water, accounting for 19% of the total ammonium uptake rate. Nitrate production via coupled regeneration/nitrification of organic N was about one‐half as large as nitrification of streamwater ammonium. Nitrate uptake lengths were longer and more variable than those for ammonium, ranging from 101 m to infinity. Nitrate uptake rate varied from 0 to 29 μg·m−2·min−1 and was ∼1.6 times greater than assimilatory ammonium uptake rate early in the tracer addition. A sixfold decline in instream gross primary production rate resulting from a sharp decline in light level with leaf emergence had little effect on ammonium uptake rate but reduced nitrate uptake rate by nearly 70%. At the end of the addition, 64–79% of added 15N was accounted for, either in biomass within the 125‐m stream reach (33–48%) or as export of 15N‐NH4 (4%), 15N‐NO3 (23%), and fine particulate organic matter (4%) from the reach. Much of the 15N not accounted for was probably lost downstream as transport of particulate organic N during a storm midway through the experiment or as dissolved organic N produced within the reach. Turnover rates of a large portion of the 15N taken up by biomass compartments were high (0.04–0.08 per day), although a substantial portion of the 15N in Porella (34%), FBOM (21%), and decomposing wood (17%) at the end of the addition was retained 75 d later, indicating relatively long‐term retention of some N taken up from water. In total, our results showed that ammonium retention and nitrification rates were high in Walker Branch, and that the downstream loss of N was primarily as nitrate and was controlled largely by nitrification, assimilatory demand for N, and availability of ammonium to meet that demand. Our results are consistent with recent 15N tracer experiments in N‐deficient forest soils that showed high rates of nitrification and the importance of nitrate uptake in regulating losses of N. Together these studies demonstrate the importance of 15N tracer experiments for improving our understanding of the complex processes controlling N cycling and loss in ecosystems

The JBEI quantitative metabolic modeling library (jQMM): a python library for modeling microbial metabolism

Modeling of microbial metabolism is a topic of growing importance in biotechnology. Mathematical modeling helps provide a mechanistic understanding for the studied process, separating the main drivers from the circumstantial ones, bounding the outcomes of experiments and guiding engineering approaches. Among different modeling schemes, the quantification of intracellular metabolic fluxes (i.e. the rate of each reaction in cellular metabolism) is of particular interest for metabolic engineering because it describes how carbon and energy flow throughout the cell. In addition to flux analysis, new methods for the effective use of the ever more readily available and abundant -omics data (i.e. transcriptomics, proteomics and metabolomics) are urgently needed

Machine learning in bioprocess development: From promise to practice

Fostered by novel analytical techniques, digitalization and automation, modern bioprocess development provides high amounts of heterogeneous experimental data, containing valuable process information. In this context, data-driven methods like machine learning (ML) approaches have a high potential to rationally explore large design spaces while exploiting experimental facilities most efficiently. The aim of this review is to demonstrate how ML methods have been applied so far in bioprocess development, especially in strain engineering and selection, bioprocess optimization, scale-up, monitoring and control of bioprocesses. For each topic, we will highlight successful application cases, current challenges and point out domains that can potentially benefit from technology transfer and further progress in the field of ML

A hybrid of ant colony optimization and flux variability analysis to improve the production of l-phenylalanine and biohydrogen

In silico metabolic engineering has shown many successful results in genome - scale model reconstruction and modification of metabolic network by implementing reaction deletion strategies to improve microbial strain such as production yield and growth rate. While improving the metabolites production, optimization algorithm has been implemented gradually in previous studies to identify the near - optimal sets of reaction knockout to obtain the best results. However, previous works implemented other algorithms that differ than this study which faced with several issues such as premature convergence and able to only produce low production yield because of ineffective algorithm and existence of complex metabolic data. The lack of effective genome models is because of the presence thousands of reactions in the metabolic network caused complex and high dimensional data size that contains competing pathway of non - desirable product. Indeed, the suitable population size and knockout number for this new algorithm have been tested previously. This study proposes an algorithm that is a hybrid of the ant colony optimization algorithm and flux variability analysis (ACOFVA) to predict near - optimal sets of reactions knockout in an effort to improve the growth rates and the production rate of L - phenylalanine and biohydrogen in Saccharomyces cerevisiae and cyanobacteria Synechocystis sp PCC6803 respectively

Model transformation of metabolic networks using a Petri net based framework

The different modeling approaches in Systems Biology create models with different levels of detail. The transformation techniques in Petri net theory can provide a solid framework for zooming between these different levels of abstraction and refinement. This work presents a Petri net based approach to Metabolic Engineering that implements model reduction methods to reduce the complexity of large-scale metabolic networks. These methods can be complemented with kinetics inference to build dynamic models with a smaller number of parameters. The central carbon metabolism model of E. coli is used as a test-case to illustrate the application of these concepts. Model transformation is a promising mechanism to facilitate pathway analysis and dynamic modeling at the genome-scale level.(undefined