scholarly journals Computational Design and Analysis of Modular Cells for Large Libraries of Exchangeable Product Synthesis Modules

2021 ◽  
Author(s):  
Sergio Garcia ◽  
Cong T Trinh
Keyword(s):  
Renewable Resources ◽  
Plant Biomass ◽  
New Product ◽  
Computational Design ◽  
Computational Method ◽  
E Coli ◽  
Genetic Manipulations ◽  
Endogenous Production ◽  
Target Molecules ◽  
Rapid Generation

Microbial metabolism can be harnessed to produce a large library of useful chemicals from renewable resources such as plant biomass. However, it is laborious and expensive to create microbial biocatalysts to produce each new product. To tackle this challenge, we have recently developed modular cell (ModCell) design principles that enable rapid generation of production strains by assembling a modular (chassis) cell with exchangeable production modules to achieve overproduction of target molecules. Previous computational ModCell design methods are limited to analyze small libraries of around 20 products. In this study, we developed a new computational method,named ModCell-HPC, capable of designing modular cells for large libraries with hundredths of products with a highly-parallel and multi-objective evolutionary algorithm. We demonstrated ModCell-HPC to design Escherichia coli modular cells towards a library of 161 endogenous production modules. From these simulations, we identified E. coli modular cells with few genetic manipulations that can produce dozens of molecules in a growth-coupled manner under different carbons sources. These designs revealed key genetic manipulations at the chassis and module levels to accomplish versatile modular cells. Furthermore, we used ModCell-HPC to identify design features that allow an existing modular cell to be re-purposed towards production of new molecules. Overall, ModCell-HPC is a useful tool towards more efficient and generalizable design of modular cells to help reduce research and development cost in biocatalysis.

Properties and Biotechnological Application of mutant Derivatives of Mini-intein PRP8 from Penicillium chrysogenum with Improved Control of C-terminal Processing

2019 ◽  
Vol 35 (6) ◽  
pp. 91-101
Author(s):  
F.A. Klebanov ◽  
S.E. Cheperegin ◽  
D.G. Kozlov
Keyword(s):  
Penicillium Chrysogenum ◽  
Protein Denaturation ◽  
Biologically Active ◽  
Cultivation Temperature ◽  
Target Proteins ◽  
E Coli ◽  
Complete Inactivation ◽  
Target Molecules ◽  
Proteins And Peptides

Mutant variants of mini-intein PRP8 from Penicillium chrysogenum (Int4b) with improved control of C-terminal processing were characterized. The presented variants can serve as a basis for self-removed polypeptide tags capable of carrying an affine label and allowing to optimize the process of obtaining target proteins and peptides in E. coli cells. They allow to synthesize target molecules in the composition of soluble and insoluble hybrid proteins (fusions), provide their afnne purification, autocatalytic processing and obtaining mature target products. The presented variants have a number of features in comparison with the known prototypes. In particular the mutant mini-intein Int4bPRO, containing the L93P mutation, has temperature-dependent properties. At cultivation temperature below 30 °C it allows the production of target molecules as part of soluble fusions, but after increasing of cultivation temperature to 37 °C it directs the most of synthesized fusions into insoluble intracellular aggregates. The transition of Int4bPRO into insoluble form is accompanied by complete inactivation of C-terminal processing. Further application of standard protein denaturation-renaturation procedures enable efficiently reactivate Int4bPRO and to carry out processing of its fusions in vitro. Two other variants, Int4b56 and Int4b36, containing a point mutation T62N or combination of mutations D144N and L146T respectively, have a reduced rate of C-terminal processing. Their use in E. coli cells allows to optimize the biosynthesis of biologically active target proteins and peptides in the composition of soluble fusions, suitable for afnne purification and subsequent intein-dependent processing without the use of protein denaturation-renaturation procedures. intein, fusion, processing, processing rate, gelonin The work was supported within the framework of the State Assignment no. 595-00003-19 PR.

scholarly journals Computational Design of Modular Robots Based on Genetic Algorithm and Reinforcement Learning

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 471
Author(s):  
Jai Hoon Park ◽  
Kang Hoon Lee
Keyword(s):  
Genetic Algorithm ◽  
Reinforcement Learning ◽  
Design Space ◽  
Learning Algorithm ◽  
Computational Design ◽  
Computational Method ◽  
Learning Ability ◽  
Modular Robots ◽  
Control Mechanisms ◽  
Candidate Structure

Designing novel robots that can cope with a specific task is a challenging problem because of the enormous design space that involves both morphological structures and control mechanisms. To this end, we present a computational method for automating the design of modular robots. Our method employs a genetic algorithm to evolve robotic structures as an outer optimization, and it applies a reinforcement learning algorithm to each candidate structure to train its behavior and evaluate its potential learning ability as an inner optimization. The size of the design space is reduced significantly by evolving only the robotic structure and by performing behavioral optimization using a separate training algorithm compared to that when both the structure and behavior are evolved simultaneously. Mutual dependence between evolution and learning is achieved by regarding the mean cumulative rewards of a candidate structure in the reinforcement learning as its fitness in the genetic algorithm. Therefore, our method searches for prospective robotic structures that can potentially lead to near-optimal behaviors if trained sufficiently. We demonstrate the usefulness of our method through several effective design results that were automatically generated in the process of experimenting with actual modular robotics kit.

scholarly journals Endogenous Production Networks

Econometrica ◽  
2020 ◽  
Vol 88 (1) ◽  
pp. 33-82 ◽  
Author(s):  
Daron Acemoglu ◽  
Pablo D. Azar
Keyword(s):  
Existence And Uniqueness ◽  
New Product ◽  
Production Network ◽  
Production Networks ◽  
Endogenous Production ◽  
Cost Reductions ◽  
Production Technologies ◽  
Dynamic Version ◽  
Input Prices ◽  
Endogenous Technology

We develop a tractable model of endogenous production networks. Each one of a number of products can be produced by combining labor and an endogenous subset of the other products as inputs. Different combinations of inputs generate (prespecified) levels of productivity and various distortions may affect costs and prices. We establish the existence and uniqueness of an equilibrium and provide comparative static results on how prices and endogenous technology/input choices (and thus the production network) respond to changes in parameters. These results show that improvements in technology (or reductions in distortions) spread throughout the economy via input–output linkages and reduce all prices, and under reasonable restrictions on the menu of production technologies, also lead to a denser production network. Using a dynamic version of the model, we establish that the endogenous evolution of the production network could be a powerful force towards sustained economic growth. At the root of this result is the fact that the arrival of a few new products expands the set of technological possibilities of all existing industries by a large amount—that is, if there are n products, the arrival of one more new product increases the combinations of inputs that each existing product can use from 2 n−1 to 2 n , thus enabling significantly more pronounced cost reductions from choice of input combinations. These cost reductions then spread to other industries via lower input prices and incentivize them to also adopt additional inputs.

scholarly journals Development of High Affinity and High Specificity Inhibitors of Matrix Metalloproteinase 14 through Computational Design and Directed Evolution

2017 ◽  
Vol 292 (8) ◽  
pp. 3481-3495 ◽  
Author(s):  
Valeria Arkadash ◽  
Gal Yosef ◽  
Jason Shirian ◽  
Itay Cohen ◽  
Yuval Horev ◽  
...  
Keyword(s):  
Directed Evolution ◽  
Surface Display ◽  
High Specificity ◽  
Computational Design ◽  
Computational Method ◽  
Yeast Surface Display ◽  
Mmp Inhibitor ◽  
Starting Point ◽  
Yeast Surface

Degradation of the extracellular matrices in the human body is controlled by matrix metalloproteinases (MMPs), a family of more than 20 homologous enzymes. Imbalance in MMP activity can result in many diseases, such as arthritis, cardiovascular diseases, neurological disorders, fibrosis, and cancers. Thus, MMPs present attractive targets for drug design and have been a focus for inhibitor design for as long as 3 decades. Yet, to date, all MMP inhibitors have failed in clinical trials because of their broad activity against numerous MMP family members and the serious side effects of the proposed treatment. In this study, we integrated a computational method and a yeast surface display technique to obtain highly specific inhibitors of MMP-14 by modifying the natural non-specific broad MMP inhibitor protein N-TIMP2 to interact optimally with MMP-14. We identified an N-TIMP2 mutant, with five mutations in its interface, that has an MMP-14 inhibition constant (Ki) of 0.9 pm, the strongest MMP-14 inhibitor reported so far. Compared with wild-type N-TIMP2, this variant displays ∼900-fold improved affinity toward MMP-14 and up to 16,000-fold greater specificity toward MMP-14 relative to other MMPs. In an in vitro and cell-based model of MMP-dependent breast cancer cellular invasiveness, this N-TIMP2 mutant acted as a functional inhibitor. Thus, our study demonstrates the enormous potential of a combined computational/directed evolution approach to protein engineering. Furthermore, it offers fundamental clues into the molecular basis of MMP regulation by N-TIMP2 and identifies a promising MMP-14 inhibitor as a starting point for the development of protein-based anticancer therapeutics.

scholarly journals Reciprocal Regulation of l-Arabinose and d-Xylose Metabolism in Escherichia coli

2015 ◽  
Vol 198 (3) ◽  
pp. 386-393 ◽  
Author(s):  
Santosh Koirala ◽  
Xiaoyi Wang ◽  
Christopher V. Rao
Keyword(s):  
Escherichia Coli ◽  
Plant Biomass ◽  
Single Cells ◽  
Xylose Utilization ◽  
Xylose Metabolism ◽  
Reciprocal Regulation ◽  
Content Type ◽  
E Coli ◽  
Metabolic Genes ◽  
Mixed Populations

ABSTRACTGlucose is known to inhibit the transport and metabolism of many sugars inEscherichia coli. This mechanism leads to its preferential consumption. Far less is known about the preferential utilization of nonglucose sugars inE. coli. Two exceptions arel-arabinose andd-xylose. Previous studies have shown thatl-arabinose inhibitsd-xylose metabolism inEscherichia coli. This repression results froml-arabinose-bound AraC binding to the promoter of thed-xylose metabolic genes and inhibiting their expression. This mechanism, however, has not been explored in single cells. Both thel-arabinose andd-xylose utilization systems are known to exhibit a bimodal induction response to their cognate sugar, where mixed populations of cells either expressing the metabolic genes or not are observed at intermediate sugar concentrations. This suggests thatl-arabinose can only inhibitd-xylose metabolism inl-arabinose-induced cells. To understand how cross talk between these systems affects their response, we investigatedE. coliduring growth on mixtures ofl-arabinose andd-xylose at single-cell resolution. Our results showed that mixed, multimodal populations ofl-arabinose- andd-xylose-induced cells occurred at intermediate sugar concentrations. We also found thatd-xylose inhibited the expression of thel-arabinose metabolic genes and that this repression was due to XylR. These results demonstrate that a strict hierarchy does not exist betweenl-arabinose andd-xylose as previously thought. The results may also aid in the design ofE. colistrains capable of simultaneous sugar consumption.IMPORTANCEGlucose,d-xylose, andl-arabinose are the most abundant sugars in plant biomass. Developing efficient fermentation processes that convert these sugars into chemicals and fuels will require strains capable of coutilizing these sugars. Glucose has long been known to repress the expression of thel-arabinose andd-xylose metabolic genes inEscherichia coli. Recent studies found thatl-arabinose also represses the expression of thed-xylose metabolic genes. In the present study, we found thatd-xylose also represses the expression of thel-arabinose metabolic genes, leading to mixed populations of cells capable of utilizingl-arabinose andd-xylose. These results further our understanding of mixed-sugar utilization and may aid in strain design.

scholarly journals Computational design of treatment strategies for proactive therapy on atopic dermatitis using optimal control theory

2017 ◽  
Vol 375 (2096) ◽  
pp. 20160285 ◽  
Author(s):  
Panayiotis Christodoulides ◽  
Yoshito Hirata ◽  
Elisa Domínguez-Hüttinger ◽  
Simon G. Danby ◽  
Michael J. Cork ◽  
...  
Keyword(s):  
Optimal Control ◽  
Atopic Dermatitis ◽  
Differential Evolution Algorithm ◽  
Treatment Strategies ◽  
Disease Status ◽  
Computational Design ◽  
Optimal Treatment ◽  
Computational Method ◽  
Mathematical Methods ◽  
Proactive Therapy

Atopic dermatitis (AD) is a common chronic skin disease characterized by recurrent skin inflammation and a weak skin barrier, and is known to be a precursor to other allergic diseases such as asthma. AD affects up to 25% of children worldwide and the incidence continues to rise. There is still uncertainty about the optimal treatment strategy in terms of choice of treatment, potency, duration and frequency. This study aims to develop a computational method to design optimal treatment strategies for the clinically recommended ‘proactive therapy’ for AD. Proactive therapy aims to prevent recurrent flares once the disease has been brought under initial control. Typically, this is done by using an anti-inflammatory treatment such as a potent topical corticosteroid intensively for a few weeks to ‘get control’, followed by intermittent weekly treatment to suppress subclinical inflammation to ‘keep control’. Using a hybrid mathematical model of AD pathogenesis that we recently proposed, we computationally derived the optimal treatment strategies for individual virtual patient cohorts, by recursively solving optimal control problems using a differential evolution algorithm. Our simulation results suggest that such an approach can inform the design of optimal individualized treatment schedules that include application of topical corticosteroids and emollients, based on the disease status of patients observed on their weekly hospital visits. We demonstrate the potential and the gaps of our approach to be applied to clinical settings. This article is part of the themed issue ‘Mathematical methods in medicine: neuroscience, cardiology and pathology’.

scholarly journals Model-driven design and evolution of non-trivial synthetic syntrophic pairs

2018 ◽  
Author(s):  
Colton J. Lloyd ◽  
Zachary A. King ◽  
Troy E. Sandberg ◽  
Ying Hefner ◽  
Connor A. Olson ◽  
...  
Keyword(s):  
Time Course ◽  
Computational Method ◽  
Community Formation ◽  
Healthcare Applications ◽  
Adaptive Laboratory Evolution ◽  
Genetic Changes ◽  
E Coli ◽  
Model Driven ◽  
Metabolic Functions ◽  
Community Fitness

AbstractSynthetic microbial communities are attractive for applied biotechnology and healthcare applications through their ability to efficiently partition complex metabolic functions. By pairing auxotrophic mutants in co-culture, nascentE. colicommunities can be established where strain pairs are metabolically coupled. Intuitive synthetic communities have been demonstrated, but the full space of cross-feeding metabolites has yet to be explored. A novel algorithm, OptAux, was constructed to design 66 multi-knockoutE. coliauxotrophic strains that require significant metabolite cross-feeding when paired in co-culture. Three OptAux predicted auxotrophic strains were co-cultured with an L-histidine auxotroph and validated via adaptive laboratory evolution (ALE). Time-course sequencing revealed the genetic changes employed by each strain to achieve higher community fitness and provided insights on mechanisms for sharing and 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 a novel computational method to elucidate metabolic changes that empower community formation and thus guide the optimization of co-cultures for a desired application.

scholarly journals Expanding the space of protein geometries by computational design of de novo fold families

2020 ◽  
Author(s):  
Xingjie Pan ◽  
Michael Thompson ◽  
Yang Zhang ◽  
Lin Liu ◽  
James S. Fraser ◽  
...  
Keyword(s):  
De Novo ◽  
Design Method ◽  
Computational Design ◽  
Computational Method ◽  
Structural Elements ◽  
New Paradigm ◽  
Biophysical Characterization ◽  
Structure Space ◽  
Naturally Occurring ◽  
Designer Proteins

AbstractNaturally occurring proteins use a limited set of fold topologies, but vary the precise geometries of structural elements to create distinct shapes optimal for function. Here we present a computational design method termed LUCS that mimics nature’s ability to create families of proteins with the same overall fold but precisely tunable geometries. Through near-exhaustive sampling of loop-helix-loop elements, LUCS generates highly diverse geometries encompassing those found in nature but also surpassing known structure space. Biophysical characterization shows that 17 (38%) out of 45 tested LUCS designs were well folded, including 16 with designed non-native geometries. Four experimentally solved structures closely match the designs. LUCS greatly expands the designable structure space and provides a new paradigm for designing proteins with tunable geometries customizable for novel functions.One Sentence SummaryA computational method to systematically sample loop-helix-loop geometries expands the structure space of designer proteins.

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