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Results for “Synthetic Biology”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2016

An algebraic approach to parameter optimization in bimolecular bistable systems

In a synthetic biological network it may often be desirable to maximize or minimize parameters such as reaction rates, fluxes and total concentrations of reagents, while preserving a given dynamic behavior. We consider the problem of parameter optimization in biomolecular bistable circuits. We show that, under some assumptions often satisfied by bistable biological networks, it is possible to derive algebraic conditions on the parameters that determine when bistability occurs. These (global) algebraic conditions can be included as nonlinear constraints in a parameter optimization problem. We derive bistability conditions using Sturm's theorem for Gardner and Collins toggle switch. Then we optimize its nominal parameters to improve switching speed and robustness to a subset of uncertain parameters.

Systems Biology↗

bioRxiv2016

Robust ergodicity and tracking in antithetic integral control of stochastic biochemical reaction networks

Controlling stochastic reactions networks is a challenging problem with important implications in various fields such as systems and synthetic biology. Various regulation motifs have been discovered or posited over the recent years, the most recent one being the so-called Antithetic Integral Control (AIC) motif [1]. Several favorable properties for the AIC motif have been demonstrated for classes of reaction networks that satisfy certain irreducibility, ergodicity and output controllability conditions. Here we address the problem of verifying these conditions for large sets of reaction networks with fixed topology using two different approaches. The first one is quantitative and relies on the notion of interval matrices while the second one is qualitative and is based on sign properties of matrices. The obtained results lie in the same spirit as those obtained in [1] where properties of reaction networks are independently characterized in terms of control theoretic concepts, linear programming conditions and graph theoretic conditions.

Systems Biology↗

bioRxiv2016

ATP homeostasis underlies optimal glucose consumption by Saccharomyces cerevisiae

Glycolysis is central to energy metabolism in most organisms, and is highly regulated to enable optimal growth. In the yeast Saccharomyces cerevisiae, feedback mechanisms that control flux through glycolysis span transcriptional control to metabolite levels in the cell. Using a cellobiose consumption pathway, we decoupled glucose sensing from carbon utilization, revealing new modular layers of control that induce ATP consumption to drive rapid carbon fermentation. Alterations of the beta subunit of phosphofructokinase (PFK2), H+-plasma membrane ATPase (PMA1), and glucose sensors (SNF3, RGT2) revealed the importance of coupling extracellular glucose sensing to manage ATP levels in the cell. Controlling the upper bound of cellular ATP levels may be a general mechanism used to regulate energy levels in cells, via a regulatory network that can be uncoupled from ATP concentrations under perceived starvation conditions.\n\nImportanceLiving cells are fine-tuned through evolution to thrive in their native environments. Genome alterations to create organisms for specific biotechnological applications may result in unexpected and undesired phenotypes. We used a minimal synthetic biological system in the yeast Saccharomyces cerevisiae as a platform to reveal novel connections between carbon sensing, starvation conditions and energy homeostasis.

Systems Biology↗

bioRxiv2016

Control mechanisms for stochastic biochemical systems via computation of reachable sets

Controlling the behaviour of cells by rationally guiding molecular processes is an overarching aim of much of synthetic biology. Molecular processes, however, are notoriously noisy and frequently non-linear. We present an approach to studying the impact of control measures on motifs of molecular interactions, that addresses the problems faced in biological systems: stochasticity, parameter uncertainty, and non-linearity. We show that our reachability analysis formalism can describe the potential behaviour of biological (naturally evolved as well as engineered) systems, and provides a set of bounds on their dynamics at the level of population statistics: for example, we can obtain the possible ranges of means and variances of mRNA and protein expression levels, even in the presence of uncertainty about model parameters.

Systems Biology↗

bioRxiv2016

QTL-guided metabolic engineering of a complex trait

Engineering complex phenotypes for industrial and synthetic biology applications is difficult and often confounds rational design. Bioethanol production from lignocellulosic feedstocks is a complex trait that requires multiple host systems to utilize, detoxify, and metabolize a mixture of sugars and inhibitors present in plant hydrolysates. Here, we demonstrate an integrated approach to discovering and optimizing host factors that impact fitness of Saccharomyces cerevisiae during fermentation of a Miscanthus x giganteus plant hydrolysate. We first used high-resolution Quantitative Trait Loci (QTL) mapping and systematic Bulk Reciprocal Hemizygosity analysis (bRHA) to discover 17 loci that differentiate hydrolysate tolerance between an industrially related (JAY291) and a laboratory (S288C) strain. We then used this data to identify a subset of favorable allelic loci that were most amenable for strain engineering. Guided by this \"genetic blueprint\", and using a dual-guide Cas9-based method to efficiently perform multi-kilobase locus replacements, we engineered an S288C strain with superior hydrolysate tolerance than JAY291. Our methods should be generalizable to engineering any complex trait in S. cerevisiae, as well as other organisms.

Bioengineering↗

bioRxiv2016

Design Specifications for Cellular Regulation

A critical feature of all cellular processes is the ability to control the rate of gene or protein expression and metabolic flux in changing environments through regulatory feedback. We review the many ways that regulation is represented through causal, logical and dynamical components. Formalizing the nature of these components promotes effective comparison among distinct regulatory networks and provides a common framework for the potential design and control of regulatory systems in synthetic biology.

systems biology↗

bioRxiv2016

Short CT-rich motifs can trigger context-specific silencing of gene expression in bacteria

We use an oligonucleotide library of over 10000 variants together with a synthetic biology approach to identify an insulation mechanism encoded within a subset of {sigma}54 promoters. Insulation manifests itself as dramatically reduced protein expression for a downstream gene that may be expressed by transcriptional read-through. The insulation we observe is strongly associated with the presence of short CT-rich motifs (3-5 bp), positioned within 25 bp upstream of the Shine-Dalgarno (SD) motif of the silenced gene. We hypothesize that insulation is effected by binding of the RBS to the upstream CT-rich motif. We provide evidence to support this hypothesis using mutations to the CT-rich motif and gene expression measurements on multiple sequence variants. Modelling is also consistent with this hypothesis. We show that the strength of the silencing, effected by insulation, depends on the location and number of CT-rich motifs encoded within the promoters. Finally, we show that in E.coli these insulator sequences are preferentially encoded within {sigma}54 promoters as compared to other promoter types, suggesting a regulatory role for these sequences in natural contexts. Our findings suggest that context-related regulatory effects may often be due to sequence-specific interactions encoded sparsely by short motifs that are not easily detected by lower throughput studies. Such short sequence-specific phenomena can be uncovered with a focused OL design that filters out the sequence noise, as exemplified herein.

systems biology↗

bioRxiv2016

Reprogramming protein kinase substrate specificity through synthetic mutations

Protein kinase specificity is largely imparted through substrate binding pocket motifs. Missense mutations in these regions are frequently associated with human disease, and in some cases can alter substrate specificity. However, current efforts at decoding the influence of mutations on substrate specificity have been focused on disease-associated mutations. Here, we adapted the Proteomic Peptide Library (ProPeL) approach for determining kinase specificity to the task of exploring structure-function relationships in kinase specificity by interrogating the effects of synthetic mutation. We established a specificity model for the wild-type DYRK1A kinase with unprecedented resolution. Using existing crystallographic and sequence homology data, we rationally designed mutations that precisely reprogrammed the DYRK1A kinase at the P+1 position to mimic the substrate preferences of a related kinase, CK II. This study illustrates a new synthetic biological approach to reprogram kinase specificity by design, and a powerful new paradigm to investigate structure-function relationships underpinning kinase substrate specificity.

biochemistry↗

bioRxiv2017

Co-adaption of tRNA Gene Copy Number and Amino Acid Usage Influences Translation Rates in Three Life Domains

The cellular translation process should obey the principle of maximizing efficiency and minimizing resource and energy costs. Here, we validated this principle by focusing on the basic translation components of tRNAs and amino acids. To most efficiently utilize these components, we reasoned that the quantities of the 20 tRNAs and their corresponding amino acids would be consistent in an organism. The two values should match at both the organismal and protein scales. For the former, they co-vary to meet the need to translate more proteins in fast-growing or larger cells. For the latter, they are consistent to different extents for various proteins in an organism to comply with different needs of translation speed. In this work, 310 out of 410 genomes in three domains had significant co-adaptions between the tRNA gene copy number and amino acid composition, and thus validating the principle at the organism scale. Furthermore, fast-growing bacteria co-adapt better than slow-growing ones. Highly expressed proteins and those connected to acute responses have better co-adaption, illustrating the principle at the individual protein scale. Experimentally, manipulating the tRNA gene copy number to optimize co-adaption between enhanced green fluorescent protein (EGFP) and tRNA gene set of Escherichia coli indeed lifted the translation rate (speed). Our results also contribute to revealing a translation rate-associated factor with universal and global effects. From a practical perspective, our findings suggest a strategy to increase the expression of target proteins and have implications for designing chassis cells in the field of synthetic biology field.

genomics↗

bioRxiv2017

Genome-wide analysis of cell-gene interactions

The study presents an analysis of how different cellular functions link cell size to the expression of synthetic genes in E. coli. The Size-Expression interaction was mapped with a two-gene genetic probe across 3800 single-gene deletion strains. Through regression analysis, expression-specific effects and gene-specific effects were derived from size effects and generic expression effects, respectively. The entire compendium of cell functions broadly mapped to four systems of distinct primary influence on the Size-Expression map. Specifically, membrane structural components primarily affected size, whereas protein and RNA stability primarily affected gene expression. In addition, major Size-Expression shifts showed no substantial gene-specific effects unless they were mediated by key components of the protein synthesis apparatus.\n\nSubject Category: Synthetic Biology

systems biology↗

bioRxiv2017

Maintaining maximal metabolic flux by gene expression control

Many evolutionarily successful bacteria attain high growth rates across growth-permissive conditions. They express metabolic networks that synthesise all cellular components at a high rate. Metabolic reaction rates are bounded by the concentration of the catalysing enzymes and cells have finite resources available for enzyme synthesis. Therefore, bacteria that grow fast should express needed metabolic enzymes at precisely tuned concentrations. To maintain fast growth in a dynamic environment, cells should adjust gene expression of metabolic enzymes. The activity of many of the associated transcription factors is regulated by their binding to intracellular metabolites. We study optimal metabolite-mediated regulation of metabolic-gene expression that preserves maximisation of metabolic fluxes across varying conditions. We logically derive the underlying control logic of this type of optimal regulation, which we term Specific Flux (q) Optimization by Robust Adaptive Control (qORAC), and illustrate it with several examples. We show that optimal metabolic flux can be maintained in the face of K changing parameters only if the number of transcription-factor-binding metabolites is at least equal to K. qORAC-regulation of metabolism can generally be achieved with basic biochemical interactions, indicating that metabolism can operate close to optimality. The theory that we present is directly applicable to synthetic biology, biotechnology and fundamental studies of the regulation of metabolism.

systems biology↗

bioRxiv2017

Synthetic analysis of natural variants yields insights into the evolution and function of auxin signaling F-box proteins in Arabidopsis thaliana

The evolution of complex body plans in land plants has been paralleled by gene duplication and divergence within nuclear auxin-signaling networks. A deep mechanistic understanding of auxin signaling proteins therefore may allow rational engineering of novel plant architectures. Towards that end, we analyzed natural variation in the auxin receptor F-box family of wild accessions of the reference plant Arabidopsis thaliana and used this information to populate a structure/function map. We employed a synthetic assay to identify natural hypermorphic F-box variants, and then assayed auxin-associated phenotypes in accessions expressing these variants. To more directly measure the impact of the strongest variant in our synthetic assay on auxin sensitivity, we generated transgenic plants expressing this allele. Together, our findings link evolved sequence variation to altered molecular performance and auxin sensitivity. This approach demonstrates the potential for combining synthetic biology approaches with quantitative phenotypes to harness the wealth of available sequence information and guide future engineering efforts of diverse signaling pathways.

plant biology↗

bioRxiv2017

Refactoring the Genetic Code for Increased Evolvability

The standard genetic code is robust to mutations and base-pairing errors during transcription and translation. Point mutations are most likely to be synonymous or preserve the chemical properties of the original amino acid. Saturation mutagenesis experiments suggest that in some cases the best performing mutant requires a replacement of more than a single nucleotide within a codon. These replacements are essentially inaccessible to common error-based laboratory engineering techniques that alter single nucleotide per mutation event, due to the extreme rarity of adjacent mutations. In this theoretical study, we suggest a radical reordering of the genetic code that maximizes the mutagenic potential of single nucleotide replacements. We explore several possible genetic codes that allow a greater degree of accessibility to the mutational landscape and may result in a hyper-evolvable organism serving as an ideal platform for directed evolution experiments. We then conclude by evaluating potential applications for recoded organisms within the synthetic biology field.\n\nSignificance StatementThe conservative nature of the genetic code prevents bioengineers from efficiently accessing the full mutational landscape of a gene using common error-prone methods. Here we present two computational approaches to generate alternative genetic codes with increased accessibility. These new codes allow mutational transition to a larger pool of amino acids and with a greater degree of chemical differences, using a single nucleotide replacement within the codon, thus increasing evolvability both at the single gene and at the genome levels. Given the widespread use of these techniques for strain and protein improvement along with more fundamental evolutionary biology questions, the use of recoded organisms that maximize evolvability should significantly improve the efficiency of directed evolution, library generation and fitness maximization.

bioengineering↗

bioRxiv2017

Organoid And Tissue Patterning Through Phase Separation: Use Of A Vertex Model To Relate Dynamics Of Patterning To Underlying Biophysical Parameters

AO_SCPLOWBSTRACTC_SCPLOWExactly a century ago, DArcy Thompson set an agenda for understanding tissue development in terms of underlying biophysical, mathematically-tractable mechanisms. One such mechanism, discovered by Steinberg in the 1960s, is adhesion-mediated sorting of cell mixtures into homotypic groups. Interest in this phase separation mechanism has recently surged, partly because of its use to create synthetic biological patterning mechanisms and partly because it has been found to drive events critical to the formation of organoids from stem cells, making the process relevant to biotechnology as well as to basic development. Here, we construct quantitative model of patterning by phase separation, informed by laboratory data, and use it to explore the relationship between degree of adhesive difference and speed, type and extent of resultant patterning. Our results can be used three ways; to predict the outcome of mix-ing cells with known properties, to estimate the properties required to make some designed organoid system, or to estimate underlying cellular properties from observed behaviour.

developmental biology↗

bioRxiv2017

Chaperone Network Based On Cellular Response Reveals The Non-Optimal Response To Perturbed Proteostasis

The proteostasis network (PN) comprises a plethora of proteins that are dedicated to aid in protein folding; some with over-lapping functions. Despite this, there are multiple pathophysiological states associated with depletion of chaperones. This is counter-intuitive assuming cells have the ability to re-program transcriptional outputs in accordance with its proteostasic limitations. To this effect, we have used S. cerevisiae to understand the route a cell takes as a response when challenged with different proteostasis impairments. Using 14 single deletion strains of genes of Protein Quality Control (PQC) system, we quantify their proteostasis impairment and the transcriptional response. In most cases cellular response was incapable of restoring proteostasis. The response did not activate proteostasis components or pathways that could complement the function of the missing PQC gene. Over-expression of alternate machineries, could restore part of the proteostasis defect in deletion strains. We posit that epistasis guided synthetic biology approaches may be helpful in realizing the true potential of the cellular chaperone machinery.

genetics↗

bioRxiv2017

Metabolic Constraints And Quantitative Design Principles In Gene Expression During Adaption Of Yeast To Heat Shock

Microorganisms evolved adaptive responses that enable them to survive stressful challenges in ever changing environments by adjusting metabolism through the modulation of gene expression, protein levels and activity, and flow of metabolites. More frequent challenges allow natural selection ampler opportunities to select from a larger number of phenotypes that are compatible with survival. Understanding the causal relationships between physiological and metabolic requirements that are needed for cellular stress adaptation and gene expression changes that are used by organisms to achieve those requirements may have a significant impact in our ability to interpret and/or guide evolution.\n\nHere, we study those causal relationships during heat shock adaptation in the yeast Saccharomyces cerevisiae. We do so by combining dozens of independent experiments measuring whole genome gene expression changes during stress response with a nonlinear simplified kinetic model of central metabolism.\n\nThis combination is used to create a quantitative, multidimensional, genotype-to-phenotype mapping of the metabolic and physiological requirements that enable cell survival to the feasible changes in gene expression that modulate metabolism to achieve those requirements. Our results clearly show that the feasible changes in gene expression that enable survival to heat shock are specific for this stress. In addition, they suggest that genetic programs for adaptive responses to desiccation/rehydration and to pH shifts might be selected by physiological requirements that are qualitatively similar, but quantitatively different to those for heat shock adaptation. In contrast, adaptive responses to other types of stress do not appear to be constrained by the same qualitative physiological requirements. Our model also explains at the mechanistic level how evolution might find different sets of changes in gene expression that lead to metabolic adaptations that are equivalent in meeting physiological requirements for survival. Finally, our results also suggest that physiological requirements for heat shock adaptation might be similar between unicellular ascomycetes that live in similar environments. Our analysis is likely to be scalable to other adaptive response and might inform efforts in developing biotechnological applications to manipulate cells for medical, biotechnological, or synthetic biology purposes.

systems biology↗

bioRxiv2017

Qinichelins, novel catecholate-hydroxamate siderophores synthesized via a multiplexed convergent biosynthesis pathway

The explosive increase in genome sequencing and the advances in bioinformatic tools have revolutionized the rationale for natural product discovery from actinomycetes. In particular, this has revealed that actinomycete genomes contain numerous orphan gene clusters that have the potential to specify many yet unknown bioactive specialized metabolites, representing a huge unexploited pool of chemical diversity. Here, we describe the discovery of a novel group of catecholate-hydroxamate siderophores termed qinichelins (2-5) from Streptomyces sp. MBT76. Correlation between the metabolite levels and the protein expression profiles identified the biosynthetic gene cluster (BGC; named qch) most likely responsible for qinichelin biosynthesis. The structure of the molecules was elucidated by bioinformatics, mass spectrometry and NMR. Synthesis of the qinichelins requires the interplay between four gene clusters, for its synthesis and for precursor supply. This biosynthetic complexity provides new insights into the challenges scientists face when applying synthetic biology approaches for natural product discovery.\n\nPride repository reviewer account details:\n\nURL: https://www.ebi.ac.uk/pride/archive/login\n\nProject accession: PXD006577\n\nUsername: reviewer35793@ebi.ac.uk\n\nPassword: 3H0iM1FK

microbiology↗

bioRxiv2017

Understanding the dynamics of scaffold-mediated signaling

Many signaling networks involve scaffold proteins that bind multiple kinases in kinase cascades. While scaffolds play a fundamental role in regulating signaling, few hypotheses regarding their function have been rigorously examined. Here, we used dynamical models of scaffold signaling to investigate the impact scaffolds have on network behavior. We considered two paradigms of scaffold assembly: as either the nucleation point for assembly of discrete multi-subunit proteins (the machine paradigm) or a platform upon which kinases independently associate (the ensemble paradigm). We found that several well-accepted hypotheses regarding the role of scaffolds in regulating signal response either do not hold or depend critically on the assembly paradigm employed. In addition to providing novel insights into the function of scaffold proteins, our work suggests experiments that could distinguish between assembly paradigms. Our findings should also inform attempts to target scaffold proteins for therapeutic intervention and the design of scaffolds for synthetic biology.

systems biology↗