Design Of Biological Systems Research Articles

The transcriptional landscape of a rewritten bacterial genome reveals control elements and genome design principles

Sequence rewriting enables low-cost genome synthesis and the design of biological systems with orthogonal genetic codes. The error-free, robust rewriting of nucleotide sequences can be achieved with a complete annotation of gene regulatory elements. Here, we compare transcription in Caulobacter crescentus to transcription from plasmid-borne segments of the synthesized genome of C. ethensis 2.0. This rewritten derivative contains an extensive amount of supposedly neutral mutations, including 123’562 synonymous codon changes. The transcriptional landscape refines 60 promoter annotations, exposes 18 termination elements and links extensive transcription throughout the synthesized genome to the unintentional introduction of sigma factor binding motifs. We reveal translational regulation for 20 CDS and uncover an essential translational regulatory element for the expression of ribosomal protein RplS. The annotation of gene regulatory elements allowed us to formulate design principles that improve design schemes for synthesized DNA, en route to a bright future of iteration-free programming of biological systems.

A synthetic biology approach for the design of genetic algorithms with bacterial agents

ABSTRACT Bacteria have been a source of inspiration for the design of evolutionary algorithms. At the beginning of the twentieth century synthetic biology was born, a discipline whose goal is the design of biological systems that do not exist in nature, for example, programmable synthetic bacteria. In this paper, we introduce as a novelty the designing of evolutionary algorithms where all the steps are conducted by synthetic bacteria. To this end, we designed a genetic algorithm, which we have named BAGA, illustrating its utility solving simple instances of optimisation problems such as function optimisation, 0/1 knapsack problem, Hamiltonian path problem. The results obtained open the possibility of conceiving evolutionary algorithms inspired by principles, mechanisms and genetic circuits from synthetic biology. In summary, we can conclude that synthetic biology is a source of inspiration either for the design of evolutionary algorithms or for some of their steps, as shown by the results obtained in our simulation experiments.

Using fish lateral line sensing to improve seismic acquisition and processing.

Bioengineering, which studies the principles and design of biological systems, is a field that has inspired the development of several technologies that are currently in use. In this work, we use concepts from the fish lateral line sensing mechanism and apply them to seismic imaging processing. The lateral line is a sensory system composed of an integrated array of mechanical sensors spanning along the fish body. We compare the array of sensors along body fish with the seismic acquisition, which employs an array of equally spaced identical mechanical sensors to image the Earth’s subsurface. In both situations, the mechanical sensors capture and process mechanical vibrations from the environment to produce useful information. We explore the strategy of using the low-pass and high-pass sensors schema of fish lateral line to improve the seismic technique. We use the full-wave inversion method to compare the conventional acquisition procedure of identical sensors with alternative sets of different sensors, which mimics the fish lateral line. Our results show that the alternate sensors arrangement surpasses the performance of the conventional acquisition method, using just half of the input information. The results point at an image processing technique that is computationally more efficient and economical than the usual seismic processing method.

Centre for Genomic Regulation - a hub for Integrative Biology in Barcelona.

Centre for Genomic Regulation - a hub for Integrative Biology in Barcelona.

Engineering modular and tunable genetic amplifiers for scaling transcriptional signals in cascaded gene networks.

Synthetic biology aims to control and reprogram signal processing pathways within living cells so as to realize repurposed, beneficial applications. Here we report the design and construction of a set of modular and gain-tunable genetic amplifiers in Escherichia coli capable of amplifying a transcriptional signal with wide tunable-gain control in cascaded gene networks. The devices are engineered using orthogonal genetic components (hrpRS, hrpV and PhrpL) from the hrp (hypersensitive response and pathogenicity) gene regulatory network in Pseudomonas syringae. The amplifiers can linearly scale up to 21-fold the transcriptional input with a large output dynamic range, yet not introducing significant time delay or significant noise during signal amplification. The set of genetic amplifiers achieves different gains and input dynamic ranges by varying the expression levels of the underlying ligand-free activator proteins in the device. As their electronic counterparts, these engineered transcriptional amplifiers can act as fundamental building blocks in the design of biological systems by predictably and dynamically modulating transcriptional signal flows to implement advanced intra- and extra-cellular control functions.

A control theoretic framework for modular analysis and design of biomolecular networks

Control theory has been instrumental for the analysis and design of a number of engineering systems, including aerospace and transportation systems, robotics and intelligent machines, manufacturing chains, electrical, power, and information networks. In the past several years, the ability of de novo creating biomolecular networks and of measuring key physical quantities has come to a point in which quantitative analysis and design of biological systems is possible. While a modular approach to analyze and design complex systems has proven critical in most control theory applications, it is still subject of debate whether a modular approach is viable in biomolecular networks. In fact, biomolecular networks display context-dependent behavior, that is, the input/output dynamical properties of a module change once this is part of a network. One cause of context dependence, similar to what found in many engineering systems, is retroactivity, that is, the effect of loads applied on a module by downstream systems. In this paper, we focus on retroactivity and review techniques, based on nonlinear control and dynamical systems theory, that we have developed to quantify the extent of modularity of biomolecular systems and to establish modular analysis and design techniques.

Bayesian design strategies for synthetic biology

We discuss how statistical inference techniques can be applied in the context of designing novel biological systems. Bayesian techniques have found widespread application and acceptance in the systems biology community, where they are used for both parameter estimation and model selection. Here we show that the same approaches can also be used in order to engineer synthetic biological systems by inferring the structure and parameters that are most likely to give rise to the dynamics that we require a system to exhibit. Problems that are shared between applications in systems and synthetic biology include the vast potential spaces that need to be searched for suitable models and model parameters; the complex forms of likelihood functions; and the interplay between noise at the molecular level and nonlinearity in the dynamics owing to often complex feedback structures. In order to meet these challenges, we have to develop suitable inferential tools and here, in particular, we illustrate the use of approximate Bayesian computation and unscented Kalman filtering-based approaches. These partly complementary methods allow us to tackle a number of recurring problems in the design of biological systems. After a brief exposition of these two methodologies, we focus on their application to oscillatory systems.

A framework and model system to investigate linear system behavior in Escherichia coli

BackgroundThe ability to compose biological systems from smaller elements that act independently of the other upon assembly may help make the forward engineering of biological systems practical. Engineering biology in this manner is made difficult by the inherent nonlinear response of organisms to genetic devices. Devices are inevitably coupled to one another in the cell because they share the same transcriptional machinery for expression. Thus, new properties can emerge when devices that had been characterized in isolation are expressed concurrently. We show in this report that, similar to physical systems, the Escherichia coli (E. coli) transcriptional system can exhibit linear behavior under "small" perturbation conditions. This, in turn, allows devices to be treated as independent modules.ResultsWe developed a framework and model system consisting of three devices to investigate linear system behavior in E. coli. Our framework employed the transfer curve concept to determine the amount of nonlinearity elicited by the E. coli transcriptional system in response to the devices. To this effect, the model system was quantitatively characterized using real-time quantitative PCR to produce device transfer curves (DTCs). Two of the devices encoded the bacterial neomycin phosphotransferase II (nptII) and chloramphenicol acetyl transferase (cat), while the third encoded the jellyfish-originating green fluorescent protein (gfp). The gfp device was the most nonlinear in our system, with nptII and cat devices eliciting linear responses. Superposition experiments verified these findings, with independence among the three devices having been lost when gfp was present at copy numbers above the lowest one used.ConclusionsWe show that linear system behavior is possible in E. coli. Elucidation of the mechanism underlying the nonlinearity observed in gfp may lead to design rules that ensure linear system behavior, enabling the accurate prediction of the quantitative behavior of a system assembled from individually characterized devices. Our work suggests that biological systems follow principles similar to physical ones, and that concepts borrowed from the latter (such as DTCs) may be of use in the characterization and design of biological systems.

Systems engineering without an engineer: Why we need systems biology

AbstractIn this review, we address the justification for creating a new scientific discipline, and suggest possible goals for systems biology. We argue that systems biology is distinct from genomics and bioinformatics, as well as older disciplines such as medicine, evolutionary biology, and traditional molecular biology. Systems biology has the potential to address questions about the quantitative behavior and design of biological systems, which are not the central focus of other areas of biology. We discuss spontaneously arising biological oscillators as an example of a phenomenon that requires a systems‐biological explanation. We also argue that there is, from evolutionary biology, an emerging view of organisms as “poorly oiled machines,” and that systems biology is needed to understand and predict the behavior of organisms that have not been rationally constructed. © 2007 Wiley Periodicals, Inc. Complexity, 2007

Systems Biology and Design of Biological Systems

Advances in the postgenomic technology produce huge data regarding molecular interactions. Systems biology requires a model-based analysis to understand the molecular architecture of biological systems. Systems biology consists of four stages: network identification, system analysis, system control, and system design. First, a large-scale biochemical network model is reconstructed in computer by elucidating gene functions or by inferring gene regulation networks from postgenomic data. Second, we analyze the network architectures that generate robust properties to various types of perturbations and explore some design principles underlying molecular architectures. Third, dynamic behaviors are controlled at the molecular level. Finally, we develop new technologies to rationally design a biochemical system at the molecular levels.

The irresistible biology of resistin.

Fuel sources have historically triggered many battles throughout the globe and will likely continue to do so. The design of biological systems is no different. Adipose tissue holds the richest energy sources and exerts substantial influence on the organism. While this fundamental truth has eluded scientists for a long time, it is now clear that adipocytes have established effective communication networks and gained control over important trade routes to maintain their influential status. To this end, these innocent-looking cells appear to utilize two main tools: fatty acids and polypeptide hormones. It is likely that they have additional, yet unknown, signaling mechanisms. It has been known for some time that obesity is associated with insulin resistance and diabetes. Yet how and why these problems are so closely linked at the molecular level has not been clear. As in most such cases, lack of knowledge led to many conspiracy theories — my favorite one being that agents produced by fat cells affect metabolic control at other sites. An example of such an agent is the inflammatory cytokine TNF-α, which is produced in excess in obesity and causally linked to insulin resistance (1). Many other such agents have now been added to this list, although unlike TNF-α, some do not mediate adverse effects on metabolism. In fact, some, such as leptin and adiponectin, work to improve the metabolic status of their fellow organs (2).