Optimization Of Biological Systems Research Articles

DBTL bioengineering cycle for part characterization and refactoring

The Design-Build-Test-Learn (DBTL) cycle is a crucial framework in Synthetic Biology for the development and optimization of biological systems. However, the manual nature of the cycle poses limitations in terms of time and labor. This paper focuses on the application of automation techniques to the DBTL cycle, specifically in the testing and characterization of standard bioparts, which are essential components of genetic circuits. By automating the testing process, throughput, reliability, and reproducibility can be significantly improved. This paper discusses the challenges associated with manual testing methods and explores various automation strategies and technologies that can address these challenges. High-throughput screening methods, laboratory robotics, and data analysis algorithms are key elements in the automation process. As a case study, we utilize the automated DBTL cycle to refactor a biosensor, aiming to enhance its performance. Integrating automation in the DBTL cycle offers numerous advantages, including increased efficiency, standardization, and quality control of bioparts. It also enables the exploration of larger design spaces and rapid prototyping of complex genetic systems. Here, we show the advantages of using the DBTL cycle to refactor a biosensor that presents improved performance and can be readily used in more complex circuits.

Thermodynamic principle to enhance enzymatic activity using the substrate affinity

Understanding how to tune enzymatic activity is important not only for biotechnological applications, but also to elucidate the basic principles guiding the design and optimization of biological systems in nature. So far, the Michaelis-Menten equation has provided a fundamental framework of enzymatic activity. However, there is still no concrete guideline on how the parameters should be optimized towards higher activity. Here, we demonstrate that tuning the Michaelis-Menten constant ({K}_{m}) to the substrate concentration ([{{{{{rm{S}}}}}}]) enhances enzymatic activity. This guideline ({K}_{m}=[{{{{{rm{S}}}}}}]) was obtained mathematically by assuming that thermodynamically favorable reactions have higher rate constants, and that the total driving force is fixed. Due to the generality of these thermodynamic considerations, we propose {K}_{m}=[{{{{{rm{S}}}}}}] as a general concept to enhance enzymatic activity. Our bioinformatic analysis reveals that the {K}_{m} and in vivo substrate concentrations are consistent across a dataset of approximately 1000 enzymes, suggesting that even natural selection follows the principle {K}_{m}=[{{{{{rm{S}}}}}}].

Entropy Generation and Control: Criteria to Calculate Flow Optimization in Biological Systems

Living beings are composite thermodynamic systems in non-equilibrium conditions. Within this context, there are a number of thermodynamic potential differences (forces) between them and the surroundings, as well as internally. These forces lead to flows, which, ultimately, are essential to life itself, but, at the same time, are associated with entropy generation, i.e., a loss of useful work. The maintenance of homeostatic conditions, the tenet of physiology, demands the regulation of these flows by control of variables. However, due to the very nature of these systems, the regulation of flows and control of variables become entangled in closed loops. Here, we show how to combine entropy generation with respect to a process, and control of parameters (in such a process) in order to create a criterium of optimal ways to regulate changes in flows, the coefficient of flow-entropy (CJσ). We demonstrate the restricted possibility to obtain an increase in flow along with a decrease in entropy generation, and the more general situation of increases in flow along with increases in entropy generation of the process. In this scenario, the CJσ aims to identify the best way to combine the gain in flow and the associated loss of useful work. As an example, we analyze the impact of vaccination effort in the spreading of a contagious disease in a population, showing that the higher the vaccination effort the higher the control over the spreading and the lower the loss of useful work by the society.

Statistical Design of Experiments for Synthetic Biology.

The design and optimization of biological systems is an inherently complex undertaking that requires careful balancing of myriad synergistic and antagonistic variables. However, despite this complexity, much synthetic biology research is predicated on One Factor at A Time (OFAT) experimentation; the genetic and environmental variables affecting the activity of a system of interest are sequentially altered while all other variables are held constant. Beyond being time and resource intensive, OFAT experimentation crucially ignores the effect of interactions between factors. Given the ubiquity of interacting genetic and environmental factors in biology this failure to account for interaction effects in OFAT experimentation can result in the development of suboptimal systems. To address these limitations, an increasing number of studies have turned to Design of Experiments (DoE), a suite of methods that enable efficient, systematic exploration and exploitation of complex design spaces. This review provides an overview of DoE for synthetic biologists. Key concepts and commonly used experimental designs are introduced, and we discuss the advantages of DoE as compared to OFAT experimentation. We dissect the applicability of DoE in the context of synthetic biology and review studies which have successfully employed these methods, illustrating the potential of statistical experimental design to guide the design, characterization, and optimization of biological protocols, pathways, and processes.

Structural Orientation and Anisotropy in Biological Materials: Functional Designs and Mechanics

AbstractBiological materials exhibit anisotropic characteristics because of the anisometric nature of their constituents and their preferred alignment within interfacial matrices. The regulation of structural orientations is the basis for material designs in nature and may offer inspiration for man‐made materials. Here, how structural orientation and anisotropy are designed into biological materials to achieve diverse functionalities is revisited. The orientation dependencies of differing mechanical properties are introduced based on a 2D composite model with wood and bone as examples; as such, anisotropic architectures and their roles in property optimization in biological systems are elucidated. Biological structural orientations are designed to achieve extrinsic toughening via complicated cracking paths, robust and releasable adhesion from anisotropic contact, programmable dynamic response by controlled expansion, enhanced contact damage resistance from varying orientations, and simultaneous optimization of multiple properties by adaptive structural reorientation. The underlying mechanics and material‐design principles that could be reproduced in man‐made systems are highlighted. Finally, the potential and challenges in developing a better understanding to implement such natural designs of structural orientation and anisotropy are discussed in light of current advances. The translation of these biological design principles can promote the creation of new synthetic materials with unprecedented properties and functionalities.

How to ‘find’ an error minimized genetic code: neutral emergence as an alternative to direct Darwinian selection for evolutionary optimization

Error minimization (EM) of the standard genetic code (SGC) refers to the assignment of amino acids to codons in such a way that the deleterious impact of mutations is reduced. The SGC is nearly optimal for the property of EM, compared to randomly generated codes, and prompts the question of how the property arose. Brute force searching of alternative genetic codes is unlikely to have occurred, given the high number of alternative codes. Therefore, a heuristic search of ‘code space’, the space of alternative codes, would have been necessary. Uncovering the nature of this heuristic search is key to understanding the evolution of the genetic code, and consequently the origin of life. Scenarios that rely on direct selection for the property of EM require codon reassignments to sample code space, but these are problematic mechanistically. Alternatively, it has been shown that EM may have emerged in a neutral fashion as a byproduct of the process of genetic code expansion. In this scenario, similar amino acids are added to similar codons via the gene duplication of tRNAs and aminoacyl-tRNA synthetases. Mimicking this process via simulation indeed produces high levels of EM in the resulting genetic codes. These observations imply that optimization has occurred by an alternative to direct selection, commonly viewed as the only form of evolutionary optimization followed in nature. I propose that the neutral emergence of EM produced by code expansion is a genetic algorithm but unlike direct selection, the local selection criterion (amino acid and codon similarity) is distant from the global fitness function (EM), leading to the emergent optimization of EM. By presenting this counter example I clarify how evolutionary optimization in biological systems is not restricted to direct selection, and emphasize that additional processes may lead to the production of beneficial traits, via ‘non-Darwinian optimization’.

Introducing the concept of biocatalysis in the classroom: The conversion of cholesterol to provitamin D3.

Biocatalysis is a fundamental concept in biotechnology. The topic integrates knowledge of several disciplines; therefore, it was included in the course "design and optimization of biological systems" which is offered in the biochemistry curricula. We selected the ciliate tetrahymena as an example of a eukaryotic system with potential for the biotransformation of sterol metabolites of industrial interest; in particular, we focused on the conversion of cholesterol to provitamin D3. The students work with wild type and recombinant strains and learn how sterol pathways could be modified to obtain diverse sterol moieties. During the course the students identify and measure the concentration of sterols. They also search for related genes by bioinformatic analysis. Additionally, the students compare biotransformation rates, growing the ciliate in plate and in a bioreactor. Finally, they use fluorescence microscopy to localize an enzyme involved in biotransformation. The last day each team makes an oral presentation, explaining the results obtained and responds to a series of key questions posed by the teachers, which determine the final mark. In our experience, this course enables undergraduate students to become acquainted with the principles of biocatalysis as well as with standard and modern techniques, through a simple and robust laboratory exercise, using a biological system for the conversion of valuable pharmaceutical moieties. © 2016 by The International Union of Biochemistry and Molecular Biology, 45(2):105-114, 2017.

Superhydrophobicity of the gecko toe pad: biological optimization versus laboratory maximization.

While many gecko-inspired hierarchically structured surfaces perform as well as or better than the natural adhesive system, these designs often fail to function across a variety of contexts. For example, the gecko can adhere to rough, wet and dirty surfaces; however, most synthetic mimics cannot maintain function when faced with a similar situation. The solution to this problem lies in a more thorough investigation of the natural system. Here, we review the adhesive system of the gecko toe pad, as well as the far less-well-studied anti-adhesive system that results from the chemistry and structure of the toe pad (superhydrophobicity). This paradoxical relationship serves as motivation to study functional optimization at the system level. As an example, we experimentally investigate the role of surface lipids in adhesion and anti-adhesion, and find a clear performance trade-off related to shear adhesion in air on a hydrophilic surface. This represents the first direct investigation of the role of surface lipids in gecko adhesion and anti-adhesion, and supports the argument that a system-level approach is necessary to elucidate optimization in biological systems. Without such an approach, bioinspired designs will be limited in functionality and context, especially compared to the natural systems they mimic.This article is part of the themed issue 'Bioinspired hierarchically structured surfaces for green science'.

Teaching the principle of biological optimization

Among the important principles in biology that should be taught in biological engineering educational programs is the principle of optimization, what it means, why it is important, and how it comes about. This material can be presented at numerous levels throughout the curriculum. Understanding of this principle can lead biological engineers to expect it in many, if not all, biological system applications. Understanding optimization in biological systems can help understand the predictive power of evolutionary principles and what to expect from living things incorporated in designs.

Applications of genetically-encoded biosensors for the construction and control of biosynthetic pathways

Cells are filled with biosensors, molecular systems that measure the state of the cell and respond by regulating host processes. In much the same way that an engineer would monitor a chemical reactor, the cell uses these sensors to monitor changing intracellular environments and produce consistent behavior despite the variable environment. While natural systems derive a clear benefit from pathway regulation, past research efforts in engineering cellular metabolism have focused on introducing new pathways and removing existing pathway regulation. Synthetic biology is a rapidly growing field that focuses on the development of new tools that support the design, construction, and optimization of biological systems. Recent advances have been made in the design of genetically-encoded biosensors and the application of this class of molecular tools for optimizing and regulating heterologous pathways. Biosensors to cellular metabolites can be taken directly from natural systems, engineered from natural sensors, or constructed entirely in vitro. When linked to reporters, such as antibiotic resistance markers, these metabolite sensors can be used to report on pathway productivity, allowing high-throughput screening for pathway optimization. Future directions will focus on the application of biosensors to introduce feedback control into metabolic pathways, providing dynamic control strategies to increase the efficient use of cellular resources and pathway reliability.

MORE SCIENCE, ONLINE ONLY

THE AMERICAN Chemical Society plans to introduce two new online-only, peer-reviewed journals that will publish research related to biological systems and synthetic biology, and to polymer science. The journals will publish their first full issues in January 2012. ACS Synthetic Biology will cover approaches to understanding how cells, tissues, and organisms are organized and function in natural and artificial systems, and the application of synthetic biology in engineering these systems. The journal is particularly interested in studies on the design, programming, and optimization of biological systems; computational methods to aid the design of synthetic systems; and integrative applied approaches to understanding disease and metabolism. “The applications of synthetic biology impact all areas of biotechnology, including agriculture, chemical and fuel synthesis, materials, and pharmaceuticals,” notes Christopher A. Voigt, editor-in-chief of ACS Synthetic Biology . Voigt recently joined the department of bi...

Methodological aspects of scaling in biology

Interest in the scaling approach to problems of biological design has increased dramatically in the past few years. But thus far no systematic attempt has been made to review the possible pitfalls attendant upon this approach. As a beginning, the problems which can arise from rounding exponents, or taking standard errors at face value, or expressing dependent variables in ratio form are discussed. There follows a discussion of fitting specific functions to scaling data, of the special needs for documentation and of the potential value to be derived from suitable computer programs in scaling studies. Finally, the possible difficulties of demonstrating global optimization in biological systems, the risks of dimensional analysis and the value and nature of scaling models are discussed.

Biotechnology and aroma production

AbstractThe acceptability of foods chiefly depends on their flavour. Natural flavour is formed biochemically by processes of ripening, refining or fermentation or by the effect of microorganisms. In many cases, however, only aroma precursors are formed in the course of biological‐biochemical processes whereas the total aroma compositions are produced by chemical reactions (e. g. by heat effect) in a second step.Thus the application of biological systems for the formation of flavour compositions can be taken into consideration. By means of literature as well as of own experimental work a review about such possibilities is given. The following biotechnologically respectively food technologically interesting fields are dealt with: protein hydrolysates and plasteins; cleavage of lipids; aroma producing enzymes in fruits and vegetables; flavour enhancers; milk products; ripening of raw sausage; fruit aroma; mushroom flavour; alcoholic beverages; bread flavour; foods produced by fermentation; plant cell and tissue cultures. Problems arising in the enhancement of microbial activity and optimization of biological systems as to flavour production are discussed.

Learning and optimization in biological systems

A model of sequential actions implying learning is proposed, starting from the concept of acybernetical action introduced in a previous paper (Teodorescu, 1977). Sequential actions of this kind (e.g. defence actions occurring repeatedly in a biological population, some motor subroutines such as walking, swimming etc.) are always goal-oriented ones. Furthermore, in performing them, some constraints must be observed. Finally, each action must be optimal with respect to a given criterion, otherwise it becomes highly expensive (or, perhaps, inefficient) for the living organism(s). To take into account these rather natural features of any cybernetical action, a specific tool is used, namely, themessage operator defined in (Teodorescu, 1976b). The procedure of deriving an action model with the aid of message operators is illustrated by means of an experimental paradigm concerning the visually induced behaviour in insects (Reichardt and Poggio, 1976). Next, by taking into account certain recovery processes, the concept of alearning sequence (i.e. model of sequential action involving learning) is defined, and some examples are given to illustrate learning in biological systems. The related concepts offull subsequence, node andmemory cycle are also defined, and some theorems concerning memory cycles are stated and proved. Furthermore,a measure of the learning effort is suggested. It is used to show that, under certain conditions, learning occurs even in the case of many different active messages. Finally, by using the proposed models, the relationship between learning and optimization is investigated, proving thatoptimization is deeply implicated in any learning process. More exctly, as far as sequential actions with recovery are concerned, optimization is a necessary (but not sufficient) condition for learning. Thus, the proposed model of action may be regarded as a useful tool, which enables to investigate some unknown aspects related to learning in living organisms, in a general, computeroriented way.