Models for synthetic biology

Synthetic biological engineering is emerging from biology as a distinct discipline based on quantification. The technologies propelling synthetic biology are not new, nor is the concept of designing novel biological molecules. What is new is the emphasis on system behavior

Genomics and synthetic biology as a viable option to intensify sustainable use of biodiversity

The Amazon basin is an area of mega-biodiversity. Different models have been proposed^1-8^ for the establishment of an effective conservation policy, increasing sustainability and adding value for biodiversity. Currently, a broad spectrum of technologies from genomics to synthetic biology is available, and these permit the collection, manipulation and effective evaluation of countless organisms, metabolic pathways and molecules that exist as potential products of a large, biodiverse ecosystem. The use of Genomics and synthetic biology may constitute an important tool and be a viable option for the prospection, evaluation and manipulation of biodiversity as advocated as well as be useful for developing methods for sustainable use and the production of novel molecules

Evolving cell models for systems and synthetic biology

This paper proposes a new methodology for the automated design of cell models for systems and synthetic biology. Our modelling framework is based on P systems, a discrete, stochastic and modular formal modelling language. The automated design of biological models comprising the optimization of the model structure and its stochastic kinetic constants is performed using an evolutionary algorithm. The evolutionary algorithm evolves model structures by combining different modules taken from a predefined module library and then it fine-tunes the associated stochastic kinetic constants. We investigate four alternative objective functions for the fitness calculation within the evolutionary algorithm: (1) equally weighted sum method, (2) normalization method, (3) randomly weighted sum method, and (4) equally weighted product method. The effectiveness of the methodology is tested on four case studies of increasing complexity including negative and positive autoregulation as well as two gene networks implementing a pulse generator and a bandwidth detector. We provide a systematic analysis of the evolutionary algorithm’s results as well as of the resulting evolved cell models

Discipline building in synthetic biology

International audienceDespite the multidisciplinary dimension of the researches conducted under the umbrella synthetic biology, the founders of this new research area in the United States adopted a disciplinary profile to shape its institutional identity. In so doing they took inspiration from two already established fields with very different disciplinary patterns. The analogy with synthetic chemistry suggested by the term 'synthetic biology' is not the unique model. Information technology is clearly another source of inspiration. The purpose of the paper focused on the US context is to emphasize the diversity of views and agendas coexisting under the disciplinary label synthetic biology, as the two models analysed are only presented as two extreme postures in the community. The paper discusses the question: in which directions the two models shape this emerging field? Do they chart two divergent futures for synthetic biology

Explorative Synthetic Biology in AI: Criteria of Relevance and a Taxonomy for Synthetic Models of Living and Cognitive Processes

This article tackles the topic of the special issue “Biology in AI: New Frontiers in Hardware, Software and Wetware Modeling of Cognition” in two ways. It addresses the problem of the relevance of hardware, software, and wetware models for the scientific understanding of biological cognition, and it clarifies the contributions that synthetic biology, construed as the synthetic exploration of cognition, can offer to artificial intelligence (AI). The research work proposed in this article is based on the idea that the relevance of hardware, software, and wetware models of biological and cognitive processes—that is, the concrete contribution that these models can make to the scientific understanding of life and cognition—is still unclear, mainly because of the lack of explicit criteria to assess in what ways synthetic models can support the experimental exploration of biological and cognitive phenomena. Our article draws on elements from cybernetic and autopoietic epistemology to define a framework of reference, for the synthetic study of life and cognition, capable of generating a set of assessment criteria and a classification of forms of relevance, for synthetic models, able to overcome the sterile, traditional polarization of their evaluation between mere imitation and full reproduction of the target processes. On the basis of these tools, we tentatively map the forms of relevance characterizing wetware models of living and cognitive processes that synthetic biology can produce and outline a programmatic direction for the development of “organizationally relevant approaches” applying synthetic biology techniques to the investigative field of (embodied) AI

Design and Analysis of Genetically Constructed Logic Gates

Synthetic biology, comprising many aspects including in vivo, in vitro and in silico techniques, models and methods, programming paradigms and tools, is a rapidly growing field with promising potential in building new synthetically constructed devices and systems. Synthetic biology features unconventional biological systems that do not naturally exist in nature. In this paper, we discuss a software platform, Infobiotics Workbench, developed to perform in silico experiments for synthetic biology systems. We utilise the tool on an unconventional system, a genetic logic gate

Cell-Free Synthetic Biology Platform for Engineering Synthetic Biological Circuits and Systems

Synthetic biology brings engineering disciplines to create novel biological systems for biomedical and technological applications. The substantial growth of the synthetic biology field in the past decade is poised to transform biotechnology and medicine. To streamline design processes and facilitate debugging of complex synthetic circuits, cell-free synthetic biology approaches has reached broad research communities both in academia and industry. By recapitulating gene expression systems in vitro, cell-free expression systems offer flexibility to explore beyond the confines of living cells and allow networking of synthetic and natural systems. Here, we review the capabilities of the current cell-free platforms, focusing on nucleic acid-based molecular programs and circuit construction. We survey the recent developments including cell-free transcription– translation platforms, DNA nanostructures and circuits, and novel classes of riboregulators. The links to mathematical models and the prospects of cell-free synthetic biology platforms will also be discussed.11Yscopu