Systems Biology: Integrating Engineering with Biological Research

Simple SummaryThis review provides a roadmap to develop safe and effective OV-based future therapeutics by using several novel synthetic and system biology strategies. Integration of system and synthetic biology can improve the genetic design of OVs backbone, maintaining enough virulence-associated genes. Furthermore, specific computation pipelines can refine target discovery in several types of cancer, identifying the MHC-I and II-restricted peptide repertoire recognized by T-cells and reinforcing anticancer immune responses. Using these versatile approaches could encourage the development of a next-generation of OVs-based therapies, overcoming current challenges such as on-target, off-tumor effects, and variable clinical responses. The efficacy and safety profile of the current OVs-based regimens could be further enhanced with additional long-lasting clinical effects.Oncolytic virus (OV)-based immunotherapy is mainly dependent on establishing an efficient cell-mediated antitumor immunity. OV-mediated antitumor immunity elicits a renewed antitumor reactivity, stimulating a T-cell response against tumor-associated antigens (TAAs) and recruiting natural killer cells within the tumor microenvironment (TME). Despite the fact that OVs are unspecific cancer vaccine platforms, to further enhance antitumor immunity, it is crucial to identify the potentially immunogenic T-cell restricted TAAs, the main key orchestrators in evoking a specific and durable cytotoxic T-cell response. Today, innovative approaches derived from systems biology are exploited to improve target discovery in several types of cancer and to identify the MHC-I and II restricted peptide repertoire recognized by T-cells. Using specific computation pipelines, it is possible to select the best tumor peptide candidates that can be efficiently vectorized and delivered by numerous OV-based platforms, in order to reinforce anticancer immune responses. Beyond the identification of TAAs, system biology can also support the engineering of OVs with improved oncotropism to reduce toxicity and maintain a sufficient portion of the wild-type virus virulence. Finally, these technologies can also pave the way towards a more rational design of armed OVs where a transgene of interest can be delivered to TME to develop an intratumoral gene therapy to enhance specific immune stimuli.

Chapter 1 - Systems biology, synthetic biology, and metabolic engineering

Paving the way for synthetic biology‐based bioremediation in Europe

An important and challenging problem in systems biology is the inference of gene regulatory networks from short non-stationary time series of transcriptional profiles. A popular approach that has been widely applied to this end is based on dynamic Bayesian networks (DBNs), although traditional homogeneous DBNs fail to model the non-stationarity and time-varying nature of the gene regulatory processes. Various authors have therefore recently proposed combining DBNs with multiple changepoint processes to obtain time varying dynamic Bayesian networks (TV-DBNs). However, TV-DBNs are not without problems. Gene expression time series are typically short, which leaves the model over-flexible, leading to over-fitting or inflated inference uncertainty. In the present paper, we introduce a Bayesian regularization scheme that addresses this difficulty. Our approach is based on the rationale that changes in gene regulatory processes appear gradually during an organism's life cycle or in response to a changing environment, and we have integrated this notion in the prior distribution of the TV-DBN parameters. We have extensively tested our regularized TV-DBN model on synthetic data, in which we have simulated short non-homogeneous time series produced from a system subject to gradual change. We have then applied our method to real-world gene expression time series, measured during the life cycle of Drosophila melanogaster, under artificially generated constant light condition in Arabidopsis thaliana, and from a synthetically designed strain of Saccharomyces cerevisiae exposed to a changing environment.

In this article I address the interactions between biological knowledge and ideas about the kinds of entity that are suited to appropriation. I start by arguing that commodification and reductionism are closely linked, and that patenting suits entities that are discrete and isolable, such as those that are the focus of molecular biology. I then turn to the new field of systems biology, which recognizes that traditional reductionist approaches to biology are no longer adequate and attempts to provide a more integrative understanding of biological systems. In doing this, systems biology has to deal with emergent phenomena. But patenting does not suit the dynamic and interactive complexity that is the object of study in systems biology. If systems biology rejects reductionism where does that leave commodification? I examine attempts to commodify predictive computational models in systems biology. I then turn to systems biology's sister discipline, synthetic biology, which deals with emergence by reducing the complexity of biological systems. By factoring out messy contingencies, synthetic biology is, in theory, well suited to commodification. Drawing on both these examples I explore how ideas about appropriation, including open source, are influencing the nature and course of research in biology.

The central aim of synthetic biology is to create and exploit biological processes that do not occur naturally. In many cases this will involve one or more steps in genetic engineering to create an organism that so far has not been found in nature. In contrast, systems biology aims to describe biological processes in their entirety, be it the complete genome of an organism, community or ecosystem, or the processes occurring in such biological systems. Systems biology adopts a top-down approach; in contrast, synthetic biology requires a bottom-up approach: first a problem is identified; data from many possible sources, including systems biology, can then be searched for possible solutions. The current thematic issue explores some of the applications of synthetic biology. While systems biology strives to provide a complete description of what is available naturally, synthetic biology exploits this information to design solutions to a specific problem. The study by Quirino and Beier nicely illustrates this principle. It is just one of the contributions to this thematic issue that originated from poster or lecture presentations during the 5th FEMS Congress of Microbiology held in Leipzig in July 2013. Our scope was limited by an unexpected paucity of contributions in Leipzig exploring exciting contributions being made across Europe, for example, to the use of synthetic biology to generate replacements for naturally synthesized antibiotics. Also in strikingly low abundance were presentations on small RNAs: their global occurrence; their roles in the regulation of gene expression; and their potential for medical exploitation as antisense RNA. We are therefore pleased to include the article by Philipps et al . ‘Expression of Porphyromonas gingivalis small RNA in response to hemin availability identified using microarray and RNA-seq analysis’. Genetic engineering is undoubtedly one of the cornerstones of synthetic biology, not least for the production of recombinant …

Network architecture plays a crucial role in governing the dynamics of any biological network. Further, network structures have been shown to remain conserved across organisms for a given phenotype. Therefore, the mapping between network structures and the output functionality not only aids in understanding of biological systems but also finds application in synthetic biology and therapeutics. Based on the approaches involved, most of the efforts hitherto invested in this field can be classified into three broad categories, namely, computational efforts, rule-based methods and systems-theoretic approaches. The present review provides a qualitative and quantitative study of all three approaches in the light of three well-researched biological phenotypes, namely, oscillation, toggle switching, and adaptation. We also discuss the advantages, limitations, and future research scope for all three approaches along with their possible applications to other emergent properties of biological relevance.

Proteins are a ubiquitous and indispensable element for every living organism, from simple bacteria to mammals. Already in the simplest organisms, there exist some thousands of different protein species that take up a great variety of structures, and thus different roles, letting them precisely orchestrate the functioning of each cell. Despite this diversity of functions and shapes, all proteins are emerging from a same root: the DNA that encodes all proteins, in a same way as a dictionary contains the definition of each word. When a cell needs a specific protein, it will therefore "read" this "DNA dictionary" and translate it into another "language": from a nucleotide sequence of the DNA to an amino acids sequence, which is the basis of each protein. This process of "reading" the DNA to form a protein, or in better terms the protein synthesis, lies at the heart of every organism. Indeed 80% of the cellular energy is devoted to protein synthesis. The main mechanisms of this process are the same for all proteins and for all kingdoms of life. A good understanding of this process is therefore essential to biology; any malfunctioning could potentially lead to diseases and, on the other hand, any of the steps of protein synthesis could be a prospective drug target. This is already the case of various antibiotics. In addition to that, a good understanding of this system is also valuable in recombinant vaccine and recombinant drug production, in order to help improve the yield of these proteins. Recombinant proteins technology is used for example to synthesize the hepatitis B vaccine in yeast cells or to synthesize the recombinant human insulin in Escherichia coli cells. This is done by inserting into the organism a DNA plasmid that encodes the given protein so that the transfected organism will then synthesize this protein nearly as if it was from its own DNA. A further benefit from an in -depth knowledge of protein synthesis relates to circuit design in synthetic biology. There, the goal is to design cells that will respond to their environment in a predefined manner, and again, this is done by inserting specific genes into the cells. Understanding protein synthesis can help to estimate the sensibility of such a system as well as help to define characteristics of its response. Nowadays, the many facets of protein synthesis and its regulation are getting increasingly better understood. Nevertheless, it also becomes increasingly more evident that the classical approach of studying every component in isolation should be left aside and the system or cell should be studied as a whole, due to the interconnections of all of its elements: we have entered in the systems biology era. With the recent advances in genomics, transcriptomics, proteomics and other –omics technologies, we are able to measure the state of cells under different conditions in a high -throughput manner, enabling some global, genome -scale view as aimed at by systems biology. The huge amount of data collected by these high -throughput techniques poses a new challenge: how can we efficiently integrate these data to make some sense out of them for gaining deeper understanding and for the design and optimization of novel systems. A general answer is that computational approaches are needed. A model can be built to represent the system, and its outputs can then be compared to the experimental measurements. The great advantage of the modeling and simulation approach is that we can build many different in silico systems to test and to compare which one best represents our current knowledge. This in silico system can then be subjected to different "virtual" conditions, with the goal of observing how the system would behave in response to these conditions, which can be repeated for many cases and conditions in a very cost - and time -effective way in comparison to an in vitro or in vivo experiment. In this thesis, we aim at integrating such high -throughput data into a model for a better understanding of protein synthesis. We mainly focus on the often -neglected steps of translation, to observe their possible influence and regulation on the system. For this, a model incorporating all the steps of translation is built, including the various intermediate translation elongation steps. We then develop a novel, efficient, exact stochastic algorithm, targeted here to simulate translation at the genome‐scale, accounting for the competition between mRNAs for shared resources. This algorithm could easily be adapted to other systems than translation as well. Another novelty is further introduced with a methodology to analyze and estimate polysome sizes from experimental measurements in prokaryotes. Integrating various experimental measurements into our model of translation, we additionally estimate translation characteristics at the genome-scale for prokaryotic and eukaryotic cells, and we observe how the system has been optimized to cope with the cellular needs. We further estimate the sensitivity of protein synthesis on different perturbations like changes in initiation, elongation, termination rates, changes in ribosome availabilities or mRNA copy numbers, or changes following starvation conditions. Taken together, the results from this thesis show that the regulation at the translation steps is stronger than is commonly assumed and it can have many implications on the system.

Biologists have used a reductionist approach to investigate the essence of life. In the last years, scientific disciplines have merged with the aim of studying life on a global scale in terms of molecules and their interactions. Based on high-throughput measurements, Systems Biology adopts mathematical modeling and computational simulation to reconstruct natural biological systems. Synthetic Biology seeks to engineer artificial biological systems starting from standard molecular compounds coding in DNA. Can Systems and Synthetic Biology be combined with the idea of creating a new science—‘SYS Biology’ that will not demarcate natural and artificial realities? What will this approach bring to medicine?

Synthetic biology is built on genetic engineering and principles of design engineering, which provides control over the biological functions of interest. This chapter explores the uses, processes, and applications of genetic engineering in synthetic biology. The chapter provides a brief history and course of development of the field of synthetic biology and genetic engineering and their unbreakable association. Next, the chapter delves into materials and methods and the applications of synthetic biology. This includes discussing the generally used components of genetic engineering to design new functions into organisms and even the general steps that are part of any synthetic biology experiment. Lastly, the chapter also explains the use of the materials and methodology discussed in solving a specific problem related to amodel mentioned in the paper titled"Development of Integrase-mediated differentiation circuits to improve evolutionary stability in E. coli." It explains how by using genetic engineering a synthetic biology-related problem was solved efficiently.

Systems biology has arisen through the convergence of theoretical, computational, and mathematical modeling of systems and the need to understand the wealth of information being rapidly generated in biology. Systems biology by its nature requires collaborations between scientists with expertise in biology, chemistry, computer sciences, engineering, mathematics, and physics. Successful integration of these disciplines depends on bringing to bear both social and technological tools: namely, consortia that help forge collaborations and common understanding, software tools that permit analysis of vast and complex data, and agreed-upon standards that enable researchers to communicate and reuse each other's results in practical and unambiguous ways. In this presentation, I will discuss several international projects (SBML, SBGN, and BioModels.net) aimed at addressing the last issue.An important prerequisite for effective sharing of computational models is reaching agreement on how to communicate them, both between software and between humans. The Systems Biology Markup Language (SBML) project is an effort to create a machine-readable format for representing computational models at the biochemical reaction level. By supporting SBML as an input and output format, different software tools can operate on the same representation of a model, removing chance for errors in translation and assuring a common starting point for analyses and simulations. SBML has become the most successful effort in this direction so far, with nearly 100 software tools supporting it today.A recently-created sister project is the Systems Biology Graphical Notation (SBGN) project. It addresses the issue of consistent human communication, by attempting to add more rigor and consistency to the graphical network diagrams that often accompany published research on models of biological reaction systems. The real payoff will come when more people and software adopt such a common visual notation and it becomes as familiar to them as circuit schematics are to electronics engineers.Finally, when developing and publishing computational models, it is only natural to want to put them into a database. The BioModels.net project is an effort to (1) provide a free, centralized, publicly-accessible database of human-curated computational models in SBML and other structured formats; (2) define agreed-upon standards for model curation; and (2) define agreed-upon vocabularies for annotating models with connections to biological data resources.

BackgroundMany difficult problems in evolutionary genomics are related to mutations that have weak effects on fitness, as the consequences of mutations with large effects are often simple to predict. Current systems biology has accumulated much data on mutations with large effects and can predict the properties of knockout mutants in some systems. However experimental methods are too insensitive to observe small effects.ResultsHere I propose a novel framework that brings together evolutionary theory and current systems biology approaches in order to quantify small effects of mutations and their epistatic interactions in silico. Central to this approach is the definition of fitness correlates that can be computed in some current systems biology models employing the rigorous algorithms that are at the core of much work in computational systems biology. The framework exploits synergies between the realism of such models and the need to understand real systems in evolutionary theory. This framework can address many longstanding topics in evolutionary biology by defining various 'levels' of the adaptive landscape. Addressed topics include the distribution of mutational effects on fitness, as well as the nature of advantageous mutations, epistasis and robustness. Combining corresponding parameter estimates with population genetics models raises the possibility of testing evolutionary hypotheses at a new level of realism.ConclusionEvoSysBio is expected to lead to a more detailed understanding of the fundamental principles of life by combining knowledge about well-known biological systems from several disciplines. This will benefit both evolutionary theory and current systems biology. Understanding robustness by analysing distributions of mutational effects and epistasis is pivotal for drug design, cancer research, responsible genetic engineering in synthetic biology and many other practical applications.

Synthetic biology is an emerging technology that uses the principles of physics, chemistry, mathematics, and biology. Synthetic biology is the technology enabler that could be applied across all fields such as agricultural, food, health, chemical, and industrial manufacturing sectors. In this chapter, several applications of synthetic biology across all disciplines are delineated, and challenges associated with the implementation of synthetic biology technologies are addressed in detail.

Calculating life? Duelling discourses in interdisciplinary systems biology

3rd congress on applied synthetic biology in Europe (Costa da Caparica, Portugal, February 2016)