systems biology research topics

Systems biology: current status and challenges

• Published: 13 January 2020
• Volume 77 , pages 379–380, ( 2020 )

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• Anze Zupanic   ORCID: orcid.org/0000-0003-3303-9086 1 ,
• Hans C. Bernstein   ORCID: orcid.org/0000-0003-2913-7708 2 , 3 &
• Ines Heiland   ORCID: orcid.org/0000-0002-9124-5112 4  

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We put together a special issue on current approaches in systems biology with a focus on mathematical modeling of metabolic networks. Mathematical models have increasingly been used to unravel molecular mechanisms of complex dynamic biological processes. We here provide a short introduction into the topics covered in this special issue, highlighting current developments and challenges.

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Singh D, Lercher MJ (2019) Network reduction methods for genome-scale metabolic models. Cell Mol Life Sci. https://doi.org/10.1007/s00018-019-03383-z

Article   PubMed   Google Scholar  

de Groot DH, Lischke J, Muolo R, Planque R, Bruggeman FJ, Teusink B (2019) The common message of constraint-based optimization approaches: overflow metabolism is caused by two growth-limiting constraints. Cell Mol Life Sci. https://doi.org/10.1007/s00018-019-03380-2

Park H, McGill SL, Arnold AD, Carlson RP (2019) Pseudomonad reverse carbon catabolite repression, interspecies metabolite exchange, and consortial division of labor. Cell Mol Life Sci. https://doi.org/10.1007/s00018-019-03377-x

Article   PubMed   PubMed Central   Google Scholar  

Ewald J, Sieber P, Garde R, Lang SN, Schuster S, Ibrahim B (2019) Trends in mathematical modeling of host-pathogen interactions. Cell Mol Life Sci. https://doi.org/10.1007/s00018-019-03382-0

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Mazat JP, Devin A, Ransac S (2019) Modelling mitochondrial ROS production by the respiratory chain. Cell Mol Life Sci. https://doi.org/10.1007/s00018-019-03381-1

Holzheu P, Kummer U (2019) Computational systems biology of cellular processes in Arabidopsis thaliana : an overview. Cell Mol Life Sci. https://doi.org/10.1007/s00018-019-03379-9

Shaw R, Cheung CYM (2019) Multi-tissue to whole plant metabolic modelling. Cell Mol Life Sci. https://doi.org/10.1007/s00018-019-03384-y

Clarelli F, Liang J, Martinecz A, Heiland I, Abel Zur Wiesch P (2019) Multi-scale modeling of drug binding kinetics to predict drug efficacy. Cell Mol Life Sci. https://doi.org/10.1007/s00018-019-03376-y

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Zupanic, A., Bernstein, H.C. & Heiland, I. Systems biology: current status and challenges. Cell. Mol. Life Sci. 77 , 379–380 (2020). https://doi.org/10.1007/s00018-019-03410-z

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Systems biology: current status and challenges

Anze zupanic.

1 Department of Biotechnology and Systems Biology, National Institute of Biology, Vecna Pot 111, 1000 Ljubljana, Slovenia

Hans C. Bernstein

2 Faculty of Biosciences, Fisheries and Economics, UiT, The Arctic University of Norway, Tromsø, Norway

3 The Arctic Centre for Sustainable Energy, UiT, The Arctic University of Norway, Tromsø, Norway

Ines Heiland

4 Department of Arctic and Marine Biology, UiT The Arctic University of Norway, Biologibygget, Framstedet 39, 9037 Tromsø, Norway

We put together a special issue on current approaches in systems biology with a focus on mathematical modeling of metabolic networks. Mathematical models have increasingly been used to unravel molecular mechanisms of complex dynamic biological processes. We here provide a short introduction into the topics covered in this special issue, highlighting current developments and challenges.

Systems biology has a wide range of definitions and covers an even wider range of different approaches and topics. We here refer to systems biology as an area of research that uses mathematical modelling in tight interconnection with experimental approaches to understand the mechanisms of complex biological systems and predict their behaviour across scales—molecular-to-organismal. This special issue focuses on metabolic modelling within this context where topics range from single-cell systems to multi-tissue and whole-body models. There are generally two different approaches to metabolic modelling. One is the dynamical modelling of detailed targeted pathways using kinetic rate laws, which allows us to describe steady-state fluxes and the dynamics of metabolite concentrations. As kinetic rates are often measured only for a limited number of reactions, these models usually cover only a small part of cellular metabolism. These approaches are also often used to describe signal transduction pathways. Interestingly, most dynamic models to date have been built for higher eukaryotes, mainly mammals. In contrast, whole cell or genome-wide metabolic models are still mainly used to analyze microbial systems. Genome-scale modelling approaches describe the whole-cell metabolic networks using methods known as ‘constraint-based metabolic modelling’. The latter are largely based on the assumption of evolutionary optimality of cellular metabolism. The disadvantage of these models is that the concentration of modelled internal metabolites—those that do not represent sources or sinks to the system—cannot be considered independently from each other. In addition, simulations of this type strongly depend on the particular assumptions made about optimization and corresponding optimization functions used to constrain the solution space. To overcome these limitations, more research groups have engaged ‘hybrid modelling approaches’, either scaling up of dynamic models or simplifying genome-scale models. Targeting the latter, the review provided by Singh and Lercher [ 1 ] discusses model reduction strategies that shall enable detailed dynamic description of genome-scale metabolism through model reduction.

Notwithstanding drawbacks, both dynamic- and genome-scale metabolic modelling approaches have been very successful in both biotechnology and for the prediction of metabolic alteration in disease. A number of different approaches and model systems, ranging from bacteria to human, are presented in this special issue:

De Groot et al. [ 2 ] analyze general metabolic features of model organisms, such as Escherichia coli and Saccharomyces cerevisiae . By comparing several models available to date, they identify modelling constraints that lead to the robust prediction of the often-discussed counterintuitive effect of overflow metabolism. In contrast, Park et al. [ 3 ] discuss why pathogenetic bacteria such as pseudomonads in isolation or bacterial communities often behave differently than the model organisms and show that their (evolutionary) success may be achieved through the adaptation of alternative metabolic strategies with respect to nutrient usage. The reviews by Ewald et al. [ 4 ] and Pecht et al. [ 5 ] build upon multicellular and multi-species systems by reviewing current modelling approaches to study host–pathogen interactions.

In recent years, there has been a concerted effort to improve our understanding of the metabolism of multicellular eukaryotes, such as humans or plants. Although examples of genome-scale modelling exist for these systems, their predictive capacity still remains behind those for single-cell organisms. Thus, dynamic metabolic modelling approaches describing specific pathways of interest are very common. As an example, Mazat et al. [ 6 ] provide a review of modelling approaches and current knowledge of ROS production in mitochondria. While there are fewer plant studies compared to human and mammalian ones, an increasing number of systems biology studies are looking into resistance of plants to environmental stress and accompanying metabolic/nutritional changes. In this respect, Holzheu and Kummer [ 7 ] review current modelling approaches used to study the model plant Arabidopsis thaliana and provide examples on how they have increased our understanding of plant metabolism and their potential for agricultural and medical practice.

Most models to date only target one level of organization, and real multiscale approaches are still limited. One reason is that the level of detail needs to be adjusted when going from single cell, over multicellular systems and tissues to the whole-body level, which requires to make assumptions that in turn may limit the predictive capacity and the possibilities for emerging behaviour. As part of this special issue, Shaw and Cheung [ 8 ] discuss the advantages and disadvantages of multi-tissue whole-plant modelling approaches in comparison with single-tissue approaches.

Challenges for multiscale modelling approaches do not only arise from limitations in our ability to mathematically represent a biological system. The challenges are inherent to the complex biology observed in many of our study systems and from limitations imposed from experimental observation. Different techniques need to be used to study different levels of organization. Sometimes, experimental data are only available from in vitro studies, while in vivo measurement can be very different or impossible. This topic is discussed in the review provided by Clarelli et al. [ 9 ], which emphasizes these limitations in the context of predicting in vivo antibiotic responses.

The reviews provided in this special issue cover different methods and examples, in which systems biology was used to further our understanding of biology. Many more have been developed in recent years, covering all levels of organization, time scales as well as using different mathematical approaches, ranging from cellular automata to logical networks. As the field has expanded and more researchers have started using systems biology approaches in their work, the number of meetings covering systems biology has also increased. For example, the conferences of the International Study Group for Systems Biology (ISGSB— isgsb.org ), which also served as the seed for this special issue, are held on a biannual basis, whereas the larger International Conference in Systems Biology (ICSB) is held every year. The next ISGSB conference will be held in Stellenbosch, South Africa, from the 14–19 September 2020, whereas the next ICSB will be held in Connecticut from 10–16 October 2020 ( http://icsb2020.bioscience-ct.net/ ).

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Research Article

From systems to biology: A computational analysis of the research articles on systems biology from 1992 to 2013

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft

Affiliations Center for Biology and Society, School of Life Sciences, Arizona State University, Tempe, Arizona, United States of America, School of Humanities and Social Science, Chinese University of Hong Kong, Shenzhen, Shenzhen, Guangdong Province, China

Roles Conceptualization, Funding acquisition, Project administration, Resources, Supervision, Writing – review & editing

* E-mail: [email protected]

Affiliation Center for Biology and Society, School of Life Sciences, Arizona State University, Tempe, Arizona, United States of America

• Yawen Zou, 
• Manfred D. Laubichler

• Published: July 25, 2018
• https://doi.org/10.1371/journal.pone.0200929
• Reader Comments

Systems biology is a discipline that studies biological systems from a holistic and interdisciplinary perspective. It brings together biologists, mathematicians, computer scientists, physicists, and engineers, so it has both biology-oriented components and systems-oriented components. We applied several computational tools to analyze the bibliographic information of published articles in systems biology to answer the question: Did the research topics of systems biology become more biology-oriented or more systems-oriented from 1992 to 2013? We analyzed the metadata of 9923 articles on systems biology from the Web of Science database. We identified the most highly cited 330 references using computational tools and through close reading we divided them into nine categories of research types in systems biology. Interestingly, we found that articles in one category, namely, systems biology’s applications in medical research, increased tremendously. This finding was corroborated by computational analysis of the abstracts, which also suggested that the percentages of topics on vaccines, diseases, drugs and cancers increased over time. In addition, we analyzed the institutional backgrounds of the corresponding authors of those 9923 articles and identified the most highly cited 330 authors over time. We found that before the mid-1990s, systems-oriented scientists had made the most referenced contributions. However, in recent years, researchers from biology-oriented institutions not only represented a huge percentage of the total number of researchers, but also had made the most referenced contributions. Notably, interdisciplinary institutions only produced a small percentage of researchers, but had made disproportionate contributions to this field.

Citation: Zou Y, Laubichler MD (2018) From systems to biology: A computational analysis of the research articles on systems biology from 1992 to 2013. PLoS ONE 13(7): e0200929. https://doi.org/10.1371/journal.pone.0200929

Editor: Ingo Brigandt, University of Alberta, CANADA

Received: October 23, 2017; Accepted: July 4, 2018; Published: July 25, 2018

Copyright: © 2018 Zou, Laubichler. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All data files are available from the Figshare database (DOI: 10.6084/m9.figshare.5422594 ).

Funding: ML was funded through a grant from National Science Foundation held at the Arizona State University under SES 1243575. YZ received funding from the China Scholarship Council under scholarship 2011635028. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Systems biology has emerged over the past two decades, aiming to examine organisms from a systematic and holistic perspective. It is characterized by large datasets and modeling. The development of high-throughput technologies in the 1990s brought forth a deluge of data to biology. Without mathematical models and computational simulations, however, the data could not be understood at the time. In the early 1990s, systems biology had strong mathematical and computational traditions, and was drastically different from traditional biological disciplines such as evolutionary biology, molecular biology, and development biology. Early systems biologists have made many attempts to improve on the methodology such as developing various algorithms for dealing with large datasets and formalisms that model a specific network including gene regulatory networks, signal transduction networks, and metabolic networks [ 1 ]. With these developments many sub-disciplines of systems biology have been established, such as systems pharmacology [ 2 ], which explores the mechanisms behind drugs by means of systems biology methods, and cancer systems biology, which is a field that applies computational modeling to reveal the carcinogenic pathways [ 3 ]. A systems biology approach offers many advantages to biologists, especially medical scientists because first, behind many complex diseases such as cardiovascular disease, respiratory diseases, and diabetes, there are complex biological networks, instead of one or several genes. Second, the diagnose and cure for complex diseases requires a systematic understanding of these complex networks, and systems biology approach makes the modeling, perturbation, and prediction of complex networks possible. Because the above reasons, systems biology approach offers a proactive instead of reactive approach [ 4 ].

Systems biology is an interdisciplinary field, where biologists, referred to here as biology-oriented scientists, including evolutionary biologists, developmental biologists, molecular biologist, pharmacists, etc., and engineers, computer scientists, and mathematicians, referred to as systems-oriented scientists, work together. Systems-oriented scientists have contributed greatly to the advancement of systems biology. For example, Hiroaki Kitano published the most highly cited article in systems biology, edited the first monograph on systems biology, founded the Systems Biology Institute in Tokyo, and organized the first International Conference of Systems Biology in Tokyo in 2000 [ 5 , 6 ]. Kitano was trained as an engineer and is the head of Sony Computer Science Laboratories. Another example is Albert-Laszlo Barabási, whose work on network theory has won him many awards in systems biology. He was trained as a physicist, yet he publishes widely in systems biology (e.g. [ 7 ]).

As increasing number of tools provided by systems biology prove to be more and more powerful, more and more biologists are joining the field of systems biology. In the past, the modelers were often from mathematics or engineering backgrounds, and had little training in biology. Nowadays the trend is that modelers have realized that biological systems are not simply analogical to physical systems or chemical systems, and therefore they need to understand the functions and mechanisms of biological systems [ 8 ]. This has led to more and more modelers and biologists working together, with the models playing a role in simulation, prediction, and guidance of real experiments [ 9 ].

Scientists from different backgrounds have different epistemologies and methodologies, thus the subjects of their research inevitably differ, with some being biology-oriented and others systems-oriented. One may therefore wonder how biology-oriented and systems-oriented research topics changed within systems biology. This study examines the history of systems biology from 1992 to 2013. Some scholars argue that systems biology’s early roots emerged in the middle half of the twentieth century such as theories about cybernetics proposed by Norbert Wiener in 1948, Denis Noble’s heart model in 1960, and Ludwig von Bertalanffy’s General Systems Theory [ 10 , 11 , 12 ]. We argue that these early work failed to establish systems biology as a legitimate discipline so we did not choose to an older time. We chose the year 1992 instead of a more recent time because although there were very few articles containing the term “systems biology” in the 1990s, we argue that technologies that enabled the generation of big data and algorithms for the modeling of complex systems had been developed in the 1990s. Conceptually, modern systems biology had its early roots in the early 1990s. Leroy Hood wrote in a 1992 book that “the future of biology will depend upon the analysis of complex systems and networks” [ 13 ].

We utilized several computational tools to analyze the metadata of the systems biology literature to answer the following questions: From 1992 to 2013, how did the topics of systems biology research change and did this change reflect a shift towards more biology-oriented topics? From what types of institutions did the systems biologists come and how did their institutions change over time? Our research allows system biologists and biologists in other fields to better track the history, dynamics, and future trends of this discipline. For policy makers, our research is also interesting to them because this study showcases the knowledge about and tools to investigate the institutional backgrounds of the practitioners in systems biology.

Materials and methods

The growing number of publications in a scientific field like systems biology makes it hard to identify trends and study frontiers simply by analyzing a few key papers. Bibliometrics can provide analysis tools to address these difficulties. Scholars have applied bibliographic analysis to study the history of business, science, art, and engineering [ 14 ]. Bibliographic analysis is a good way to assess the influence and quality of literature by deciding which work gets cited most and which author has the most citations [ 15 ]. Earlier attempts to analyze citation data from the Web of Science (WoS), such as those by Science, Technology, and Society scholar Susan Cozzens in the 1990s, were limited as many computational tools for the analysis of big data were not yet available [ 16 ]. However, after more than a decade of development, information scientists have produced many tools and approaches for citation analysis, which can overcome the difficulty faced by Cozzens. These include the ISI citation index, CiteSpace, HistCite, VOSViewer, to name a few [ 17 , 18 , 19 ].

In the WoS database, we searched for documents containing the term “systems biology” in their topics (including titles, abstracts, and keywords) and published from 1992 to 2013. Then by selecting those published in English, we narrowed the sample down to 9923 articles, which included research articles, reviews, editorial materials, proceeding papers, and meeting abstracts. One thing to note is that the first article in our query that contains the term “systems biology” was published in 1997 and there were few articles in the 1990s. Despite this limitation, we can use bibliographic analysis tools to get the relevant references of those 9923 articles. If one article cites about 20 references on average, then the total number of references are about 200,000. One such tool is CiteSpace, an open-access bibliometric tool, which can help analyze the references and also count which references were cited most by those 9923 articles automatically using their built-in functions [ 20 ]. The 9923 articles can be called the “research front” and the references of those 9923 articles can be called the “intellectual base” of systems biology.

We downloaded the bibliographic information for these 9923 articles. All data files are available from the Figshare database (accession number 10.6084/m9.figshare.5422594). For every article, WoS can bulk export all the bibliographic information, such as authors, title, abstract, references, and publishing year. To answer our questions, we first picked out the most cited references, and then analyzed the research categories of the most cited articles using close reading. Next, we used machine learning techniques to study the topics of systems biology embedded in the thousands of abstracts.

Analysis of the most cited references for the types of research (examining the intellectual base of systems biology).

The built-in functions of CiteSpace can analyze the citation frequencies of each reference, and produced a list of the most highly cited references from the year 1992 to 2013 automatically. We divided the years from 1992 to 2013 into 11 time slices, and picked 30 articles for each time slice, which included both research articles and reviews. We chose the number 30 because if we had chosen less than 30 references for each time slice, we could not have obtained statistically significant result, while choosing a sample size larger than 30 was not feasible because in early years there were not so many cited articles. For a full list of these articles, see S1 Table . After CiteSpace picked out 330 references, we manually searched for and downloaded the full texts for each reference, and then carefully read them to determine whether the articles were systems-oriented or biology-oriented and how the number of articles in each category changed over time.

This requires certain criteria. Because systems biology is an interdisciplinary science, there is no 100 percent biology-oriented or systems-oriented research. However, because any research has to be focused on a certain area, it is possible to classify these articles into a few categories, and for each category, it is easier to say whether each is more systems-oriented or biology-oriented. For previous bibliographic analysis, researchers have categorized publications of a field in categories to show the “big picture” of that field [ 14 , 21 ]. We have to admit that it is a formidable task to categorize publications of a scientific field as diverse as systems biology. For our analysis we derived our categories from within the literature (based on close reading) and also from categories identified by other historians and philosophers of biology.

The historians and philosophers of science Ulrich Krohs and Werner Callebaut once argued that three roots of systems biology must be discerned to account properly for the structure of systems biology, namely, pathway modeling, biological cybernetics, and -omics [ 22 ]. We agree that pathway modeling and -omics have fueled the advancements of systems biology during the past two decades. Cybernetics may be very important in the mid-twentieth century and contributed to systems biology’s theory as a root. However, in the literature on systems biology published over the last two decades, it is hard to see the influence of cybernetics as comparable to that of pathway modeling and -omics. We argue that Krohs and Callebaut’s categorization is simplistic. Systems biology is a very interdisciplinary field and has many diverse areas; therefore, we used more categories than just the three proposed by Krohs and Callebaut, and most of the articles in systems biology fall in a rather straightforward way into the nine categories we propose in Table 1 .

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https://doi.org/10.1371/journal.pone.0200929.t001

Based on close reading of these 330 articles, we used nine categories of research and we introduce what each category means and why each is either systems-oriented or biology-oriented (see Table 1 ). Biology-oriented research includes the following four categories: -omics-related research, high-throughput technologies, applications in engineering and medicine, and biological mechanisms. Systems-oriented research includes network properties, software development, Metabolic Flux Analysis, database development, and algorithms, equations, and modeling. For a more detailed description of each category, please see S1 Text .

Analysis of topics found in abstracts using topic modeling.

Through the previous step, we analyzed the categories of systems biology research through manual reading of 330 articles and how the number of articles in each category changed. However, we wanted to analyze the research categories in a larger sample size, say thousands of articles, and see if the results of the latter corroborated with our close reading. Because we could not possibly have read all of them, we relied on topic modeling. A topic here can be broadly interpreted as a subfield, a category, or a research type. A good thing about topic modeling is that it can analyze millions of words quickly without humans reading them [ 23 ]. Topic modeling has many applications in the humanities, social sciences, and bioinformatics, including the study of Twitter messages to identify trends and studying the corpus of thousands of research papers or newspaper articles to show how ideas in a specific field have changed over time [ 23 , 24 , 25 ]. We used topic modeling to analyze the 8809 abstracts of articles published from 2003 to 2013 that were retrieved from the WoS Citation data. The starting year was set at 2003 because before 2003, each year only had a few publications and the number of articles is positively related to the accuracy of the results of topic modeling. Out of the 9634 articles that were published from 2003 to 2013, 8809 articles have abstracts.

Topic modeling uses probabilistic models to generate topics (each topic is represented by a cluster of words) through automatic reading of unstructured natural language [ 26 ]. One of the most widely used topic model is the Latent Dirichlet Allocation (LDA) model.

The main mechanism of topic modeling is as follows: First, the model sets a fixed number of topics and a fixed number of words in each topic. The basic idea is to view a document as a distribution of topics and a topic as a distribution of words. Second, the model randomly assigns the words in a document to a topic and calculates two probabilities: P (topic/document) and P (word/topic). Third, the LDA model utilizes Bayesian inference to adjust the word assignments iteratively until it reaches a relatively stable state. Each iteration involves assigning a word to a topic, and updating the P (word/document) and P (topic/document) to infer and update P (word/topic) [ 26 ]. The actual modeling process utilizes more complicated algorithms.

There are many tools that can implement topic modeling, for example the MALLET (Machine Learning for Language E Toolkit) and the Stanford Topic Modeling Toolbox [ 27 , 28 ]. We used MALLET, a software developed by Andrew McCallum at the University of Massachusetts, and it is based on an LDA model and a set of different algorithms. Words like “doi,” “paper,” “ab,” “research,” “results” and “elsevier” were compiled in an extra list of stopwords, which are words that MALLET ignores, in addition to the default one that MALLET includes. The number of topics was set at 20.

We can also analyze how the topics change over time. To offer a historical perspective, identifying topics in thousands of articles is an interesting task, but it is even more useful to see the topic trends over time [ 23 ]. MALLET also returned the composition of topics in all the documents over time. In our case, each document is an abstract of an article. For example, for a document, MALLET returned the probabilities of each of the 20 topics. If every topic is equally represented in articles, then the probability of each topic should be 5%. If the probability of a topic is higher, that means that this document has a higher probability of containing that topic. For example, if an article has a topic whose probability is higher than 40%, then this suggests that this article is indeed related to that topic. Similarly, if an article has a topic whose probability is only 1%, then it is unlikely that this article contains that topic. In our study, we consider a probability of 10% to be the threshold for considering that the topic is significant. A Python code was written to calculate the number of articles that contained a topic the probability of which was higher than 10% for a certain year. We then calculated the percentage of that number for all the articles published in that specific year.

Analyzing the institutions of authors

We used Tethne to determine which authors were the most highly cited over the years (30 authors for each of the 11 time slices). Tethne is a Python package developed by Erick Peirson at the Digital Innovation Group at Arizona State University for bibliographic and corpus analysis. The tool was written in Python, and it is open source. It works with WoS, JSTOR, and Scopus data to visualize patterns and trends in the scientific literature.

Using Tethne, we analyzed the references for all 9923 articles. Each reference includes the information of the first author’s name and the publishing year. For each time slice, we calculated the number of the references an author published in that time slice, and arranged the authors based on that number. Then we picked out the top 30 authors with the highest number of references in each time slice for the analysis in the next step.

We were not only interested in the most highly cited 330 authors, but all the authors of those 9923 articles. Therefore, we retrieved each author’s affiliation at the time of publication from either WoS data or Google Scholar. We analyzed these affiliations to determine which category of institution the author was affiliated with. The reason we chose their affiliations instead of other information was a trade-off between the content and the accessibility of that information. For example, a person’s description on one’s own website may be a more accurate assessment of what one is doing, but this information is hard to get for thousands of authors, and harder to compare in an objective way. Affiliations are easier to obtain and can accurately reveal the institutional backgrounds for researchers of systems biology.

We built a word list by retrieving an identifying word from the affiliations of the most highly cited 330 authors. For example, if a department name has the word “anatomy,” it is likely to be a biology-oriented institution, and we used “anatomy” as an identifying word. Next, we developed a model to study the affiliations of the 9634 first authors of articles published from 2003 to 2013 in the WoS database. The reason for starting from 2003 was that in that year, 118 articles were published whereas in the previous year, only 30 articles were published. In statistics, 30 is usually considered the minimal sample size. Because we started with the year 2003, the articles we looked at was 9634 instead of 9932. Based on the word list generated by analyzing the 330 authors, we wrote a Python code to design a model that automatically labels the institutions of thousands authors.

The model is described as follows: the automatic labeling of the institutions was based on the first word, usually the department name that matched the word list. For example, in the affiliation of “Max Planck Inst Mol Plant Physiol, D-14476 Potsdam, Brandenburg, Germany,” the first word that matched the word list was “plant,” so it was labeled automatically as a “biology-oriented” institution. For institutions names that contained a word that indicated the institution’s orientation but is not on our list, we labeled them manually and added the identifying word to our word list. We then ran the process iteratively. However, some institutions were still hard to define because the affiliations retrieved from the WoS citation data did not have a specific department and only the university, so we labeled these manually as “unidentifiable,” for example, affiliation like “Los Alamos Natl Lab, Los Alamos, NM 87545 USA” would be labeled as unidentifiable. Another situation where an institution was hard to identify was that it is a foreign institution with a name like “Tech Univ Dresden, Inst Lebensmittel & Bioverfahrenstech, D-01069 Dresden, Germany.” The word list that we used to identify institution categories contain 193 words (See S2 Table ).

Research categories of the most highly cited references in the intellectual base

We selected the most highly cited 30 references in 11 time slices from 1992 to 2013 (a total of 330 references). Excluding books and references that are hard to label, the results of these 330 references that fall either into biology-oriented or systems-oriented categories are depicted in Figs 1 and 2 , respectively. Out of the 330 articles, CiteSpace picked out 14 references that could not be placed into these nine categories. They either focus only on topics tangentially related to systems biology, or are hard to put into any category, so they were not included in the nine categories. Each line represents a category. Fig 1 shows the number of references in four biology-oriented categories. The development of high-throughput technologies (the red line) is the category that has the highest number of articles in early stages, but this category decreased afterwards. This suggests that many early biologists involved in systems biology by developing new technologies.

The y axis stands for the number of articles among 30 most highly cited articles for a category.

https://doi.org/10.1371/journal.pone.0200929.g001

The y axis stands for the number of articles among 30 most highly cited articles for each time slice.

https://doi.org/10.1371/journal.pone.0200929.g002

Omics research (the solid blue line) began to emerge in the late 1990s and peaked around 2002. Omics research has changed from simply getting the sequence of a genome in the 1990s to actually mapping the interactions of biological molecule, be it proteins, or genes, or metabolites after 2000. An example of an article in this category is the comprehensive study of protein-protein interactions in yeast. Peter Uets and his colleagues discovered 957 possible interactions of more than 1000 proteins [ 29 ]. After that omics research decreased while the applications of systems biology (the purple line) began to increase in early 2000s, and reached a plateau from 2006 to 2010.

Then in the last four years, the rise of systems biology’s applications in medical fields and engineering became very significant. 13 out of the 30 most highly cited articles from 2012 to 2013 belong to this category. For example, one such highly cited reference in 2010 is on systems vaccinology, describing how systems approach has changed the way scientists develop vaccines [ 30 ]. Despite the fact that vaccines work well in preventing diseases, the mechanism for how they work remained largely unknown before application of a systems approach. Scientists are now starting to use systems approaches to identify the gene regulatory network after the injection of vaccines, and predict the later responses. It can help identify high-risk individuals and prevent potential harmful consequences of vaccine failures to those individuals [ 30 ].

Fig 2 shows the number of references in five systems-oriented categories. From 1992 to 1995, two categories, namely, metabolic flux analysis (the light blue dotted line), algorithms equations and models (the purple dotted line), are the top two categories. However, only the category of algorithms, equations and models maintains the same importance, and metabolic flux analysis decreases over time. This suggests that algorithms, equations and models are still very central to systems biology research, which is claimed by other scholars [ 31 ].

Database management (the dark blue solid line) emerged around 2000 and continue to be a strong presence in later years. These include the Gene Ontology, KEGG database, BioGRID, Reactome, BiGG, IntAct, STRING, to name a few [ 32 , 33 ]. A database is not a place where biologists dump their data, because scientists need to figure out how to store the data, how to search for data quickly, how to manage database structure, and how to develop a standard of data format that is compatible to more databases [ 34 ]. This knowledge can be classified as data science in general, which requires the input of systems-oriented scientists.

Topics found in the abstracts of the research front

We were not only interested in the change of research types of the most highly cited references, but also in the trend of those 9923 systems biology articles. The result of the topic modeling based on thousands of articles is similar to our manual reading of the most highly cited articles. S3 Table shows the machine learning results of topics using MALLET and the labels that we assigned by reading the words in topics. The machine learning model returns the following 20 topics: biology, models, metabolomics, diseases, proteomics, synthetic biology, database and software, cell biology, systems theory, algorithms, immune system, network properties, network, genomics, technologies and tools, drug and cancer, regulation, pathway.

We were not only interested in what these topics are, but also the temporal change of topics. Figs 3 and 4 show the topics that have significant patterns of increasing or decreasing. Fig 3 shows that the percentage of articles that contain Topics 11, 14 and 17 increased over time. Topic 11 is about the research related to immune systems and vaccines, Topic 14 is about disease, and Topic 17 is about drugs and cancer. These are related to the applications of systems biology, which is similar to our finding about the articles in this category among the most highly cited articles based on close reading, which showed an increase.

The y axis represents the percentage of articles published in that year that contains a topic with a probability higher than ten percent.

https://doi.org/10.1371/journal.pone.0200929.g003

The y axis represents the percentage of articles published in that year that contain a topic with a probability higher than ten percent.

https://doi.org/10.1371/journal.pone.0200929.g004

Fig 4 shows how the percentage of articles that contained topics 9, and 16 decreases. Topic 9 includes some general terms about systems biology, and Topic 16 is about high-throughput technologies, which includes words like “high,” “throughput,” “technologies,” and “techniques.” The result is similar to our findings about the percentage of articles in the category of high-throughput technologies among the most highly cited articles, which is decreasing over the years (The percentage for articles that contained each of the 20 topics can be seen in S4 Table ).

The institutional contexts for systems biologists

Table 2 lists the affiliations of the first most highly cited authors in each time slice. The affiliations show the department, the university or institution, and geographical information such as city and country. The table shows that the most highly cited authors in each time slice changed quickly throughout the years, suggesting that systems biology was evolving quickly. The institutions they are affiliated with are very diverse and interdisciplinary. We classified the institutions of the top 30 authors from 11 time slices into four categories: biology-oriented, systems-oriented, interdisciplinary and systems biology institutions.

https://doi.org/10.1371/journal.pone.0200929.t002

Biology-oriented institutions have words like “cancer,” or “genetics” that are related to the life sciences. We noticed that a fair number of authors are from a medical institution, and even pharmaceutical companies, like Glaxosmithkline and Syngenta. Systems-oriented institutions include those related to “statistics,” “mathematics,” “physics,” “chemistry,” and other non-biology disciplines. We also found that some researchers are from companies such as Microsoft, Siemens, and Sony. “Interdisciplinary institutions” refer to those that related to interdisciplinary field such as “biochemistry,” “biophysics,” “bioengineering,” “biotechnology,” and “bioinformatics.” Systems biology institutes include those that specifically use the words like “systems biology,” or “biosystem,” or “biosyst.” For some institutions that are hard to tell which category they belong to, we did not label them and include them in the calculation.

We used Tethne to determine which authors were the most highly cited over the years (30 authors for each of the 11 time slices). The results of the categorization of the affiliations of the 330 highly cited authors are shown in Fig 5 . In each time slice, the top 30 authors’ institutions were plotted in four color-coded bars to represent the four categories. The figure shows that the number of authors from systems-oriented institutions (red bar) was first almost the same as biology-oriented institutions, but their number diminished over the years. For example, in the time frame of 1992 and 1993, one of the most cited authors is Daniel T. Gillespie, a physicist working at the Research Department of Naval Weapons Center at the time. His work on stochastic simulation algorithm in chemical kinetics contributed to the simulation method adopted by later systems biologists [ 35 ]. He contributed to the foundation of systems biology while this discipline was still in its “early roots” stage, and he did not mention systems biology in his work at the time.

The y axis stands for the number of authors in a category among the top most highly cited 30 authors.

https://doi.org/10.1371/journal.pone.0200929.g005

Scientists from interdisciplinary institutions (green bar) have fueled the advancement of systems biology not only in the early days of systems biology, but also in more recent years. These institutions have provided a place for early systems biologists to stay. While in the 1990s, there were no systems biology institutions (purple bar), which only emerged within the slice from 2000 to 2001. In each time slice after 2001, there were a few authors coming from systems biology institutions. The number of the most cited authors from a biology-oriented institution (blue) tends to fluctuate over the years, but this category has the highest number compared to other categories in every slice except in the time slice from 1992 to 2013.

Fig 6 shows the result of all the first authors who published between 2003 and 2013 retrieved from WoS citation data. Out of all 9876 authors who published between the years 2003 and 2013, a total of 779 (7.89%) were unidentifiable. We excluded those articles when calculating the percentage of four categories. After taking out the unidentifiable authors’ affiliations, it shows that the percentage of each category has remained quite constant, which means that from 2003 to 2013, the institutional context for systems biologists did not change much.

The number in each category changed little from 2003 to 2013. The y axis stands for the percentage of each type of institution.

https://doi.org/10.1371/journal.pone.0200929.g006

Between the years 2003 and 2013, on average 60.85% of authors came from a biology-oriented institution; 21.16% came from a systems-oriented institution; 13.75% were from an interdisciplinary institution; and 4.23% were from a systems biology institution. These statistics suggest that the institutional context for all authors publishing on systems biology is different than that for the people who published the most highly cited articles on systems biology. Notably, the latter had a larger percentage of authors who were affiliated with interdisciplinary and systems-oriented institutions. This is an interesting observation that the people who were cited most and the people who published in a field have different patterns in terms of their affiliations, and we will discuss that in the next section.

This study suggests that systems biology is becoming more and more biology-oriented, especially with its biological applications. Among the most recent highly cited articles, the articles in the category of applications of systems biology have increased tremendously. Over the years among the most-highly cited authors, fewer authors were from systems-oriented institutions. The trends of topics based on topic modeling suggested that more recently a higher percentage of articles includes topics about immune system and vaccines, diseases, drugs and cancer.

Our research findings are echoed in some other scholars’ observations about systems biology. Alan Aderem argues that “biology dictates what new technology and computational tools should be developed, and once developed, these tools open new frontiers in biology for exploration. Thus, biology drives technology and computation, and in turn, technology and computation revolutionize biology” [ 36 ]. Some other scholars also suggest that biology needs to be more important within systems biology in regard to two aspects, first, that more biologists should be involved in systems biology, and second, that more empirical biological problems need to be addressed [ 37 ]. Jane Calvert and Joan H. Fujimura carried out interviews with many researchers in systems biology, including biologists, mathematicians, physicists, and computer scientists [ 37 ]. One of the biologists said, “I think biologists need to drive systems biology, because if it’s driven by computation or engineers, without a depth of training in biology, they lose that sense, they tend to treat molecules as nodes and edges without a sense of how they’re performing their functions” [ 37 ]. Physicists and computer scientists might also agree with that, because often those who want to model a biological system sometimes have difficulty find a biological expert to help them to link the model to specific biological problems [ 38 ]. Along with the findings of the interviews, a systems biologist, Sui Huang, claims that biologists have become active players in systems biology because what they need to understand now is not a single gene or a protein, but networks of genes or proteins, and systems biology approach can help address these needs [ 39 ]. Huang’s article claims that systems biologists should divert their research “back to biology in systems biology” [ 39 ]. Today, systems biology has captured more biological phenomena, properties, objects such as various regulatory pathways, data, theories, and methods than its precursor did in the middle of the twentieth century. Biologists have seen the utility of applying a systems biology approach to understand the evolution and function of biological networks.

Our study shows a clear upward trend towards applications in systems biology. These applications have been centered on understanding cancer better, transforming drug discovery, and making preventative vaccines. System biology, compared to evolutionary biology, developmental biology, etc., can reveal the mechanisms behind complex diseases. System biology and medicine have become so closely related that this has led to the development of a new field called "systems medicine." We agree with Leroy Hood's point of view that the future of medicine will become "predictive, personalized, preventative and participatory" [ 40 ]. This also means a new era for biologists, and this study’s results indicate that the growth of this field is very fast, and we believe that in the near future we may be able to see some breakthroughs.

Our results on the institutional context of scholars who have published in this field also has implication for policy makers and funding agencies. The results indicate that among the most highly cited authors, while those from systems-oriented institutions have decreased over the years, those from interdisciplinary fields have come to contribute more to systems biology. Another interesting finding is the institutional contexts of the most cited authors and general authors, which suggest that the scholars who lead a field are sometimes different from those who publish in that same field. Therefore, it has been suggested by previous scholars that we should create interdisciplinary environments on purpose to foster innovation, and this study’s results agree with that suggestion [ 41 ]. Furthermore, the percentage of scholars from an interdisciplinary field or a field outside of biology could be translated into a message that the funding agencies should prioritize the funding of interdisciplinary projects and institutions, because they may better lead the starting of a new discipline, or make a higher impact on existing fields.

Our results should not be interpreted to mean that systems biology is abandoning the systems-oriented components like modeling or databases in recent years. The opposite is true. As mentioned in the beginning of the article, system biology is characterized by big data and modeling, and the integration of mechanistic and mathematical explanations is and still will be underway [ 42 ]. What we mean is that modeling has changed from being a primary research subject into tools that help reveal biological mechanisms or solving a real-life problem. Systems-oriented components still play a very important role, but more biological components are integrated and transform systems biology to align better with other biological disciplines.

Some limitations of this research include: First, bibliographic analysis only allowed us to pick the most highly cited references, because it can be assumed that high citations can be interpreted as high impact and high quality. Therefore, these highly cited articles are more interesting and some argue that they can shed some light on the trend of this field, but we admit that there is a possibility that they do not fully represent the entire field. Second, we carefully categorized those articles through close reading, and the application of machine learning technique to analyze a much larger number of articles. While this enabled us to overcome the limitation that we could not read them all and it corroborated the results of close reading, machine learning techniques have some limitations in it, and it still cannot have the accuracy of human reading.

Supporting information

S1 table. 330 most highly cited references and their categories..

https://doi.org/10.1371/journal.pone.0200929.s001

S2 Table. The word list used to identify the categories of institutions.

https://doi.org/10.1371/journal.pone.0200929.s002

S3 Table. Machine learning results of topics using MALLET.

https://doi.org/10.1371/journal.pone.0200929.s003

S4 Table. The percentage for articles that contained each of the 20 topics.

https://doi.org/10.1371/journal.pone.0200929.s004

S1 Text. A detailed description of nine categories of research in systems biology.

https://doi.org/10.1371/journal.pone.0200929.s005

Acknowledgments

We would like to thank many people who have contributed to this project, including Jane Maienschein, Erick Peirson, Julia Damerow, Kenneth D. Aiello, and Deryc Painter. We also thank the anonymous reviewers for comments that improved this manuscript.

• View Article
• Google Scholar
• PubMed/NCBI
• 5. Kitano H, editor. Foundations of systems biology. Cambridge: The MIT Press; 2001.
• 10. Wiener N. Cybernetics, or control and communication in the Animal and the Machine. Cambridge: MIT Press; 1948.
• 11. von Bertalanffy L. General systems theory. New York: George Braziler; 1969.
• 13. Kevles DJ, Hood L, editors. The code of codes. Cambridge: Harvard University Press; 1992.
• 15. Moed HF. Citation analysis in research evaluation (Vol. 9). Amsterdam: Springer Netherlands; 2006.
• 17. De Bellis N. Bibliometrics and citation analysis: from the science citation index to cybermetrics. Lanham: Scarecrow Press; 2009.
• 22. Krohs U, Callebaut W. Data without models merging with models without data. In: Boogerd F, Bruggeman FJ, Hofmeyr JHS, Westerhoff HV, editors. Systems biology: Philosophical foundations. Amsterdam: Elsevier; 2007. pp. 181–213.
• 27. McCallum AK. MALLET: A Machine Learning for Language Toolkit [Internet]. University of Massachusetts Amherst; 2002. Available from: http://mallet.cs.umass.edu .
• 28. Ramage D, Rosen E. Stanford topic modeling toolbox [Internet]. Stanford University; 2001. Available from: http://nlp.stanford.edu/software.tmt.tmt-0.4/
• 38. Hlavacek W. Two challenges of systems biology. In: Stumpt MP, Balding DJ, Girolami M, editors. Handbook of Statistical Systems Biology. Chichester: John Wiley & Sons; 2011.
• 41. Lattuca LR. Creating interdisciplinarity: Interdisciplinary research and teaching among college and university faculty. Nashville: Vanderbilt University Press; 2001.

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Systems biology.

Systems biology is an interdisciplinary field that focuses on complex interactions in biological systems in order to improve the design of molecular and cell-based technologies.  Instead of studying one biological component at a time, scientists use systems biology approaches to obtain, integrate and analyze complex data from multiple experimental sources to understand how molecules act together within the network of interaction that makes up life.

IBB investigators are using engineering approaches to develop a quantitative understanding of cell function and to apply this understanding for improved technologies to study biological systems such as the immune system or the nervous system. In addition, scientists and engineers use mathematics and computation to reliably model, predict, manipulate and optimize biomedical systems for the advancement of medicine, drug development, and biotechnologies. 

Researchers are focusing on understanding the complexity of gene and protein networks involved in individual cell signaling, communication between cells in communities, and cellular metabolic pathways. In order to study biological systems, tools are required for collecting information across a larger scale than traditional biological or biochemical methods. The technology platforms under development for high-throughput data collection and analysis at IBB focus on genomics, epigenetics, proteomics, and metabolomics.

Systems Biology Faculty

Amirali Aghazadeh

Kyle Allison

Turgay Ayer

Mark Borodovsky

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Ahmet Coskun

James Dahlman

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Constantine Dovrolis

Alan Emanuel

Christoph Fahrni

Andrei Fedorov

Facundo Fernandez

Greg Gibson

Daniel Goldman

Christine Heitsch

Lynn Kamerlin

Peter Kasson

Melissa Kemp

Kostas Konstantinidis

Joel Kostka

Julia Kubanek

Nael McCarty

Patrick McGrath

Julien Meaud

Roman Mezencev

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Annalise Paaby

Boris Prilutsky

Felipe Garcia Quiroz

Frank Rosenzweig

A. Fatih Sarioglu

Minoru Shinohara

Saurabh Sinha, Ph.D.

Terry Snell

Mark Styczynski

Shuichi Takayama

Matthew Torres

Denis V. Tsygankov

Eberhard O. Voit

Marvin Whiteley

Peter Yunker

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Science Transforming Health

ISB researchers, along with the global scientific community at large, immediately focused their attention on the COVID-19 pandemic and the virus that causes the disease – SARS-CoV-2. ISB President Dr. Jim Heath and scientists across labs and disciplines at ISB have worked to uncover the secrets behind COVID-19.

Healthy Aging

Research suggests that it may be possible to eliminate the correlation between age and diseases. ISB is working to leverage systems biology approaches to understand the mechanistic links among the processes that accompany and/or lead to aging.

Understanding of the human microbiome has grown exponentially over the past decade. ISB researchers are unlocking the mysteries of the human microbiome and translating our scientific knowledge into therapies for a number of complex diseases.

Cancer research poses many interconnected complexities related to early detection, stratification and treatment. The cross-disciplinary holistic, integrative nature of systems biology makes ISB researchers well suited to tackle the complexity of these diseases. 

Brain Health

ISB, along with a collaborative network of partners, is pioneering a multimodal approach that combines personal data, lifestyle factors, cognitive training and systems medicine — and is rigorously testing these new approaches in clinical trials. This approach is critical to prevent, slow, and even reverse many neurological conditions before they become irreversible.

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Scientific Wellness embodies P4 medicine (predictive, preventive, personalized and participatory), and is being applied to help individuals improve their health. The platform provides new approaches for drug target discovery, and is driving profound economic, policy and social change.

Pregnancy Health

By using a systems approach to pregnancy health, ISB is gaining a more comprehensive understanding of pathological changes that occur in pregnancy, which will identify women most at risk of developing pregnancy complications.

Environment

Achieving environmental sustainability is one of civilization’s historic challenges. ISB uses systems science to improve the understanding of the interactions of microbes and ecosystems and create a new generation of sustainable tools and strategies that can be deployed to address this challenge.

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Systems Biology

View Principal Investigators in Systems Biology

While most researchers continue to break down disease into smaller and smaller pieces in an effort to understand how they work, systems biologists take the opposite approach by looking at how all the pieces of a system interact—be it at the level of the population, organism, tissue, or cell—and then put the pieces together. Systems biology is a conceptual framework that researchers use to further understand complex biological processes.

Intramural Research Program (IRP) scientists work to understand how and why systems exhibit properties that cannot be concluded from studies of the individual components. Systems biology is a powerful tool to test theories about health and disease, particularly the outcomes of therapeutic interventions. Understanding cancer cells at the molecular level gives one type of insight, but understanding how these malignant cells interact in an organ offers a very different viewpoint.

Systems biology often involves collaborative work between multiple disciplines, including:

• Cell Biology

During this time of great discovery, as the research community generates increasingly specific genomic and proteomic data, predicting system behavior remains elusive. The IRP is a valuable resource for those wishing to apply new insights and collaborate across disciplines.

To learn more about how systems biology is making a difference in our understanding of health and disease, visit the Systems Biology Interest Group Web site .

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Noncanonical amino acids are efficiently incorporated into proteins by optimizing mRNA codon usage.

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Rapid discovery and evolution of nanosensors containing fluorogenic amino acids

Binding-activated optical sensors are powerful tools, but their development can be slow and laborious. Here, authors introduce a platform to expedite biosensor discovery and evolution using genetically encodable fluorogenic amino acids.

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Continuous multiplexed phage genome editing using recombitrons

Phage genomes are readily engineered using retron recombineering.

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Elastin-like polypeptide coacervates as reversibly triggerable compartments for synthetic cells

Compartmentalization within living cells is vital to orchestrate intracellular processes, but effective compartmentalization and organization within synthetic cells remains a key challenge. Here, the authors report a lab-on-a-chip system to reversibly trigger the formation of peptide-based coacervates as membraneless organelles via pH/temperature/osmolyte variations within cell-mimicking confinements.

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Fate induction in CD8 CAR T cells through asymmetric cell division

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Genetically engineered synthetic cells activate cargo release upon temperature shift

We combine RNA thermometer genetic switches, cell-free protein expression and synthetic cell design to create cell-sized systems that can initiate the synthesis of soluble proteins at defined temperatures. We show that when these switches are used to control the expression of a pore-forming membrane protein, temperature-controlled cargo release is achieved, with potential future applications in biomedicine.

Quantitative synthetic biology

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49 Most Interesting Biology Research Topics

August 21, 2023

In need of the perfect biology research topics—ideas that can both showcase your intellect and fuel your academic success? Lost in the boundless landscape of possible biology topics to research? And afraid you’ll never get a chance to begin writing your paper, let alone finish writing? Whether you’re a budding biologist hoping for a challenge or a novice seeking easy biology research topics to wade into, this blog offers curated and comprehensible options.

And if you’re a high school or transfer student looking for opportunities to immerse yourself in biology, consider learning more about research opportunities for high school students , top summer programs for high school students , best colleges for studying biomedical engineering , and best colleges for studying biology .

What is biology?

Well, biology explores the web of life that envelops our planet, from the teeny-tiny microbes to the big complex ecosystems. Biology investigates the molecular processes that define existence, deciphers the interplay of genes, and examines all the dynamic ways organisms interact with their environments. And through biology, you can gain not only knowledge, but a deeper appreciation for the interconnectedness of all living things. Pretty cool!

There are lots and lots of sub-disciplines within biology, branching out in all directions. Throughout this list, we won’t follow all of those branches, but we will follow many. And while none of these branches are truly simple or easy, some might be easier than others. Now we’ll take a look at a few various biology research topics and example questions that could pique your curiosity.

Climate change and ecosystems

The first of our potentially easy biology research topics: climate change and ecosystems. Investigate how ecosystems respond and adapt to the changing climate. And learn about shifts in species distributions , phenology , and ecological interactions .

1) How are different ecosystems responding to temperature changes and altered precipitation patterns?2) What are the implications of shifts in species distributions for ecosystem stability and functioning?

2) Or how does phenology change in response to climate shifts? And how do those changes impact species interactions?

3) Which underlying genetic and physiological mechanisms enable certain species to adapt to changing climate conditions?

4) And how do changing climate conditions affect species’ abilities to interact and form mutualistic relationships within ecosystems?

Microbiome and human health

Intrigued by the relationship between the gut and the rest of the body? Study the complex microbiome . You could learn how gut microbes influence digestion, immunity, and even mental health.

5) How do specific gut microbial communities impact nutrient absorption?

6) What are the connections between the gut microbiome, immune system development, and susceptibility to autoimmune diseases?

7) What ethical considerations need to be addressed when developing personalized microbiome-based therapies? And how can these therapies be safely and equitably integrated into clinical practice?

8) Or how do variations in the gut microbiome contribute to mental health conditions such as anxiety and depression?

9) How do changes in diet and lifestyle affect the composition and function of the gut microbiome? And what are the subsequent health implications?

Urban biodiversity conservation

Next, here’s another one of the potentially easy biology research topics. Examine the challenges and strategies for conserving biodiversity in urban environments. Consider the impact of urbanization on native species and ecosystem services. Then investigate the decline of pollinators and its implications for food security or ecosystem health.

10) How does urbanization influence the abundance and diversity of native plant and animal species in cities?

11) Or what are effective strategies for creating and maintaining green spaces that support urban biodiversity and ecosystem services?

12) How do different urban design and planning approaches impact the distribution of wildlife species and their interactions?

13) What are the best practices for engaging urban communities in biodiversity conservation efforts?

14) And how can urban agriculture and rooftop gardens contribute to urban biodiversity conservation while also addressing food security challenges?

Bioengineering

Are you a problem solver at heart? Then try approaching the intersection of engineering, biology, and medicine. Delve into the field of synthetic biology , where researchers engineer biological systems to create novel organisms with useful applications.

15) How can synthetic biology be harnessed to develop new, sustainable sources of biofuels from engineered microorganisms?

16) And what ethical considerations arise when creating genetically modified organisms for bioremediation purposes?

17) Can synthetic biology techniques be used to design plants that are more efficient at withdrawing carbon dioxide from the atmosphere?

18) How can bioengineering create organisms capable of producing valuable pharmaceutical compounds in a controlled and sustainable manner?

19) But what are the potential risks and benefits of using engineered organisms for large-scale environmental cleanup projects?

Neurobiology

Interested in learning more about what makes creatures tick? Then this might be one of your favorite biology topics to research. Explore the neural mechanisms that underlie complex behaviors in animals and humans. Shed light on topics like decision-making, social interactions, and addiction. And investigate how brain plasticity and neurogenesis help the brain adapt to learning, injury, and aging.

20) How does the brain’s reward circuitry influence decision-making processes in situations involving risk and reward?

21) What neural mechanisms underlie empathy and social interactions in both humans and animals?

22) Or how do changes in neural plasticity contribute to age-related cognitive decline and neurodegenerative diseases?

23) Can insights from neurobiology inform the development of more effective treatments for addiction and substance abuse?

24) What are the neural correlates of learning and memory? And how can our understanding of these processes be applied to educational strategies?

Plant epigenomics

While this might not be one of the easy biology research topics, it will appeal to plant enthusiasts. Explore how epigenetic modifications in plants affect their ability to respond and adapt to changing environmental conditions.

25) How do epigenetic modifications influence the expression of stress-related genes in plants exposed to temperature fluctuations?

26) Or what role do epigenetic changes play in plants’ abilities to acclimate to changing levels of air pollution?

27) Can certain epigenetic modifications be used as indicators of a plant’s adaptability to new environments?

28) How do epigenetic modifications contribute to the transgenerational inheritance of traits related to stress resistance?

29) And can targeted manipulation of epigenetic marks enhance crop plants’ ability to withstand changing environmental conditions?

Conservation genomics

Motivated to save the planet? Conservation genomics stands at the forefront of modern biology, merging the power of genetics with the urgent need to protect Earth’s biodiversity. Study genetic diversity, population dynamics, and how endangered species adapt in response to environmental changes.

30) How does genetic diversity within endangered species influence their ability to adapt to changing environmental conditions?

31) What genetic factors contribute to the susceptibility of certain populations to diseases, and how can this knowledge inform conservation strategies?

32) How can genomic data be used to inform captive breeding and reintroduction programs for endangered species?

33) And what are the genomic signatures of adaptation in response to human-induced environmental changes, such as habitat fragmentation and pollution?

34) Or how can genomics help identify “hotspots” of biodiversity that are particularly important for conservation efforts?

Zoonotic disease transmission

And here’s one of the biology research topics that’s been on all our minds in recent years. Investigate the factors contributing to the transmission of zoonotic diseases , like COVID-19. Then posit strategies for prevention and early detection.

35) What are the ecological and genetic factors that facilitate the spillover of zoonotic pathogens from animals to humans?

36) Or how do changes in land use, deforestation, and urbanization impact the risk of zoonotic disease emergence?

37) Can early detection and surveillance systems be developed to predict and mitigate the spread of zoonotic diseases?

38) How do social and cultural factors influence human behaviors that contribute to zoonotic disease transmission?

39) And can strategies be implemented to improve global pandemic preparedness?

Bioinformatics

Are you a data fanatic? Bioinformatics involves developing computational tools and techniques to analyze and interpret large biological datasets. This enables advancements in genomics, proteomics, and systems biology. So delve into the world of bioinformatics to learn how large-scale genomic and molecular data are revolutionizing biological research.

40) How can machine learning algorithms predict the function of genes based on their DNA sequences?

41) And what computational methods can identify potential drug targets by analyzing protein-protein interactions in large biological datasets?

42) Can bioinformatics tools be used to identify potential disease-causing mutations in human genomes and guide personalized medicine approaches?

43) What are the challenges and opportunities in analyzing “omics” data (genomics, proteomics, transcriptomics) to uncover novel biological insights?

44) Or how can bioinformatics contribute to our understanding of microbial diversity, evolution, and interactions within ecosystems?

Regenerative medicine

While definitely not one of the easy biology research topics, regenerative medicine will appeal to those interested in healthcare. Research innovative approaches to stimulate tissue and organ regeneration, using stem cells, tissue engineering, and biotechnology. And while you’re at it, discover the next potential medical breakthrough.

45) How can stem cells be directed to differentiate into specific cell types for tissue regeneration, and what factors influence this process?

46) Or what are the potential applications of 3D bioprinting in creating functional tissues and organs for transplantation?

47) How can bioengineered scaffolds enhance tissue regeneration and integration with host tissues?

48) What are the ethical considerations surrounding the use of stem cells and regenerative therapies in medical treatments?

49) And can regenerative medicine approaches be used to treat neurodegenerative disorders and restore brain function?

Biology Research Topics – Final thoughts

So as you take your next steps, try not to feel overwhelmed. And instead, appreciate the vast realm of possibilities that biology research topics offer. Because the array of biology topics to research is as diverse as the ecosystems it seeks to understand. And no matter if you’re only looking for easy biology research topics, or you’re itching to unravel the mysteries of plant-microbe interactions, your exploration will continue to deepen what we know of the world around us.

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Mariya holds a BFA in Creative Writing from the Pratt Institute and is currently pursuing an MFA in writing at the University of California Davis. Mariya serves as a teaching assistant in the English department at UC Davis. She previously served as an associate editor at Carve Magazine for two years, where she managed 60 fiction writers. She is the winner of the 2015 Stony Brook Fiction Prize, and her short stories have been published in Mid-American Review , Cutbank , Sonora Review , New Orleans Review , and The Collagist , among other magazines.

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• Frontiers in Systems Biology
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Education in Systems Biology 2022

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To question and explore the natural world is an exercise we have all performed as children. Systems Biology takes those same “big-life” questions we asked and explores them through the use of systematic mathematical and computational approaches and integrating them with experimentation. Currently, there is a ...

Keywords : Education, Systems Biology, Systems Thinking, Early-Stage Researcher

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