Bayesian Inference and Sequential Tempered Markov Chain Monte Carlo for Synthetic Biological Systems.
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Antibody-free processes are disclosed that provide accurate quantification of a wide variety of low-abundance target analytes in complex samples. The processes can employ high-pressure, high-resolution chromatographic separations for analyte enrichment. Intelligent selection of target fractions may be performed via on-line Selected Reaction Monitoring (SRM) or off-line rapid screening of internal standards. Quantification may be performed on individual or multiplexed fractions. Applications include analyses of, e.g., very low abundance proteins or candidate biomarkers in plasma, cell, or tissue samples without the need for affinity-specific reagents.
Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developed SBML Level 3 over the past decade. Its modular form consists of a core suited to representing reaction-based models and packages that extend the core with features suited to other model types including constraint-based models, reaction-diffusion models, logical network models, and rule-based models. The format leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single-cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and how SBML Level 3 provides the foundation needed to support this evolution.
The 2024 International Conference on Microbiome Engineering (ICME) took place November 12-14 at Tufts University in Medford, MA. ICME connects experts from academia and industry to share the most recent developments in the field of microbiome engineering. This includes genetically engineered organisms that function within microbiomes, control of microbiomes through environmental/nutrient modifications, and inference of engineering principles from analysis of synthetic and natural microbiomes. The conference is unique and distinct from other microbiome conferences in that it specifically highlights the integration of engineering design principles with microbiome research (others are more focused on basic biological principles). The conference thus integrates synthetic biology, systems biology, microbial ecology, and bioinformatics across a range of application spaces from the environment to manufacturing, food, and human health. This project utilized support from the Department of Energy’s (DOE) Office of Biological and Environmental Research (BER) to help trainees and early career faculty attend ICME.
The biological reduction of CO 2 into organic carbon is integral to the formation and maintenance of all life on earth. An urgent need to mitigate the effects of CO 2 on our atmosphere has provided an imperative to exploit biological CO 2 fixation for the conversion of CO2 into valuable and energy rich fuels. Photosynthesis uses only sunlight, water, and CO2 as starting materials to form all biomass.
Plant-derived biomass is the most abundant biogenic carbon source on Earth. Despite this, only a small clade of organisms known as white-rot fungi (WRF) can efficiently break down both the polysaccharide and lignin components of plant cell walls. This unique ability imparts a key role for WRF in global carbon cycling and highlights their potential utilization in diverse biotechnological applications. To date, research on WRF has primarily focused on their extracellular 'digestive enzymes', while knowledge of their intracellular metabolism remains underexplored. Systems biology has been demonstrated to be a powerful approach to elucidate biological processes in numerous organisms, including WRF. Thus, here we review systems biology methods applied to WRF to date, highlight observations related to their intracellular metabolism, and conduct a comparative extracellular proteomic analysis to establish further correlations between WRF species, enzymes, and cultivation conditions. Lastly, we discuss biotechnological opportunities of WRF as well as challenges and future research directions.
Rapid population growth, a rise in global living standards, and economic competitiveness have intensified the need for sustainable, low-cost biofuels and bioproducts production. Industrial biotechnology using microbial cell factories – and waste and/or renewable feedstocks – is one of the most attractive approaches for addressing this need, particularly when large-scale chemical synthesis is untenable. Unfortunately, designing, building, and optimizing biosynthetic pathways in cells remains a complex challenge. With support from the Department of Energy, we worked to address this challenge in a new interdisciplinary venture that established the world’s first clostridial Foundry for Biosystems Design (cBioFAB). Working both in vitro (cell-free) and in vivo, the goal of this project was to interweave and advance state-of-the-art computational modeling, genome editing, omics measurements, systems-biology analyses, and cell-free technologies to expand the set of platform organisms that meet DOE bioenergy goals. Specifically, we manufactured fuel and chemical intermediates via existing and de novo pathways. This report covers the outcomes of our research project.
As a major supporter of basic genome-enabled research, BER’s Biological Systems Science Division (BSSD) fosters scientific discovery by funding - fundamental biological research across disciplines in conjunction with enabling investigational tools and computational capabilities that include world-class user facilities. The overarching goal of BSSD is to provide the necessary fundamental science to understand, predict, manipulate, and design biological systems that underpin innovations for bioenergy and bioproduct production and enhance understanding of natural, DOE-relevant environmental processes (Biological Systems Science Division Strategic Plan, 2021). To accelerate the U.S. bioeconomy, BSSD pursues innovative science underpinning advances in sustainable biofuels and bioproducts and the development of next-generation technologies and computational resources for systems biology research. The 2024 BSSD Enabling Capabilities and Resources (ECR) Principal Investigator (PI) meeting brought together PIs across the BSSD ECR portfolio to confer on shared interests and opportunities. The meeting was held concurrently with the Genomic Science program (GSP) PI meeting to optimize collaboration on research to advance bioenergy and the bioeconomy. Rick Stevens of Argonne National Laboratory gave a keynote on How Generative Artificial Intelligence Can Impact Biological Research (see Keynote: How Generative Artificial Intelligence Can Impact Biological Research, this page). Plenary presentations included several joint sessions that illuminated the integration and understanding of the larger BSSD mission. GSP’s objective is to provide systems-level understanding of plants, microbes, and their communities through its Bioenergy Research, Biosystems Design, and Environmental Microbiome Research portfolios. The objective of the ECR portfolio is to support development of computational and instrumental platforms to advance fundamental GSP research—and BER more broadly— toward the overall goal of understanding the functional principles of living systems and their response to environmental challenges.
Gaining a thorough understanding of biological systems requires building our knowledge about biological processes from the level of atoms and electrons, and up to whole organisms. Such comprehensive knowledge will allow for a predictive understanding of complex biological systems behavior. It will guide us in the design and development of novel therapeutics and vaccines to tackle existing health threats and to prepare for future pandemics, and it will provide information necessary to create new biomaterials and bio-inspired technologies through manipulation of biological macromolecules, their assemblies, single cells and even microorganisms. Reaching these goals will require a synergistic combination of multiple experimental techniques with molecular calculations and predictive simulations, and the design and development of new techniques and capabilities that bridge current knowledge and technology gaps. Neutron scattering provides unique information about the biomacromolecular structure and function and can play a major role in achieving these goals. A workshop was held to engage the scientific community in identifying pressing challenges in biochemistry, structural biology, enzymology and structure-guided drug design not solved with the current neutron scattering technologies or utilizing other structural biology techniques such as X-ray crystallography, NMR, and cryo-EM. The workshop brought together structural biology, biochemistry and computational experts, as well as early career researchers and students, creating a forum for discussing scientific advancement and collaboration. The workshop included a one-day satellite training workshop where graduate students and postdoctoral researchers were educated in the application of neutron crystallography and small-angle scattering in structural biology. Furthermore, the Instrument Scientific Advisory Board (ISAB) for the development of a macromolecular neutron diffractometer at ORNL’s Second Target Station was introduced at the workshop. The major outcome was that neutrons can provide atomic-level understanding of biomacromolecular structure, function and dynamics which is of paramount importance for addressing the identified challenges. Neutron crystallography, in particular, can resolve long-standing biochemical issues regarding enzyme function by delineating the underlying chemistry and can have a major impact on the design of small-molecule therapeutics, especially in combination with molecular computation (quantum chemistry and molecular dynamics simulations) and the emerging artificial intelligence (AI)-assisted drug design technologies. The unique properties of neutrons, including their high sensitivity to hydrogen and their non-destructive nature, make them ideal probes of biological matter. There is a palpable need in the scientific community to expand and enhance the impact of neutron sciences on biology. Neutron crystallography is the only structural biology method capable of determining positions of all hydrogen atoms in proteins, nucleic acids and their complexes at near-physiological temperatures and of unstable species at cryogenic temperatures. Moreover, neutron analysis is non-ionizing, non-destructive and does not perturb the structure or redox chemistry of active site metal centers and clusters in proteins, which can be invaluable for studying radiation-sensitive metalloprotein complexes. Further, neutron energies used in scattering applications are similar to atomic motions, permitting neutron spectroscopies to characterize the dynamics of biomacromolecules on the picosecond to microsecond timescales. The different sensitivities of neutrons to protium (H) and deuterium (D) isotopes of hydrogen allow enhanced visibility of specific parts of biological complexes through isotopic labeling. The impact of neutrons will be most powerful when neutron scattering is combined with complementary experimental techniques that use photons and electrons, and with high-performance computing. The interconnection and mutuality of the experimental and theoretical capabilities will drive discoveries in biological and health sciences to generate more complete picture of complex biological systems. The major limitation in the field of biological neutron crystallography has been signal-to-noise, demanding large samples that are difficult to produce for the majority of biomacromolecules and limiting the applicability of this technique in biological sciences. A neutron crystallography instrument at the Second Target Station will revolutionize biological science with neutrons by engaging a large scientific community of structural biologists, enabling successful neutron diffraction experiments from radically smaller biomacromolecular crystals, resolving unanswered biochemical questions, and meaningfully contributing to rational drug design. The meeting highlighted 10 grand challenges that will be addressed with this advanced capability over the next decade and beyond, and the recommendations required to help address them are given below.
Selenium is released into the environment via anthropogenic activities such as coal combustion. The U.S. Environmental Protection Agency’s Effluent Limitation Guideline (ELG) Rule limits the release of selenium species into U.S. waterways. To meet the ELG limit for selenium, coal-fired power plants must treat the wastewater produced by the flue gas desulfurization (FGD) systems. Selenium removal is of interest because small changes of the concentrations of selenium in the environment can cause detrimental ecological consequences. Biological treatment is used to remediate this wastewater, but little is known about the individual species in the microbial consortium that reduce toxic water-soluble selenium oxyanions to insoluble elemental selenium. To optimize current biological treatment capabilities of this water, the selenium-reducing capabilities must be leveraged. In order to do so, the selenium-reducing bacteria (SeRB) in biological treatment systems must be characterized. This study will be the first to characterize the microbial consortia from a real FGD WW biological treatment system and provide insights on how to better achieve selenium removal via biological treatment. This work will provide insights into the microorganisms predominantly responsible for the reduction of selenium oxyanions in FGD wastewater biological treatment systems, which can be used to better optimize more efficient biological treatment strategies of this industrial waste.
Plant–microbe interactions are of rising interest in plant sustainability, biomass production, plant biology, and systems biology. These interactions have been a challenge to detect until recent advancements in mass spectrometry imaging. Plants and microbes interact in four main regions within the plant, the rhizosphere, endosphere, phyllosphere, and spermosphere. This mini review covers the challenges within investigations of plant and microbe interactions. We highlight the importance of sample preparation and comparisons among time-of-flight secondary ion mass spectroscopy (ToF-SIMS), matrix-assisted laser desorption/ionization (MALDI), laser desorption ionization (LDI/LDPI), and desorption electrospray ionization (DESI) techniques used for the analysis of these interactions. Using mass spectral imaging (MSI) to study plants and microbes offers advantages in understanding microbe and host interactions at the molecular level with single-cell and community communication information. More research utilizing MSI has emerged in the past several years. We first introduce the principles of major MSI techniques that have been employed in the research of microorganisms. An overview of proper sample preparation methods is offered as a prerequisite for successful MSI analysis. Traditionally, dried or cryogenically prepared, frozen samples have been used; however, they do not provide a true representation of the bacterial biofilms compared to living cell analysis and chemical imaging. New developments such as microfluidic devices that can be used under a vacuum are highly desirable for the application of MSI techniques, such as ToF-SIMS, because they have a subcellular spatial resolution to map and image plant and microbe interactions, including the potential to elucidate metabolic pathways and cell-to-cell interactions. Promising results due to recent MSI advancements in the past five years are selected and highlighted. The latest developments utilizing machine learning are captured as an important outlook for maximal output using MSI to study microorganisms.
During the last three decades, a series of key technological improvements turned atomic force microscopy (AFM) into a nanoscopic laboratory to directly observe and chemically characterize molecular and cell biological systems under physiological conditions. In this work, we review key technological improvements that have established AFM as an analytical tool to observe and quantify native biological systems from the micro- to the nanoscale. Native biological systems include living tissues, cells, and cellular components such as single or complexed proteins, nucleic acids, lipids, or sugars. We showcase the procedures to customize nanoscopic chemical laboratories by functionalizing AFM tips and outline the advantages and limitations in applying different AFM modes to chemically image, sense, and manipulate biosystems at (sub)nanometer spatial and millisecond temporal resolution. We further discuss theoretical approaches to extract the kinetic and thermodynamic parameters of specific biomolecular interactions detected by AFM for single bonds and extend the discussion to multiple bonds. Finally, we highlight the potential of combining AFM with optical microscopy and spectroscopy to address the full complexity of biological systems and to tackle fundamental challenges in life sciences.
Methods and systems for the biological conversion of pretreated or solubilized coal or waste coal into biofuels. Coal (10) may be pretreated perhaps in a pretreatment reactor (13). Pretreated coal or even solubilized coal may be introduced into a processing reactor such as a bioreactor (16) containing a plurality of microorganisms (9) such as oleaginous microorganisms which can convert at least some of the pretreated or solubilized coal into lipids (19) or biomass (18), which then may be used directly or as a precursor for various products such as biofuels, feedstock, or the like.
Motivation: Representing biological networks as graphs is a powerful approach to reveal underlying patterns, signatures, and critical components from high-throughput biomolecular data. However, graphs do not natively capture the multi-way relationships present among genes and proteins in biological systems such as protein complexes, metabolic reactions, and signal transduction pathways. Hypergraphs are generalizations of graphs that naturally model multi-way interactions in data, and we therefore seek to understand how they can more faithfully identify, and potentially predict, complex relationships in genomic expression data sets. Results: We compiled a novel data set of transcriptional host response to pathogenic viral infections and formulated relationships between genes as a hypergraph where hyperedges are differentially expressed genes and vertices represent conditions. We find that hypergraph betweenness centrality is a superior method for identification of genes important to viral response when compared with graph centrality. Our results demonstrate the utility of using hypergraphs to represent complex biological systems, and highlight potentially interesting biological results about host response to highly pathogenic viruses.
In many biological systems, the curvature of the surfaces cells live on influences their collective properties. Curvature should likewise influence the behavior of active colloidal particles. We show using molecular simulation of self-propelled active particles on surfaces of Gaussian curvature (both positive and negative) how curvature sign and magnitude can alter the system's collective behavior. Curvature acts as a geometrical lens and shifts the critical density of motility-induced phase separation (MIPS) to lower values for positive curvature and higher values for negative curvature, which we explain theoretically by the nature of parallel lines in spherical and hyperbolic space. Curvature also fluidizes dense MIPS clusters due to the emergence of defect patterns disrupting the crystalline order inside the clusters. Using our findings, we engineer three confining surfaces that strategically combine regions of different curvature to produce a host of novel dynamical behaviors, including cyclic MIPS on spherocylinders, directionally biased cyclic MIPS on spherocones, and position dependent cluster fluctuations on metaballs.
Biomanufacturing is a key component of biotechnology that uses biological systems to produce bioproducts of commercial relevance, which are of great interest to the energy, material, pharmaceutical, food, and agriculture industries. Biotechnology-based approaches, such as synthetic biology and metabolic engineering are heavily reliant on “omics” driven systems biology to characterize and understand metabolic networks. Knowledge gained from systems biology experiments aid the development of synthetic biology tools and the advancement of metabolic engineering studies toward establishing robust industrial biomanufacturing platforms. In this review, we discuss recent advances in “omics” technologies, compare the pros and cons of the different “omics” technologies, and discuss the necessary requirements for carrying out multi-omics experiments. We highlight the influence of “omics” technologies on the production of biofuels and bioproducts by metabolic engineering. Finally, we discuss the application of “omics” technologies to agricultural and food biotechnology, and review the impact of “omics” on current COVID-19 research.
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White-rot fungi (WRF) are the most effective lignin-degrading organisms in nature, making them essential to Earth’s carbon cycle. Lignin is a highly methoxylated, heterogeneous biopolymer derived from plants. However, the pathways WRF use to metabolize methoxylated aromatic monomericcompounds as carbon sources remain unidentified. Here, we employ a systems biology approach to elucidate the intracellular catabolism of vanillate – a monomethoxylated aromatic compound – in two white-rot fungi (WRF), Gelatoporia subvermispora and Trametes versicolor. We identified and biochemically validated a four-enzyme pathway that converts vanillate into ß-ketoadipate – a metabolite that enters central carbon metabolism. This pathway deviates from typical bacterial pathways, where vanillate is initially demethylated and ring-cleaved by intradiol dioxygenases; instead, oxidative decarboxylation occurs prior to ring cleavage by extradiol dioxygenases. Thus, we conducted an in-depth investigation of ring cleavage and further downstream catabolism by the identified fungal enzymes using biochemical and structural approaches. This revealed non-canonical enzymes, including a highly substrate-specific extradiol dioxygenase and a metal-free, promiscuous reductase, the latter capable of acting on catabolic intermediates derived from both methoxylated and non-methoxylated aromatic compounds. This work emphasizes the potential of WRF and their enzymes to advance lignin valorization and enhance our understanding of their role during wood decay.