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High performance computing has a great potential to provide a range of significant benefits for investigating biological systems. These systems often present large modelling problems with many coupled subsystems, such as when studying colonies of bacteria cells. The aim to understand cell colonies has generated substantial interest as they can have strong economic and societal impacts through their roles in in industrial bioreactors and complex community structures, called biofilms, found in clinical settings. Investigating these communities through realistic models can rapidly exceed the capabilities of current serial software. Here, we introduce BMX, a software system developed for the high performance modelling of large cell communities by utilising GPU acceleration. BMX builds upon the AMRex adaptive mesh refinement package to efficiently model cell colony formation under realistic laboratory conditions. Using simple test scenarios with varying nutrient availability, we show that BMX is capable of correctly reproducing observed behavior of bacterial colonies on realistic time scales demonstrating a potential application of high performance computing to colony modelling. The open source software is available from the zenodo repository https://doi.org/10.5281/zenodo.8084270 under the BSD-2-Clause licence.

We demonstrate and characterize a first-principles approach to modeling the mass action dynamics of metabolism. Starting from a basic definition of entropy expressed as a multinomial probability density using Boltzmann probabilities with standard chemical potentials, we derive and compare the free energy dissipation and the entropy production rates. We express the relation between entropy production and the chemical master equation for modeling metabolism, which unifies chemical kinetics and chemical thermodynamics. Because prediction uncertainty with respect to parameter variability is frequently a concern with mass action models utilizing rate constants, we compare and contrast the maximum entropy model, which has its own set of rate parameters, to a population of standard mass action models in which the rate constants are randomly chosen. We show that a maximum entropy model is characterized by a high probability of free energy dissipation rate and likewise entropy production rate, relative to other models. We then characterize the variability of the maximum entropy model predictions with respect to uncertainties in parameters (standard free energies of formation) and with respect to ionic strengths typically found in a cell. [ABSTRACT FROM AUTHOR]

Machine learning is increasingly recognized as a promising technology in the biological, biomedical, and behavioral sciences. There can be no argument that this technique is incredibly successful in image recognition with immediate applications in diagnostics including electrophysiology, radiology, or pathology, where we have access to massive amounts of annotated data. However, machine learning often performs poorly in prognosis, especially when dealing with sparse data. This is a field where classical physics-based simulation seems to remain irreplaceable. In this review, we identify areas in the biomedical sciences where machine learning and multiscale modeling can mutually benefit from one another: Machine learning can integrate physics-based knowledge in the form of governing equations, boundary conditions, or constraints to manage ill-posted problems and robustly handle sparse and noisy data; multiscale modeling can integrate machine learning to create surrogate models, identify system dynamics and parameters, analyze sensitivities, and quantify uncertainty to bridge the scales and understand the emergence of function. With a view towards applications in the life sciences, we discuss the state of the art of combining machine learning and multiscale modeling, identify applications and opportunities, raise open questions, and address potential challenges and limitations. We anticipate that it will stimulate discussion within the community of computational mechanics and reach out to other disciplines including mathematics, statistics, computer science, artificial intelligence, biomedicine, systems biology, and precision medicine to join forces towards creating robust and efficient models for biological systems.

Experimental measurements or computational model predictions of the post-translational regulation of enzymes needed in a metabolic pathway is a difficult problem. Consequently, regulation is mostly known only for well-studied reactions of central metabolism in various model organisms. In this study, we use two approaches to predict enzyme regulation policies and investigate the hypothesis that regulation is driven by the need to maintain the solvent capacity in the cell. The first predictive method uses a statistical thermodynamics and metabolic control theory framework while the second method is performed using a hybrid optimization-reinforcement learning approach. Efficient regulation schemes were learned from experimental data that either agree with theoretical calculations or result in a higher cell fitness using maximum useful work as a metric. As previously hypothesized, regulation is herein shown to control the concentrations of both immediate and downstream product concentrations at physiological levels. Model predictions provide the following two novel general principles: (1) the regulation itself causes the reactions to be much further from equilibrium instead of the common assumption that highly non-equilibrium reactions are the targets for regulation; and (2) the minimal regulation needed to maintain metabolite levels at physiological concentrations maximizes the free energy dissipation rate instead of preserving a specific energy charge. The resulting energy dissipation rate is an emergent property of regulation which may be represented by a high value of the adenylate energy charge. In addition, the predictions demonstrate that the amount of regulation needed can be minimized if it is applied at the beginning or branch point of a pathway, in agreement with common notions. The approach is demonstrated for three pathways in the central metabolism of E. coli (gluconeogenesis, glycolysis-tricarboxylic acid (TCA) and pentose phosphate-TCA) that each require different regulation schemes. It is shown quantitatively that hexokinase, glucose 6-phosphate dehydrogenase and glyceraldehyde phosphate dehydrogenase, all branch points of pathways, play the largest roles in regulating central metabolism.

Fueled by breakthrough technology developments, the biological, biomedical, and behavioral sciences are now collecting more data than ever before. There is a critical need for time- and cost-efficient strategies to analyze and interpret these data to advance human health. The recent rise of machine learning as a powerful technique to integrate multimodality, multifidelity data, and reveal correlations between intertwined phenomena presents a special opportunity in this regard. However, machine learning alone ignores the fundamental laws of physics and can result in ill-posed problems or non-physical solutions. Multiscale modeling is a successful strategy to integrate multiscale, multiphysics data and uncover mechanisms that explain the emergence of function. However, multiscale modeling alone often fails to efficiently combine large datasets from different sources and different levels of resolution. Here we demonstrate that machine learning and multiscale modeling can naturally complement each other to create robust predictive models that integrate the underlying physics to manage ill-posed problems and explore massive design spaces. We review the current literature, highlight applications and opportunities, address open questions, and discuss potential challenges and limitations in four overarching topical areas: ordinary differential equations, partial differential equations, data-driven approaches, and theory-driven approaches. Towards these goals, we leverage expertise in applied mathematics, computer science, computational biology, biophysics, biomechanics, engineering mechanics, experimentation, and medicine. Our multidisciplinary perspective suggests that integrating machine learning and multiscale modeling can provide new insights into disease mechanisms, help identify new targets and treatment strategies, and inform decision making for the benefit of human health.

Transcriptional and translational feedback loops in fungi and animals drive circadian rhythms in transcript levels that provide output from the clock, but post-transcriptional mechanisms also contribute. To determine the extent and underlying source of this regulation, we applied newly developed analytical tools to a long-duration, deeply sampled, circadian proteomics time course comprising half of the proteome. We found a quarter of expressed proteins are clock regulated, but >40% of these do not arise from clock-regulated transcripts, and our analysis predicts that these protein rhythms arise from oscillations in translational rates. Our data highlighted the impact of the clock on metabolic regulation, with central carbon metabolism reflecting both transcriptional and post-transcriptional control and opposing metabolic pathways showing peak activities at different times of day. The transcription factor CSP-1 plays a role in this metabolic regulation, contributing to the rhythmicity and phase of clock-regulated proteins.

We report the application of a recently proposed approach for modeling biological systems using a maximum entropy production rate principle in lieu of having in vivo rate constants. The method is applied in four steps: (1) a new ordinary differential equation (ODE) based optimization approach based on Marcelins 1910 mass action equation is used to obtain the maximum entropy distribution; (2) the predicted metabolite concentrations are compared to those generally expected from experiments using a loss function from which post-translational regulation of enzymes is inferred; (3) the system is re-optimized with the inferred regulation from which rate constants are determined from the metabolite concentrations and reaction fluxes; and finally (4) a full ODE-based, mass action simulation with rate parameters and allosteric regulation is obtained. From the last step, the power characteristics and resistance of each reaction can be determined. The method is applied to the central metabolism of Neurospora crassa and the flow of material through the three competing pathways of upper glycolysis, the non-oxidative pentose phosphate pathway, and the oxidative pentose phosphate pathway are evaluated as a function of the NADP/NADPH ratio. It is predicted that regulation of phosphofructokinase (PFK) and flow through the pentose phosphate pathway are essential for preventing an extreme level of fructose 1,6-bisphophate accumulation. Such an extreme level of fructose 1,6-bisphophate would otherwise result in a glassy cytoplasm with limited diffusion, dramatically decreasing the entropy and energy production rate and, consequently, biological competitiveness.

For 26 years, it has been assumed by some that the thermodynamics of open-system biochemical reactions must be executed by performing Legendre transformations on the terms involving the species whose concentrations are being held fixed. In contrast, standard nontransformed thermodynamics applies to chemical processes. However, it has recently been shown that such biochemical reactions may be accurately examined using either method. The papers that report this finding use the hydrolysis of ATP at fixed pH and pMg as an example. This biochemical process comprises 14 equilibrium reactions involving 17 chemical species. Consequently, the chemical and mathematical complexity is so high that the underlying principles leading to the equivalence of the two methods tend to become lost. Furthermore, the details of such an example are too complex for classroom presentation. This paper makes these principles abundantly clear by the thermodynamic examination of the simple case of a unimolecular isomerization conducted under both open and closed conditions. For the open system, the analysis is conducted using both Legendre-transformed and nontransformed methods. The results are shown to be identical provided that the chemical potentials of the terms on which the transform is performed are held constant. More importantly, the analysis makes the underlying reasons for the equivalence of the two methods very clear and shows when they will not be equivalent. The model is ideally suited for classroom presentation because of its chemical and mathematical simplicity.

A wide range of length and time scales are relevant to pharmacology, especially in drug development, drug design and drug delivery. Therefore, multiscale computational modeling and simulation methods and paradigms that advance the linkage of phenomena occurring at these multiple scales have become increasingly important. Multiscale approaches present in silico opportunities to advance laboratory research to bedside clinical applications in pharmaceuticals research. This is achievable through the capability of modeling to reveal phenomena occurring across multiple spatial and temporal scales, which are not otherwise readily accessible to experimentation. The resultant models, when validated, are capable of making testable predictions to guide drug design and delivery. In this review we describe the goals, methods, and opportunities of multiscale modeling in drug design and development. We demonstrate the impact of multiple scales of modeling in this field. We indicate the common mathematical and computational techniques employed for multiscale modeling approaches used in pharmacometric and systems pharmacology models in drug development and present several examples illustrating the current state-of-the-art models for (1) excitable systems and applications in cardiac disease; (2) stem cell driven complex biosystems; (3) nanoparticle delivery, with applications to angiogenesis and cancer therapy; (4) host-pathogen interactions and their use in metabolic disorders, inflammation and sepsis; and (5) computer-aided design of nanomedical systems. We conclude with a focus on barriers to successful clinical translation of drug development, drug design and drug delivery multiscale models.

Rattlesnake Cave is located on the western shore of Lake Abert in the northern Great Basin of southeast Oregon, one of hundreds of archaeological sites in the Lake Abert/Chewaucan Basin. The site was dug by collectors in the 1950s, and recovered materials were donated to the Fort Rock Valley Historical Society and Homestead Museum in the early 1990s. We analyze 77 artifacts in the assemblage, which includes cordage, basketry, moccasins, as well as wood, bone, and stone tools. We report new radiocarbon (14C) dates for the site, and the results of energy dispersive x-ray fluorescence (EDXRF) on one basalt and nine obsidian bifaces, matching their chemical signatures to regional geologic sources. We discuss the place of Rattlesnake Cave in the broader context of the northern Great Basin while demonstrating how museum collections may contribute to addressing anthropological research questions.

Adovasio et al. (1986) described Early Holocene basketry from the northern Great Basin as "simple twined and undecorated." Cressman (1986) reported the presence of decorated basketry during the Early Holocene, which he characterized as a "climax of cultural development" in the Fort Rock Basin in Oregon. These contrasting interpretations are the product of relatively small basketry assemblages reliably dated to the Early Holocene from this area, as well as the questionable recovery context of some critical specimens. We report on the direct AMS dating of a number of basketry specimens central to this issue. Early Holocene basketry from the northern Great Basin does include decorated and complex structures; however, since most of the dated specimens fall toward the end of the Early Holocene, the evidence presented here does not provide definitive closure to the issue.

Large-amplitude gravity and magnetic highs over northeast Iowa are interpreted to reflect a buried intrusive complex composed of mafic-ultramafic rocks, the northeast Iowa intrusive complex (NEIIC), intruding Yavapai province (1.8-1.72 Ga) rocks. The age of the complex is unproven, although it has been considered to be Keweenawan (~1.1 Ga). Because only four boreholes reach the complex, which is covered by 200-700 m of Paleozoic sedimentary rocks, geophysical methods are critical to developing a better understanding of the nature and mineral resource potential of the NEIIC. Lithologic and cross-cutting relations interpreted from high-resolution aeromagnetic and airborne gravity gradient data are presented in the form of a preliminary geologic map of the basement Precambrian rocks. Numerous magnetic anomalies are coincident with airborne gravity gradient (AGG) highs, indicating widespread strongly magnetized and dense rocks of likely mafic-ultramafic composition. A Yavapai-age metagabbro unit is interpreted to be part of a layered intrusion with subvertical dip. Another presumed Yavapai unit has low density and weak magnetization, observations consistent with felsic plutons. Northeast-trending, linear magnetic lows are interpreted to reflect reversely magnetized diabase dikes and have properties consistent with Keweenawan rocks. The interpreted dikes are cut in places by normally magnetized mafic-ultramafic rocks, suggesting that the latter represent younger Keweenawan rocks. Distinctive horseshoe-shaped magnetic and AGG highs correspond with a known gabbro, and surround rocks with weaker magnetization and lower density. Here, informally called the Decorah complex, the source body has notable geophysical similarities to Keweenawan alkaline ring complexes, such as the Coldwell and Killala Lake complexes, and Mesoproterozoic anorogenic complexes, such as the Kiglapait, Hettasch, and Voisey's Bay intrusions in Labrador. Results presented here suggest that much of the NEIIC is composed of such complexes, and broadly speaking, may be a discontinuous group of several intrusive bodies. Most units are cut by suspected northwest-trending faults imaged as magnetic lineaments, and one produces apparent sinistral fault separation of a dike in the eastern part of the survey area. The location, trend, and apparent sinistral sense of motion are consistent with the suspected faults being part of the Belle Plaine fault zone, a complex transform fault zone within the Midcontinent rift system that is here proposed to correspond with a major structural discontinuity. De grandes cretes magnetiques et gravimetriques sur le nord-est de l'Iowa sont interpretees comme etant le reflet d'un complexe intrusif enfoui compose de roches mafiques et ultramafiques, soit le complexe intrusif du nord-est de l'Iowa (NEIIC), lequel penetre les roches de la Province de Yavapai (1,8-1,72 Ga). L'age du complexe n'a pas ete prouve, bien qu'il soit considere comme datant du Keweenawien (~1.1 Ga). Puisque seulement quatre sondages ont atteint le complexe, lequel est recouvert de 200 a 700 m de roches sedimentaires paleozoiques, les methodes geophysiques sont essentielles au developpement d'une meilleure comprehension de la nature et du potentiel mineral du NEIIC, Des relations lithologiques et de recoupement, interpretees a partir de donnees aeromagnetiques et de gradients de gravite aeroportes a haute resolution, sont presentees sous la forme d'une carte geologique preliminaire des roches du socle precambrien. De nombreuses anomalies magnetiques coincident avec les cretes des gradients de gravite aeroportes (AGG), indiquant des roches denses fortement magnetisees, probablement de composition mafique-ultramafique. Une unite de metagabbro d'age Yavapai est interpretee comme faisant partie d'une intrusion stratifiee avec un pendage subvertical. Une autre unite, presumee Yavapai, a une faible densite et une faible magnetisation; ce qui concorde avec des plutons felsiques. Des creux magnetiques, a tendance nord-est, sont interpretes comme etant le reflet de dykes de diabase a magnetisation inverse; leurs proprietes concordent avec celles de roches keweenawiennes. Les dykes interpretes sont par endroits recoupes par des roches mafiques-ultramafiques a magnetisation normale, suggerant que ces dernieres representent des roches keweenawiennes plus jeunes. Des cretes distinctes magnetiques et d'AGG en forme de fer a cheval correspondent a un gabbro connu et elles entourent des roches a magnetisation plus faible et a densite moindre. L'amas source, ici nomme de maniere informelle complexe de Decorah, presente des similarites geophysiques remarquables aux complexes annulaires alcalins keweenawiens, tels que les complexes de Coldwell et de Killala Lake, et a des complexes anorogeniques datant du Mesoproterozoique, tels que les intrusions de Kiglapait, de Hettasch et de Voisey's Bay au Labrador. Selon les resultats presentes ici, une grande partie du NEIIC serait composee de tels complexes et, de maniere generale, elle pourrait constituer un groupe discontinu de plusieurs amas intrusifs. La plupart des unites sont recoupees par des failles presumees a tendance nord-ouest dont l'imagerie donne des lineaments magnetiques; une unite a donne une separation, par une faille apparente senestre, d'un dyke dans la partie est du secteur a l'etude. L'endroit, la tendance et le sens senestre apparent de mouvement concordent avec le fait que les failles en question appartiendraient a la zone de faille de Belle Plaine, une zone de failles transformantes dans le systeme de rifts medio-continental que l'on propose ici correspondre a une discontinuite structurale majeure. [Traduit par la Redaction], Introduction Mineral exploration trends indicate that exploration for deposits in crystalline rocks under sedimentary cover is increasing, as exposed and shallowly buried deposits are discovered less frequently (Witherly 2012; Williams [...]

Carbon dots (CDots) are small carbon nanoparticles with effective surface passivation by organic functionalization. In the reported work, the surface functionalization of preexisting small carbon nanoparticles with N-ethylcarbazole (NEC) was achieved by the NEC radical addition. Due to the major difference in microwave absorption between the carbon nanoparticles and organic species such as NEC, the nanoparticles could be selectively heated via microwave irradiation to enable the hydrogen abstraction in NEC to generate NEC radicals, followed by in situ additions of the radicals to the nanoparticles. The resulting NEC-CDots were characterized by microscopy and spectroscopy techniques including quantitative proton and 13C NMR methods. The optical spectroscopic properties of the dot sample were found to be largely the same as those of CDots from other organic functionalization schemes. The high structural stability of NEC-CDots benefiting from the radical addition functionalization is highlighted and discussed. [ABSTRACT FROM AUTHOR]

In the Lake Superior region of North America, deposition of most banded iron formations (BIFs) ended abruptly 1.85 Ga ago, coincident with the oceanic impact of the giant Sudbury extraterrestrial bolide. We propose a new model in which this impact produced global mixing of shallow oxic and deep anoxic waters of the Paleoproterozoic ocean, creating a suboxic redox state for deep seawater. This suboxic state, characterized by only small concentrations of dissolved [O.sub.2] (~1 [micro]M), prevented transport of hydrothermally derived Fe(II) from the deep ocean to continental-margin settings, ending an ~1.1 billion-year-long period of episodic BIF mineralization. The model is supported by the nature of Precambrian deep-water exhalative chemical sediments, which changed from predominantly sulfide facies prior to ca. 1.85 Ga to mainly oxide facies thereafter. doi: 10.1130/G30259A.1