Unveiling the potential of specific growth rate control in fed-batch fermentation: bridging the gap between product quantity and quality.

During the epoch of sustainable development, leveraging cellular systems for production of diverse chemicals via fermentation has garnered attention. Industrial fermentation, extending beyond strain efficiency and optimal conditions, necessitates a profound understanding of microorganism growth characteristics. Specific growth rate (SGR) is designated as a key variable due to its influence on cellular physiology, product synthesis rates and end-product quality. Despite its significance, the lack of real-time measurements and robust control systems hampers SGR control strategy implementation. The narrative in this contribution delves into the challenges associated with the SGR control and presents perspectives on various control strategies, integration of soft-sensors for real-time measurement and control of SGR. The discussion highlights practical and simple SGR control schemes, suggesting their seamless integration into industrial fermenters. Recommendations provided aim to propose new algorithms accommodating mechanistic and data-driven modelling for enhanced progress in industrial fermentation in the context of sustainable bioprocessing.

Significant impact of time-of-day variation on metformin pharmacokinetics.

AIMS/HYPOTHESIS: The objective was to investigate if metformin pharmacokinetics is modulated by time-of-day in humans using empirical and mechanistic pharmacokinetic modelling techniques on a large clinical dataset. This study also aimed to generate and test hypotheses on the underlying mechanisms, including evidence for chronotype-dependent interindividual differences in metformin plasma and efficacy-related tissue concentrations. METHODS: A large clinical dataset consisting of individual metformin plasma and urine measurements was analysed using a newly developed empirical pharmacokinetic model. Causes of daily variation of metformin pharmacokinetics and interindividual variability were further investigated by a literature-informed mechanistic modelling analysis. RESULTS: A significant effect of time-of-day on metformin pharmacokinetics was found. Daily rhythms of gastrointestinal, hepatic and renal processes are described in the literature, possibly affecting drug pharmacokinetics. Observed metformin plasma levels were best described by a combination of a rhythm in GFR, renal plasma flow (RPF) and organic cation transporter (OCT) 2 activity. Furthermore, the large interindividual differences in measured metformin concentrations were best explained by individual chronotypes affecting metformin clearance, with impact on plasma and tissue concentrations that may have implications for metformin efficacy. CONCLUSIONS/INTERPRETATION: Metformin's pharmacology significantly depends on time-of-day in humans, determined with the help of empirical and mechanistic pharmacokinetic modelling, and rhythmic GFR, RPF and OCT2 were found to govern intraday variation. Interindividual variation was found to be partly dependent on individual chronotype, suggesting diurnal preference as an interesting, but so-far underappreciated, topic with regard to future personalised chronomodulated therapy in people with type 2 diabetes.

Physiologically based modelling of dermal absorption and kinetics of consumer-relevant chemicals: A case study with exposure to bisphenol A from thermal paper.

Bisphenol A (BPA) is one of the best studied industrial chemicals in terms of exposure, toxicity, and toxicokinetics. This renders it an ideal candidate to exploit the recent advancements in physiologically based pharmacokinetic (PBPK) modelling to support risk assessment of BPA specifically, and of other consumer-relevant hazardous chemicals in general. Using the exposure from thermal paper as a case scenario, this study employed the multi-phase multi-layer mechanistic dermal absorption (MPML MechDermA) model available in the Simcyp® Simulator to simulate the dermal toxicokinetics of BPA at local and systemic levels. Sensitivity analysis helped to identify physicochemical and physiological factors influencing the systemic exposure to BPA. The iterative modelling process was as follows: (i) development of compound files for BPA and its conjugates, (ii) setting-up of a PBPK model for intravenous administration, (iii) extension for oral administration, and (iv) extension for exposure via skin (i.e., hand) contact. A toxicokinetic study involving hand contact to BPA-containing paper was used for model refinement. Cumulative urinary excretion of total BPA had to be employed for dose reconstruction. PBPK model performance was verified using the observed serum BPA concentrations. The predicted distribution across the skin compartments revealed a depot of BPA in the stratum corneum (SC). These findings shed light on the role of the SC to act as temporary reservoir for lipophilic chemicals prior to systemic absorption, which inter alia is relevant for the interpretation of human biomonitoring data and for establishing the relationship between external and internal measures of exposure.

Revealing the extent of sea otter impacts on bivalve prey through multi-trophic monitoring and mechanistic models.

Sea otters are apex predators that can exert considerable influence over the nearshore communities they occupy. Since facing near extinction in the early 1900s, sea otters are making a remarkable recovery in Southeast Alaska, particularly in Glacier Bay, the largest protected tidewater glacier fjord in the world. The expansion of sea otters across Glacier Bay offers both a challenge to monitoring and stewardship and an unprecedented opportunity to study the top-down effect of a novel apex predator across a diverse and productive ecosystem. Our goal was to integrate monitoring data across trophic levels, space, and time to quantify and map the predator-prey interaction between sea otters and butter clams Saxidomus gigantea, one of the dominant large bivalves in Glacier Bay and a favoured prey of sea otters. We developed a spatially-referenced mechanistic differential equation model of butter clam dynamics that combined both environmental drivers of local population growth and estimates of otter abundance from aerial survey data. We embedded this model in a Bayesian statistical framework and fit it to clam survey data from 43 intertidal and subtidal sites across Glacier Bay. Prior to substantial sea otter expansion, we found that butter clam density was structured by an environmental gradient driven by distance from glacier (represented by latitude) and a quadratic effect of current speed. Estimates of sea otter attack rate revealed spatial heterogeneity in sea otter impacts and a negative relationship with local shoreline complexity. Sea otter exploitation of productive butter clam habitat substantially reduced the abundance and altered the distribution of butter clams across Glacier Bay, with potential cascading consequences for nearshore community structure and function. Spatial variation in estimated sea otter predation processes further suggests that community context and local environmental conditions mediate the top-down influence of sea otters on a given prey. Overall, our framework provides high-resolution insights about the interaction among components of this food web and could be applied to a variety of other systems involving invasive species, epidemiology or migration.

An integrated modelling approach for targeted degradation: insights on optimization, data requirements and PKPD predictions from semi- or fully-mechanistic models and exact steady state solutions.

The value of an integrated mathematical modelling approach for protein degraders which combines the benefits of traditional turnover models and fully mechanistic models is presented. Firstly, we show how exact solutions of the mechanistic models of monovalent and bivalent degraders can provide insight on the role of each system parameter in driving the pharmacological response. We show how on/off binding rates and degradation rates are related to potency and maximal effect of monovalent degraders, and how such relationship can be used to suggest a compound optimization strategy. Even convoluted exact steady state solutions for bivalent degraders provide insight on the type of observations required to ensure the predictive capacity of a mechanistic approach. Specifically for PROTACs, the structure of the exact steady state solution suggests that the total remaining target at steady state, which is easily accessible experimentally, is insufficient to reconstruct the state of the whole system at equilibrium and observations on different species (such as binary/ternary complexes) are necessary. Secondly, global sensitivity analysis of fully mechanistic models for PROTACs suggests that both target and ligase baselines (actually, their ratio) are the major sources of variability in the response of non-cooperative systems, which speaks to the importance of characterizing their distribution in the target patient population. Finally, we propose a pragmatic modelling approach which incorporates the insights generated with fully mechanistic models into simpler turnover models to improve their predictive ability, hence enabling acceleration of drug discovery programs and increased probability of success in the clinic.

A mechanistic model captures livestock trading, disease dynamics, and compensatory behaviour in response to control measures.

Trade is a complex, multi-faceted process that can contribute to the spread and persistence of disease. We here develop novel mechanistic models of supply. Our model is framed within a livestock trading system, where farms form and end trade partnerships with rates dependent on current demand, with these trade partnerships facilitating trade between partners. With these time-varying, stock dependent partnership and trade dynamics, our trading model goes beyond current state of the art modelling approaches. By studying instantaneous shocks to farm-level supply and demand we show that behavioural responses of farms lead to trading systems that are highly resistant to shocks with only temporary disturbances to trade observed. Individual adaptation in response to permanent alterations to trading propensities, such that animal flows are maintained, illustrates the ability for farms to find new avenues of trade, minimising disruptions imposed by such alterations to trade that common modelling approaches cannot adequately capture. In the context of endemic disease control, we show that these adaptations hinder the potential beneficial reductions in prevalence such changes to trading propensities have previously been shown to confer. Assessing the impact of a common disease control measure, post-movement batch testing, highlights the ability for our model to measure the stress on multiple components of trade imposed by such control measures and also highlights the temporary and, in some cases, the permanent disturbances to trade that post-movement testing has on the trading system.

Modelling the effects of variability in feeding rate on growth - a vital step for DEB-TKTD modelling.

A major limitation of dietary toxicity studies on rodents is that food consumption often differs between treatments. The control treatment serves as a reference of how animals would have grown if not for the toxicant in their diet, but this comparison unavoidably conflates the effects of toxicity and feeding rate on body weight over time. A key advantage of toxicity models based on dynamic energy budget theory (DEB) is that chemical stress and food consumption are separate model inputs, so their effects on growth rate can be separated. To reduce data requirements, DEB convention is to derive a simplified feeding input, f, from food availability; its value ranges from zero (starvation) to one (food available ad libitum). Observed food consumption in dietary toxicity studies shows that, even in the control treatment, rats limit their food consumption, contradicting DEB assumptions regarding feeding rate. Relatively little work has focused on addressing this mismatch, but accurately modelling the effects of food intake on growth rate is essential for the effects of toxicity to be isolated. This can provide greater insight into the results of chronic toxicity studies and allows accurate extrapolation of toxic effects from laboratory data. Here we trial a new method for calculating f, based on the observed relationships between food consumption and body size in laboratory rats. We compare model results with those of the conventional DEB method and a previous effort to calculate f using observed food consumption data. Our results showed that the new method improved model accuracy while modelled reserve dynamics closely followed observed body fat percentage over time. The new method assumes that digestive efficiency increases with body size. Verifying this relationship through data collection would strengthen the basis of DEB theory and support the case for its use in ecological risk assessment.

Demographic models of the reproductive process: Past, interlude, and future.

After 30 years of active development, mechanistic models of the reproductive process nearly stopped attracting scholarly interest in the early 1980s. In the following decades, fertility research continued to thrive, relying on solid descriptive work and detailed analysis of micro-level data. The absence of systematic modelling efforts, however, has also made the field more fragmented, with empirical research, theory building, and forecasting advancing along largely disconnected channels. In this paper we outline some of the drivers of this process, from the popularization of user-friendly statistical software to the limitations of early family building models. We then describe a series of developments in computational modelling and statistical computing that can contribute to the emergence of a new generation of mechanistic models. Finally, we introduce a concrete example of this new kind of model, and show how they can be used to formulate and test theories coherently and make informed projections.

Dispersal in heterogeneous environments drives population dynamics and control of tsetse flies.

Spatio-temporally heterogeneous environments may lead to unexpected population dynamics. Knowledge is needed on local properties favouring population resilience at large scale. For pathogen vectors, such as tsetse flies transmitting human and animal African trypanosomosis, this is crucial to target management strategies. We developed a mechanistic spatio-temporal model of the age-structured population dynamics of tsetse flies, parametrized with field and laboratory data. It accounts for density- and temperature-dependence. The studied environment is heterogeneous, fragmented and dispersal is suitability-driven. We confirmed that temperature and adult mortality have a strong impact on tsetse populations. When homogeneously increasing adult mortality, control was less effective and induced faster population recovery in the coldest and temperature-stable locations, creating refuges. To optimally select locations to control, we assessed the potential impact of treating them and their contribution to the whole population. This heterogeneous control induced a similar population decrease, with more dispersed individuals. Control efficacy was no longer related to temperature. Dispersal was responsible for refuges at the interface between controlled and uncontrolled zones, where resurgence after control was very high. The early identification of refuges, which could jeopardize control efforts, is crucial. We recommend baseline data collection to characterize the ecosystem before implementing any measures.

A dynamic model for spread of livestock-associated methicillin-resistant Staphylococcus aureus on a pig farm, incorporating bacterial load and human exposure through air.

Livestock-associated methicillin-resistant Staphylococcus aureus (LA-MRSA) is widely distributed in the pig population in many countries, where its presence is undesirable, because as an opportunistic human pathogen, it poses a threat to human health. At present, there is a lack of knowledge regarding successful methods for eradication of LA-MRSA on a pig farm, which does not involve emptying the farm and culling all pigs. Some studies have reported an association between levels of LA-MRSA in the barn air and LA-MRSA carriage among humans entering or working in the pig barns. Therefore, interventions that are able to reduce the amount of LA-MRSA carried by the pigs and/or the concentration of LA-MRSA in the barn air, might be highly relevant if aiming for reducing the spread of LA-MRSA into the general human population. In the present study, an existing agent-based simulation model for spread of LA-MRSA within a pig herd was extended to also include LA-MRSA load and spread through air. This makes it possible to use the model for studying the air exposure to LA-MRSA for humans entering the pig barns. The model was used for simulating various types of interventions in contaminated herds. At present quantitative data for nasal carriage of LA-MRSA in pigs are sparse, and many knowledge gaps regarding spread of LA-MRSA remain. Thus, our goal of building this model was not to provide exact values for risk reduction, but to avail a model that can be used for studying the effect of various types of interventions mechanistically, once more relevant data become available. Collection of more data on the influence of load is crucial for getting a better understanding of which possible interventions strategies, that might still have some potential in countries, where LA-MRSA has already spread to the majority of the pig population.

Mechanistic modelling of the large-scale Lassa fever epidemics in Nigeria from 2016 to 2019.

Lassa fever, also known as Lassa hemorrhagic fever, is a virus that has generated recurrent outbreaks in West Africa. We use mechanistic modelling to study the Lassa fever epidemics in Nigeria from 2016-19. Our model describes the interaction between human and rodent populations with the consideration of quarantine, isolation and hospitalization processes. Our model supports the phenomenon of forward bifurcation where the stability between disease-free equilibrium and endemic equilibrium exchanges. Moreover, our model captures well the incidence curves from surveillance data. In particular, our model is able to reconstruct the periodic rodent and human forces of infection. Furthermore, we suggest that the three major epidemics from 2016-19 can be modelled by properly characterizing the rodent (or human) force of infection while the estimated human force of infection also present similar patterns across outbreaks. Our results suggest that the initial susceptibility likely increased across the three outbreaks from 2016-19. Our results highlight the similarity of the transmission dynamics driving three major Lassa fever outbreaks in the endemic areas.

The biogeography of thermal risk for terrestrial ectotherms: Scaling of thermal tolerance with body size and latitude.

Many organisms are shrinking in size in response to global warming. However, we still lack a comprehensive understanding of the mechanisms linking body size and temperature of organisms across their geographical ranges. Here we investigate the biophysical mechanisms determining the scaling of body temperature with size across latitudes in terrestrial ectotherms. Using biophysical models, we simulated operative temperatures experienced by lizard-like ectotherms as a function of microclimatic variables, body mass and latitude and used them to generate null predictions for the effect of size on temperature across geographical gradients. We then compared model predictions against empirical data on lizards' field body temperature (Tb ) and thermal tolerance limits (CTmax and CTmin ). Our biophysical models predict that the allometric scaling of operative temperatures with body size varies with latitude, with a positive relationship at low latitudes that vanishes with increasing latitude. The analyses of thermal traits of lizards show a significant interaction of body size and latitude on Tb and CTmax and no effect of body mass on CTmin , consistent with model's predictions. The estimated scaling coefficients are within the ranges predicted by the biophysical model. The effect of body mass, however, becomes non-significant after controlling for the phylogenetic relatedness between species. We propose that large-bodied terrestrial ectotherms exhibit higher risk of overheating at low latitudes, while size differences in thermal sensitivity vanish towards higher latitudes. Our work highlights the potential of combining mechanistic models with empirical data to investigate the mechanisms underpinning broad-scale patterns and ultimately provide a null model to develop baseline expectations for further empirical research.

Mechanistic home range analysis reveals drivers of space use patterns for a non-territorial passerine.

Home ranging is a near-ubiquitous phenomenon in the animal kingdom. Understanding the behavioural mechanisms that give rise to observed home range patterns is thus an important general question, and mechanistic home range analysis (MHRA) provides the tools to address it. However, such analysis has hitherto been principally restricted to scent-marking territorial animals, so its potential breadth of application has not been tested. Here, we apply MHRA to a population of long-tailed tits Aegithalos caudatus, a non-territorial passerine, in the non-breeding season where there is no clear 'central place' near which birds need to remain. The aim is to uncover the principal movement mechanisms underlying observed home range formation. Our foundational models consist of memory-mediated conspecific avoidance between flocks, combined with attraction to woodland. These are then modified to incorporate the effects of flock size and relatedness (i.e. kinship), to uncover the effect of these on the mechanisms of home range formation. We found that a simple model of spatial avoidance, together with attraction to the central parts of woodland areas, accurately captures long-tailed tit home range patterns. Refining these models further, we show that the magnitude of spatial avoidance by a flock is negatively correlated to both the relative size of the flock (compared to its neighbour) and the relatedness of the flock with its neighbour. Our study applies MHRA beyond the confines of scent-marking, territorial animals, so paves the way for much broader taxonomic application. These could potentially help uncover general properties underlying the emergence of animal space use patterns. This is also the first study to apply MHRA to questions of relatedness and flock size, thus broadening the potential possible applications of this suite of analytic techniques.

A mechanistic modelling approach for the determination of the mechanisms of inhibition by cyclosporine on the uptake and metabolism of atorvastatin in rat hepatocytes using a high throughput uptake method.

Determine the inhibition mechanism through which cyclosporine inhibits the uptake and metabolism of atorvastatin in fresh rat hepatocytes using mechanistic models applied to data generated using a high throughput oil spin method.Atorvastatin was incubated in fresh rat hepatocytes (0.05-150 nmol/ml) with or without 20 min pre-incubation with 10 nmol/ml cyclosporine and sampled over 0.25-60 min using a high throughput oil spin method. Micro-rate constant and macro-rate constant mechanistic models were ranked based on goodness of fit values.The best fitting model to the data was a micro-rate constant mechanistic model including non-competitive inhibition of uptake and competitive inhibition of metabolism by cyclosporine (Model 2). The association rate constant for atorvastatin was 150-fold greater than the dissociation rate constant and 10-fold greater than the translocation into the cell. The association and dissociation rate constants for cyclosporine were 7-fold smaller and 10-fold greater, respectively, than atorvastatin. The simulated atorvastatin-transporter-cyclosporine complex derived using the micro-rate constant parameter estimates increased in line with the incubation concentration of atorvastatin.The increased amount of data generated with the high throughput oil spin method, combined with a micro-rate constant mechanistic model helps to explain the inhibition of uptake by cyclosporine following pre-incubation.

Interactions between different predator-prey states: a method for the derivation of the functional and numerical response.

In this paper we introduce a formal method for the derivation of a predator's functional response from a system of fast state transitions of the prey or predator on a time scale during which the total prey and predator densities remain constant. Such derivation permits an explicit interpretation of the structure and parameters of the functional response in terms of individual behaviour. The same method is also used here to derive the corresponding numerical response of the predator as well as of the prey.

Computational disease model of phenobarbital-induced acute attacks in an acute intermittent porphyria mouse model.

INTRODUCTION: Acute intermittent porphyria (AIP) is characterized by hepatic over-production of the heme precursors when aminolevulinic acid (ALA)-synthase 1 is induced by endogenous or environmental factors. The aim of this study was to develop a semi-mechanistic computational model to characterize urine accumulation of heme precursors during acute attacks based on experimental pharmacodynamics data and support the development of new therapeutic strategies. METHODS: Male AIP mice received recurrent phenobarbital challenge starting on days 1, 9, 16 and 30. 24-h urine excretion of ALA, porphobilinogen (PBG) and porphyrins from challenges D1, D9 and D30 constituted the training data set to build the mechanistic model using the population approach. In a second study, porphyrin and porphyrin precursor excretion from challenge D16 were used as a validation data set. RESULTS: The computational model presented the following features: (i) urinary excretion of ALA, PBG and porphyrins was governed by unmeasured circulating heme precursor amounts, (ii) the circulating amounts of ALA and PBG were the precursors of circulating amounts of PBG and porphyrins, respectively, and (iii) the phenobarbital effect linearly increased the synthesis of circulating ALA and PBG levels. The model displayed good parameter precision (coefficient of variation below 32% in all parameters), and adequately described the experimental data. Finally, a theoretical hemin effect was implemented to illustrate the applicability of the model to dosage optimization in drug therapies. CONCLUSIONS: A semi-mechanistic disease model was successfully developed to describe the temporal evolution of urinary heme precursor excretion during recurrent biochemical-induced acute attacks in AIP mice. This model represents the first computational approach to explore and optimize current and new therapies.

Mechanistic Image-Based Modelling: Concepts and Applications.

Advancements in imaging techniques have led to a rapid growth of available imaging data. Interpretation of the imaging data and extraction of biologically, physiologically and/or medically relevant information, however, remains challenging. In contrast, mechanistic computational modelling provides a means to formalise and dissect mechanisms governing the behaviour of complex systems. However, its application often is limited due to the lack of relevant data for model building and validation. Exploitation of the imaging data to build, parameterise and validate computational models gives rise to an image-based modelling approach. In this chapter, we introduce the basics of the mechanistic image-based modelling approach and review its application in developmental biology and biomedical research as well as for medical device development and drug discovery and development. Implementation of image-based modelling in pharmaceutical industry holds promise to further advance model-informed drug discovery and development and aids substantially in our understanding of drug pharmacokinetic, pharmacodynamic and ultimately de-risk drug development.

Problems, challenges and promises: perspectives on precision medicine.

The 'precision medicine (systems medicine)' concept promises to achieve a shift to future healthcare systems with a more proactive and predictive approach to medicine, where the emphasis is on disease prevention rather than the treatment of symptoms. The individualization of treatment for each patient will be at the centre of this approach, with all of a patient's medical data being computationally integrated and accessible. Precision medicine is being rapidly embraced by biomedical researchers, pioneering clinicians and scientific funding programmes in both the European Union (EU) and USA. Precision medicine is a key component of both Horizon 2020 (the EU Framework Programme for Research and Innovation) and the White House's Precision Medicine Initiative. Precision medicine promises to revolutionize patient care and treatment decisions. However, the participants in precision medicine are faced with a considerable central challenge. Greater volumes of data from a wider variety of sources are being generated and analysed than ever before; yet, this heterogeneous information must be integrated and incorporated into personalized predictive models, the output of which must be intelligible to non-computationally trained clinicians. Drawing primarily from the field of 'oncology', this article will introduce key concepts and challenges of precision medicine and some of the approaches currently being implemented to overcome these challenges. Finally, this article also covers the criticisms of precision medicine overpromising on its potential to transform patient care.

Daytime depression in temperature-normalised stem CO2 efflux in young poplar trees is dominated by low turgor pressure rather than by internal transport of respired CO2.

Daytime decreases in temperature-normalised stem CO2 efflux (EA_D ) are commonly ascribed to internal transport of respired CO2 (FT ) or to an attenuated respiratory activity due to lowered turgor pressure. The two are difficult to separate as they are simultaneously driven by sap flow dynamics. To achieve combined gradients in turgor pressure and FT , sap flow rates in poplar trees were manipulated through severe defoliation, severe drought, moderate defoliation and moderate drought. Turgor pressure was mechanistically modelled using measurements of sap flow, stem diameter variation, and soil and stem water potential. A mass balance approach considering internal and external CO2 fluxes was applied to estimate FT . Under well-watered control conditions, both turgor pressure and sap flow, as a proxy of FT , were reliable predictors of EA_D . After tree manipulation, only turgor pressure was a robust predictor of EA_D . Moreover, FT accounted for < 15% of EA_D . Our results suggest that daytime reductions in turgor pressure and associated constrained growth are the main cause of EA_D in young poplar trees. Turgor pressure is determined by both carbohydrate supply and water availability, and should be considered to improve our widely used but inaccurate temperature-based predictions of woody tissue respiration in global models.

Evidence Synthesis for Stochastic Epidemic Models.

In recent years, the role of epidemic models in informing public health policies has progressively grown. Models have become increasingly realistic and more complex, requiring the use of multiple data sources to estimate all quantities of interest. This review summarises the different types of stochastic epidemic models that use evidence synthesis and highlights current challenges.