Surrogate modeling and control of medical digital twins.

The vision of personalized medicine is to identify interventions that maintain or restore a person's health based on their individual biology. Medical digital twins, computational models that integrate a wide range of health-related data about a person and can be dynamically updated, are a key technology that can help guide medical decisions. Such medical digital twin models can be high-dimensional, multi-scale, and stochastic. To be practical for healthcare applications, they often need to be simplified into low-dimensional surrogate models that can be used for optimal design of interventions. This paper introduces surrogate modeling algorithms for the purpose of optimal control applications. As a use case, we focus on agent-based models (ABMs), a common model type in biomedicine for which there are no readily available optimal control algorithms. By deriving surrogate models that are based on systems of ordinary differential equations, we show how optimal control methods can be employed to compute effective interventions, which can then be lifted back to a given ABM. The relevance of the methods introduced here extends beyond medical digital twins to other complex dynamical systems.

Control of Medical Digital Twins with Artificial Neural Networks.

The objective of personalized medicine is to tailor interventions to an individual patient's unique characteristics. A key technology for this purpose involves medical digital twins, computational models of human biology that can be personalized and dynamically updated to incorporate patient-specific data collected over time. Certain aspects of human biology, such as the immune system, are not easily captured with physics-based models, such as differential equations. Instead, they are often multi-scale, stochastic, and hybrid. This poses a challenge to existing model-based control and optimization approaches that cannot be readily applied to such models. Recent advances in automatic differentiation and neural-network control methods hold promise in addressing complex control problems. However, the application of these approaches to biomedical systems is still in its early stages. This work introduces dynamics-informed neural-network controllers as an alternative approach to control of medical digital twins. As a first use case for this method, the focus is on agent-based models, a versatile and increasingly common modeling platform in biomedicine. The effectiveness of the proposed neural-network control method is illustrated and benchmarked against other methods with two widely-used agent-based model types. The relevance of the method introduced here extends beyond medical digital twins to other complex dynamical systems.

Control of Medical Digital Twins with Artificial Neural Networks.

The objective of personalized medicine is to tailor interventions to an individual patient's unique characteristics. A key technology for this purpose involves medical digital twins, computational models of human biology that can be personalized and dynamically updated to incorporate patient-specific data collected over time. Certain aspects of human biology, such as the immune system, are not easily captured with physics-based models, such as differential equations. Instead, they are often multi-scale, stochastic, and hybrid. This poses a challenge to existing model-based control and optimization approaches that cannot be readily applied to such models. Recent advances in automatic differentiation and neural-network control methods hold promise in addressing complex control problems. However, the application of these approaches to biomedical systems is still in its early stages. This work introduces dynamics-informed neural-network controllers as an alternative approach to control of medical digital twins. As a first use case for this method, the focus is on agent-based models, a versatile and increasingly common modeling platform in biomedicine. The effectiveness of the proposed neural-network control method is illustrated and benchmarked against other methods with two widely-used agent-based model types. The relevance of the method introduced here extends beyond medical digital twins to other complex dynamical systems.

Prospective predictors of electronic nicotine delivery system initiation in tobacco naive young adults: A machine learning approach.

The use of electronic nicotine delivery systems (ENDS) is increasing among young adults. However, there are few studies regarding predictors of ENDS initiation in tobacco-naive young adults. Identifying the risk and protective factors of ENDS initiation that are specific to tobacco-naive young adults will enable the creation of targeted policies and prevention programs. This study used machine learning (ML) to create predictive models, identify risk and protective factors for ENDS initiation for tobacco-naive young adults, and the relationship between these predictors and the prediction of ENDS initiation. We used nationally representative data of tobacco-naive young adults in the U.S drawn from the Population Assessment of Tobacco and Health (PATH) longitudinal cohort survey. Respondents were young adults (18-24 years) who had never used any tobacco products in Wave 4 and who completed Waves 4 and 5 interviews. ML techniques were used to create models and determine predictors at 1-year follow-up from Wave 4 data. Among the 2,746 tobacco-naive young adults at baseline, 309 initiated ENDS use at 1-year follow-up. The top five prospective predictors of ENDS initiation were susceptibility to ENDS, increased days of physical exercise specifically designed to strengthen muscles, frequency of social media use, marijuana use and susceptibility to cigarettes. This study identified previously unreported and emerging predictors of ENDS initiation that warrant further investigation and provided comprehensive information on the predictors of ENDS initiation. Furthermore, this study showed that ML is a promising technique that can aid ENDS monitoring and prevention programs.

Computational models in systems biology: standards, dissemination, and best practices.

Mathematical and computational models are a key technology in systems biology. Progress in the field depends on the replicability and reproducibility of their properties and behavior. For this, an essential requirement is a set of clear standards for model specification and dissemination. This review covers existing standards, and it highlights the most important areas where further work is required. This includes the specification of agent-based models, an increasingly common modeling approach.

E-Cigarette Use in Young Adult Never Cigarette Smokers with Disabilities: Results from the Behavioral Risk Factor Surveillance System Survey.

Young adult never cigarette smokers with disabilities may be at particular risk for adopting e-cigarettes, but little attention has been paid to these people. This study examines the associations between different types of disability and e-cigarette use in this population. Young adult never-smokers from the 2016-2017 Behavioral Risk Factor Surveillance System (BRFSS) survey who were either never or current e-cigarette users (n = 79,177) were selected for the analysis. The Least Absolute Shrinkage and Selection Operator (LASSO) algorithm was used to select confounders for multivariable logistic regression models. Multivariable logistic regression models were used to determine the associations between current e-cigarette use and different types of disability after incorporating BRFSS survey design and adjusting for confounders. Young adult never-smokers who reported any disability had increased odds (OR 1.44, 95% CI 1.18-1.76) of e-cigarette use compared to those who reported no disability. Young adult never-smokers who reported self-care, cognitive, vision, and independent living disabilities had higher odds of e-cigarette use compared to those who reported no disability. There was no statistically significant difference in the odds of e-cigarette use for those reporting hearing and mobility disabilities compared to those who reported no disability. This study highlights the need for increased public education and cessation programs for this population.

Factors Associated with E-Cigarette Use in U.S. Young Adult Never Smokers of Conventional Cigarettes: A Machine Learning Approach.

E-cigarette use is increasing among young adult never smokers of conventional cigarettes, but the awareness of the factors associated with e-cigarette use in this population is limited. The goal of this work was to use machine learning (ML) algorithms to determine the factors associated with current e-cigarette use among US young adult never cigarette smokers. Young adult (18-34 years) never cigarette smokers from the 2016 and 2017 Behavioral Risk Factor Surveillance System (BRFSS) who reported current or never e-cigarette use were used for the analysis (n = 79,539). Variables associated with current e-cigarette use were selected by two ML algorithms (Boruta and Least absolute shrinkage and selection operator (LASSO)). Odds ratios were calculated to determine the association between e-cigarette use and the variables selected by the ML algorithms, after adjusting for age, gender and race/ethnicity and incorporating the BRFSS complex design. The prevalence of e-cigarette use varied across states. Factors previously reported in the literature, such as age, race/ethnicity, alcohol use, depression, as well as novel factors associated with e-cigarette use, such as disabilities, obesity, history of diabetes and history of arthritis were identified. These results can be used to generate further hypotheses for research, increase public awareness and help provide targeted e-cigarette education.

Sensitivity of comorbidity network analysis.

OBJECTIVES: Comorbidity network analysis (CNA) is a graph-theoretic approach to systems medicine based on associations revealed from disease co-occurrence data. Researchers have used CNA to explore epidemiological patterns, differentiate populations, characterize disorders, and more; but these techniques have not been comprehensively evaluated. Our objectives were to assess the stability of common CNA techniques. MATERIALS AND METHODS: We obtained seven co-occurrence data sets, most from previous CNAs, coded using several ontologies. We constructed comorbidity networks under various modeling procedures and calculated summary statistics and centrality rankings. We used regression, ordination, and rank correlation to assess these properties' sensitivity to the source of data and construction parameters. RESULTS: Most summary statistics were robust to variation in link determination but somewhere sensitive to the association measure. Some more effectively than others discriminated among networks constructed from different data sets. Centrality rankings, especially among hubs, were somewhat sensitive to link determination and highly sensitive to ontology. As multivariate models incorporated additional effects, comorbid associations among low-prevalence disorders weakened while those between high-prevalence disorders shifted negative. DISCUSSION: Pairwise CNA techniques are generally robust, but some analyses are highly sensitive to certain parameters. Multivariate approaches expose additional conceptual and technical limitations to the usual pairwise approach. CONCLUSION: We conclude with a set of recommendations we believe will help CNA researchers improve the robustness of results and the potential of follow-up research.

Management of rare movement disorders in Europe: outcome of surveys of the European Reference Network for Rare Neurological Diseases.

BACKGROUND AND PURPOSE: The diagnosis of rare movement disorders is difficult and specific management programmes are not well defined. Thus, in order to capture and assess care needs, the European Reference Network for Rare Neurological Diseases has performed an explorative care need survey across all European Union (EU) countries. METHODS: This is a multicentre, cross-sectional study. A survey about the management of different rare movement disorders (group 1, dystonia, paroxysmal dyskinesia and neurodegeneration with brain iron accumulation; group 2, ataxias and hereditary spastic paraparesis; group 3, atypical parkinsonism; group 4, choreas) was sent to an expert in each group of disorders from each EU country. RESULTS: Some EU countries claimed for an increase of teaching courses. Genetic testing was not readily available in a significant number of countries. Regarding management, patients' accessibility to tertiary hospitals, to experts and to multidisciplinary teams was unequal between countries and groups of diseases. The availability of therapeutic options, such as botulinum toxin or more invasive treatments like deep brain stimulation, was limited in some countries. CONCLUSIONS: The management of these conditions in EU countries is unequal. The survey provides evidence that a European care-focused network that is able to address the unmet rare neurological disease care needs and inequalities is highly warranted.

Systems biology of ferroptosis: A modeling approach.

Ferroptosis is a recently discovered form of iron-dependent regulated cell death (RCD) that occurs via peroxidation of phospholipids containing polyunsaturated fatty acid (PUFA) moieties. Activating this form of cell death is an emerging strategy in cancer treatment. Because multiple pathways and molecular species contribute to the ferroptotic process, predicting which tumors will be sensitive to ferroptosis is a challenge. We thus develop a mathematical model of several critical pathways to ferroptosis in order to perform a systems-level analysis of the process. We show that sensitivity to ferroptosis depends on the activity of multiple upstream cascades, including PUFA incorporation into the phospholipid membrane, and the balance between levels of pro-oxidant factors (reactive oxygen species, lipoxogynases) and antioxidant factors (GPX4). We perform a systems-level analysis of ferroptosis sensitivity as an outcome of five input variables (ACSL4, SCD1, ferroportin, transferrin receptor, and p53) and organize the resulting simulations into 'high' and 'low' ferroptosis sensitivity groups. We make a novel prediction corresponding to the combinatorial requirements of ferroptosis sensitivity to SCD1 and ACSL4 activity. To validate our prediction, we model the ferroptotic response of an ovarian cancer stem cell line following single- and double-knockdown of SCD1 and ACSL4. We find that the experimental outcomes are consistent with our simulated predictions. This work suggests that a systems-level approach is beneficial for understanding the complex combined effects of ferroptotic input, and in predicting cancer susceptibility to ferroptosis.

Clinical epidemiology and outcomes of ventilator-associated pneumonia in critically ill adult patients: protocol for a large-scale systematic review and planned meta-analysis.

BACKGROUND: An increasing number of studies have investigated the clinical epidemiology and outcomes of ventilator-associated pneumonia (VAP) in intensive care units. However, these findings have not been clearly defined in broad subgroups of mechanically ventilated adults. Hence, this protocol for a systematic review and meta-analysis is designed to better understand the clinical and epidemiological features of VAP in these patient populations by establishing its overall prognosis of and risk factors for morbidity and mortality and to determine the differences in clinical and economic outcomes between VAP and non-VAP patients. METHODS: This present review will systematically search available full-text articles without date and language restrictions and indexed in PubMed, CENTRAL, CINAHL, Web of Science, and EMBASE databases. In addition, reference lists and citations of retrieved articles and relevant medical and nursing journals will be manually reviewed. Supplementary search in other databases involving trials, reviews, and grey literatures, including conference proceedings, theses, and dissertations, will be performed. Study investigators will be contacted to clarify missing or unpublished data. All prognostic studies meeting the pre-defined eligibility criteria will be included. The study selection, risk of bias assessment, data extraction, and grading of the quality of evidence will be carried out in duplicate, involving independent evaluation by two investigators with consensus or a third-party adjudication. The degree of inter-rater agreement will be calculated using the kappa statistic. For meta-analysis, dichotomous and continuous outcome measures will be pooled using odds ratios and standardized mean differences with 95% confidence intervals, respectively. The Mantel-Haenszel or inverse variance methods with random effects model will be used as a guide for analysis. The heterogeneity of each outcome measure will be assessed using both X2 and I2 statistics. In addition, sensitivity and subgroup analyses will be performed to ensure consistency of pooled results. The review protocol described herein is in accordance with the PRISMA-P standards. DISCUSSION: The investigation of the epidemiological profiles, prognostic factors, and outcomes associated with VAP is critical for the identification of high-risk groups of mechanically ventilated patients and evaluation of possible clinical endpoints. This may provide substantial links for improved VAP prevention practices targeting modifiable risk factors. Implications for future research directions are discussed. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42017048158.

A mathematical model of combined CD8 T cell costimulation by 4-1BB (CD137) and OX40 (CD134) receptors.

Combined agonist stimulation of the TNFR costimulatory receptors 4-1BB (CD137) and OX40(CD134) has been shown to generate supereffector CD8 T cells that clonally expand to greater levels, survive longer, and produce a greater quantity of cytokines compared to T cells stimulated with an agonist of either costimulatory receptor individually. In order to understand the mechanisms for this effect, we have created a mathematical model for the activation of the CD8 T cell intracellular signaling network by mono- or dual-costimulation. We show that supereffector status is generated via downstream interacting pathways that are activated upon engagement of both receptors, and in silico simulations of the model are supported by published experimental results. The model can thus be used to identify critical molecular targets of T cell dual-costimulation in the context of cancer immunotherapy.

Control of Intracellular Molecular Networks Using Algebraic Methods.

Many problems in biology and medicine have a control component. Often, the goal might be to modify intracellular networks, such as gene regulatory networks or signaling networks, in order for cells to achieve a certain phenotype, what happens in cancer. If the network is represented by a mathematical model for which mathematical control approaches are available, such as systems of ordinary differential equations, then this problem might be solved systematically. Such approaches are available for some other model types, such as Boolean networks, where structure-based approaches have been developed, as well as stable motif techniques. However, increasingly many published discrete models are mixed-state or multistate, that is, some or all variables have more than two states, and thus the development of control strategies for multistate networks is needed. This paper presents a control approach broadly applicable to general multistate models based on encoding them as polynomial dynamical systems over a finite algebraic state set, and using computational algebra for finding appropriate intervention strategies. To demonstrate the feasibility and applicability of this method, we apply it to a recently developed multistate intracellular model of E2F-mediated bladder cancerous growth and to a model linking intracellular iron metabolism and oncogenic pathways. The control strategies identified for these published models are novel in some cases and represent new hypotheses, or are supported by the literature in others as potential drug targets. Our Macaulay2 scripts to find control strategies are publicly available through GitHub at https://github.com/luissv7/multistatepdscontrol.

A Systems Biology Approach to Understanding the Pathophysiology of High-Grade Serous Ovarian Cancer: Focus on Iron and Fatty Acid Metabolism.

Ovarian cancer (OVC) is the most lethal of the gynecological malignancies, with diagnosis often occurring during advanced stages of the disease. Moreover, a majority of cases become refractory to chemotherapeutic approaches. Therefore, it is important to improve our understanding of the molecular dependencies underlying the disease to identify novel diagnostic and precision therapeutics for OVC. Cancer cells are known to sequester iron, which can potentiate cancer progression through mechanisms that have not yet been completely elucidated. We developed an algorithm to identify novel links between iron and pathways implicated in high-grade serous ovarian cancer (HGSOC), the most common and deadliest subtype of OVC, using microarray gene expression data from both clinical sources and an experimental model. Using our approach, we identified several links between fatty acid (FA) and iron metabolism, and subsequently developed a network for iron involvement in FA metabolism in HGSOC. FA import and synthesis pathways are upregulated in HGSOC and other cancers, but a link between these processes and iron-related genes has not yet been identified. We used the network to derive hypotheses of specific mechanisms by which iron and iron-related genes impact and interact with FA metabolic pathways to promote tumorigenesis. These results suggest a novel mechanism by which iron sequestration by cancer cells can potentiate cancer progression, and may provide novel targets for use in diagnosis and/or treatment of HGSOC.

Applications of network analysis to routinely collected health care data: a systematic review.

Objective: To survey network analyses of datasets collected in the course of routine operations in health care settings and identify driving questions, methods, needs, and potential for future research. Materials and Methods: A search strategy was designed to find studies that applied network analysis to routinely collected health care datasets and was adapted to 3 bibliographic databases. The results were grouped according to a thematic analysis of their settings, objectives, data, and methods. Each group received a methodological synthesis. Results: The search found 189 distinct studies reported before August 2016. We manually partitioned the sample into 4 groups, which investigated institutional exchange, physician collaboration, clinical co-occurrence, and workplace interaction networks. Several robust and ongoing research programs were discerned within (and sometimes across) the groups. Little interaction was observed between these programs, despite conceptual and methodological similarities. Discussion: We use the literature sample to inform a discussion of good practice at this methodological interface, including the concordance of motivations, study design, data, and tools and the validation and standardization of techniques. We then highlight instances of positive feedback between methodological development and knowledge domains and assess the overall cohesion of the sample.

The effect of long-term bisphosphonate therapy on trabecular bone strength and microcrack density.

OBJECTIVES: Bisphosphonates (BP) are the first-line treatment for preventing fragility fractures. However, concern regarding their efficacy is growing because bisphosphonate is associated with over-suppression of remodelling and accumulation of microcracks. While dual-energy X-ray absorptiometry (DXA) scanning may show a gain in bone density, the impact of this class of drug on mechanical properties remains unclear. We therefore sought to quantify the mechanical strength of bone treated with BP (oral alendronate), and correlate data with the microarchitecture and density of microcracks in comparison with untreated controls. METHODS: Trabecular bone from hip fracture patients treated with BP (n = 10) was compared with naïve fractured (n = 14) and non-fractured controls (n = 6). Trabecular cores were synchrotron scanned and micro-CT scanned for microstructural analysis, including quantification of bone volume fraction, microarchitecture and microcracks. The specimens were then mechanically tested in compression. RESULTS: BP bone was 28% lower in strength than untreated hip fracture bone, and 48% lower in strength than non-fractured control bone (4.6 MPa vs 6.4 MPa vs 8.9 MPa). BP-treated bone had 24% more microcracks than naïve fractured bone and 51% more than non-fractured control (8.12/cm2vs 6.55/cm2vs 5.25/cm2). BP and naïve fracture bone exhibited similar trabecular microarchitecture, with significantly lower bone volume fraction and connectivity than non-fractured controls. CONCLUSION: BP therapy had no detectable mechanical benefit in the specimens examined. Instead, its use was associated with substantially reduced bone strength. This low strength may be due to the greater accumulation of microcracks and a lack of any discernible improvement in bone volume or microarchitecture. This preliminary study suggests that the clinical impact of BP-induced microcrack accumulation may be significant.Cite this article: A. Jin, J. Cobb, U. Hansen, R. Bhattacharya, C. Reinhard, N. Vo, R. Atwood, J. Li, A. Karunaratne, C. Wiles, R. Abel. The effect of long-term bisphosphonate therapy on trabecular bone strength and microcrack density. Bone Joint Res 2017;6:602-609. DOI: 10.1302/2046-3758.610.BJR-2016-0321.R1.

Addressing current challenges in cancer immunotherapy with mathematical and computational modelling.

The goal of cancer immunotherapy is to boost a patient's immune response to a tumour. Yet, the design of an effective immunotherapy is complicated by various factors, including a potentially immunosuppressive tumour microenvironment, immune-modulating effects of conventional treatments and therapy-related toxicities. These complexities can be incorporated into mathematical and computational models of cancer immunotherapy that can then be used to aid in rational therapy design. In this review, we survey modelling approaches under the umbrella of the major challenges facing immunotherapy development, which encompass tumour classification, optimal treatment scheduling and combination therapy design. Although overlapping, each challenge has presented unique opportunities for modellers to make contributions using analytical and numerical analysis of model outcomes, as well as optimization algorithms. We discuss several examples of models that have grown in complexity as more biological information has become available, showcasing how model development is a dynamic process interlinked with the rapid advances in tumour-immune biology. We conclude the review with recommendations for modellers both with respect to methodology and biological direction that might help keep modellers at the forefront of cancer immunotherapy development.

Effects of research complexity and competition on the incidence and growth of coauthorship in biomedicine.

BACKGROUND: Investigations into the factors behind coauthorship growth in biomedical research have mostly focused on specific disciplines or journals, and have rarely controlled for factors in combination or considered changes in their effects over time. Observers often attribute the growth to the increasing complexity or competition (or both) of research practices, but few attempts have been made to parse the contributions of these two likely causes. OBJECTIVES: We aimed to assess the effects of complexity and competition on the incidence and growth of coauthorship, using a sample of the biomedical literature spanning multiple journals and disciplines. METHODS: Article-level bibliographic data from PubMed were combined with publicly available bibliometric data from Web of Science and SCImago over the years 1999-2007. We selected four predictors of coauthorship were selected, two (study type, topical scope of the study) associated with complexity and two (financial support for the project, popularity of the publishing journal) associated with competition. A negative binomial regression model was used to estimate the effects of each predictor on coauthorship incidence and growth. A second, mixed-effect model included the journal as a random effect. RESULTS: Coauthorship increased at about one author per article per decade. Clinical trials, supported research, and research of broader scope produced articles with more authors, while review articles credited fewer; and more popular journals published higher-authorship articles. Incidence and growth rates varied widely across journals and were themselves uncorrelated. Most effects remained statistically discernible after controlling for the publishing journal. The effects of complexity-associated factors held constant or diminished over time, while competition-related effects strengthened. These trends were similar in size but not discernible from subject-specific subdata. CONCLUSIONS: Coauthorship incidence rates are multifactorial and vary with factors associated with both complexity and competition. Coauthorship growth is likewise multifactorial and increasingly associated with research competition.

Characteristics of a large sample of candidates for permanent ventricular pacing included in the Biventricular Pacing for Atrio-ventricular Block to Prevent Cardiac Desynchronization Study (BioPace).

AIMS: The general clinical profile of European pacemaker recipients who require predominant ventricular pacing (VP) is scarcely known. We examined the demographic and clinical characteristics of the 1808 participants (out of 1833 randomized patients) of the ongoing Biventricular Pacing for Atrio-ventricular Block to Prevent Cardiac Desynchronization (BioPace) study. METHODS AND RESULTS: BioPace recruited patients between May 2003 and September 2007 predominantly in European medical centres. We analysed demographic data and described clinical characteristics and electrophysiological parameters prior to device implantation in 1808 enrolled patients. The mean age ± standard deviation (SD) of the 1808 patients was 73.5 ± 9.2 years, 1235 (68%) were men, 654 (36%) presented without structural heart disease, 547 (30%) had ischemic, 355 (20%) hypertensive, 146 (8%) valvular, and 102 (6%) non-ischemic dilated cardiomyopathy. Mean left ventricular ejection fraction was 55.4 ± 12.3%. The main pacing indications were (a) permanent and intermittent atrioventricular (AV) block in 973 (54%), (b) atrial fibrillation with slow ventricular rate in 313 (17%), and (c) miscellaneous bradyarrhythmias in 522 (29%) patients. Mean QRS duration was 118.5 ± 30.5 ms, left bundle branch block was present in 316 (17%), and atrial tachyarrhythmias in 426 (24%) patients. CONCLUSION: To the best of our knowledge, this sample is a representative source of description of the general profile of European pacemaker recipients who require predominant VP. Patients' characteristics included advanced age, predominantly male gender, preserved left ventricular systolic function, high-grade AV block, narrow QRS complex, and atrial tachyarrhythmias, the latter being present in nearly one-fourth of the cohort.

Behaviour of postnatally growth-impaired mice during malnutrition and after partial weight recovery.

OBJECTIVES: Early malnutrition is a highly prevalent condition in developing countries. Different rodent models of postnatal early malnutrition have been used to approach the subject experimentally, inducing early malnutrition by maternal malnutrition, temporal maternal separation, manipulation of litter size or the surgical nipple ligation to impair lactation. Studies on the behaviour of (previously) malnourished animals using animal models have produced sometimes contradictory results regarding the effects of early postnatal malnutrition and have been criticized for introducing potential confounding factors. The present paper is a first report on the behavioural effects of early malnutrition induced by an alternative approach: mice nursed by α-casein-deficient knockout dams showed a severe growth delay during early development and substantial catch-up growth after weaning when compared with animals nursed by wild-type females. METHODS: Established behavioural tests were used to study the consequences of early postnatal malnutrition on mouse pups at weaning and after partial weight recovery. RESULTS: Despite the impaired growth, the only behavioural difference between malnourished and normally growing animals was found in exploratory behaviour during acute malnutrition at the time of weaning. After partial catch-up in weight early protein malnourished animals showed no indication of lasting effects on general activity, emotionality and exploration, memory, and pain reactivity. DISCUSSION: These results suggest that the role of early nutrition on behavioural development after recovery in animal models may have been overestimated. Further careful examination of this animal model in terms of maternal care and offspring behaviour will be necessary to confirm if mice nursed by α-casein-deficient dams offer an alternative to existing models while eliminating potential confounding factors.