Abstract Background Social jet lag (SJL), the discrepancy timing between work nights and free nights, reflects schedule-related circadian misalignment. Time-stamped CPAP adherence records may provide objective, longitudinal estimates of sleep timing and could augment conventional CPAP reports by adding information on sleep regularity and weekday–weekend misalignment. Objectives To quantify CPAP-derived SJL in two independent clinical cohorts, characterize its behavioral correlates and age-related patterns, and assess cross-site reproducibility. Methods We analyzed CPAP-derived sleep timing in patients from Rutgers-RWJ Health (RWJ, N = 1,437) and Hackensack Meridian Health (HMH, N = 1,510) with at least 31 valid nights and at least one valid work night and free night. Mid-sleep on work nights (MSW) and free nights (MSF) was estimated using circular statistics. SJL was defined as the absolute circular difference between MSF and MSW and categorized as none (<1 h), moderate (1–2 h), or severe (≥2 h). Sleep duration, free-night rebound, age-stratified prevalence, and cross-site differences were evaluated using nonparametric and categorical tests. Results SJL was right-skewed at both sites, with median values below 0.5 h at RWJ and HMH. SJL >1 h was present in 21.2% and 16.4% of patients, respectively; severe SJL occurred in 4.0% and 2.8%. Moderate and severe SJL were associated with shorter work-night sleep and greater free-night rebound, consistent with weekday restriction and weekend compensation. SJL prevalence and variability were highest in younger and middle-aged adults, particularly those aged 26–50 years, and declined markedly after age 65. Core timing phenotypes, including MSW, MSF, and free-night rebound, were highly reproducible across sites despite modest differences in absolute sleep duration and overall SJL prevalence. Conclusions In CPAP-treated cohorts, SJL is common but usually modest, is associated with weekday sleep restriction and free-night rebound, and declines substantially with age. These findings support the use of routinely collected CPAP data as a scalable, low-burden source of device-anchored circadian screening phenotypes. CPAP-derived SJL may augment standard adherence reports by helping identify patients who warrant further behavioral, circadian, or activity-based assessment.

Independent of the suprachiasmatic nucleus, peripheral clocks can be strongly entrained by dietary signals. Although feeding time has been widely studied, the effects of food quality—particularly nutrient availability and stress—on peripheral circadian entrainment and metabolic regulation remain less understood. We developed a semi-mechanistic mathematical model of peripheral clock synchronization and clock-controlled ribosome biogenesis (RiBi) in response to feeding/fasting cycles and rhythms in dietary essential amino acid (EAA) availability. The model integrates EAA-sensitive signaling through mammalian target of rapamycin complex 1 (mTORC1) and the general control nonderepressible 2 (GCN2)-mediated integrated stress response (ISR), together with ribosomal protein expression as a metabolic endpoint. We used the model to examine circadian entrainment under nutrient stress, adaptation during transitions between feeding schedules with EAA insufficiency, and stress-related mechanisms that may restore circadian and metabolic function. Simulations showed that mTORC1 and GCN2-ISR signaling jointly regulate metabolic entrainability and stress adaptation and are required to maintain circadian synchronization and RiBi dynamics during nutrient stress. The model also predicted that differences in homeostatic adaptation can produce individualized recovery trajectories after transient dietary disruption. Finally, appropriate modulation of GCN2-ISR signaling mitigated disruption-associated RiBi hyperactivation by leveraging dietary EAA rhythms to restore clock function. These findings identify dietary EAA stress and its regulatory pathways as important determinants of peripheral circadian entrainment and metabolic adaptation, supporting the development of personalized nutrition-based strategies for circadian disruption-related chronic disease.

Over 90% of investigational drugs fail during clinical development, largely due to poor translation of pharmacokinetic, efficacy, and toxicity data from preclinical to clinical settings. The high costs and ethical concerns associated with translational failures highlight the need for more efficient and reliable preclinical tools. Human-relevant new approach methodologies (NAMs), including advanced in vitro systems, in silico mechanistic models, and computational techniques like artificial intelligence and machine learning, can improve translational success, as evident by several literature examples. Case studies on physiologically based pharmacokinetic modeling and quantitative systems pharmacology applications demonstrate the potential of NAMs in improving translational accuracy, reducing reliance on animal studies. Additionally, mechanistic modeling approaches for drug-induced liver injury and tumor microenvironment models have provided critical insights into drug safety and efficacy. We propose a structured and iterative "a priori in silico" workflow that integrates NAM components to actively guide preclinical study designa step toward more predictive and resource-efficient drug development. The proposed workflow can enable in vivo predictions to guide the design of reduced and optimal preclinical studies. The findings from these preclinical studies can then be used to refine computational models to enhance the accuracy of human predictions or guide additional preclinical studies, as needed. To conclude, integrating computational and in vitro NAM approaches can optimize preclinical drug development, improving translational accuracy and reducing clinical trial failures. This paradigm shift is further supported by global regulations, such as the FDA Modernization Act 2.0 and EMA directive 2010/63/EU, underscoring the regulatory momentum toward adopting human-relevant NAMs as the new standard in preclinical drug development.

Background:Gout, the most common inflammatory arthritis worldwide, shows clear seasonal variation in flares. Traditional epidemiology provides important insights but often lacks real-time resolution. Digital behavior, such as online search patterns, offers a scalable, timely complement that can capture seasonal trends in disease-related activity. Objective:This study aimed to determine whether public interest in gout, as expressed through Google (Google LLC) search queries, exhibits seasonal variation across countries, US states, and metropolitan areas, and to assess the influence of symptom- and language-specific search terms. We evaluated whether a bimodal (semiannual) seasonal pattern better described certain queries, providing further insight into complex behavioral rhythms. Methods:We retrieved monthly Google Trends data for gout-related queries from January 1, 2014, to December 31, 2024, covering 70 countries, all 50 US states, and 36 major cities in the United States and Canada. Queries included generic terms, symptom descriptors, and language-specific translations in 14 languages. We applied cosinor modeling to assess seasonality and calculated the amplitude and phase of fitted sinusoidal curves. Significance was assessed using P values and adjusted for multiple comparisons using the Benjamini-Hochberg false discovery rate (FDR) within and across subgroups, and the Bonferroni method. To explore bimodal patterns, we compared 12- and 6-month harmonics using changes in Akaike and Bayesian information criteria. Results:We found robust seasonal variation in gout-related search interest across multiple geographic and linguistic categories. Statistically significant seasonality (original P<.05) was detected in 41 of 70 countries using English terms, with 36 remaining significant after within-group FDR correction and 39 under pooled FDR; only 7 remained significant after Bonferroni adjustment. Among 20 countries using language-specific queries, 15 showed consistent seasonality across all 3 non-Bonferroni methods, while 2 met the Bonferroni threshold. In the United States, 49 out of 50 states and 33 out of 36 cities demonstrated significant seasonality (Bonferroni-adjusted significance in 9 and 10 units, respectively). For symptom- and treatment-related search terms, 18 of 21 exhibited seasonality under multiple correction methods. Peaks in search volume generally occurred in late spring or early summer in the northern hemisphere, with corresponding seasonal shifts in the southern hemisphere. Bimodal patterns were uncommon but identified for terms such as "obesity" and "swollen big toe," suggesting more complex cyclic interest in certain contexts. Conclusions:Google search activity reflects the seasonal dynamics of gout flares, highlighting infodemiology as a population-scale complement to traditional surveillance. This approach may anticipate care needs, guide digital health strategies, and improve preparedness for seasonal, climate-sensitive conditions, while emphasizing the importance of geographic, climatic, and linguistic context in interpreting trends.

Quantitative Systems Pharmacology (QSP) has emerged as a cornerstone of modern drug development, providing a robust framework to integrate data from preclinical and clinical studies, enhance decision-making, and optimize therapeutic strategies. By modeling biological systems and drug interactions, QSP enables predictions of outcomes, optimization of dosing regimens, and personalized medicine applications. Recent advancements in artificial intelligence (AI) and machine learning (ML) hold the potential to significantly transform QSP by enabling enhanced data extraction, fostering the development of hybrid mechanistic ML models, and supporting the introduction of surrogate models and digital twins. This manuscript explores the transformative role of AI and ML in reshaping QSP modeling workflows. AI/ML tools now enable automated literature mining, the generation of dynamic models from data, and the creation of hybrid frameworks that blend mechanistic insights with data-driven approaches. Large Language Models (LLMs) further revolutionize the field by transitioning AI/ML from merely a tool to becoming an active partner in QSP modeling. By facilitating interdisciplinary collaboration, lowering barriers to entry, and democratizing QSP workflows, LLMs empower researchers without deep coding expertise to engage in complex modeling tasks. Additionally, the integration of Artificial General Intelligence (AGI) holds the potential to autonomously propose, refine, and validate models, further accelerating innovation across multiscale biological processes. Key challenges remain in integrating AI/ML into QSP workflows, particularly in ensuring rigorous validation pipelines, addressing ethical considerations, and establishing robust regulatory frameworks to address the reliability and reproducibility of AI-assisted models. Moreover, the complexity of multiscale biological integration, effective data management, and fostering interdisciplinary collaboration present ongoing hurdles. Despite these challenges, the potential of AI/ML to enhance hybrid model development, improve model interpretability, and democratize QSP modeling offers an exciting opportunity to revolutionize drug development and therapeutic innovation. This work highlights a pathway toward a transformative era for QSP, leveraging advancements in AI and ML to address these challenges and drive innovation in the field.

Study objectives:This study examines age-related variations in activity patterns using actigraphy data from the National Health and Nutrition Examination Survey (NHANES). By analyzing sleep onset, wake times, and daily activity levels across different age groups, we aim to uncover key changes in chronotype and physical engagement with aging. From a systems-biology perspective, minute-level rest-activity traces are emergent outputs of coupled circadian-homeostatic-behavioral networks. Treating actigraphy as a high-throughput phenotyping readout, we use NHANES to extract system-level markers (phase, amplitude, and transition dynamics) that reflect network organization across the lifespan. Methods:Actigraphy data from NHANES (2011-2013) were analyzed using machine learning techniques to identify distinct activity clusters among four age groups (19-30, 31-50, 51-70, 71-80). We implemented an unsupervised machine learning pipeline that clustered average-day actigraphy profiles, enabling the identification of distinct, age-dependent rest-activity phenotypes from the NHANES dataset. Sleep-wake cycles, activity intensities, and circadian periodicities were assessed through clustering and statistical modeling. Key metrics, including winding down activity and time to alertness, were derived to evaluate age-related variations. Results:Younger individuals exhibited delayed chronotypes with later sleep and wake times, whereas older adults showed advanced and more structured schedules. Winding down periods lengthened with age, and overall activity levels declined progressively. Time to alertness showed a strong correlation with wake time in younger groups but diminished with age, indicating a weakening circadian influence. Conclusion:Aging is associated with shifts in sleep-wake cycles and activity patterns, reflecting biological and behavioral adaptations. These findings highlight the importance of personalized interventions to support optimal activity and sleep alignment across the lifespan. Insights from actigraphy data can inform public health strategies and clinical approaches to aging-related changes in physical activity and circadian regulation. These age-stratified, interpretable "dynamical phenotypes" provide observables to calibrate and validate systems-level models of sleep-wake regulation and behavior-physiology coupling, supporting hypothesis generation and intervention design in systems biology.

Obstructive sleep apnea is a prevalent condition effectively treated by continuous positive airway pressure (CPAP) therapy. CPAP adherence data, routinely gathered in clinical practice, include detailed information regarding both duration and timing of use. The purpose of the present study was to develop a systematic way to measure the diurnal pattern of CPAP adherence data and to see if distinct patterns exist in a clinical cohort. Machine learning techniques were employed to analyze CPAP adherence data. A cohort of 200 unselected patients was assessed and a cluster analysis was subsequently performed. Application of this methodology to 17 patients with different visually noted patterns was carried out to further assess performance. Each 30-day period of CPAP use for each patient was characterized by 4 variables describing the time of day of initiation and discontinuation of CPAP use, as well as the consistency of use during those times. Further analysis identified 6 distinct clusters, reflecting different timing and adherence patterns. Specifically, clusters with relatively normal timing vs delayed timing were identified. Finally, application of this methodology showed generally good performance with limitations in the ability to characterize shift worker and non-24 rhythms. This study demonstrates a methodology for analysis of diurnal patterns from CPAP adherence data. Furthermore, distinct timing and adherence patterns are demonstrated. The potential impact of these patterns on the beneficial effects of CPAP requires elucidation. Scharf MT, Androulakis IP. Novel assessment of CPAP adherence data reveals distinct diurnal patterns. J Clin Sleep Med. 2025;21(3):493–502.

As a potent endogenous regulator of homeostasis, the circadian time-keeping system synchronizes internal physiology to periodic changes in the external environment to enhance survival. Adapting endogenous rhythms to the external time is accomplished hierarchically with the central pacemaker located in the suprachiasmatic nucleus (SCN) signaling the hypothalamus-pituitary-adrenal (HPA) axis to release hormones, notably cortisol, which help maintain the body's circadian rhythm. Given the essential role of HPA-releasing hormones in regulating physiological functions, including immune response, cell cycle, and energy metabolism, their daily variation is critical for the proper function of the circadian timing system. In this review, we focus on cortisol and key fundamental properties of the HPA axis and highlight their importance in controlling circadian dynamics. We demonstrate how systems-driven, mathematical modeling of the HPA axis complements experimental findings, enhances our understanding of complex physiological systems, helps predict potential mechanisms of action, and elucidates the consequences of circadian disruption. Finally, we outline the implications of circadian regulation in the context of personalized chronotherapy. Focusing on the chrono-pharmacology of synthetic glucocorticoids, we review the challenges and opportunities associated with moving toward personalized therapies that capitalize on circadian rhythms.

From cells to society: untangling the web of stress, inflammation, and social determinants of health

The circadian timing system and integrated stress response (ISR) systems are fundamental regulatory mechanisms that maintain body homeostasis. The central circadian pacemaker in the suprachiasmatic nucleus (SCN) governs daily rhythms through interactions with peripheral oscillators via the hypothalamus-pituitary-adrenal (HPA) axis. On the other hand, ISR signaling is pivotal for preserving cellular homeostasis in response to physiological changes. Notably, disrupted circadian rhythms are observed in cases of impaired ISR signaling. In this work, we examine the potential interplay between the central circadian system and the ISR, mainly through the SCN and HPA axis. We introduce a semimechanistic mathematical model to delineate SCN's capacity for indirectly perceiving physiological stress through glucocorticoid-mediated feedback from the HPA axis and orchestrating a cellular response via the ISR mechanism. Key components of our investigation include evaluating general control nonderepressible 2 (GCN2) expression in the SCN, the effect of physiological stress stimuli on the HPA axis, and the interconnected feedback between the HPA and SCN. Simulation revealed a critical role for GCN2 in linking ISR with circadian rhythms. Experimental findings have demonstrated that a Gcn2 deletion in mice leads to rapid re-entrainment of the circadian clock following jetlag as well as to an elongation of the circadian period. These phenomena are well replicated by our model, which suggests that both the swift re-entrainment and prolonged period can be ascribed to a reduced robustness in neuronal oscillators. Our model also offers insights into phase shifts induced by acute physiological stress and the alignment/misalignment of physiological stress with external light-dark cues. Such understanding aids in strategizing responses to stressful events, such as nutritional status changes and jetlag. NEW & NOTEWORTHY This study is the first theoretical work to investigate the complex interaction between integrated stress response (ISR) sensing and central circadian rhythm regulation, encompassing the suprachiasmatic nucleus (SCN) and hypothalamus-pituitary-adrenal (HPA) axis. The findings carry implications for the development of dietary or pharmacological interventions aimed at facilitating recovery from stressful events, such as jetlag. Moreover, they provide promising prospects for potential therapeutic interventions that target circadian rhythm disruption and various stress-related disorders.

The central circadian pacemaker in the suprachiasmatic nuclei (SCN) aligns the phase and period of autonomous molecular oscillators in peripheral cells to daily light/dark cycles via physiological, neuronal, hormonal, and metabolic signals. Among different entrainment factors, temperature entrainment has been proposed as an essential alternative for inducing and sustaining circadian rhythms in vitro. While the synchronization mechanisms for hormones such as glucocorticoids have been widely studied, little is known about the crucial role of body temperature as a systemic cue. In this work, we develop a semi-mechanistic mathematical model describing the entrainment of peripheral clocks to temperature rhythms. The model incorporates a temperature sensing-transduction cascade involving a heat shock transcription factor-1 (HSF1) and heat shock response (HSR) pathway to simulate the entrainment of clock genes. The model is used to investigate the mammalian temperature entrainment and synchronization of cells subject to temperature oscillations of different amplitudes and magnitudes and examine the effects of transitioning between temperature schedules. Our computational analyses of the system’s dynamic responses reveal that 1) individual cells gradually synchronize to the rhythmic temperature signal by resetting their intrinsic phases to achieve coherent dynamics while oscillations are abolished in the absence of temperature rhythmicity; 2) alterations in the amplitude and period of temperature rhythms impact the peripheral synchronization behavior; 3) personalized synchronization strategies allow for differential, adaptive responses to temperature rhythms. Our results demonstrate that temperature can be a potent entrainer of circadian rhythms. Therefore, in vitro systems subjected to temperature modulation can serve as a potential tool for studying the adjustment or disruption of circadian rhythms.

Synchronizing the circadian timing system (CTS) to external light/dark cycles is crucial for homeostasis maintenance and environmental adaptation. The CTS is organized hierarchically, with the central pacemaker located in the suprachiasmatic nuclei (SCN) generating coherent oscillations that are entrained to light/dark cycles. These oscillations regulate the release of glucocorticoids by the hypothalamus-pituitary-adrenal (HPA) axis, which acts as a systemic entrainer of peripheral clocks throughout the body. The SCN adjusts its network plasticity in response to variations in photoperiod, leading to changes in the rhythmic release of glucocorticoids and ultimately impacting peripheral clocks. However, the effects of photoperiod-induced variations of glucocorticoids on the synchronization of peripheral clocks are not fully understood, and the interaction between jetlag adaption and photoperiod changes is unclear. This study presents a semi-mechanistic mathematical model to investigate how the CTS responds to changes in photoperiod. Specifically, the study focuses on the entrainment properties of a system composed of the SCN, HPA axis, and peripheral clocks. The results show that high-amplitude glucocorticoid rhythms lead to a more coherent phase distribution in the periphery. In addition, our study investigates the effect of photoperiod exposure on jetlag recovery time and phase shift, proposing different interventional strategies for eastward and westward jetlag. The findings suggest that decreasing photic exposure before jetlag during eastward traveling and after jetlag during westward traveling can accelerate jetlag readaptation. The study provides insights into the mechanisms of CTS organization and potential recovery strategies for transitions between time zones and lighting zones.

In this paper, we study several existing quantitative models of the hypothalamic–pituitary–adrenal (HPA) axis from a control systems theory viewpoint, that is, we suppose that we can act on the dynamics of the HPA axis throughout some parameters, which are the system inputs. In particular, we will focus on flatness and Liouvillian properties of the considered control systems of the HPA axis. We first study the minimal three-dimensional model of Bangsgaard and Ottesen (2017, Math. Biosci., 287:24–35) and the semi-mechanistic four-dimensional model of Gupta et al. (2007, Theor. Biol. Medical Model., 4(1):8) which are shown to be flat, and then, we consider the more involved and important model proposed in Rao & Androulakis (2019, Sci. Rep., 9(1):11212; 2020, IFAC-PapersOnLine, 53(2):15858–15863), with seven states, for which we prove that for the nominal values of the parameters involved in the model, flatness no longer holds. The more involved model satisfies however a similar but weaker property than flatness: it is a Liouvillian system.

Quantitative systems pharmacology (QSP) modeling is applied to address essential questions in drug development, such as the mechanism of action of a therapeutic agent and the progression of disease. Meanwhile, machine learning (ML) approaches also contribute to answering these questions via the analysis of multi-layer 'omics' data such as gene expression, proteomics, metabolomics, and high-throughput imaging. Furthermore, ML approaches can also be applied to aspects of QSP modeling. Both approaches are powerful tools and there is considerable interest in integrating QSP modeling and ML. So far, a few successful implementations have been carried out from which we have learned about how each approach can overcome unique limitations of the other. The QSP + ML working group of the International Society of Pharmacometrics QSP Special Interest Group was convened in September, 2019 to identify and begin realizing new opportunities in QSP and ML integration. The working group, which comprises 21 members representing 18 academic and industry organizations, has identified four categories of current research activity which will be described herein together with case studies of applications to drug development decision making. The working group also concluded that the integration of QSP and ML is still in its early stages of moving from evaluating available technical tools to building case studies. This paper reports on this fast-moving field and serves as a foundation for future codification of best practices.

Quantitative Systems Pharmacology (QSP) models capture the physiological underpinnings driving the response to a drug and express those in a semi-mechanistic way, often involving ordinary differential equations (ODEs). The process of developing a QSP model generally starts with the definition of a set of reasonable hypotheses that would support a mechanistic interpretation of the expected response which are used to form a network of interacting elements. This is a hypothesis-driven and knowledge-driven approach, relying on prior information about the structure of the network. However, with recent advances in our ability to generate large datasets rapidly, often in a hypothesis-neutral manner, the opportunity emerges to explore data-driven approaches to establish the network topologies and models in a robust, repeatable manner. In this paper, we explore the possibility of developing complex network representations of physiological responses to pharmaceuticals using a logic-based analysis of available data and then convert the logic relations to dynamic ODE-based models. We discuss an integrated pipeline for converting data to QSP models. This pipeline includes using k-means clustering to binarize continuous data, inferring likely network relationships using a Best-Fit Extension method to create a Boolean network, and finally converting the Boolean network to a continuous ODE model. We utilized an existing QSP model for the dual-affinity re-targeting antibody flotetuzumab to demonstrate the robustness of the process. Key output variables from the QSP model were used to generate a continuous data set for use in the pipeline. This dataset was used to reconstruct a possible model. This reconstruction had no false-positive relationships, and the output of each of the species was similar to that of the original QSP model. This demonstrates the ability to accurately infer relationships in a hypothesis-neutral manner without prior knowledge of a system using this pipeline.

Quantitative systems pharmacology (QSP) modeling is applied to address essential questions in drug development, such as the mechanism of action of a therapeutic agent and the progression of disease. Meanwhile, machine learning (ML) approaches also contribute to answering these questions via the analysis of multi-layer 'omics' data such as gene expression, proteomics, metabolomics, and high-throughput imaging. Furthermore, ML approaches can also be applied to aspects of QSP modeling. Both approaches are powerful tools and there is considerable interest in integrating QSP modeling and ML. So far, a few successful implementations have been carried out from which we have learned about how each approach can overcome unique limitations of the other. The QSP + ML working group of the International Society of Pharmacometrics QSP Special Interest Group was convened in September, 2019 to identify and begin realizing new opportunities in QSP and ML integration. The working group, which comprises 21 members representing 18 academic and industry organizations, has identified four categories of current research activity which will be described herein together with case studies of applications to drug development decision making. The working group also concluded that the integration of QSP and ML is still in its early stages of moving from evaluating available technical tools to building case studies. This paper reports on this fast-moving field and serves as a foundation for future codification of best practices.

Gout, the most common inflammatory arthritis worldwide, is an auto-inflammatory metabolic disease that leads to monosodium urate crystal deposition. Hyperuricaemia is a significant risk factor for the development of gout; however, hyperuricaemia alone is not sufficient to induce gout.Gout flares have circadian rhythms. Gout flares vary during the day and have strong seasonality, with flares being more common in the spring. The reasons for the predominance of flares in the spring are unclear since serum urate (SU) levels show seasonal variation; however, SU levels are highest in the summer.Immune function varies significantly throughout the year, with enhanced immune responses increasing during the winter. In addition, chronic disruption of circadian rhythms is associated with metabolic syndrome and diseases driven by metabolism. The most telling example relates to Xanthine oxidase (XOD/XDH). The analysis of XOD/XDH established its circadian regulation and demonstrated that inhibition of the activity of XOD is characterised by distinct, crossregulating diurnal/seasonal patterns of activity.The gastrointestinal microbiota of gout patients is highly distinct from healthy individuals. In a small series of gout patients, Bacteroides caccae and Bacteroides xylanisolvens were found to be enriched. Bacteroidales levels were highest during the spring and summer, and loading values were highest in the spring.Our review discusses gout's circadian rhythm and seasonality, possible influences of the microbiome on gout due to our new knowledge that Bacteroidales levels were highest during spring when gout is most common, and potential opportunities for treatment based on our current understanding of this interaction.

Computational systems biology (CSB) is a field that emerged primarily as the product of research activities. As such, it grew in several directions in a distributed and uncoordinated manner making the area appealing and fascinating. The idea of not having to follow a specific path but instead creating one fueled innovation. As the field matured, several interdisciplinary graduate programs emerged attempting to educate future generations of computational systems biologists. These educational initiatives coordinated the dissemination of information across student populations that had already decided to specialize in this field. However, we are now entering an era where CSB, having established itself as a valuable research discipline, is attempting the next major step: Entering undergraduate curricula. As interesting as this endeavor may sound, it has several difficulties, mainly because the field is not uniformly defined. In this manuscript, we argue that this diversity is a significant advantage and that several incarnations of an undergraduate-level CSB biology course could, and should, be developed tailored to programmatic needs. In this manuscript, we share our experiences creating a course as part of a Biomedical Engineering program.