Multitype Bellman-Harris branching model provides biological predictors of early stages of adult hippocampal neurogenesis

A comprehensive review of computational cell cycle models in guiding cancer treatment strategies

This article reviews the current knowledge and recent advancements in computational modeling of the cell cycle. It offers a comparative analysis of various modeling paradigms, highlighting their unique strengths, limitations, and applications. Specifically, the article compares deterministic and stochastic models, single-cell versus population models, and mechanistic versus abstract models. This detailed analysis helps determine the most suitable modeling framework for various research needs. Additionally, the discussion extends to the utilization of these computational models to illuminate cell cycle dynamics, with a particular focus on cell cycle viability, crosstalk with signaling pathways, tumor microenvironment, DNA replication, and repair mechanisms, underscoring their critical roles in tumor progression and the optimization of cancer therapies. By applying these models to crucial aspects of cancer therapy planning for better outcomes, including drug efficacy quantification, drug discovery, drug resistance analysis, and dose optimization, the review highlights the significant potential of computational insights in enhancing the precision and effectiveness of cancer treatments. This emphasis on the intricate relationship between computational modeling and therapeutic strategy development underscores the pivotal role of advanced modeling techniques in navigating the complexities of cell cycle dynamics and their implications for cancer therapy.

Mathematics of neural stem cells: Linking data and processes

Adult stem cells are described as a discrete population of cells that stand at the top of a hierarchy of progressively differentiating cells. Through their unique ability to self-renew and differentiate, they regulate the number of end-differentiated cells that contribute to tissue physiology. The question of how discrete, continuous, or reversible the transitions through these hierarchies are and the precise parameters that determine the ultimate performance of stem cells in adulthood are the subject of intense research. In this review, we explain how mathematical modelling has improved the mechanistic understanding of stem cell dynamics in the adult brain. We also discuss how single-cell sequencing has influenced the understanding of cell states or cell types. Finally, we discuss how the combination of single-cell sequencing technologies and mathematical modelling provides a unique opportunity to answer some burning questions in the field of stem cell biology.

Glial PAMPering and DAMPening of Adult Hippocampal Neurogenesis

Adult neurogenesis represents a mature brain's capacity to integrate newly generated neurons into functional circuits. Impairment of neurogenesis contributes to the pathophysiology of various mood and cognitive disorders such as depression and Alzheimer's Disease. The hippocampal neurogenic niche hosts neural progenitors, glia, and vasculature, which all respond to intrinsic and environmental cues, helping determine their current state and ultimate fate. In this article we focus on the major immune communication pathways and mechanisms through which glial cells sense, interact with, and modulate the neurogenic niche. We pay particular attention to those related to the sensing of and response to innate immune danger signals. Receptors for danger signals were first discovered as a critical component of the innate immune system response to pathogens but are now also recognized to play a crucial role in modulating non-pathogenic sterile inflammation. In the neurogenic niche, viable, stressed, apoptotic, and dying cells can activate danger responses in neuroimmune cells, resulting in neuroprotection or neurotoxicity. Through these mechanisms glial cells can influence hippocampal stem cell fate, survival, neuronal maturation, and integration. Depending on the context, such responses may be appropriate and on-target, as in the case of learning-associated synaptic pruning, or excessive and off-target, as in neurodegenerative disorders.

Intermittent Hypoxia and Effects on Early Learning/Memory: Exploring the Hippocampal Cellular Effects of Pediatric Obstructive Sleep Apnea

This review provides an update on the neurocognitive phenotype of pediatric obstructive sleep apnea (OSA). Pediatric OSA is associated with neurocognitive deficits involving memory, learning, and executive functioning. Adenotonsillectomy (AT) is presently accepted as the first-line surgical treatment for pediatric OSA, but the executive function deficits do not resolve postsurgery, and the timeline for recovery remains unknown. This finding suggests that pediatric OSA potentially causes irreversible damage to multiple areas of the brain. The focus of this review is the hippocampus, 1 of the 2 major sites of postnatal neurogenesis, where new neurons are formed and integrated into existing circuitry and the mammalian center of learning/memory functions. Here, we review the clinical phenotype of pediatric OSA, and then discuss existing studies of OSA on different cell types in the hippocampus during critical periods of development. This will set the stage for future study using preclinical models to understand the pathogenesis of persistent neurocognitive dysfunction in pediatric OSA.

A multiscale mathematical model of cell dynamics during neurogenesis in the mouse cerebral cortex

Background

Neurogenesis in the murine cerebral cortex involves the coordinated divisions of two main types of progenitor cells, whose numbers, division modes and cell cycle durations set up the final neuronal output. To understand the respective roles of these factors in the neurogenesis process, we combine experimental in vivo studies with mathematical modeling and numerical simulations of the dynamics of neural progenitor cells. A special focus is put on the population of intermediate progenitors (IPs), a transit amplifying progenitor type critically involved in the size of the final neuron pool.

Results

A multiscale formalism describing IP dynamics allows one to track the progression of cells along the subsequent phases of the cell cycle, as well as the temporal evolution of the different cell numbers. Our model takes into account the dividing apical progenitors (AP) engaged into neurogenesis, both neurogenic and proliferative IPs, and the newborn neurons. The transfer rates from one population to another are subject to the mode of division (proliferative, or neurogenic) and may be time-varying. The model outputs are successfully fitted to experimental cell numbers from mouse embryos at different stages of cortical development, taking into account IPs and neurons, in order to adjust the numerical parameters. We provide additional information on cell kinetics, such as the mitotic and S phase indexes, and neurogenic fraction.

Conclusions

Applying the model to a mouse mutant for Ftm/Rpgrip1l, a gene involved in human ciliopathies with severe brain abnormalities, reveals a shortening of the neurogenic period associated with an increased influx of newborn IPs from apical progenitors at mid-neurogenesis. Our model can be used to study other mouse mutants with cortical neurogenesis defects and can be adapted to study the importance of progenitor dynamics in cortical evolution and human diseases.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-3018-8) contains supplementary material, which is available to authorized users.

Imaging Mass Spectrometry: A New Tool to Assess Molecular Underpinnings of Neurodegeneration

Neurodegenerative diseases are prevalent and devastating. While extensive research has been done over the past decades, we are still far from comprehensively understanding what causes neurodegeneration and how we can prevent it or reverse it. Recently, systems biology approaches have led to a holistic examination of the interactions between genome, metabolome, and the environment, in order to shed new light on neurodegenerative pathogenesis. One of the new technologies that has emerged to facilitate such studies is imaging mass spectrometry (IMS). With its ability to map a wide range of small molecules with high spatial resolution, coupled with the ability to quantify them at once, without the need for a priori labeling, IMS has taken center stage in current research efforts in elucidating the role of the metabolome in driving neurodegeneration. IMS has already proven to be effective in investigating the lipidome and the proteome of various neurodegenerative diseases, such as Alzheimer's, Parkinson's, Huntington's, multiple sclerosis, and amyotrophic lateral sclerosis. Here, we review the IMS platform for capturing biological snapshots of the metabolic state to shed more light on the molecular mechanisms of the diseased brain.

Mathematical modelling reveals unexpected inheritance and variability patterns of cell cycle parameters in mammalian cells

The cell cycle is the fundamental process of cell populations, it is regulated by environmental cues and by intracellular checkpoints. Cell cycle variability in clonal cell population is caused by stochastic processes such as random partitioning of cellular components to progeny cells at division and random interactions among biomolecules in cells. One of the important biological questions is how the dynamics at the cell cycle scale, which is related to family dependencies between the cell and its descendants, affects cell population behavior in the long-run. We address this question using a “mechanistic” model, built based on observations of single cells over several cell generations, and then extrapolated in time. We used cell pedigree observations of NIH 3T3 cells including FUCCI markers, to determine patterns of inheritance of cell-cycle phase durations and single-cell protein dynamics. Based on that information we developed a hybrid mathematical model, involving bifurcating autoregression to describe stochasticity of partitioning and inheritance of cell-cycle-phase times, and an ordinary differential equation system to capture single-cell protein dynamics. Long-term simulations, concordant with in vitro experiments, demonstrated the model reproduced the main features of our data and had homeostatic properties. Moreover, heterogeneity of cell cycle may have important consequences during population development. We discovered an effect similar to genetic drift, amplified by family relationships among cells. In consequence, the progeny of a single cell with a short cell cycle time had a high probability of eventually dominating the population, due to the heritability of cell-cycle phases. Patterns of epigenetic heritability in proliferating cells are important for understanding long-term trends of cell populations which are either required to provide the influx of maturing cells (such as hematopoietic stem cells) or which started proliferating uncontrollably (such as cancer cells).

All cells in multicellular organisms obey orchestrated sequences of signals to ensure developmental and homeostatic fitness under a variety of external stimuli. However, there also exist self-perpetuating stem-cell populations, the function of which is to provide a steady supply of differentiated progenitors that in turn ensure persistence of organism functions. This “cell production engine” is an important element of biological homeostasis. A similar process, albeit distorted in many respects, plays a major role in cancer development; here the robustness of homeostasis contributes to difficulty in eradication of malignancy. An important role in homeostasis seems to be played by generation of heterogeneity among cell phenotypes, which then can be shaped by selection and other genetic forces. In the present paper, we present a model of a cultured cell population, which factors in relationships among related cells and the dynamics of cell growth and important proteins regulating cell division. We find that the model not only maintains homeostasis, but that it also responds to perturbations in a manner that is similar to that exhibited by the Wright-Fisher model of population genetics. The model-cell population can become dominated by the progeny of the fittest individuals, without invoking advantageous mutations. If confirmed, this may provide an alternative mode of evolution of cell populations.

Modeling Reveals the Dependence of Hippocampal Neurogenesis Radiosensitivity on Age and Strain of Rats

Cognitive dysfunction following radiation treatment for brain cancers in both children and adults have been correlated to impairment of neurogenesis in the hippocampal dentate gyrus. Various species and strains of rodent models have been used to study radiation-induced changes in neurogenesis and these investigations have utilized only a limited number of doses, dose-fractions, age and time after exposures conditions. In this paper, we have extended our previous mathematical model of radiation-induced hippocampal neurogenesis impairment of C57BL/6 mice to delineate the time, age, and dose dependent alterations in neurogenesis of a diverse strain of rats. To the best of our knowledge, this is the first predictive mathematical model to be published about hippocampal neurogenesis impairment for a variety of rat strains after acute or fractionated exposures to low linear energy transfer (low LET) radiation, such as X-rays and γ-rays, which are conventionally used in cancer radiation therapy. We considered four compartments to model hippocampal neurogenesis and its impairment following radiation exposures. Compartments include: (1) neural stem cells (NSCs), (2) neuronal progenitor cells or neuroblasts (NB), (3) immature neurons (ImN), and (4) glioblasts (GB). Additional consideration of dose and time after irradiation dependence of microglial activation and a possible shift of NSC proliferation from neurogenesis to gliogenesis at higher doses is established. Using a system of non-linear ordinary differential equations (ODEs), characterization of rat strain and age-related dynamics of hippocampal neurogenesis for unirradiated and irradiated conditions is developed. The model is augmented with the description of feedback regulation on early and late neuronal proliferation following radiation exposure. Predictions for dose-fraction regimes compared to acute radiation exposures, along with the dependence of neurogenesis sensitivity to radiation on age and strain of rats are discussed. A major result of this work is predictions of the rat strain and age dependent differences in radiation sensitivity and sub-lethal damage repair that can be used for predictions for arbitrary dose and dose-fractionation schedules.

Revealing age-related changes of adult hippocampal neurogenesis using mathematical models

New neurons are continuously generated in the dentate gyrus of the adult hippocampus. This continuous supply of newborn neurons is important to modulate cognitive functions. Yet the number of newborn neurons declines with age. Increasing Wnt activity upon loss of dickkopf 1 can counteract both the decline of newborn neurons and the age-related cognitive decline. However, the precise cellular changes underlying the age-related decline or its rescue are fundamentally not understood. The present study combines a mathematical model and experimental data to address features controlling neural stem cell (NSC) dynamics. We show that available experimental data fit a model in which quiescent NSCs may either become activated to divide or may undergo depletion events, such as astrocytic transformation and apoptosis. Additionally, we demonstrate that old NSCs remain quiescent longer and have a higher probability of becoming re-activated than depleted. Finally, our model explains that high NSC-Wnt activity leads to longer time in quiescence while enhancing the probability of activation. Altogether, our study shows that modulation of the quiescent state is crucial to regulate the pool of stem cells throughout the life of an animal.

Summary: New deterministic and stochastic mathematical models are proposed to investigate adult neurogenesis in young, old and perturbed hippocampus, and quantified using population-level and clonal experimental data.

A simulation model of neuroprogenitor proliferation dynamics predicts age-related loss of hippocampal neurogenesis but not astrogenesis

Adult hippocampal neuroprogenitors give rise to both neurons and astrocytes. As neuroprogenitors are lost with increased age, neurogenesis concomitantly decreases. However, the dynamics of neuron and astrocyte generation throughout adulthood has not been systematically examined. Here, we analyzed the hippocampal niche both longitudinally (from 2 h to 30d of cell life) and transversally (from 1 m to 12 m of age) and generated a Marsaglia polar random simulation model to predict newborn cell dynamics. The sharp decrease in newborn neuron production throughout adulthood was largely predicted by the number of proliferating neuroprogenitors at each age. In contrast, newborn astrocyte decay was slower and associated with their increased yield in mature mice. As a result, the niche shifted from neurogenic to neuro/astrogenic with increased age. Our data provide a simple “end-point” model to understand the hippocampal niche changes across adulthood and suggest yet unexplored functions of newborn astrocytes for the aging hippocampal circuitry.

The International Conference on Intelligent Biology and Medicine (ICIBM) 2016: putting systems biology to work

Between December 8–10, 2016, the International Conference on Intelligent Biology and Medicine (ICIBM 2016) was held in Houston, Texas, USA. The conference included eight scientific sessions, four tutorials, one poster session, four highlighted talks and four keynotes that covered topics in 3D genome structure analysis and visualization, next generation sequencing analysis, computational drug discovery, medical informatics, cancer genomics and systems biology. Systems biology has been a main theme in ICIBM 2016, with exciting advances were presented in many areas of systems biology. Here, we selected seven high quality papers to be published in BMC Systems Biology.

Lunatic fringe-mediated Notch signaling regulates adult hippocampal neural stem cell maintenance

Hippocampal neural stem cells (NSCs) integrate inputs from multiple sources to balance quiescence and activation. Notch signaling plays a key role during this process. Here, we report that Lunatic fringe (Lfng), a key modifier of the Notch receptor, is selectively expressed in NSCs. Further, Lfng in NSCs and Notch ligands Delta1 and Jagged1, expressed by their progeny, together influence NSC recruitment, cell cycle duration, and terminal fate. We propose a new model in which Lfng-mediated Notch signaling enables direct communication between a NSC and its descendants, so that progeny can send feedback signals to the ‘mother’ cell to modify its cell cycle status. Lfng-mediated Notch signaling appears to be a key factor governing NSC quiescence, division, and fate.

DOI:http://dx.doi.org/10.7554/eLife.24660.001