Publications by authors named "Natalia L Komarova"

125 Publications

Multi-scale network targeting: A holistic systems-biology approach to cancer treatment.

Prog Biophys Mol Biol 2021 Aug 21. Epub 2021 Aug 21.

Department of Mathematics, University of California Irvine, Irvine, CA, 92697, USA. Electronic address:

The vulnerabilities of cancer at the cellular and, recently, with the introduction of immunotherapy, at the tissue level, have been exploited with variable success. Evaluating the cancer system vulnerabilities at the organismic level through analysis of network topology and network dynamics can potentially predict novel anti-cancer drug targets directed at the macroscopic cancer networks. Theoretical work analyzing the properties and the vulnerabilities of the multi-scale network of cancer needs to go hand-in-hand with experimental research that uncovers the biological nature of the relevant networks and reveals new targetable vulnerabilities. It is our hope that attacking cancer on different spatial scales, in a concerted integrated approach, may present opportunities for novel ways to prevent treatment resistance.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.pbiomolbio.2021.08.004DOI Listing
August 2021

Quantifying the dynamics of viral recombination during free virus and cell-to-cell transmission in HIV-1 infection.

Virus Evol 2021 Jan 22;7(1):veab026. Epub 2021 Mar 22.

Department of Basic Science, New York University College of Dentistry, 921 Schwartz Building, 345 East 24th Street, New York, NY 10010-9403, USA.

Recombination has been shown to contribute to human immunodeficiency virus-1 (HIV-1) evolution , but the underlying dynamics are extremely complex, depending on the nature of the fitness landscapes and of epistatic interactions. A less well-studied determinant of recombinant evolution is the mode of virus transmission in the cell population. HIV-1 can spread by free virus transmission, resulting largely in singly infected cells, and also by direct cell-to-cell transmission, resulting in the simultaneous infection of cells with multiple viruses. We investigate the contribution of these two transmission pathways to recombinant evolution, by applying mathematical models to experimental data on the growth of fluorescent reporter viruses under static conditions (where both transmission pathways operate), and under gentle shaking conditions, where cell-to-cell transmission is largely inhibited. The parameterized mathematical models are then used to extrapolate the viral evolutionary dynamics beyond the experimental settings. Assuming a fixed basic reproductive ratio of the virus (independent of transmission pathway), we find that recombinant evolution is fastest if virus spread is driven only by cell-to-cell transmission and slows down if both transmission pathways operate. Recombinant evolution is slowest if all virus spread occurs through free virus transmission. This is due to cell-to-cell transmission 1, increasing infection multiplicity; 2, promoting the co-transmission of different virus strains from cell to cell; and 3, increasing the rate at which point mutations are generated as a result of more reverse transcription events. This study further resulted in the estimation of various parameters that characterize these evolutionary processes. For example, we estimate that during cell-to-cell transmission, an average of three viruses successfully integrated into the target cell, which can significantly raise the infection multiplicity compared to free virus transmission. In general, our study points towards the importance of infection multiplicity and cell-to-cell transmission for HIV evolution.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/ve/veab026DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8117450PMC
January 2021

Network models and the interpretation of prolonged infection plateaus in the COVID19 pandemic.

Epidemics 2021 06 8;35:100463. Epub 2021 May 8.

Department of Population Health and Disease Prevention, Program in Public Health, Susan and Henry Samueli College of Health Science, University of California Irvine, Irvine, CA, 92697, United States. Electronic address:

Non-pharmaceutical intervention measures, such as social distancing, have so far been the only means to slow the spread of SARS-CoV-2. In the United States, strict social distancing during the first wave of virus spread has resulted in different types of infection dynamics. In some states, such as New York, extensive infection spread was followed by a pronounced decline of infection levels. In other states, such as California, less infection spread occurred before strict social distancing, and a different pattern was observed. Instead of a pronounced infection decline, a long-lasting plateau is evident, characterized by similar daily new infection levels. Here we show that network models, in which individuals and their social contacts are explicitly tracked, can reproduce the plateau if network connections are cut due to social distancing measures. The reason is that in networks characterized by a 2D spatial structure, infection tends to spread quadratically with time, but as edges are randomly removed, the infection spreads along nearly one-dimensional infection "corridors", resulting in plateau dynamics. Further, we show that plateau dynamics are observed only if interventions start sufficiently early; late intervention leads to a "peak and decay" pattern. Interestingly, the plateau dynamics are predicted to eventually transition into an infection decline phase without any further increase in social distancing measures. Additionally, the models suggest that a second wave becomes significantly less pronounced if social distancing is only relaxed once the dynamics have transitioned to the decline phase. The network models analyzed here allow us to interpret and reconcile different infection dynamics during social distancing observed in various US states.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.epidem.2021.100463DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8105306PMC
June 2021

Role of high-dose exposure in transmission hot zones as a driver of SARS-CoV-2 dynamics.

J R Soc Interface 2021 03 31;18(176):20200916. Epub 2021 Mar 31.

Department of Microbiology and Immunology and Baker Institute, Cornell University College of Veterinary Medicine, Cornell University, Ithaca, NY 14853, USA.

Epidemiological data about SARS-CoV-2 spread indicate that the virus is not transmitted uniformly in the population. The transmission tends to be more effective in select settings that involve exposure to relatively high viral dose, such as in crowded indoor settings, assisted living facilities, prisons or food processing plants. To explore the effect on infection dynamics, we describe a new mathematical model where transmission can occur (i) in the community at large, characterized by low-dose exposure and mostly mild disease, and (ii) in so-called transmission hot zones, characterized by high-dose exposure that can be associated with more severe disease. The model yields different types of epidemiological dynamics, depending on the relative importance of hot zone and community transmission. Interesting dynamics occur if the rate of virus release/deposition from severely infected people is larger than that of mildly infected individuals. Under this assumption, we find that successful infection spread can hinge upon high-dose hot zone transmission, yet the majority of infections are predicted to occur in the community at large with mild disease. In this regime, residual hot zone transmission can account for continued virus spread during community lockdowns, and the suppression of hot zones after community interventions are relaxed can cause a prolonged lack of infection resurgence following the reopening of society. This gives rise to the notion that targeted interventions specifically reducing virus transmission in the hot zones have the potential to suppress overall infection spread, including in the community at large. Epidemiological trends in the USA and Europe are interpreted in light of this model.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1098/rsif.2020.0916DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8098709PMC
March 2021

Effect of feedback regulation on stem cell fractions in tissues and tumors: Understanding chemoresistance in cancer.

J Theor Biol 2021 01 29;509:110499. Epub 2020 Oct 29.

Department of Mathematics, University of California Irvine, Irvine, CA 92697, United States. Electronic address:

While resistance mutations are often implicated in the failure of cancer therapy, lack of response also occurs without such mutants. In bladder cancer mouse xenografts, repeated chemotherapy cycles have resulted in cancer stem cell (CSC) enrichment, and consequent loss of therapy response due to the reduced susceptibility of CSCs to drugs. A particular feedback loop present in the xenografts has been shown to promote CSC enrichment in this system. Yet, many other regulatory loops might also be operational and might promote CSC enrichment. Their identification is central to improving therapy response. Here, we perform a comprehensive mathematical analysis to define what types of regulatory feedback loops can and cannot contribute to CSC enrichment, providing guidance to the experimental identification of feedback molecules. We derive a formula that reveals whether or not the cell population experiences CSC enrichment over time, based on the properties of the feedback. We find that negative feedback on the CSC division rate or positive feedback on differentiated cell death rate can lead to CSC enrichment. Further, the feedback mediators that achieve CSC enrichment can be secreted by either CSCs or by more differentiated cells. The extent of enrichment is determined by the CSC death rate, the CSC self-renewal probability, and by feedback strength. Defining these general characteristics of feedback loops can guide the experimental screening for and identification of feedback mediators that can promote CSC enrichment in bladder cancer and potentially other tumors. This can help understand and overcome the phenomenon of CSC-based therapy resistance.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jtbi.2020.110499DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8445233PMC
January 2021

Role of high-dose exposure in transmission hot zones as a driver of SARS-CoV2 dynamics.

medRxiv 2020 Oct 9. Epub 2020 Oct 9.

Department of Microbiology and Immunology and Baker Institute, Cornell University College of Veterinary Medicine, Cornell University, Ithaca, NY 14853.

Epidemiological data on the spread of SARS-CoV-2 in the absence and presence of various non-pharmaceutical interventions indicate that the virus is not transmitted uniformly in the population. Transmission tends to be more effective in select settings that involve exposure to relatively high viral dose, such as in crowded indoor settings, assisted living facilities, prisons, or food processing plants. To explore the effect on infection dynamics, we describe a new mathematical model where transmission can occur (i) in the community at large, characterized by low dose exposure and mostly mild disease, and (ii) in so called transmission hot zones, characterized by high dose exposure that can be associated with more severe disease. Interestingly, we find that successful infection spread can hinge upon high-dose hot zone transmission, yet the majority of infections are predicted to occur in the community at large with mild disease. This gives rise to the prediction that targeted interventions that specifically reduce virus transmission in the hot zones (but not in the community at large) have the potential to suppress overall infection spread, including in the community at large. The model can further reconcile seemingly contradicting epidemiological observations. While in some locations like California, strict stay-home orders failed to significantly reduce infection prevalence, in other locations, such as New York and several European countries, stay-home orders lead to a pronounced fall in infection levels, which remained suppressed for some months after re-opening of society. Differences in hot zone transmission levels during and after social distancing interventions can account for these diverging infection patterns. These modeling results warrant further epidemiological investigations into the role of high dose hot zone transmission for the maintenance of SARS-CoV-2 spread.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1101/2020.10.07.20208231DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7553173PMC
October 2020

Patterns of the COVID-19 pandemic spread around the world: exponential versus power laws.

J R Soc Interface 2020 09 30;17(170):20200518. Epub 2020 Sep 30.

Department of Population Health and Disease Prevention, Program in Public Health, Susan and Henry Samueli College of Health Science, University of California Irvine, Irvine, CA 92697, USA.

We have analysed the COVID-19 epidemic data of more than 174 countries (excluding China) in the period between 22 January and 28 March 2020. We found that some countries (such as the USA, the UK and Canada) follow an exponential epidemic growth, while others (like Italy and several other European countries) show a power law like growth. Regardless of the best fitting law, many countries can be shown to follow a common trajectory that is similar to Italy (the epicentre at the time of analysis), but with varying degrees of delay. We found that countries with 'younger' epidemics, i.e. countries where the epidemic started more recently, tend to exhibit more exponential like behaviour, while countries that were closer behind Italy tend to follow a power law growth. We hypothesize that there is a universal growth pattern of this infection that starts off as exponential and subsequently becomes more power law like. Although it cannot be excluded that this growth pattern is a consequence of social distancing measures, an alternative explanation is that it is an intrinsic epidemic growth law, dictated by a spatially distributed community structure, where the growth in individual highly mixed communities is exponential but the longer term, local geographical spread (in the absence of global mixing) results in a power law. This is supported by computer simulations of a metapopulation model that gives rise to predictions about the growth dynamics that are consistent with correlations found in the epidemiological data. Therefore, seeing a deviation from straight exponential growth may be a natural progression of the epidemic in each country. On the practical side, this indicates that (i) even in the absence of strict social distancing interventions, exponential growth is not an accurate predictor of longer term infection spread, and (ii) a deviation from exponential spread and a reduction of estimated doubling times do not necessarily indicate successful interventions, which are instead indicated by a transition to a reduced power or by a deviation from power law behaviour.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1098/rsif.2020.0518DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7536045PMC
September 2020

Mutant Evolution in Spatially Structured and Fragmented Expanding Populations.

Genetics 2020 09 13;216(1):191-203. Epub 2020 Jul 13.

Department of Mathematics, University of California Irvine, California 92697

Mutant evolution in spatially structured systems is important for a range of biological systems, but aspects of it still require further elucidation. Adding to previous work, we provide a simple derivation of growth laws that characterize the number of mutants of different relative fitness in expanding populations in spatial models of different dimensionalities. These laws are universal and independent of "microscopic" modeling details. We further study the accumulation of mutants and find that, with advantageous and neutral mutants, more of them are present in spatially structured, compared to well-mixed colonies of the same size. The behavior of disadvantageous mutants is subtle: if they are disadvantageous through a reduction in division rates, the result is the same, and it is the opposite if the disadvantage is due to a death rate increase. Finally, we show that in all cases, the same results are observed in fragmented, nonspatial patch models. This suggests that the patterns observed are the consequence of population fragmentation, and not spatial restrictions We provide an intuitive explanation for the complex dependence of disadvantageous mutant evolution on spatial restriction, which relies on desynchronized dynamics in different locations/patches, and plays out differently depending on whether the disadvantage is due to a lower division rate or a higher death rate. Implications for specific biological systems, such as the evolution of drug-resistant cell mutants in cancer or bacterial biofilms, are discussed.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1534/genetics.120.303422DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7463292PMC
September 2020

Evolutionary dynamics of culturally transmitted, fertility-reducing traits.

Proc Biol Sci 2020 04 15;287(1925):20192468. Epub 2020 Apr 15.

Department of Mathematics, University of California, Irvine, CA 92697, USA.

Human populations in many countries have undergone a phase of demographic transition, characterized by a major reduction in fertility at a time of increased resource availability. A key stylized fact is that the reduction in fertility is preceded by a reduction in mortality and a consequent increase in population density. Various theories have been proposed to account for the demographic transition process, including maladaptation, increased parental investment in fewer offspring, and cultural evolution. None of these approaches, including formal cultural evolutionary models of the demographic transitions, have addressed a possible direct causal relationship between a reduction in mortality and the subsequent decline in fertility. We provide mathematical models in which favours the cultural selection of low-fertility traits. This occurs because reduced mortality slows turnover in the model, which allows the cultural transmission advantage of low-fertility traits to outrace their reproductive disadvantage. For mortality to be a crucial determinant of outcome, a cultural transmission bias is required where slow reproducers exert higher social influence. Computer simulations of our models that allow for exogenous variation in the death rate can reproduce the central features of the demographic transition process, including substantial reductions in fertility within only one to three generations. A model assuming continuous evolution of reproduction rates through imitation errors predicts fertility to fall below replacement levels if death rates are sufficiently low. This can potentially explain the very low preferred family sizes in Western Europe.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1098/rspb.2019.2468DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7211447PMC
April 2020

Beyond the pair approximation: Modeling colonization population dynamics.

Phys Rev E 2020 Mar;101(3-1):032404

Department of Mathematics, University of California Irvine, Irvine, California 92697, USA.

The process of range expansion (colonization) is one of the basic types of biological dynamics, whereby a species grows and spreads outwards, occupying new territories. Spatial modeling of this process is naturally implemented as a stochastic cellular automaton, with individuals occupying nodes on a rectangular grid, births and deaths occurring probabilistically, and individuals only reproducing onto unoccupied neighboring spots. In this paper we derive several approximations that allow prediction of the expected range expansion dynamics, based on the reproduction and death rates. We derive several approximations, where the cellular automaton is described by a system of ordinary differential equations that preserves correlations among neighboring spots (up to a distance). This methodology allows us to develop accurate approximations of the population size and the expected spatial shape, at a fraction of the computational time required to simulate the original stochastic system. In addition, we provide simple formulas for the steady-state population densities for von Neumann and Moore neighborhoods. Finally, we derive concise approximations for the speed of range expansion in terms of the reproduction and death rates, for both types of neighborhoods. The methodology is generalizable to more complex scenarios, such as different interaction ranges and multiple-species systems.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1103/PhysRevE.101.032404DOI Listing
March 2020

Effect of synaptic cell-to-cell transmission and recombination on the evolution of double mutants in HIV.

J R Soc Interface 2020 03 25;17(164):20190832. Epub 2020 Mar 25.

Department of Mathematics, Susan and Henry Samueli College of Health Sciences, University of California, Irvine, CA 92697, USA.

Recombination in HIV infection can impact virus evolution in complex ways, as has been shown both experimentally and mathematically. The effect of free virus versus synaptic, cell-to-cell transmission on the evolution of double mutants, however, has not been investigated. Here, we do so by using a stochastic agent-based model. Consistent with data, we assume spatial constraints for synaptic but not for free-virus transmission. Two important effects of the viral spread mode are observed: (i) for disadvantageous mutants, synaptic transmission protects against detrimental effects of recombination on double mutant persistence. Under free virus transmission, recombination increases double mutant levels for negative epistasis, but reduces them for positive epistasis. This reduction for positive epistasis is much diminished under predominantly synaptic transmission, and recombination can, in fact, lead to increased mutant levels. (ii) The mode of virus spread also directly influences the evolutionary fate of double mutants. For disadvantageous mutants, double mutant production is the predominant driving force, and hence synaptic transmission leads to highest double mutant levels due to increased transmission efficiency. For advantageous mutants, double mutant spread is the most important force, and hence free virus transmission leads to fastest invasion due to better mixing. For neutral mutants, both production and spread of double mutants are important, and hence an optimal mixture of free virus and synaptic transmission maximizes double mutant fractions. Therefore, both free virus and synaptic transmission can enhance or delay double mutant evolution. Implications for drug resistance in HIV are discussed.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1098/rsif.2019.0832DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7115232PMC
March 2020

Aspirin and the chemoprevention of cancers: A mathematical and evolutionary dynamics perspective.

Wiley Interdiscip Rev Syst Biol Med 2020 09 12;12(5):e1487. Epub 2020 Mar 12.

Department of Population Health and Disease Prevention, Program in Public Health, Susan and Henry Samueli College of Health Sciences, University of California Irvine, Irvine, California, USA.

Epidemiological data indicate that long-term low dose aspirin administration has a protective effect against the occurrence of colorectal cancer, both in sporadic and in hereditary forms of the disease. The mechanisms underlying this protective effect, however, are incompletely understood. The molecular events that lead to protection have been partly defined, but remain to be fully characterized. So far, however, approaches based on evolutionary dynamics have not been discussed much, but can potentially offer important insights. The aim of this review is to highlight this line of investigation and the results that have been obtained. A core observation in this respect is that aspirin has a direct negative impact on the growth dynamics of the cells, by influencing the kinetics of tumor cell division and death. We discuss the application of mathematical models to experimental data to quantify these parameter changes. We then describe further mathematical models that have been used to explore how these aspirin-mediated changes in kinetic parameters influence the probability of successful colony growth versus extinction, and how they affect the evolution of the tumor during aspirin administration. Finally, we discuss mathematical models that have been used to investigate the selective forces that can lead to the rise of mismatch-repair deficient cells in an inflammatory environment, and how this selection can be potentially altered through aspirin-mediated interventions. This article is categorized under: Models of Systems Properties and Processes > Mechanistic Models Analytical and Computational Methods > Analytical Methods Analytical and Computational Methods > Computational Methods.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1002/wsbm.1487DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7486281PMC
September 2020

A comprehensive in vivo and mathematic modeling-based kinetic characterization for aspirin-induced chemoprevention in colorectal cancer.

Carcinogenesis 2020 07;41(6):751-760

Center for Gastrointestinal Research, Center from Translational Genomics and Oncology, Baylor Scott and White Research Institute and Charles A. Sammons Cancer Center, Baylor University Medical Center, Dallas, TX, USA.

Accumulating evidence suggests that aspirin has anti-tumorigenic properties in colorectal cancer (CRC). Herein, we undertook a comprehensive and systematic series of in vivo animal experiments followed by 3D-mathematical modeling to determine the kinetics of aspirin's anti-cancer effects on CRC growth. In this study, CRC xenografts were generated using four CRC cell lines with and without PIK3CA mutations and microsatellite instability, and the animals were administered with various aspirin doses (0, 15, 50, and 100 mg/kg) for 2 weeks. Cell proliferation, apoptosis and protein expression were evaluated, followed by 3D-mathematical modeling analysis to estimate cellular division and death rates and their impact on aspirin-mediated changes on tumor growth. We observed that aspirin resulted in a dose-dependent decrease in the cell division rate, and a concomitant increase in the cell death rates in xenografts from all cell lines. Aspirin significantly inhibited cell proliferation as measured by Ki67 staining (P < 0.05-0.01). The negative effect of aspirin on the rate of tumor cell proliferation was more significant in xenograft tumors derived from PIK3CA mutant versus wild-type cells. A computational model of 3D-tumor growth suggests that the growth inhibitory effect of aspirin on the tumor growth kinetics is due to a reduction of tumor colony formation, and that this effect is sufficiently strong to be an important contributor to the reduction of CRC incidence in aspirin-treated patients. In conclusion, we provide a detailed kinetics of aspirin-mediated inhibition of tumor cell proliferation, which support the epidemiological data for the observed protective effect of aspirin in CRC patients.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/carcin/bgz195DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7351132PMC
July 2020

Object-Label-Order Effect When Learning From an Inconsistent Source.

Cogn Sci 2019 08;43(8):e12737

Department of Mathematics, University of California Irvine.

Learning in natural environments is often characterized by a degree of inconsistency from an input. These inconsistencies occur, for example, when learning from more than one source, or when the presence of environmental noise distorts incoming information; as a result, the task faced by the learner becomes ambiguous. In this study, we investigate how learners handle such situations. We focus on the setting where a learner receives and processes a sequence of utterances to master associations between objects and their labels, where the source is inconsistent by design: It uses both "correct" and "incorrect" object-label pairings. We hypothesize that depending on the order of presentation, the result of the learning may be different. To this end, we consider two types of symbolic learning procedures: the Object-Label (OL) and the Label-Object (LO) process. In the OL process, the learner is first exposed to the object, and then the label. In the LO process, this order is reversed. We perform experiments with human subjects, and also construct a computational model that is based on a nonlinear stochastic reinforcement learning algorithm. It is observed experimentally that OL learners are generally better at processing inconsistent input compared to LO learners. We show that the patterns observed in the learning experiments can be reproduced in the simulations if the model includes (a) an ability to regularize the input (and also to do the opposite, i.e., undermatch) and (b) an ability to take account of implicit negative evidence (i.e., interactions among different objects/labels). The model suggests that while both types of learners utilize implicit negative evidence in a similar way, there is a difference in regularization patterns: OL learners regularize the input, whereas LO learners undermatch. As a result, OL learners are able to form a more consistent system of image-utterance associations, despite the ambiguous learning task.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1111/cogs.12737DOI Listing
August 2019

Environmental spatial and temporal variability and its role in non-favoured mutant dynamics.

J R Soc Interface 2019 08 14;16(157):20180781. Epub 2019 Aug 14.

Department of Mathematics, University of California Irvine, Irvine, CA 92697, USA.

Understanding how environmental variability (or randomness) affects evolution is of fundamental importance for biology. The presence of temporal or spatial variability significantly affects the competition dynamics in populations, and gives rise to some counterintuitive observations. In this paper, we consider both birth-death (BD) or death-birth (DB) Moran processes, which are set up on a circular or a complete graph. We investigate spatial and temporal variability affecting division and/or death parameters. Assuming that mutant and wild-type fitness parameters are drawn from an identical distribution, we study mutant fixation probability and timing. We demonstrate that temporal and spatial types of variability possess fundamentally different properties. Under temporal randomness, in a completely mixed system, minority mutants experience (i) higher than neutral fixation probability and a higher mean conditional fixation time, if the division rates are affected by randomness and (ii) lower fixation probability and lower mean conditional fixation time if the death rates are affected. Once spatial restrictions are imposed, however, these effects completely disappear, and mutants in a circular graph experience neutral dynamics, but only for the DB update rule in case (i) and for the BD rule in case (ii) above. In contrast to this, in the case of spatially variable environment, both for BD/DB processes, both for complete/circular graph and both for division/death rates affected, minority mutants experience a higher than neutral probability of fixation. Fixation time, however, is increased by randomness on a circle, while it decreases for complete graphs under random division rates. A basic difference between temporal and spatial kinds of variability is the types of correlations that occur in the system. Under temporal randomness, mutants are spatially correlated with each other (they simply have equal fitness values at a given moment of time; the same holds for wild-types). Under spatial randomness, there are subtler, temporal correlations among mutant and wild-type cells, which manifest themselves by cells of each type 'claiming' better spots for themselves. Applications of this theory include cancer generation and biofilm dynamics.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1098/rsif.2018.0781DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6731496PMC
August 2019

Multiple infection of cells changes the dynamics of basic viral evolutionary processes.

Evol Lett 2019 Feb 31;3(1):104-115. Epub 2018 Dec 31.

Department of Ecology and Evolutionary Biology, 321 Steinhaus Hall University of California Irvine CA 92697.

The infection of cells by multiple copies of a given virus can impact viral evolution in a variety of ways, yet some of the most basic evolutionary dynamics remain underexplored. Using computational models, we investigate how infection multiplicity affects the fixation probability of mutants, the rate of mutant generation, and the timing of mutant invasion. An important insight from these models is that for neutral and disadvantageous phenotypes, rare mutants initially enjoy a fitness advantage in the presence of multiple infection of cells. This arises because multiple infection allows the rare mutant to enter more target cells and to spread faster, while it does not accelerate the spread of the resident wild-type virus. The rare mutant population can increase by entry into both uninfected and wild-type-infected cells, while the established wild-type population can initially only grow through entry into uninfected cells. Following this initial advantageous phase, the dynamics are governed by drift or negative selection, respectively, and a higher multiplicity reduces the chances that mutants fix in the population. Hence, while increased infection multiplicity promotes the presence of neutral and disadvantageous mutants in the short-term, it makes it less likely in the longer term. We show how these theoretical insights can be useful for the interpretation of experimental data on virus evolution at low and high multiplicities. The dynamics explored here provide a basis for the investigation of more complex viral evolutionary processes, including recombination, reassortment, as well as complementary/inhibitory interactions.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1002/evl3.95DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6369963PMC
February 2019

The role of telomere shortening in carcinogenesis: A hybrid stochastic-deterministic approach.

J Theor Biol 2019 01 10;460:144-152. Epub 2018 Oct 10.

Department of Mathematics, University of California, Irvine, California, United States; Department of Ecology and Evolutionary Biology, University of California, Irvine, California, United States.

Genome instability is a characteristic of most cancers, contributing to the acquisition of genetic alterations that drive tumor progression. One important source of genome instability is linked to telomere dysfunction in cells with critically short telomeres that lack p53-mediated surveillance of genomic integrity. Here we research the probability that cancer emerges through an evolutionary pathway that includes a telomere-induced phase of genome instability. To implement our models we use a hybrid stochastic-deterministic approach, which allows us to perform large numbers of simulations using biologically realistic population sizes and mutation rates, circumventing the traditional limitations of fully stochastic algorithms. The hybrid methodology should be easily adaptable to a wide range of evolutionary problems. In particular, we model telomere shortening and the acquisition of two mutations: Telomerase activation and p53 inactivation. We find that the death rate of unstable cells, and the number of cell divisions that p53 mutants can sustain beyond the normal senescence setpoint determine the likelihood that the first double mutant originates in a cell with telomere-induced instability. The model has applications to an influential telomerase-null mouse model and p16 silenced human cells. We end by discussing algorithmic performance and a measure for the accuracy of the hybrid approximation.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jtbi.2018.09.003DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6234035PMC
January 2019

Musical trends and predictability of success in contemporary songs in and out of the top charts.

R Soc Open Sci 2018 May 16;5(5):171274. Epub 2018 May 16.

Department of Mathematics, University of California Irvine, Irvine, CA 92697, USA.

We analyse more than 500 000 songs released in the UK between 1985 and 2015 to understand the dynamics of success (defined as 'making it' into the top charts), correlate success with acoustic features and explore the predictability of success. Several multi-decadal trends have been uncovered. For example, there is a clear downward trend in 'happiness' and 'brightness', as well as a slight upward trend in 'sadness'. Furthermore, songs are becoming less 'male'. Interestingly, successful songs exhibit their own distinct dynamics. In particular, they tend to be 'happier', more 'party-like', less 'relaxed' and more 'female' than most. The difference between successful and average songs is not straightforward. In the context of some features, successful songs pre-empt the dynamics of all songs, and in others they tend to reflect the past. We used random forests to predict the success of songs, first based on their acoustic features, and then adding the 'superstar' variable (informing us whether the song's artist had appeared in the top charts in the near past). This allowed quantification of the contribution of purely musical characteristics in the songs' success, and suggested the time scale of fashion dynamics in popular music.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1098/rsos.171274DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5990848PMC
May 2018

Passenger mutations can accelerate tumour suppressor gene inactivation in cancer evolution.

J R Soc Interface 2018 06;15(143)

Department of Mathematics, Rowland Hall, University of California, Irvine, CA 92697, USA.

Carcinogenesis is an evolutionary process whereby cells accumulate multiple mutations. Besides the 'driver mutations' that cause the disease, cells also accumulate a number of other mutations with seemingly no direct role in this evolutionary process. They are called passenger mutations. While it has been argued that passenger mutations render tumours more fragile due to reduced fitness, the role of passenger mutations remains understudied. Using evolutionary computational models, we demonstrate that in the context of tumour suppressor gene inactivation (and hence fitness valley crossing), the presence of passenger mutations can accelerate the rate of evolution by reducing overall population fitness and increasing the relative fitness of intermediate mutants in the fitness valley crossing pathway. Hence, the baseline rate of tumour suppressor gene inactivation might be faster than previously thought. Conceptually, parallels are found in the field of turbulence and pattern formation, where instabilities can be driven by perturbations that are damped (disadvantageous), but provide a richer set of pathways such that a system can achieve some desired goal more readily. This highlights, through a number of novel parallels, the relevance of physical sciences in oncology.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1098/rsif.2017.0967DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6030626PMC
June 2018

Quantitative study of color category boundaries.

J Opt Soc Am A Opt Image Sci Vis 2018 Apr;35(4):B165-B183

We use World Color Survey (WCS) data to design quantitative methods to study color categorization, with the focus on the "geometric" properties of categories, in particular, on studying their shape, and creating a consistent methodology to identify category boundaries. We introduce the notion of "No Man's Land" and "Some Man's Land" to distinguish color chips that belong to no color category and those that belong to some color category. We introduce a "color-stimulus-strength" function that characterizes color boundaries. While categories may come in a variety of shapes, and their boundaries are nonuniform and can vary in thickness, there are universal patterns that emerge. For example, the boundary-to-category-mass ratio is a decreasing function of category strength (i.e., stronger categories have relatively thinner boundaries), and boundary mass obeys a "square root"-like law as a function of category mass (i.e., roughly speaking, color categories behave like 2D circles). We further identify a relationship between color boundaries and Shannon's entropy, which can be calculated by using the field data of the WCS. We find that depending on the informational content of a given chip, it can belong to three distinct types: (I) strongly belonging to a color category; (II) belonging to a boundary between two or more categories; (III) not belonging to a category or a boundary. The last two cases can be interpreted in terms of evolution and temporal dynamics of color categories.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1364/JOSAA.35.00B165DOI Listing
April 2018

Spatial evolution of regularization in learned behavior of animals.

Math Biosci 2018 05 14;299:103-116. Epub 2018 Mar 14.

Department of Mathematics, University of California Irvine, Irvine, CA 92697, USA; Department of Ecology and Evolutionary Biology, University of California Irvine, Irvine, CA 92697, USA. Electronic address:

Stochastic population dynamics of learned traits are studied, where individual learners behave according to a reinforcement learner model, which is a nonlinear version of the Bush-Mosteller model. Depending on a regularization parameter (parameter a), the learners may possess different degrees of overmatching (regularization behavior, 0 ≤ a < 1), frequency matching (corresponding to a=1), or undermatching behavior (a > 1). Both non-spatial and spatial models are considered, to study the interplay of individual heterogeneity of behavior, spatial and temporal effects of learning, and the possibility of emergence of regional culture. In non-spatial models, we observe that populations of individuals learning from each other converge to a universally shared, deterministic rule (either rule "1" or rule "0"), only if they to some extent possess the ability to generalize (a < 1). Otherwise, a low-coherence solution where both rules are used intermittently by everyone, is achieved. If the evolution of the regularization ability is included, then we find that a initially evolves toward lower values, and a shared solution is established when everyone reliably uses the same rule. The spatial (2D) model has two well known limiting cases: if a=0 (the strongest degree of regularization), the model converges to a threshold voter model, and if a=1 (frequency matching), it is equivalent to the discrete diffusion equation. If 0 < a < 1 (the case where individuals regularize), spatial patterns emerge, where patches of different usage of the rule are formed. Smaller values of a lead to sharper and longer lived patches. Values of a < 1 close to unity result in probabilistic outcomes where patches only survive if they are attached to the boundary. Analytical treatment of the 1D case reveals the existence of approximate equilibria that have front structure, where spatially intermittent deterministic usage of one and the other rule are separated by interfaces whose analytical form is derived.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.mbs.2018.03.005DOI Listing
May 2018

Cooperation-based branching as a mechanism of evolutionary speciation.

J Theor Biol 2018 05 27;445:166-186. Epub 2018 Feb 27.

Department of Mathematics, University of California Irvine, Irvine, CA, 92697, United States. Electronic address:

When performing complex tasks, coexistence of organisms in a shared environment can be achieved by means of different strategies. For example, individuals can evolve to complete all parts of the complex task, choosing self-sufficiency over cooperation. On the other hand, they may choose to split parts of the task and share the products for mutual benefit, such that distinct groups of the organisms specialize on a subset of elementary tasks. In contrast to the existing theory of specialization and task sharing for cells in multicellular organisms (or colonies of social insects), here we describe a mechanism of evolutionary branching which is based on cooperation and division of labor, and where selection happens at the individual level. Using a class of mathematical models and the methodology of adaptive dynamics, we investigate the conditions for such branching into distinct cooperating subgroups to occur. We show that, as long as performing multiple tasks is associated with additional cost, branching occurs for a wide parameter range, and this scenario is stable against the invasion of cheaters. We hypothesize that over time, this can lead to evolutionary speciation. Examples from bacterial evolution and the connection with the Black Queen Hypothesis are discussed. It is our hope that the theory of diversification rooted in cooperation may inspire further ecological research to identify more evolutionary examples consistent with this speciation mechanism.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jtbi.2018.02.033DOI Listing
May 2018

Optimizing homeostatic cell renewal in hierarchical tissues.

PLoS Comput Biol 2018 02 15;14(2):e1005967. Epub 2018 Feb 15.

Department of Mathematics, University of California Irvine, Irvine, California, United States of America.

In order to maintain homeostasis, mature cells removed from the top compartment of hierarchical tissues have to be replenished by means of differentiation and self-renewal events happening in the more primitive compartments. As each cell division is associated with a risk of mutation, cell division patterns have to be optimized, in order to minimize or delay the risk of malignancy generation. Here we study this optimization problem, focusing on the role of division tree length, that is, the number of layers of cells activated in response to the loss of terminally differentiated cells, which is related to the balance between differentiation and self-renewal events in the compartments. Using both analytical methods and stochastic simulations in a metapopulation-style model, we find that shorter division trees are advantageous if the objective is to minimize the total number of one-hit mutants in the cell population. Longer division trees on the other hand minimize the accumulation of two-hit mutants, which is a more likely evolutionary goal given the key role played by tumor suppressor genes in cancer initiation. While division tree length is the most important property determining mutant accumulation, we also find that increasing the size of primitive compartments helps to delay two-hit mutant generation.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1371/journal.pcbi.1005967DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5831642PMC
February 2018

The effect of spatial randomness on the average fixation time of mutants.

PLoS Comput Biol 2017 Nov 27;13(11):e1005864. Epub 2017 Nov 27.

Department of Applied Mathematics, University of Waterloo, Waterloo, Ontario, Canada.

The mean conditional fixation time of a mutant is an important measure of stochastic population dynamics, widely studied in ecology and evolution. Here, we investigate the effect of spatial randomness on the mean conditional fixation time of mutants in a constant population of cells, N. Specifically, we assume that fitness values of wild type cells and mutants at different locations come from given probability distributions and do not change in time. We study spatial arrangements of cells on regular graphs with different degrees, from the circle to the complete graph, and vary assumptions on the fitness probability distributions. Some examples include: identical probability distributions for wild types and mutants; cases when only one of the cell types has random fitness values while the other has deterministic fitness; and cases where the mutants are advantaged or disadvantaged. Using analytical calculations and stochastic numerical simulations, we find that randomness has a strong impact on fixation time. In the case of complete graphs, randomness accelerates mutant fixation for all population sizes, and in the case of circular graphs, randomness delays mutant fixation for N larger than a threshold value (for small values of N, different behaviors are observed depending on the fitness distribution functions). These results emphasize fundamental differences in population dynamics under different assumptions on cell connectedness. They are explained by the existence of randomly occurring "dead zones" that can significantly delay fixation on networks with low connectivity; and by the existence of randomly occurring "lucky zones" that can facilitate fixation on networks of high connectivity. Results for death-birth and birth-death formulations of the Moran process, as well as for the (haploid) Wright Fisher model are presented.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1371/journal.pcbi.1005864DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5720826PMC
November 2017

Effect of cell cycle duration on somatic evolutionary dynamics.

Evol Appl 2017 12 12;10(10):1121-1129. Epub 2017 Oct 12.

Department of Ecology and Evolutionary Biology University of California Irvine CA USA.

Cellular checkpoints prevent damage and mutation accumulation in tissue cells. DNA repair is one mechanism that can be triggered by checkpoints and involves temporary cell cycle arrest and thus delayed reproduction. Repair-deficient cells avoid this delay, which has been argued to lead to a selective advantage in the presence of frequent damage. We investigate this hypothesis with stochastic modeling, using mathematical analysis and agent-based computations. We first model competition between two cell types: a cell population that enters temporary cell cycle arrest, corresponding to repair (referred to as arresting cells), and one that does not enter arrest (referred to as nonarresting cells). Although nonarresting cells are predicted to grow with a faster rate than arresting cells in isolation, this does not translate into a selective advantage in the model. Interestingly, the evolutionary properties of the nonarresting cells depend on the measure (or observable) of interest. When examining the average populations sizes in competition simulations, nonarresting and arresting cells display neutral dynamics. The fixation probability of nonarresting mutants, however, is lower than predicted for a neutral scenario, suggesting a selective disadvantage in this setting. For nonarresting cells to gain a selective advantage, additional mechanisms must be invoked in the model, such as small, repeated phases of tissue damage, each resulting in a brief period of regenerative growth. The same properties are observed in a more complex model where it is explicitly assumed that repair and temporary cell cycle arrest are dependent on the cell having sustained DNA damage, the rate of which can be varied. We conclude that repair-deficient cells are not automatically advantageous in the presence of frequent DNA damage and that mechanisms beyond avoidance of cell cycle delay must be invoked to explain their emergence.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1111/eva.12518DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5680637PMC
December 2017

Quantitative approach for defining basic color terms and color category best exemplars.

J Opt Soc Am A Opt Image Sci Vis 2017 Aug;34(8):1285-1300

A new method is presented that identifies basic color terms (BCTs) from color-naming data. A function is defined that measures how well a term is understood by a communicating population. BCTs are then separated from other color terms by a threshold value applied to this function. A new mathematical algorithm is proposed and analyzed for determining the best exemplar associated with each BCT. Using data provided by the World Color Survey, comparisons are made between the paper's methods and those from other studies. These comparisons show that the paper's new definition of "basicness" mostly agrees with the typical definition found in the color categorization literature, which was originally due to Kay and colleagues. The new definition, unlike the typical one, has the advantage of not relying on syntactic or semantic features of languages or color lexicons. This permits the methodology developed to be generalizable and applied to other category domains for which a construct of "basicness" could have an important role.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1364/JOSAA.34.001285DOI Listing
August 2017

Effect of aspirin on tumour cell colony formation and evolution.

J R Soc Interface 2017 09;14(134)

Department of Ecology and Evolutionary Biology, University of California, Irvine, CA 92617, USA.

Aspirin is known to reduce the risk of colorectal cancer (CRC) incidence, but the underlying mechanisms are not fully understood. In a previous study, we quantified the growth kinetics of different CRC tumour cell lines treated with varying doses of aspirin, measuring the rate of cell division and cell death. Here, we use these measured parameters to calculate the chances of successful clonal expansion and to determine the evolutionary potential of the tumour cell lines in the presence and absence of aspirin. The calculations indicate that aspirin increases the probability that a single tumour cell fails to clonally expand. Further, calculations suggest that aspirin increases the evolutionary potential of an expanding tumour cell colony. An aspirin-treated tumour cell population is predicted to result in the accumulation of more mutations (and is thus more virulent and more difficult to treat) than a cell population of the same size that grew without aspirin. This indicates a potential trade-off between delaying the onset of cancer and increasing its evolutionary potential through chemoprevention. Further work needs to investigate to what extent these findings apply to settings, and to what degree they contribute to the epidemiologically documented aspirin-mediated protection.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1098/rsif.2017.0374DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5636273PMC
September 2017

Determining the control networks regulating stem cell lineages in colonic crypts.

J Theor Biol 2017 09 29;429:190-203. Epub 2017 Jun 29.

Department of Ecology and Evolutionary Biology, University of California, Irvine, Irvine, CA 92697, USA. Electronic address:

The question of stem cell control is at the center of our understanding of tissue functioning, both in healthy and cancerous conditions. It is well accepted that cellular fate decisions (such as divisions, differentiation, apoptosis) are orchestrated by a network of regulatory signals emitted by different cell populations in the lineage and the surrounding tissue. The exact regulatory network that governs stem cell lineages in a given tissue is usually unknown. Here we propose an algorithm to identify a set of candidate control networks that are compatible with (a) measured means and variances of cell populations in different compartments, (b) qualitative information on cell population dynamics, such as the existence of local controls and oscillatory reaction of the system to population size perturbations, and (c) statistics of correlations between cell numbers in different compartments. Using the example of human colon crypts, where lineages are comprised of stem cells, transit amplifying cells, and differentiated cells, we start with a theoretically known set of 32 smallest control networks compatible with tissue stability. Utilizing near-equilibrium stochastic calculus of stem cells developed earlier, we apply a series of tests, where we compare the networks' expected behavior with the observations. This allows us to exclude most of the networks, until only three, very similar, candidate networks remain, which are most compatible with the measurements. This work demonstrates how theoretical analysis of control networks combined with only static biological data can shed light onto the inner workings of stem cell lineages, in the absence of direct experimental assessment of regulatory signaling mechanisms. The resulting candidate networks are dominated by negative control loops and possess the following properties: (1) stem cell division decisions are negatively controlled by the stem cell population, (2) stem cell differentiation decisions are negatively controlled by the transit amplifying cell population.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jtbi.2017.06.033DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5689466PMC
September 2017

Stability of Control Networks in Autonomous Homeostatic Regulation of Stem Cell Lineages.

Bull Math Biol 2018 05 15;80(5):1345-1365. Epub 2017 May 15.

Department of Mathematics and Statistics, University of Victoria, Victoria, BC, V8W 2Y2, Canada.

Design principles of biological networks have been studied extensively in the context of protein-protein interaction networks, metabolic networks, and regulatory (transcriptional) networks. Here we consider regulation networks that occur on larger scales, namely the cell-to-cell signaling networks that connect groups of cells in multicellular organisms. These are the feedback loops that orchestrate the complex dynamics of cell fate decisions and are necessary for the maintenance of homeostasis in stem cell lineages. We focus on "minimal" networks that are those that have the smallest possible numbers of controls. For such minimal networks, the number of controls must be equal to the number of compartments, and the reducibility/irreducibility of the network (whether or not it can be split into smaller independent sub-networks) is defined by a matrix comprised of the cell number increments induced by each of the controlled processes in each of the compartments. Using the formalism of digraphs, we show that in two-compartment lineages, reducible systems must contain two 1-cycles, and irreducible systems one 1-cycle and one 2-cycle; stability follows from the signs of the controls and does not require magnitude restrictions. In three-compartment systems, irreducible digraphs have a tree structure or have one 3-cycle and at least two more shorter cycles, at least one of which is a 1-cycle. With further work and proper biological validation, our results may serve as a first step toward an understanding of ways in which these networks become dysregulated in cancer.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1007/s11538-017-0283-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5690898PMC
May 2018

Determining the role of inflammation in the selection of JAK2 mutant cells in myeloproliferative neoplasms.

J Theor Biol 2017 07 10;425:43-52. Epub 2017 May 10.

Department of Mathematics, USA; Department of Ecology and Evolutionary Biology, University of California Irvine, Irvine, CA 92697, USA. Electronic address:

Myeloproliferative neoplasm (MPN) is a hematologic malignancy characterized by the clonal outgrowth of hematopoietic cells with a somatically acquired mutation most commonly in JAK2 (JAK2). This mutation endows upon myeloid progenitors cytokine independent growth and consequently leads to excessive production of myeloid lineage cells. It has been previously suggested that inflammation may play a role in the clonal evolution of JAK2 mutants. In particular, it is possible that one or more cellular kinetic parameters of hematopoietic stem cells (HSCs) are affected by inflammation, such as division or death rates of cells, and the probability of HSC differentiation. This suggests a mechanism that can steer the outcome of the cellular competition in favor of the mutants, initiating the disease. In this paper we create a number of mathematical evolutionary models, from very abstract to more concrete, that describe cellular competition in the context of inflammation. It is possible to build a model axiomatically, where only very general assumptions are imposed on the modeling components and no arbitrary (and generally unknown) functional forms are used, and still generate a set of testable predictions. In particular, we show that, if HSC death is negligible, the evolutionary advantage of mutant cells can only be conferred by an increase in differentiation probability of HSCs in the presence of inflammation, and if death plays a significant role in the dynamics, an additional mechanism may be an increase of HSC's division-to-death ratio in the presence of inflammation. Further, we show that in the presence of inflammation, the wild type cell population is predicted to shrink under inflammation (even in the absence of mutants). Finally, it turns out that if only the differentiation probability is affected by the inflammation, then the resulting steady state population of wild type cells will contain a relatively smaller percentage of HSCs under inflammation. If the division-to-death rate is also affected, then the percentage of HSCs under inflammation can either decrease or increase, depending on other parameters.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jtbi.2017.05.012DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5689470PMC
July 2017
-->