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Recently, metapopulation models for rock-paper-scissors games have been presented. Each subpopulation is represented by a node on a graph. An individual is either rock (R), scissors (S) or paper (P); it randomly migrates among subpopulations. In the present paper, we assume victory rates differ in different subpopulations. To investigate the dynamic state of each subpopulation (node), we numerically obtain the solutions of reaction-diffusion equations on the graphs with two and three nodes. In the case of homogeneous victory rates, we find each subpopulation has a periodic solution with neutral stability. However, when victory rates between subpopulations are heterogeneous, the solution approaches stable focuses. The heterogeneity of victory rates promotes the coexistence of species., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Laboratory experiments and numerical simulations have shown that the outcome of cyclic competition is significantly affected by the spatial distribution of the competitors. Short-range interaction and limited dispersion allows for coexistence of competing species that cannot coexist in a well-mixed environment. In order to elucidate the mechanisms that destroy species diversity we study the intermediate situation of imperfect mixing, typical in aquatic media, in a model of cyclic competition between toxin producing, sensitive and resistant phenotypes. It is found, that chaotic mixing, by changing the character of the spatial distribution, induces coherent oscillations in the populations. The magnitude of the oscillations increases with the strength of mixing, leading to the extinction of some species beyond a critical mixing rate. When mixing is non-uniform in space, coexistence can be sustained at much stronger mixing by the formation of partially isolated regions, that prevent global extinction. The heterogeneity of mixing may enable toxin producing and sensitive strains to coexist for very long time at strong mixing.

Abstract: We study the oscillatory dynamics in the generic three-species rock–paper–scissors games with mutations. In the mean-field limit, different behaviors are found: (a) for high mutation rate, there is a stable interior fixed point with coexistence of all species; (b) for low mutation rates, there is a region of the parameter space characterized by a limit cycle resulting from a Hopf bifurcation; (c) in the absence of mutations, there is a region where heteroclinic cycles yield oscillations of large amplitude (not robust against noise). After a discussion on the main properties of the mean-field dynamics, we investigate the stochastic version of the model within an individual-based formulation. Demographic fluctuations are therefore naturally accounted and their effects are studied using a diffusion theory complemented by numerical simulations. It is thus shown that persistent erratic oscillations (quasi-cycles) of large amplitude emerge from a noise-induced resonance phenomenon. We also analytically and numerically compute the average escape time necessary to reach a (quasi-)cycle on which the system oscillates at a given amplitude. [Copyright &y& Elsevier]

Initial community evenness has been shown to be a key factor in preserving the functional stability of an ecosystem, but has not been accounted for in previous modelling studies. We formulate a model that allows the initial evenness of the community to be varied in order to investigate the consequent impact on system diversity. We consider a community of four interacting bacterial species, and present a stochastic, spatial individual-based model simulating the ecosystem dynamics. Interactions take place on a two-dimensional lattice. The model incorporates three processes: reproduction, competition and mobility. In addition to variable initial evenness, multiple competition schemes are implemented, modelling various possible communities, which results in diverse coexistence and extinction scenarios. Simulations show that long-term system behaviour is strongly dependent on initial evenness and competition structure. The system is generally unstable; higher initial evenness has a small stabilizing effect on ecosystem dynamics by extending the time until the first extinction., (Copyright © 2015 Elsevier Ltd. All rights reserved.)

Oxygenic photoautotrophs are, paradoxically, subject to photoinhibition of their photosynthetic apparatus, in particular one of its major components, the Photosystem II (PSII). Photoinhibition is generalized across species, light conditions and habitats, imposing substantial metabolic costs that lower photosynthetic productivity and constrain the niches of photoautotrophy. As a process driven by light reaching PSII, light attenuation in optically thick samples influences both the actual extent, and the detection, of photoinhibition. Chlorophyll fluorescence is widely used to measure photoinhibition, but fluorescence-based parameters are affected by light attenuation of both downwelling incident radiation traversing the sample to reach PSII, and emitted fluorescence upwelling through the sample. We used modelling, experimental manipulation of within-sample light attenuation, and meta-analysis of published data, to show substantial, differential effects of light attenuation and depth-integration of emitted fluorescence upon measurements of photoinhibition. Numerical simulations and experimental manipulation of light attenuation indicated that PSII photoinactivation tracked using chlorophyll fluorescence can appear to be over three times lower than the inherent cellular susceptibility to photoinactivation, in optically-dense samples such as leaves or biofilms. The meta-analysis of published data showed that this general trend was unknowingly present in the literature, revealing an overall difference of more than five times between optically thick leaves and optically thin cell suspensions. Although fluorescence-based parameters may provide ecophysiologically relevant information for characterizing the sample as a whole, light attenuation and depth integration can vary between samples independently of their intrinsic physiology. They should be used with caution when aiming to quantify in absolute terms inherent photoinhibition-related parameters in optically thick samples., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.)

COVID-19 testing has become a standard approach for estimating prevalence which then assist in public health decision making to contain and mitigate the spread of the disease. The sampling designs used are often biased in that they do not reflect the true underlying populations. For instance, individuals with strong symptoms are more likely to be tested than those with no symptoms. This results in biased estimates of prevalence (too high). Typical post-sampling corrections are not always possible. Here we present a simple bias correction methodology derived and adapted from a correction for publication bias in meta analysis studies. The methodology is general enough to allow a wide variety of customization making it more useful in practice. Implementation is easily done using already collected information. Via a simulation and two real datasets, we show that the bias corrections can provide dramatic reductions in estimation error., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Parameter estimation is a major challenge in computational modeling of biological processes. This is especially the case in image-based modeling where the inherently quantitative output of the model is measured against image data, which is typically noisy and non-quantitative. In addition, these models can have a high computational cost, limiting the number of feasible simulations, and therefore rendering most traditional parameter estimation methods unsuitable. In this paper, we present a pipeline that uses Gaussian process learning to estimate biological parameters from noisy, non-quantitative image data when the model has a high computational cost. This approach is first successfully tested on a parametric function with the goal of retrieving the original parameters. We then apply it to estimating parameters in a biological setting by fitting artificial in-situ hybridization (ISH) data of the developing murine limb bud. We expect that this method will be of use in a variety of modeling scenarios where quantitative data is missing and the use of standard parameter estimation approaches in biological modeling is prohibited by the computational cost of the model., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

This paper deals with the study of spatial and spatio-temporal patterns in the reaction-diffusion FitzHugh-Nagumo model on growing curved domains. This is carried out on two exemplar cases: a torus and a sphere. We compute bifurcation boundaries for the homogeneous steady state when the homogeneous system is monostable. We exhibit Turing and Turing-Hopf bifurcations, as well as additional patterning outside of these bifurcation regimes due to the multistability of patterned states. We consider static and growing domains, where the growth is slow, isotropic, and exponential in time, allowing for a simple analytical calculation of these bifurcations in terms of model parameters. Numerical simulations allow us to discuss the role played by the growth and the curvature of the domains on the pattern selection on the torus and the sphere. We demonstrate parameter regimes where the linear theory can successfully predict the kind of pattern (homogeneous and heterogeneous oscillations and stationary spatial patterns) but not their detailed nonlinear structure. We also find parameter regimes where the linear theory fails, such as Hopf regimes which give rise to spatial patterning (depending on geometric details), where we suspect that multistability plays a key role in the departure from homogeneity. Finally we also demonstrate effects due to the evolution of nonuniform patterns under growth, suggesting important roles for growth in reaction-diffusion systems beyond modifying instability regimes., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

This paper proposes two spatio-temporal epidemic network models based on popularity and similarity optimization (PSO), called r-SI and r-SIS, respectively, in which new connections take both popularity and similarity into account. In the spatial dimension, the epidemic process is described by the diffusion equation; in the time dimension, the growth of an epidemic is described by the logistic map. Both models are represented by partial differential equations, and can be easily solved. Simulations are performed on both artificial and real networks, demonstrating the effectiveness of the two models., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Drug target interaction prediction is a very labor-intensive and expensive experimental process which has motivated researchers to focus on in silico prediction to provide information on potential interaction. In recent years, researchers have proposed several computational approaches for predicting new drug target interactions. In this paper, we present CFSBoost, a simple and computationally cheap ensemble boosting classification model for identification and prediction of drug-target interactions using evolutionary and structural features. CFSBoost uses a simple yet novel feature group selection procedure which allows the model to be computationally very cheap while being able to achieve state of the art performance. The ensemble model uses extra tree as weak learners inside a boosting scheme while holding on to the best model per iteration. We tested our method of four benchmark datasets, which are also referred as gold standard datasets. Our method was able to achieve better score in terms of area under receiver operating characteristic (auROC) curve on 2 out of the 4 datasets. It was also able to achieve higher area under precision recall (auPR) curve on 3 out of the 4 datasets. It has been argued by researchers that auPR metric is more suitable than auROC for comparison of performance on imbalanced datasets such our benchmark datasets. Our reported result shows that, despite of its simplicity in design, CFSBoost's performance is very satisfactory comparing to other literatures. We also provide 5 new possible interactions for each dataset based on CFSBoost's prediction score., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Individual animal movement has been a focus of intense research and considerable controversy over the last two decades, however the understanding of wider ecological implications of various movement behaviours is lacking. In this paper, we consider this issue in the context of pattern formation. Using an individual-based modelling approach and computer simulations, we first show that density dependence ("auto-taxis") of the individual movement in a population of random walkers typically results in the formation of a strongly heterogeneous population distribution consisting of clearly defined animal clusters or patches. We then show that, when the movement takes place in a large spatial domain, the properties of the clusters are significantly different in the populations of Brownian and non-Brownian walkers. Whilst clusters tend to be stable in the case of Brownian motion, in the population of Levy walkers clusters are dynamical so that the number of clusters fluctuates in the course of time. We also show that the population dynamics of non-Brownian walkers exhibits two different time scales: a short time scale of the relaxation of the initial condition and a long time scale when one type of dynamics is replaced by another. Finally, we show that the distribution of sample values in the populations of Brownian and non-Brownian walkers is significantly different., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

The immune system mounts a response to an infection by activating T cells. T cell activation occurs when dendritic cells, which have already interacted with the pathogen, scan a T cell that is cognate for (responsive to) the pathogen. This often occurs inside lymph nodes. The time it takes for this scanning event to occur, indeed the probability that it will occur at all, depends on many factors, including the rate that T cells and dendritic cells enter and leave the lymph node as well as the geometry of the lymph node and of course other cellular and molecular parameters. In this paper, we develop a hybrid stochastic-deterministic mathematical model at the tissue scale of the lymph node and simulate dendritic cells and cognate T cells to investigate the most important physiological factors leading to a successful and timely immune response after a vaccination. We use an agent-based model to describe the small population of cognate naive T cells and a partial differential equation description for the concentration of mature dendritic cells. We estimate the model parameters based on the known literature and measurements previously taken in our lab. We perform a parameter sensitivity analysis to quantify the sensitivity of the model results to the parameters. The results show that increasing T cell inflow through high endothelial venules, restricting cellular egress via the efferent lymph and increasing the total dendritic cell count by improving vaccinations are the among the most important physiological factors leading to an improved immune response. We also find that increasing the physical size of lymph nodes improves the overall likelihood that an immune response will take place but has a fairly weak effect on the response rate. The nature of dendritic cell trafficking through the LN (either passive or active transport) seems to have little effect on the overall immune response except if a change in overall egress time is observed., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

While deterministic metapopulation models for the spread of epidemics between populations have been well-studied in the literature, variability in disease transmission rates and interaction rates between individual agents or populations suggests the need to consider stochastic fluctuations in model parameters in order to more fully represent realistic epidemics. In the present paper, we have extended a stochastic SIS epidemic model - which introduces stochastic perturbations in the form of white noise to the force of infection (the rate of disease transmission from classes of infected to susceptible populations) - to spatial networks, thereby obtaining a stochastic epidemic metapopulation model. We solved the stochastic model numerically and found that white noise terms do not drastically change the overall long-term dynamics of the system (for sufficiently small variance of the noise) relative to the dynamics of a corresponding deterministic system. The primary difference between the stochastic and deterministic metapopulation models is that for large time, solutions tend to quasi-stationary distributions in the stochastic setting, rather than to constant steady states in the deterministic setting. We then considered different approaches to controlling the spread of a stochastic SIS epidemic over spatial networks, comparing results for a spectrum of controls utilizing local to global information about the state of the epidemic. Variation in white noise was shown to be able to counteract the treatment rate (treated curing rate) of the epidemic, requiring greater treatment rates on the part of the control and suggesting that in real-life epidemics one should be mindful of such random variations in order for a treatment to be effective. Additionally, we point out some problems using white noise perturbations as a model, but show that a truncated noise process gives qualitatively comparable behaviors without these issues., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

In this paper, the correlation coefficients between nodes in states are used as dynamic variables, and we construct SIR epidemic dynamic models with correlation coefficients by using the pair approximation method in static networks and dynamic networks, respectively. Considering the clustering coefficient of the network, we analytically investigate the existence and the local asymptotic stability of each equilibrium of these models and derive threshold values for the prevalence of diseases. Additionally, we obtain two equivalent epidemic thresholds in dynamic networks, which are compared with the results of the mean field equations., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

The human brain is a small organ which uses a disproportionate amount of the total metabolic energy production in the body. While it is well understood that the most significant energy sink is the maintenance of the neuronal membrane potential during the brain signaling activity, the role of astrocytes in the energy balance continues to be the topic of a lot of research. A key function of astrocytes, besides clearing glutamate from the synaptic clefts, is the potassium clearing after neuronal activation. Extracellular potassium plays a significant role in triggering neuronal firing, and elevated concentration of potassium may lead to abnormal firing patterns, e.g., seizures, thus emphasizing the importance of the glial K + buffering role. The predictive mathematical model proposed in this paper elucidates the role of glial potassium clearing in brain energy metabolism, integrating a detailed model of the ion dynamics which regulates neuronal firing with a four compartment metabolic model. Because of the very different characteristic time scales of electrophysiology and metabolism, care must be taken when coupling the two models to ensure that the predictions, e.g., neuronal firing frequencies and the oxygen-glucose index (OGI) of the brain during activation and rest, are in agreement with empirical observations. The temporal multi-scale nature of the problem requires the design of new computational tools to ensure a stable and accurate numerical treatment. The model predictions for different protocols, including combinations of elevated activation and ischemic episodes, are in good agreement with experimental observations reported in the literature., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Highlights • We have corrected the paper in order to present more clearly the experimental results and also to comment more the simulations done for the present Boolean model. • As demanded by reviewers, we have added a short presentation of the R. Thomas' theory, and also a recall of the earlier Boolean model proposed with him for the experiments of stimulation of Bidens pilosus. • Eventually, we have tried to present the perspectives of a hybrid approach from the continuous model previously proposed for Bidens. Abstract This paper presents a new model of memory functions in plants, which improves the previous approaches done by René Thomas and colleagues. We have decomposed the plant memory processing into three main functions: learning, storing and recalling. We propose as main mechanism for these three functions the occurrence of a calcium wave consecutive to an environmental stimulus, followed by the activation of specific genes and proteins changing their phosphorylated state. Feedback is ensured by Calcium Dependent Protein Kinases and oxidative phosphorylation. [ABSTRACT FROM AUTHOR]

Robert Rosen's (M,R) system is an abstract biological network architecture that is allegedly both irreducible to sub-models of its component states and non-computable on a Turing machine. (M,R) stands as an obstacle to both reductionist and mechanistic presentations of systems biology, principally due to its self-referential structure. If (M,R) has the properties claimed for it, computational systems biology will not be possible, or at best will be a science of approximate simulations rather than accurate models. Several attempts have been made, at both empirical and theoretical levels, to disprove this assertion by instantiating (M,R) in software architectures. So far, these efforts have been inconclusive. In this paper, we attempt to demonstrate why - by showing how both finite state machine and stream X-machine formal architectures fail to capture the self-referential requirements of (M,R). We then show that a solution may be found in communicating X-machines, which remove self-reference using parallel computation, and then synthesise such machine architectures with object-orientation to create a formal basis for future software instantiations of (M,R) systems., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

Understanding the principle of RNA-protein interactions (RPIs) is of critical importance to provide insights into post-transcriptional gene regulation and is useful to guide studies about many complex diseases. The limitations and difficulties associated with experimental determination of RPIs, call an urgent need to computational methods for RPI prediction. In this paper, we proposed a machine learning method to detect RNA-protein interactions based on sequence information. We used motif information and repetitive patterns, which have been extracted from experimentally validated RNA-protein interactions, in combination with sequence composition as descriptors to build a model to RPI prediction via a random forest classifier. About 20% of the "sequence motifs" and "nucleotide composition" features have been selected as the informative features with the feature selection methods. These results suggest that these two feature types contribute effectively in RPI detection. Results of 10-fold cross-validation experiments on three non-redundant benchmark datasets show a better performance of the proposed method in comparison with the current state-of-the-art methods in terms of various performance measures. In addition, the results revealed that the accuracy of the RPI prediction methods could vary considerably across different organisms. We have implemented the proposed method, namely rpiCOOL, as a stand-alone tool with a user friendly graphical user interface (GUI) that enables the researchers to predict RNA-protein interaction. The rpiCOOL is freely available at http://biocool.ir/rpicool.html for non-commercial uses., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

Radiotherapy is a commonly used treatment for cancer and is usually given in varying doses. At low radiation doses relatively few cells die as a direct response to radiation but secondary radiation effects, such as DNA mutation or bystander phenomena, may affect many cells. Consequently it is at low radiation levels where an understanding of bystander effects is essential in designing novel therapies with superior clinical outcomes. In this paper, we use a hybrid multiscale mathematical model to study the direct effects of radiation as well as radiation-induced bystander effects on both tumour cells and normal cells. We show that bystander responses play a major role in mediating radiation damage to cells at low-doses of radiotherapy, doing more damage than that due to direct radiation. The survival curves derived from our computational simulations showed an area of hyper-radiosensitivity at low-doses that are not obtained using a traditional radiobiological model., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

Most cancer treatments efficacy depends on tumor metastasis suppression, where tumor suppressor genes play an important role. Maspin (Mammary Serine Protease Inhibitor), an non-inhibitory serpin has been reported as a potential tumor suppressor to influence cell migration, adhesion, proliferation and apoptosis in in vitro and in vivo experiments in last two decades. Lack of computational investigations hinders its ability to go through clinical trials. Previously, we reported first computational model for maspin effects on tumor growth using artificial neural network and cellular automata paradigm with in vitro data support. This paper extends the previous in silico model by encompassing how maspin influences cell migration and the cell-extracellular matrix interaction in subcellular level. A feedforward neural network was used to define each cell behavior (proliferation, quiescence, apoptosis) which followed a cell-cycle algorithm to show the microenvironment impacts over tumor growth. Furthermore, the model concentrates how the in silico experiments results can further confirm the fact that maspin reduces cell migration using specific in vitro data verification method. The data collected from in vitro and in silico experiments formulates an unsupervised learning problem which can be solved by using different clustering algorithms. A density based clustering technique was developed to measure the similarity between two datasets based on the number of links between instances. Our proposed clustering algorithm first finds the nearest neighbors of each instance, and then redefines the similarity between pairs of instances in terms of how many nearest neighbors share the two instances. The number of links between two instances is defined as the number of common neighbors they have. The results showed significant resemblances with in vitro experimental data. The results also offer a new insight into the dynamics of maspin and establish as a metastasis suppressor gene for further molecular research., (Copyright © 2015 Elsevier Ltd. All rights reserved.)

When biological cells divide, they divide on a given angle. It has been shown experimentally that the orientation of cell division angle for a single cell can be described by a probability density function. However, the way in which the probability density function underlying cell division orientation influences population or tissue scale morphogenesis is unknown. Here we show that a computational approach, with thousands of stochastic simulations modeling growth and division of a population of cells, can be used to investigate this unknown. In this paper we examine two potential forms of the probability density function: a wrapped normal distribution and a binomial distribution. Our results demonstrate that for the wrapped normal distribution the standard deviation of the division angle, potentially interpreted as biological noise, controls the degree of tissue scale anisotropy. For the binomial distribution, we demonstrate a mechanism by which direction and degree of tissue scale anisotropy can be tuned via the probability of each division angle. We anticipate that the method presented in this paper and the results of these simulations will be a starting point for further investigation of this topic. [ABSTRACT FROM AUTHOR]

This paper develops a three-dimensional spatially distributed model of brain cellular metabolism and investigates how the locus of the synaptic activity in reference to the capillaries and diffusion affects the behavior of the model, a type of analysis which is impossible to carry out in spatially lumped models, which are shown to be consistent spatially averaged approximations of the distributed model. To avoid a geometrically detailed modeling of the complex structure of the tissue consisting of different cell types and the extracellular space, the distributed model is based on a novel multi-domain formulation of reaction-diffusion equations, accounting also for separate mitochondria. The model reduction relating the spatially distributed model and lower dimensional reduced models, including the well-mixed spatially lumped compartment model, is carefully explained. We illustrate the effects of losing the spatial resolution with a computed example which is based on a reduced one-dimensional distributed radial model, and look into how the model behaves when the locus of the synaptic activity in reference to the capillaries is changed. By averaging the fluxes and concentrations in the distributed radial model to correspond to quantities in a lumped model, and further by estimating the parameters in the lumped, we conclude that varying the locus of the synaptic activity in reference to the capillaries alters significantly the lumped model parameters. This observation seems to be consequential for parameter estimation procedures from data when the spatial resolution is insufficient to determine the locus of the activity, indicating that the model uncertainty is an inherent feature of lumped models., (Copyright © 2015 Elsevier Ltd. All rights reserved.)

A generalized lattice-spring lattice-Boltzmann model (GLLM) is introduced by adding a three-body force in the traditional lattice-spring model. This method is able to deal with bending deformation of flexible biological bodies in fluids. The interactions between elastic solids and fluid are treated with the immersed boundary-lattice Boltzmann method. GLLM is validated by comparing the present results with the existing theoretical and simulation results. As an application of GLLM, swimming of flagellum in fluid is simulated and propulsive force as a function of driven frequency and fluid structures at various Reynolds numbers 0.15-5.1 are presented in this paper., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

Cancer metastasis is a complex multistep process which allows cancer cells to establish new tumours in distant organs. The process of metastasis involves cell migration and invasion; it is what makes cancer a fatal disease. The efficiency of most cancer treatments depends on metastasis suppression. Maspin is a type II tumour metastasis suppressor which has multiple cellular effects. It has been described as a key regulatory protein in both the intracellular and extracellular environments. Maspin has been shown to reduce cell migration, invasion, proliferation and angiogenesis, and increase apoptosis and cell-cell adhesion in in vitro and in vivo experiments. The clinical data regarding the predictive effects of maspin expression are variable. To date, the whole cellular mechanisms that maspin uses to influence tumour cell behaviours have not been clearly defined. The diversity of the effects of maspin motivated us to develop an intelligent model to investigate its effects on cellular proliferation and migration. This paper reports a hybrid model of solid tumour growth in order to investigate the impact of maspin on the growth and evolutionary dynamics of the cancer cell. A feed-forward neural network was used to model the behaviours (proliferation, quiescence, apoptosis and/or movement) of each cell, which has been suggested as a suitable model of cell signalling pathways. Results show that maspin reduces migration by 10-40%, confirmed by published in vitro data. The model also shows a reduction in cell proliferation by 20-30% in the presence of maspin. So far, this is the first attempt to model the effect of maspin in a computational model to verify in vitro data. This will provide new insights into the tumour suppressive properties of maspin and inform the development of novel cancer therapies., (© 2013 Elsevier Ltd. All rights reserved.)

• A new two-patch pollination-mutualisms system including interacting species is analyzed. • Rigorous analysis on the model exhibits existence of stable positive equilibria. • Dispersal can make the pollinator approach higher total population abundance than if non-diffusing. • The result is effective even though the mutualistic plant approaches a low density. • The result is effective even though the system is in source-sink populations. In this paper, we consider a two-patch pollination-mutualism model with dispersal, which is derived from resource-service exchange between the plant and pollinator. The pollinator is assumed to persist in one patch in the presence of pollination-mutualisms, while it can (or cannot) survive alone in the other. Rigorous study on the model exhibits that solutions of the equations are nonnegative and bounded, and there exist stable positive equilibria under conditions. Theoretical analysis on the equilibria demonstrates that if the pollinator can survive alone in the other patch, a small dispersal can make the pollinator approach higher total population abundance than if non-dispersing. If the pollinator cannot survive alone in the other patch, a small or large dispersal can make the pollinator approach higher size than if non-dispersing, which is not intuitive. A novel prediction of this work is that the pollinator with dispersal can reach high population abundance even though the mutualistic plant approaches a low density, while constructing a high-quality patch for pollinator can lead to extra individuals for both plant and pollinator via dispersal. Numerical simulations confirm and extend our results. [ABSTRACT FROM AUTHOR]

In this paper we present a continuum mathematical model of vascular tumour growth which is based on a multiphase framework in which the tissue is decomposed into four distinct phases and the principles of conservation of mass and momentum are applied to the normal/healthy cells, tumour cells, blood vessels and extracellular material. The inclusion of a diffusible nutrient, supplied by the blood vessels, allows the vasculature to have a nonlocal influence on the other phases. Two-dimensional computational simulations are carried out on unstructured, triangular meshes to allow a natural treatment of irregular geometries, and the tumour boundary is captured as a diffuse interface on this mesh, thereby obviating the need to explicitly track the (potentially highly irregular and ill-defined) tumour boundary. A hybrid finite volume/finite element algorithm is used to discretise the continuum model: the application of a conservative, upwind, finite volume scheme to the hyperbolic mass balance equations and a finite element scheme with a stable element pair to the generalised Stokes equations derived from momentum balance, leads to a robust algorithm which does not use any form of artificial stabilisation. The use of a matrix-free Newton iteration with a finite element scheme for the nutrient reaction-diffusion equations allows full nonlinearity in the source terms of the mathematical model. Numerical simulations reveal that this four-phase model reproduces the characteristic pattern of tumour growth in which a necrotic core forms behind an expanding rim of well-vascularised proliferating tumour cells. The simulations consistently predict linear tumour growth rates. The dependence of both the speed with which the tumour grows and the irregularity of the invading tumour front on the model parameters is investigated., (Copyright © 2012 Elsevier Ltd. All rights reserved.)

Models of root systems are essential tools to understand how crops access and use soil resources during their development. However, scaling up such models to field scale remains a great challenge. In this paper, we detail a new approach to compute the growth of root systems based on density distribution functions. Growth was modelled as the dynamics of root apical meristems, using Partial Differential Equations. Trajectories of root apical meristems were used to deform root domains, the bounded support of root density functions, and update density distributions at each time increment of the simulation. Our results demonstrate that it is possible to predict the growth of root domains, by including developmentally meaningful parameters such as root elongation rate, gravitropic rate and branching rate. Models of this type are computationally more efficient than state-of-the-art finite volume methods. At a given prediction accuracy, computational time is over 10 times quicker; it allowed deformable models to be used to simulate ensembles of interacting plants. Application to root competition in crop-weed systems is demonstrated. The models presented in this study indicate that similar approaches could be developed to model shoot or whole plant processes with potential applications in crop and ecological modelling., (Copyright © 2012 Elsevier Ltd. All rights reserved.)

Trees are able to maintain or modify the orientation of their axes (trunks or branches) by tropic movements. For axes in which elongation is achieved but cambial growth active, the tropic movements are due to the production of a particular wood, called reaction wood which is prestressed within the growing tree. Several models have been developed to simulate the gravitropic response of axes in trees due to the formation of reaction wood, all within the frame of linear elasticity and considering the wood maturation as instantaneous. The effect viscoelasticity of wood has, to our knowledge, never been considered. The TWIG model presented in this paper aims at simulating the gravitropic movement of a tree axis at the intra-annual scale. In this work we studied both the effect of a non-instantaneous maturation process and of viscoelasticity. For this purpose, we considered the elastic case with maturation considered as an instantaneous process as the reference. The introduction of viscoelasticity in TWIG has been done by coupling TWIG to a model developed for bridges. Indeed from a purely mechanical point of view, bridges and trees are very similar: they are structures which are built in stages, they are made of several materials (composite structures), their materials are prestressed (wood is prestressed during the maturation process as a result of polymerisation of lignin and cellulose to form the secondary cell wall and concrete is prestressed during drying). Simulations gave evidence that the reorientation process of axes can be significantly influenced by the kinetics of maturation. Moreover the model has now to be tested with more experimental data of wood viscoelasticity but it appears that in the range of a relaxation time from 0 to 50 days, viscoelasticity has an important effect on the evolution of tree shape as well as on the values of prestresses., (Copyright © 2011 Elsevier Ltd. All rights reserved.)

Mitochondria have long been known to sequester cytosolic Ca(2+) and even to shape intracellular patterns of endoplasmic reticulum-based Ca(2+) signaling. Evidence suggests that the mitochondrial network is an excitable medium which can demonstrate independent Ca(2+) induced Ca(2+) release via the mitochondrial permeability transition. The role of this excitability remains unclear, but mitochondrial Ca(2+) handling appears to be a crucial element in diverse diseases as diabetes, neurodegeneration and cardiac dysfunction that also have bioenergetic components. In this paper, we extend the modular Magnus-Keizer computational model for respiration-driven Ca(2+) handling to include a permeability transition based on a channel-like pore mechanism. We demonstrate both excitability and Ca(2+) wave propagation accompanied by depolarizations qualitatively similar to those reported in cell and isolated mitochondria preparations. These waves depend on the energy state of the mitochondria, as well as other elements of mitochondrial physiology. Our results support the concept that mitochondria can transmit state dependent signals about their function across the mitochondrial network. Our model provides the tools for predictions about the internal physiology that leads to this qualitatively different Ca(2+) excitability seen in mitochondria., (Copyright © 2011 Elsevier Ltd. All rights reserved.)

Abstract: Zero-order ultrasensitivity (ZOU) is a long known and interesting phenomenon in enzyme networks. Here, a substrate is reversibly modified by two antagonistic enzymes (a ‘push–pull' system) and the fraction in modified state undergoes a sharp switching from near-zero to near-unity at a critical value of the ratio of the enzyme concentrations, under saturation conditions. ZOU and its extensions have been studied for several decades now, ever since the seminal paper of Goldbeter and Koshland (1981); however, a complete probabilistic treatment, important for the study of fluctuations in finite populations, is still lacking. In this paper, we study ZOU using a modular approach, akin to the total quasi-steady state approximation (tQSSA). This approach leads to a set of Fokker–Planck (drift–diffusion) equations for the probability distributions of the intermediate enzyme-bound complexes, as well as the modified/unmodified fractions of substrate molecules. We obtain explicit expressions for various average fractions and their fluctuations in the linear noise approximation (LNA). The emergence of a ‘critical point’ for the switching transition is rigorously established. New analytical results are derived for the average and variance of the fractional substrate concentration in various chemical states in the near-critical regime. For the total fraction in the modified state, the variance is shown to be a maximum near the critical point and decays algebraically away from it, similar to a second-order phase transition. The new analytical results are compared with existing ones as well as detailed numerical simulations using a Gillespie algorithm. [Copyright &y& Elsevier]

In this paper an advanced, clinically oriented multiscale cancer model of breast tumor response to chemotherapy is presented. The paradigm of early breast cancer treated by epirubicin according to a branch of an actual clinical trial (the Trial of Principle, TOP trial) has been addressed. The model, stemming from previous work of the In Silico Oncology Group, National Technical University of Athens, is characterized by several crucial new features, such as the explicit distinction of proliferating cells into stem cells of infinite mitotic potential and cells of limited proliferative capacity, an advanced generic cytokinetic model and an improved tumor constitution initialization technique. A sensitivity analysis regarding critical parameters of the model has revealed their effect on the behavior of the biological system. The favorable outcome of an initial step towards the clinical adaptation and validation of the simulation model, based on the use of anonymized data from the TOP clinical trial, is presented and discussed. Two real clinical cases from the TOP trial with variable molecular profile have been simulated. A realistic time course of the tumor diameter and a reduction in tumor size in agreement with the clinical data has been achieved for both cases by selection of reasonable model parameter values, thus demonstrating a possible adaptation process of the model to real clinical trial data. Available imaging, histological, molecular and treatment data are exploited by the model in order to strengthen patient individualization modeling. The expected use of the model following thorough clinical adaptation, optimization and validation is to simulate either several candidate treatment schemes for a particular patient and support the selection of the optimal one or to simulate the expected extent of tumor shrinkage for a given time instant and decide on the adequacy or not of the simulated scheme., (Copyright 2010 Elsevier Ltd. All rights reserved.)

One of the most important questions in cell biology is how cells cope with rapid changes in their environment. The range of common molecular responses includes a dramatic change in the pattern of gene expression and the elevated synthesis of so-called heat shock (or stress) proteins (HSPs). Induction of HSPs increases cell survival under stress conditions [Morimoto, R.I., 1993. Cells in stress: transcriptional activation of heat shock genes. Science 259, 1409-1410]. In this paper we propose a mathematical model of heat shock protein synthesis induced by an external temperature stimulus. Our model consists of a system of nine nonlinear ordinary differential equations describing the temporal evolution of the key variables involved in the regulation of HSP synthesis. Computational simulations of our model are carried out for different external temperature stimuli. We compare our model predictions with experimental data for three different cases-one corresponding to heat shock, the second corresponding to slow heating conditions and the third corresponding to a short heat shock (lasting about 40 min). We also present our model predictions for heat shocks carried out up to different final temperatures and finally we present a new hypothesis concerning the molecular response to stress that explains some phenomena observed in experiments.

Evolutionary game theory studies frequency dependent selection. The fitness of a strategy is not constant, but depends on the relative frequencies of strategies in the population. This type of evolutionary dynamics occurs in many settings of ecology, infectious disease dynamics, animal behavior and social interactions of humans. Traditionally evolutionary game dynamics are studied in well-mixed populations, where the interaction between any two individuals is equally likely. There have also been several approaches to study evolutionary games in structured populations. In this paper we present a simple result that holds for a large variety of population structures. We consider the game between two strategies, A and B, described by the payoff matrix(abcd). We study a mutation and selection process. For weak selection strategy A is favored over B if and only if sigma a+b>c+sigma d. This means the effect of population structure on strategy selection can be described by a single parameter, sigma. We present the values of sigma for various examples including the well-mixed population, games on graphs, games in phenotype space and games on sets. We give a proof for the existence of such a sigma, which holds for all population structures and update rules that have certain (natural) properties. We assume weak selection, but allow any mutation rate. We discuss the relationship between sigma and the critical benefit to cost ratio for the evolution of cooperation. The single parameter, sigma, allows us to quantify the ability of a population structure to promote the evolution of cooperation or to choose efficient equilibria in coordination games.

To understand the dynamics of natural species communities, a major challenge is to quantify the relationship between their assembly, stability, and underlying food web structure. To this end, two complementary aspects of food web structure can be related to community stability: sign structure, which refers to the distributions of trophic links irrespective of interaction strengths, and interaction strength structure, which refers to the distributions of interaction strengths with or without consideration of sign structure. In this paper, using data from a set of relatively well documented community food webs, I show that natural communities generally exhibit a sign structure that renders their stability sensitive to interaction strengths. Using a Lotka-Volterra type population dynamical model, I then show that in such communities, individual consumer species with high values of a measure of their total biomass acquisition rate, which I term "weighted generality", tend to undermine community stability. Thus consumer species' trophic modules (a species and all its resource links) should be "selected" through repeated immigrations and extinctions during assembly into configurations that increase the probability of stable coexistence within the constraints of the community's trophic sign structure. The presence of such constraints can be detected by the incidence and strength of certain non-random structural characteristics. These structural signatures of dynamical constraints are readily measurable, and can be used to gauge the importance of interaction-driven dynamical constraints on communities during and after assembly in natural communities.

The model proposed here links together two approaches to describe tumours: a continuous medium to describe the movement and the mechanical properties of the tissue, and a population dynamics approach to represent internal genetic inhomogeneity and instability of the tumour. In this way one can build models which cover several stages of tumour progression. In this paper we focus on describing transition from aerobic to purely glycolytic metabolism (the Warburg effect) in tumour cords. From the mathematical point of view this model leads to a free boundary problem where domains in contact are characterized by different sets of equations. Accurate stitching of the solution was possible with a modified ghost fluid method. Growth and death of the cells and uptake of the nutrients are related through ATP production and energy costs of the cellular processes. In the framework of the bi-population model this allowed to keep the number of model parameters relatively small.

In this paper, a recently developed model governing the cancer growth on a cell population level with combination of immune and chemotherapy is used to develop a reactive (feedback) mixed treatment strategy. The feedback design proposed here is based on nonlinear constrained model predictive control together with an adaptation scheme that enables the effects of unavoidable modeling uncertainties to be compensated. The effectiveness of the proposed strategy is shown under realistic human data showing the advantage of treatment in feedback form as well as the relevance of the adaptation strategy in handling uncertainties and modeling errors. A new treatment strategy defined by an original optimal control problem formulation is also proposed. This new formulation shows particularly interesting possibilities since it may lead to tumor regression under better health indicator profile.

Studying the spread of a pathogen in a managed metapopulation such as cattle herds in a geographical region often requires to take into account both the within- and between-herd transmission dynamics. This can lead to high-dimensional metapopulation systems resulting from the coupling of several within-herd transmission models. To tackle this problem, we aim in this paper at reducing the dimension of a tractable but realistic dynamical system reproducing the within-herd spread. The context chosen to illustrate our purpose is bovine viral diarrhoea virus (BVDV) transmission in a cattle herd structured in two age classes and several epidemiological states, including two infectious states (transiently and persistently infected). Different time scales, corresponding to the epidemiological and demographic processes, are identified which allow to build a reduced model. Singular perturbation technique is used to prove that, under some non-restrictive conditions on parameter values, the behaviour of the original system is quite accurately approximated by that of the reduced system. Simulations are also performed to corroborate the approximation quality. Our study illustrates the methodological interest of using singular perturbations to reduce model complexity. It also rigorously proves the biologically intuitive assumption that transiently infected individuals can be neglected in a homogeneous population, when capturing the global dynamics of BVDV spread.

The aim of the paper is to develop a procedure for an estimate of an analytical form of a hazard function for cancer patients. Although a deterministic approach based on cancer cell population dynamics yields the analytical expression, it depends on several parameters which should be estimated. On the other hand, a kernel estimate is an effective nonparametric method for estimating hazard functions. This method provides the pointwise estimate of the hazard function. Our procedure consists of two steps: in the first step we find the kernel estimate of the hazard function and in the second step the parameters in the deterministic model are obtained by the least squares method. A simulation study with different types of censorship is carried out and the developed procedure is applied to real data.

Maintenance of alternative chromatin states through cell divisions pose some fundamental constraints on the dynamics of histone modifications. In this paper, we study the systems biology of epigenetic inheritance by defining and analyzing general classes of mathematical models. We discuss how the number of modification states involved plays an essential role in the stability of epigenetic states. In addition, DNA duplication and the consequent dilution of marked histones act as a large perturbation for a stable state of histone modifications. The requirement that this large perturbation falls into the basin of attraction of the original state sometimes leads to additional constraints on effective models. Two such models, inspired by two different biological systems, are compared in their fulfilling the requirements of multistability and of recovery after DNA duplication. We conclude that in the presence of multiple histone modifications that characterize alternative epigenetic stable states, these requirements are more easily fulfilled.

We investigate the transition between limiting similarity and coexistence of a continuum in the competitive Lotka-Volterra model. It is known that there exist exceptional cases in which, contrary to the limiting similarity expectation, all phenotypes coexist along a trait axis. Earlier studies established that the distance between surviving phenotypes is in the magnitude of the niche width 2sigma provided that the carrying capacity curve differs from the exceptional one significantly enough. In this paper we studied the outcome of competition for small perturbations of the exceptional (Gaussian) carrying capacity. We found that the average distance between the surviving phenotypes goes to zero when the perturbation vanishes. The number of coexisting species in equilibrium is proportional to the negative logarithm of the perturbation. Nevertheless, the niche width provides a good order of magnitude for the distance between survivors if the perturbations are larger than 10%. Therefore, we conclude that limiting similarity is a good framework of biological thinking despite the lack of an absolute lower bound of similarity.

Phylogenetic trees describe the evolutionary history of a group of present-day species from a common ancestor. These trees are typically reconstructed from aligned DNA sequence data. In this paper we analytically address the following question: Is the amount of sequence data required to accurately reconstruct a tree significantly more than the amount required to test whether or not a candidate tree was the 'true' tree? By 'significantly', we mean that the two quantities do not behave the same way as a function of the number of species being considered. We prove that, for a certain type of model, the amount of information required is not significantly different; while for another type of model, the information required to test a tree is independent of the number of leaves, while that required to reconstruct it grows with this number. Our results combine probabilistic and combinatorial arguments.

In this paper, a mathematical model for the lactic acid fermentation in membrane bioreactor is investigated. This novel theoretical framework could result in an objective criterion on how to control the substrate concentration in order to keep a sustainable and steady output of lactic acid. Firstly, continuous input substrate is taken. The existence and local stability of two equilibria are studied. According to Poincaré-Bendixson Theorem, we obtain the conditions for the globally asymptotical stability of the equilibrium. Secondly, impulsive input substrate is also considered. Using Floquet's theorem and small-amplitude perturbation, we obtain the biomass-free periodic solution is locally stable if some conditions are satisfied. In a certain limiting case, it is shown that a nontrivial periodic solution emerges via a supercritical bifurcation. Finally, our findings are confirmed by means of numerical simulations.

Competitive intransitivity occurs when species' competitive abilities cannot be listed in a strict hierarchy, but rather form competitive loops, as in the game 'Rock-Paper-Scissors'. Indices are useful for summarizing intransitivity in communities; however, as with most indices, a great deal of information is compressed into single number. So while recent ecological theory, experiments, and natural history observations demonstrate that competitive intransitivity can promote species coexistence, the consequence of variation in the 'topology' of competitive interactions that is not accounted for by intransitivity indices is much less well understood. We use a continuous analytical model and two complementary discrete lattice models (one spatially explicit, the other aspatial) to demonstrate that such variation does indeed greatly affect species coexistence. Specifically, we show that although intransitivity indices are good at capturing broad patterns of coexistence, communities with different levels of intransitivity can have equal coexistence, and communities with equal intransitivity can have different coexistence, due to underlying variation in competitive network topology.

In vivo, cardiomyocytes interact with surrounding extracellular matrix while performing periodically a contractile behaviour, which is the main determinant of heart performance. As extracellular substrates with easily tunable stiffness properties, polyacrylamide gels (PAGs) provide valuable flexible media for studying in vitro the dynamical behaviour of cardiomyocytes responding to stiffness variations of their surrounding environment. We propose in this paper an original mechano-chemical model of the cardiac cell contraction that sheds light on the adaptive response of cardiomyocytes evidenced recently in the experiments of Qin et al. [2007. Dynamical stress characterization and energy evaluation of single cardiac myocyte actuating on flexible substrate. Biochem. Biophys. Res. Commun. 360, 352-356]. The model links the amplitude of the extracellular PAGs strain fields to the spatio-temporal variation of the intracellular stresses in every part of the cell during the sarcomeres contraction-relaxation. In a continuum mechanics framework, we derived a unified description of the sarcomere-length dependence of intracellular active stress and of its control by anisotropic calcium diffusion and autocatalytic calcium release from the sarcoplasmic reticulum. Taking benefit of our previous work on the characterization of mechanical properties of PAGs with varying stiffness, we were thus able to evaluate the active intracellular stress exerted by the cardiomyocyte on flexible PAGs with different and known Young's moduli. Interestingly, we were able to explain the intriguing increase of maximal cellular stress observed experimentally when substrate stiffness is increased. By providing an evaluation of the whole-field cell stresses and strains, this integrative approach of cardiomyocyte contraction provides a reliable basis for further analysis of additional cooperativity and mechanotransduction mechanisms involved in cell contractility regulation, notably in physiological and pathological situations where modifications of cardiac performance are linked to varied stiffness of the cardiomyocytes environment.

In this paper the qualitative dynamic behavior of reaction kinetic models of G protein signaling is examined. A simplified basic G protein signaling structure is defined, which is extended to be able to take the effect of slow transmission, RGS mediated feedback regulation and ERK-phosphatase mediated feedback regulation into account. The resulting model gives rise to an acceptable qualitative approximation of the G protein dependent and independent ERK activation dynamics that is in good agreement with the experimentally observed behavior.

This paper presents the definition, solution and validation of a stochastic model of the budding yeast cell cycle, based on Stochastic Petri Nets (SPN). A specific family of SPNs is selected for building a stochastic version of a well-established deterministic model. We describe the procedure followed in defining the SPN model from the deterministic ODE model, a procedure that can be largely automated. The validation of the SPN model is conducted with respect to both the results provided by the deterministic one and the experimental results available from literature. The SPN model catches the behavior of the wild type budding yeast cells and a variety of mutants. We show that the stochastic model matches some characteristics of budding yeast cells that cannot be found with the deterministic model. The SPN model fine-tunes the simulation results, enriching the breadth and the quality of its outcome.

In this paper, we study general protocell models aiming to understand the synchronization phenomenon of genetic material and container productions, a necessary condition to ensure sustainable growth in protocells and eventually leading to Darwinian evolution when applied to a population of protocells. Synchronization has been proved to be an emergent property in many relevant protocell models in the class of the so-called surface reaction models, assuming both linear- and non-linear dynamics for the involved chemical reactions. We here extend this analysis by introducing and studying a new class of models where the relevant chemical reactions are assumed to occur inside the protocell, in contrast with the former model where the reaction site was the external surface. While in our previous studies, the replicators were assumed to compete for resources, without any direct interaction among them, we here improve both models by allowing linear interaction between replicators: catalysis and/or inhibition. Extending some techniques previously introduced, we are able to give a quite general analytical answer about the synchronization phenomenon in this more general context. We also report on results of numerical simulations to support the theory, where applicable, and allow the investigation of cases which are not amenable to analytical calculations.

The formation of out-of-equilibrium patterns is a characteristic feature of spatially extended, biodiverse, ecological systems. Intriguing examples are provided by cyclic competition of species, as metaphorically described by the 'rock-paper-scissors' game. Both experimentally and theoretically, such non-transitive interactions have been found to induce self-organization of static individuals into noisy, irregular clusters. However, a profound understanding and characterization of such patterns is still lacking. Here, we theoretically investigate the influence of individuals' mobility on the spatial structures emerging in rock-paper-scissors games. We devise a quantitative approach to analyze the spatial patterns self-forming in the course of the stochastic time evolution. For a paradigmatic model originally introduced by May and Leonard, within an interacting particle approach, we demonstrate that the system's behavior-in the proper continuum limit-is aptly captured by a set of stochastic partial differential equations. The system's stochastic dynamics is shown to lead to the emergence of entangled rotating spiral waves. While the spirals' wavelength and spreading velocity is demonstrated to be accurately predicted by a (deterministic) complex Ginzburg-Landau equation, their entanglement results from the inherent stochastic nature of the system. These findings and our methods have important applications for understanding the formation of noisy patterns, e.g. in ecological and evolutionary contexts, and are also of relevance for the kinetics of (bio)-chemical reactions.

This paper considers the coevolution of phenotypic traits in a community comprising two competitive species subject to strong Allee effects. Firstly, we investigate the ecological and evolutionary conditions that allow for continuously stable strategy under symmetric competition. Secondly, we find that evolutionary suicide is impossible when the two species undergo symmetric competition, however, evolutionary suicide can occur in an asymmetric competition model with strong Allee effects. Thirdly, it is found that evolutionary bistability is a likely outcome of the process under both symmetric and asymmetric competitions, which depends on the properties of symmetric and asymmetric competitions. Fourthly, under asymmetric competition, we find that evolutionary cycle is a likely outcome of the process, which depends on the properties of both intraspecific and interspecific competition. When interspecific and intraspecific asymmetries vary continuously, we also find that the evolutionary dynamics may admit a stable equilibrium and two limit cycles or two stable equilibria separated by an unstable limit cycle or a stable equilibrium and a stable limit cycle.

It has recently been proposed [Dediu, D., Ladd, D.R., 2007. Linguistic tone is related to the population frequency of the adaptive haplogroups of two brain size genes, ASPM and Microcephalin. Proc. Natl Acad. Sci. USA 104(26), 10944-10949] that genetically coded linguistic biases can influence the trajectory of language change. However, the nature of such biases and the conditions under which they can become manifest have remained vague. The present paper explores computationally two plausible types of linguistic acquisition biases in a population of agents implementing realistic genetic, linguistic and demographic processes. One type of bias represents an innate asymmetric initial state (initial expectation bias) while the other an innate asymmetric facility of acquisition (rate of learning bias). It was found that only the second type of bias produces detectable effects on language through cultural transmission across generations and that such effects are produced even by weak biases present at low frequencies in the population. This suggests that learning preference asymmetries, very small at the individual level and not very frequent at the population level, can bias the trajectory of language change through the process of cultural transmission.