A general framework for modeling growth and division of mammalian cells

Background  

Modeling the cell-division cycle has been practiced for many years. As time has progressed, this work has gone from understanding the basic principles to addressing distinct biological problems, e.g., the nature of the restriction point, how checkpoints operate, the nonlinear dynamics of the cell cycle, the effect of localization, etc. Most models consist of coupled ordinary differential equations developed by the researchers, restricted to deal with the interactions of a limited number of molecules. In the future, cell-cycle modeling--and indeed all modeling of complex biologic processes--will increase in scope and detail.     

A computational framework for cortical learning

Recent physiological findings have revealed that long-term adaptation of the synaptic strengths between cortical pyramidal neurons depends on the temporal order of presynaptic and postsynaptic spikes, which is called spike-timing-dependent plasticity (STDP) or temporally asymmetric Hebbian (TAH) learning. Here I prove by analytical means that a physiologically plausible variant of STDP adapts synaptic strengths such that the presynaptic spikes predict the postsynaptic spikes with minimal error. This prediction error model of STDP implies a mechanism for cortical memory: cortical tissue learns temporal spike patterns if these spike patterns are repeatedly elicited in a set of pyramidal neurons. The trained network finishes these patterns if their beginnings are presented, thereby recalling the memory. Implementations of the proposed algorithms may be useful for applications in voice recognition and computer vision.     

A log-linear modeling framework for selective mixing.

Nonrandom mixing can significantly alter the diffusion path of an infectious disease such as AIDS that requires intimate contact. Recent attempts to model this effect have sought a general framework capable of representing both simple and arbitrarily complicated mixing structures, and of solving the balancing problem in a nonequilibrium multigroup population. Log-linear models are proposed here as a general framework for solving the first problem. This approach offers several additional benefits: The parameters used to govern the mixing have a simple, intuitive interpretation, the framework provides a statistically sound basis for the estimation of these parameters from mixing-matrix data, and the resulting estimates are easily integrated into compartmental models for diffusion. A modified selection model is proposed to solve the second problem of generalizing the selection process to nonequilibrium populations. The distribution of contacts under this model is derived and is found to satisfy the assumptions of statistical inference for log-linear models. Together these techniques provide an integrated and flexible framework for modeling the role of selective mixing in the spread of disease.     

A computational framework for influenza antigenic cartography

Influenza viruses have been responsible for large losses of lives around the world and continue to present a great public health challenge. Antigenic characterization based on hemagglutination inhibition (HI) assay is one of the routine procedures for influenza vaccine strain selection. However, HI assay is only a crude experiment reflecting the antigenic correlations among testing antigens (viruses) and reference antisera (antibodies). Moreover, antigenic characterization is usually based on more than one HI dataset. The combination of multiple datasets results in an incomplete HI matrix with many unobserved entries. This paper proposes a new computational framework for constructing an influenza antigenic cartography from this incomplete matrix, which we refer to as Matrix Completion-Multidimensional Scaling (MC-MDS). In this approach, we first reconstruct the HI matrices with viruses and antibodies using low-rank matrix completion, and then generate the two-dimensional antigenic cartography using multidimensional scaling. Moreover, for influenza HI tables with herd immunity effect (such as those from Human influenza viruses), we propose a temporal model to reduce the inherent temporal bias of HI tables caused by herd immunity. By applying our method in HI datasets containing H3N2 influenza A viruses isolated from 1968 to 2003, we identified eleven clusters of antigenic variants, representing all major antigenic drift events in these 36 years. Our results showed that both the completed HI matrix and the antigenic cartography obtained via MC-MDS are useful in identifying influenza antigenic variants and thus can be used to facilitate influenza vaccine strain selection. The webserver is available at http://sysbio.cvm.msstate.edu/AntigenMap.     

A framework for whole-cell mathematical modeling

The default framework for modeling biochemical processes is that of a constant-volume reactor operating under steady-state conditions. This is satisfactory for many applications, but not for modeling growth and division of cells. In this study, a whole-cell modeling framework is developed that assumes expanding volumes and a cell-division cycle. A spherical newborn cell is designed to grow in volume during the growth phase of the cycle. After 80% of the cycle period, the cell begins to divide by constricting about its equator, ultimately affording two spherical cells with total volume equal to twice that of the original. The cell is partitioned into two regions or volumes, namely the cytoplasm (Vcyt) and membrane (Vmem), with molecular components present in each. Both volumes change during the cell cycle; Vcyt changes in response to osmotic pressure changes as nutrients enter the cell from the environment, while Vmem changes in response to this osmotic pressure effect such that membrane thickness remains invariant. The two volumes change at different rates; in most cases, this imposes periodic or oscillatory behavior on all components within the cell. Since the framework itself rather than a particular set of reactions and components is responsible for this behavior, it should be possible to model various biochemical processes within it, affording stable periodic solutions without requiring that the biochemical process itself generates oscillations as an inherent feature. Given that these processes naturally occur in growing and dividing cells, it is reasonable to conclude that the dynamics of component concentrations will be more realistic than when modeled within constant-volume and/or steady-state frameworks. This approach is illustrated using a symbolic whole cell model.     

A computational framework for topographies of cortical areas

Self-organizing feature maps (SOFMs) represent a dimensionality-reduction algorithm that has been used to replicate feature topographies observed experimentally in primary visual cortex (V1). We used the SOFM algorithm to model possible topographies of generic sensory cortical areas containing up to five arbitrary physiological features. This study explored the conditions under which these multi-feature SOFMs contained two features that were mapped monotonically and aligned orthogonally with one another (i.e., “globally orthogonal”), as well as the conditions under which the map of one feature aligned with the longest anatomical dimension of the modeled cortical area (i.e., “dominant”). In a single SOFM with more than two features, we never observed more than one dominant feature, nor did we observe two globally orthogonal features in the same map in which a dominant feature occurred. Whether dominance or global orthogonality occurred depended upon how heavily weighted the features were relative to one another. The most heavily weighted features are likely to correspond to those physical stimulus properties transduced directly by the sensory epithelium of a particular sensory modality. Our results imply, therefore, that in the primary cortical area of sensory modalities with a two-dimensional sensory epithelium, these two features are likely to be organized globally orthogonally to one another, and neither feature is likely to be dominant. In the primary cortical area of sensory modalities with a one-dimensional sensory epithelium, however, this feature is likely to be dominant, and no two features are likely to be organized globally orthogonally to one another. Because the auditory system transduces a single stimulus feature (i.e., frequency) along the entire length of the cochlea, these findings may have particular relevance for topographic maps of primary auditory cortex. This research was supported by The McDonnell Center for Higher Brain Function, The Wallace H. Coulter Foundation and NIH grant DC008880.     

A general framework for marker-assisted selection

Early simulation studies have showed that the inclusion of epistatic components (especially the additive-by-additive effects) into marker-assisted selection (MAS) can improve selection efficiency for a short-term breeding program. In this study I extend Lande and Thompson's theory to incorporate both additive and non-additive effects into MAS with reference to the mass selection case. Four different indices are analytically examined in terms of the type of genetic components involved in the marker scores: phenotype-, general combining ability (GCA)-, and GCA and reciprocal effects-based marker scores. The phenotype-based marker index is applicable to any population of non-random mating, while the other three indices are applicable to the synthetic population derived from diallel crosses. All these indices may have higher selection efficiencies than the index with solely additive effects-associated markers as long as the detectable transient non-additive effects are present. The improvement in selection efficiency depends on the magnitude of non-additive variances and the proportion of them explained by markers. The index with the phenotype-based marker scores operates on the whole of the additive and non-additive effects, and has the largest selection efficiency. The indices with the GCA-based marker scores operate only on additive and additive-by-additive genetic variation and have relatively small selection efficiencies. Inclusion of the markers from organelle genomes can also increase selection efficiency, depending upon the proportion of the total genetic variation attributable to organelle genomes and the proportion of them explained by organelle genomic markers. Sharing of markers among different marker scores does not facilitate the improvement of selection efficiency.     

A unifying computational framework for motor control and social interaction

Recent empirical studies have implicated the use of the motor system during action observation, imitation and social interaction. In this paper, we explore the computational parallels between the processes that occur in motor control and in action observation, imitation, social interaction and theory of mind. In particular, we examine the extent to which motor commands acting on the body can be equated with communicative signals acting on other people and suggest that computational solutions for motor control may have been extended to the domain of social interaction.     

A hybrid systems framework for cellular processes

With the availability of technologies that allow us to obtain stimulus-response time series data for modeling and system identification, there is going to be an increasing need for conceptual frameworks in which to formulate and test hypotheses about intra- and inter-cellular dynamics, in general and not just dependent on a particular cell line, cell type, organism, or technology. While the semantics can be quite different, biologists and systems scientists use in many cases a similar language (notion of feedback, regulation, etc.). A more abstract system-theoretic framework for signals, systems, and control could provide the biologist with an interface between the domains. Apart from recent examples to identify functional elements and describing them in engineering terms, there have been various more abstract developments to describe dynamics at the cell level in the past. This includes Rosen's (M,R)-systems. This paper presents an abstract and general compact mathematical framework of intracellular dynamics, regulation and regime switching inspired by (M,R)-theory and based on hybrid automata.     

A career structure for general practice.

Training for general practitioners usually provides little experience in research, business management, or dealing with chronic disease. It is these areas that could provide scope for further training after becoming a general practitioner principal and provided career goals. Formally recognised research practices, perhaps with one partner coordinating research but all participating, and district research facilitators could increase both the quality and the quantity of research in general practice. Recognising the different skills of doctors in the partnership and referring patients to the most appropriate partner will improve care for patients as well as provide career development. Further training could be aimed at filling gaps in the practice''s pool of skills. Good management skills are becoming more important as practice teams get bigger and fundholding spreads. Some doctors may wish to become full time or part time managers and the idea of accredited courses for management has been raised.     

A general eco-evolutionary framework for understanding bioinvasions

Studies of bioinvasions have revealed various strategies of invasion, depending on the ecosystem invaded and the alien species concerned. Here, we consider how migration (as a demographic factor), as well as ecological and evolutionary changes, affect invasion success. We propose three main theoretical scenarios that depend on how these factors generate the match between an invader and its new environment. Our framework highlights the features that are common to, or differ among, observed invasion cases, and clarifies some general trends that have been previously highlighted in bioinvasions. We also suggest some new directions of research, such as the assessment of the time sequence of demographic, genetic and environmental changes, using detailed temporal surveys.     

A framework for modeling of juxtacrine signaling systems

Juxtacrine signaling is intercellular communication, in which the receptor of the signal (typically a protein) as well as the ligand (also typically a protein, responsible for the activation of the receptor) are anchored in the plasma membranes, so that in this type of signaling the activation of the receptor depends on direct contact between the membranes of the cells involved. Juxtacrine signaling is present in many important cellular events of several organisms, especially in the development process. We propose a generic formal model (a modeling framework) for juxtacrine signaling systems that is a class of discrete dynamic systems. It possesses desirable characteristics in a good modeling framework, such as: a) structural similarity with biological models, b) capacity of operating in different scales of time, and c) capacity of explicitly treating both the events and molecular elements that occur in the membrane, and those that occur in the intracellular environment and that are involved in the juxtacrine signaling process. We have implemented this framework and used it to develop a new three-level discrete model for the neurogenic network and its participation in neuroblast segregation. This paper presents the details of this framework and its current status.     

A framework for structured modeling of skeletal muscle

The aim of this study is to present a detailed continuum mechanics formulation, and the corresponding algorithms, to predict the deformation of skeletal muscle at different structural levels, starting from the muscle fiber level. The model is used to investigate force production and structural changes during isometric and dynamic contractions of the cat medial gastrocnemius. From a comparison with experimental data obtained in our own laboratories, we conclude that the model faithfully predicts all of the observations pertaining to force production, fascicle length and angle of pennation under various test conditions.     

SubcloneSeeker: a computational framework for reconstructing tumor clone structure for cancer variant interpretation and prioritization

Many tumors are composed of genetically divergent cell subpopulations. We report SubcloneSeeker, a package capable of exhaustive identification of subclone structures and evolutionary histories with bulk somatic variant allele frequency measurements from tumor biopsies. We present a statistical framework to elucidate whether specific sets of mutations are present within the same subclones, and the order in which they occur. We demonstrate how subclone reconstruction provides crucial information about tumorigenesis and relapse mechanisms; guides functional study by variant prioritization, and has the potential as a rational basis for informed therapeutic strategies for the patient. SubcloneSeeker is available at: https://github.com/yiq/SubcloneSeeker.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-014-0443-x) contains supplementary material, which is available to authorized users.     

A general framework for biclustering gene expression data

A large number of biclustering methods have been proposed to detect patterns in gene expression data. All these methods try to find some type of biclusters but no one can discover all the types of patterns in the data. Furthermore, researchers have to design new algorithms in order to find new types of biclusters/patterns that interest biologists. In this paper, we propose a novel approach for biclustering that, in general, can be used to discover all computable patterns in gene expression data. The method is based on the theory of Kolmogorov complexity. More precisely, we use Kolmogorov complexity to measure the randomness of submatrices as the merit of biclusters because randomness naturally consists in a lack of regularity, which is a common property of all types of patterns. On the basis of algorithmic probability measure, we develop a Markov Chain Monte Carlo algorithm to search for biclusters. Our method can also be easily extended to solve the problems of conventional clustering and checkerboard type biclustering. The preliminary experiments on simulated as well as real data show that our approach is very versatile and promising.     

An open system framework for integrating critical zone structure and function

The ??critical zone?? includes the coupled earth surface systems of vegetation, regolith and groundwater that are essential to sustaining life on the planet. The function of this zone is the result of complex interactions among physical, chemical and biological processes and understanding these interactions remains a major challenge to earth system sciences. Here we develop an integrated framework based on thermodynamic theory to characterize the critical zone as a system open to energy and mass fluxes that are forced by radiant, geochemical, and elevational gradients. We derive a statement that demonstrates the relative importance of solar radiation, water, carbon, and physical/chemical denudation mass fluxes to the critical zone energy balance. Within this framework we use rates of effective energy and mass transfer [EEMT; W m^?2] to quantify the relevant flux-gradient relations. Synthesis of existing data demonstrates that variation in energetics associated with primary production and effective precipitation explains substantial variance in critical zone structure and function. Furthermore, we observe threshold behavior in systems that transition to primary production predominance of the energy flux term. The proposed framework provides a first order approximation of non-linearity in critical zone processes that may be coupled with physical and numerical models to constrain landscape evolution.     

A computational framework for the study of confidence in humans and animals

Confidence judgements, self-assessments about the quality of a subject's knowledge, are considered a central example of metacognition. Prima facie, introspection and self-report appear the only way to access the subjective sense of confidence or uncertainty. Contrary to this notion, overt behavioural measures can be used to study confidence judgements by animals trained in decision-making tasks with perceptual or mnemonic uncertainty. Here, we suggest that a computational approach can clarify the issues involved in interpreting these tasks and provide a much needed springboard for advancing the scientific understanding of confidence. We first review relevant theories of probabilistic inference and decision-making. We then critically discuss behavioural tasks employed to measure confidence in animals and show how quantitative models can help to constrain the computational strategies underlying confidence-reporting behaviours. In our view, post-decision wagering tasks with continuous measures of confidence appear to offer the best available metrics of confidence. Since behavioural reports alone provide a limited window into mechanism, we argue that progress calls for measuring the neural representations and identifying the computations underlying confidence reports. We present a case study using such a computational approach to study the neural correlates of decision confidence in rats. This work shows that confidence assessments may be considered higher order, but can be generated using elementary neural computations that are available to a wide range of species. Finally, we discuss the relationship of confidence judgements to the wider behavioural uses of confidence and uncertainty.