Cross-talk between Rho and Rac GTPases drives deterministic exploration of cellular shape space and morphological heterogeneity

One goal of cell biology is to understand how cells adopt different shapes in response to varying environmental and cellular conditions. Achieving a comprehensive understanding of the relationship between cell shape and environment requires a systems-level understanding of the signalling networks that respond to external cues and regulate the cytoskeleton. Classical biochemical and genetic approaches have identified thousands of individual components that contribute to cell shape, but it remains difficult to predict how cell shape is generated by the activity of these components using bottom-up approaches because of the complex nature of their interactions in space and time. Here, we describe the regulation of cellular shape by signalling systems using a top-down approach. We first exploit the shape diversity generated by systematic RNAi screening and comprehensively define the shape space a migratory cell explores. We suggest a simple Boolean model involving the activation of Rac and Rho GTPases in two compartments to explain the basis for all cell shapes in the dataset. Critically, we also generate a probabilistic graphical model to show how cells explore this space in a deterministic, rather than a stochastic, fashion. We validate the predictions made by our model using live-cell imaging. Our work explains how cross-talk between Rho and Rac can generate different cell shapes, and thus morphological heterogeneity, in genetically identical populations.     

Unsupervised logic-based mechanism inference for network-driven biological processes

Modern analytical techniques enable researchers to collect data about cellular states, before and after perturbations. These states can be characterized using analytical techniques, but the inference of regulatory interactions that explain and predict changes in these states remains a challenge. Here we present a generalizable, unsupervised approach to generate parameter-free, logic-based models of cellular processes, described by multiple discrete states. Our algorithm employs a Hamming-distance based approach to formulate, test, and identify optimized logic rules that link two states. Our approach comprises two steps. First, a model with no prior knowledge except for the mapping between initial and attractor states is built. We then employ biological constraints to improve model fidelity. Our algorithm automatically recovers the relevant dynamics for the explored models and recapitulates key aspects of the biochemical species concentration dynamics in the original model. We present the advantages and limitations of our work and discuss how our approach could be used to infer logic-based mechanisms of signaling, gene-regulatory, or other input-output processes describable by the Boolean formalism.     

Fungal evolution: cellular,genomic and metabolic complexity

The question of how phenotypic and genomic complexity are inter‐related and how they are shaped through evolution is a central question in biology that historically has been approached from the perspective of animals and plants. In recent years, however, fungi have emerged as a promising alternative system to address such questions. Key to their ecological success, fungi present a broad and diverse range of phenotypic traits. Fungal cells can adopt many different shapes, often within a single species, providing them with great adaptive potential. Fungal cellular organizations span from unicellular forms to complex, macroscopic multicellularity, with multiple transitions to higher or lower levels of cellular complexity occurring throughout the evolutionary history of fungi. Similarly, fungal genomes are very diverse in their architecture. Deep changes in genome organization can occur very quickly, and these phenomena are known to mediate rapid adaptations to environmental changes. Finally, the biochemical complexity of fungi is huge, particularly with regard to their secondary metabolites, chemical products that mediate many aspects of fungal biology, including ecological interactions. Herein, we explore how the interplay of these cellular, genomic and metabolic traits mediates the emergence of complex phenotypes, and how this complexity is shaped throughout the evolutionary history of Fungi.     

Biotechnological production of bio-based long-chain dicarboxylic acids with oleogenious yeasts

Long-chain α,ω-dicarboxylic acids (DCAs) are versatile chemical intermediates of industrial importance used as building blocks for the production of polymers, lubricants, or adhesives. The majority of industrial long-chain DCAs is produced from petro-chemical resources. An alternative is their biotechnological production from renewable materials like plant oil fatty acids by microbial fermentation using oleogenious yeasts. Oleogenious yeasts are natural long-chain DCA producers, which have to be genetically engineered for high-yield DCA production. Although, some commercialized fermentation processes using engineered yeasts are reported, bio-based long-chain DCAs are still far from being a mass product. Further progress in bioprocess engineering and rational strain design is necessary to advance their further commercialization. The present article reviews the basic strategies, as well as novel approaches in the strain design of oleogenious yeasts, such as the combination of traditional metabolic engineering with system biology strategies for high-yield long-chain DCA production. Therefore a detailed overview of the involved metabolic processes for the biochemical long-chain DCA synthesis is given.     

Integrating intracellular dynamics using CompuCell3D and Bionetsolver: applications to multiscale modelling of cancer cell growth and invasion

In this paper we present a multiscale, individual-based simulation environment that integrates CompuCell3D for lattice-based modelling on the cellular level and Bionetsolver for intracellular modelling. CompuCell3D or CC3D provides an implementation of the lattice-based Cellular Potts Model or CPM (also known as the Glazier-Graner-Hogeweg or GGH model) and a Monte Carlo method based on the metropolis algorithm for system evolution. The integration of CC3D for cellular systems with Bionetsolver for subcellular systems enables us to develop a multiscale mathematical model and to study the evolution of cell behaviour due to the dynamics inside of the cells, capturing aspects of cell behaviour and interaction that is not possible using continuum approaches. We then apply this multiscale modelling technique to a model of cancer growth and invasion, based on a previously published model of Ramis-Conde et al. (2008) where individual cell behaviour is driven by a molecular network describing the dynamics of E-cadherin and β-catenin. In this model, which we refer to as the centre-based model, an alternative individual-based modelling technique was used, namely, a lattice-free approach. In many respects, the GGH or CPM methodology and the approach of the centre-based model have the same overall goal, that is to mimic behaviours and interactions of biological cells. Although the mathematical foundations and computational implementations of the two approaches are very different, the results of the presented simulations are compatible with each other, suggesting that by using individual-based approaches we can formulate a natural way of describing complex multi-cell, multiscale models. The ability to easily reproduce results of one modelling approach using an alternative approach is also essential from a model cross-validation standpoint and also helps to identify any modelling artefacts specific to a given computational approach.     

Probabilistic discovery of overlapping cellular processes and their regulation.

In this paper, we explore modeling overlapping biological processes. We discuss a probabilistic model of overlapping biological processes, gene membership in those processes, and an addition to that model that identifies regulatory mechanisms controlling process activation. A key feature of our approach is that we allow genes to participate in multiple processes, thus providing a more biologically plausible model for the process of gene regulation. We present algorithms to learn each model automatically from data, using only genomewide measurements of gene expression as input. We compare our results to those obtained by other approaches and show that significant benefits can be gained by modeling both the organization of genes into overlapping cellular processes and the regulatory programs of these processes. Moreover, our method successfully grouped genes known to function together, recovered many regulatory relationships that are known in the literature, and suggested novel hypotheses regarding the regulatory role of previously uncharacterized proteins.     

Computation of steady-state probability distributions in stochastic models of cellular networks

Cellular processes are "noisy". In each cell, concentrations of molecules are subject to random fluctuations due to the small numbers of these molecules and to environmental perturbations. While noise varies with time, it is often measured at steady state, for example by flow cytometry. When interrogating aspects of a cellular network by such steady-state measurements of network components, a key need is to develop efficient methods to simulate and compute these distributions. We describe innovations in stochastic modeling coupled with approaches to this computational challenge: first, an approach to modeling intrinsic noise via solution of the chemical master equation, and second, a convolution technique to account for contributions of extrinsic noise. We show how these techniques can be combined in a streamlined procedure for evaluation of different sources of variability in a biochemical network. Evaluation and illustrations are given in analysis of two well-characterized synthetic gene circuits, as well as a signaling network underlying the mammalian cell cycle entry.     

Quantifying the roles of space and stochasticity in computer simulations for cell biology and cellular biochemistry

Most of the fascinating phenomena studied in cell biology emerge from interactions among highly organized multimolecular structures embedded into complex and frequently dynamic cellular morphologies. For the exploration of such systems, computer simulation has proved to be an invaluable tool, and many researchers in this field have developed sophisticated computational models for application to specific cell biological questions. However, it is often difficult to reconcile conflicting computational results that use different approaches to describe the same phenomenon. To address this issue systematically, we have defined a series of computational test cases ranging from very simple to moderately complex, varying key features of dimensionality, reaction type, reaction speed, crowding, and cell size. We then quantified how explicit spatial and/or stochastic implementations alter outcomes, even when all methods use the same reaction network, rates, and concentrations. For simple cases, we generally find minor differences in solutions of the same problem. However, we observe increasing discordance as the effects of localization, dimensionality reduction, and irreversible enzymatic reactions are combined. We discuss the strengths and limitations of commonly used computational approaches for exploring cell biological questions and provide a framework for decision making by researchers developing new models. As computational power and speed continue to increase at a remarkable rate, the dream of a fully comprehensive computational model of a living cell may be drawing closer to reality, but our analysis demonstrates that it will be crucial to evaluate the accuracy of such models critically and systematically.     

辐射传输模型多尺度反演植被理化参数研究进展

植被是生态系统最重要的组成成分之一,许多与植被有关的物质能量交换过程都与植被的理化参数密切相关,因此定量估算植被的理化参数含量对监测植被生长状况、森林火灾预警以及研究全球碳氮循环过程等都具有重要意义.在众多定量反演植被理化参数的方法中,基于数学、物理学以及生物学的基本理论建立起来的辐射传输模型受到越来越多的关注.辐射传输模型描述了植被与入射辐射之间的相互作用过程和特征,相对于传统的经验/半经验方法,辐射传输模型物理意义明确,具有稳定性和可移植性强的特点.在分析国内外最新相关研究的基础上,首先从植被叶片、冠层和像元3个不同的尺度阐述反演植被理化参数的辐射传输模型.叶片尺度上主要介绍PROSPECT模型和LIBERTY模型;冠层尺度上主要介绍SAIL冠层辐射传输模型以及PROSPECT与SAIL耦合的PROSAIL叶片-冠层辐射传输模型;像元尺度的植被理化参数反演目前主要采用冠层尺度的辐射传输模型.其次,分析尺度变化下植被理化参数遥感反演所面临的主要问题,如不同尺度下模型参数敏感性的变化、辐射传输模型的选取以及混合像元的影响等.最后,总结展望植被理化参数反演多模型与多种数据源相互结合的研究趋势,以及将来具有高空间分辨率的高光谱遥感卫星升空后所带来的发展前景.     

Signal processing through a generalized module of adaptation and spatial sensing

Signal transduction in many cellular processes is accompanied by the feature of adaptation, which allows certain key signalling components to respond to temporal and/or spatial variation of external signals, independent of the absolute value of the signal. We extend and formulate a more general module which accounts for robust temporal adaptation and spatial response. In this setting, we examine various aspects of spatial and temporal signalling, as well as the signalling consequences and restrictions imposed by virtue of adaptation. This module is able to exhibit a variety of behaviour in response to temporal, spatial and spatio-temporal inputs. We carefully examine the roles of various parameters in this module and how they affect signal processing and propagation. Overall, we demonstrate how a simple module can account for a range downstream responses to a variety of input signals, and how elucidating the downstream response of many cellular components in systems with such adaptive signalling can be consequently very non-trivial.     

An integrated model of epidermal growth factor receptor trafficking and signal transduction

Endocytic trafficking of many types of receptors can have profound effects on subsequent signaling events. Quantitative models of these processes, however, have usually considered trafficking and signaling independently. Here, we present an integrated model of both the trafficking and signaling pathway of the epidermal growth factor receptor (EGFR) using a probability weighted-dynamic Monte Carlo simulation. Our model consists of hundreds of distinct endocytic compartments and approximately 13,000 reactions/events that occur over a broad spatio-temporal range. By using a realistic multicompartment model, we can investigate the distribution of the receptors among cellular compartments as well as their potential signal transduction characteristics. Our new model also allows the incorporation of physiochemical aspects of ligand-receptor interactions, such as pH-dependent binding in different endosomal compartments. To determine the utility of this approach, we simulated the differential activation of the EGFR by two of its ligands, epidermal growth factor (EGF) and transforming growth factor-alpha (TGF-alpha). Our simulations predict that when EGFR is activated with TGF-alpha, receptor activation is biased toward the cell surface whereas EGF produces a signaling bias toward the endosomal compartment. Experiments confirm these predictions from our model and simulations. Our model accurately predicts the kinetics and extent of receptor downregulation induced by either EGF or TGF-alpha. Our results suggest that receptor trafficking controls the compartmental bias of signal transduction, rather than simply modulating signal magnitude. Our model provides a new approach to evaluating the complex effect of receptor trafficking on signal transduction. Importantly, the stochastic and compartmental nature of the simulation allows these models to be directly tested by high-throughput approaches, such as quantitative image analysis.     

Modeling the topological organization of cellular processes

The cell as a dynamical system presents the characteristics of having a dynamical structure. That is, the exact phase space of the system cannot be fixed before the evolution and integrative cell models must state the evolution of the structure jointly with the evolution of the cell state. This kind of dynamical systems is very challenging to model and simulate. New programming concepts must be developed to ease their modeling and simulation. In this context, the goal of the MGS project is to develop an experimental programming language dedicated to the simulation of this kind of systems. MGS proposes a unified view on several computational mechanisms (CHAM, Lindenmayer systems, Paun systems, cellular automata) enabling the specification of spatially localized computations on heterogeneous entities. The evolution of a dynamical structure is handled through the concept of transformation which relies on the topological organization of the system components. An example based on the modeling of spatially distributed biochemical networks is used to illustrate how these notions can be used to model the spatial and temporal organization of intracellular processes.     

Constrained proteome allocation affects coexistence in models of competitive microbial communities

Microbial communities are ubiquitous and play crucial roles in many natural processes. Despite their importance for the environment, industry and human health, there are still many aspects of microbial community dynamics that we do not understand quantitatively. Recent experiments have shown that the structure and composition of microbial communities are intertwined with the metabolism of the species that inhabit them, suggesting that properties at the intracellular level such as the allocation of cellular proteomic resources must be taken into account when describing microbial communities with a population dynamics approach. In this work, we reconsider one of the theoretical frameworks most commonly used to model population dynamics in competitive ecosystems, MacArthur’s consumer-resource model, in light of experimental evidence showing how proteome allocation affects microbial growth. This new framework allows us to describe community dynamics at an intermediate level of complexity between classical consumer-resource models and biochemical models of microbial metabolism, accounting for temporally-varying proteome allocation subject to constraints on growth and protein synthesis in the presence of multiple resources, while preserving analytical insight into the dynamics of the system. We first show with a simple experiment that proteome allocation needs to be accounted for to properly understand the dynamics of even the simplest microbial community, i.e. two bacterial strains competing for one common resource. Then, we study our consumer-proteome-resource model analytically and numerically to determine the conditions that allow multiple species to coexist in systems with arbitrary numbers of species and resources.Subject terms: Biodiversity, Microbial ecology, Microbial ecology, Bacterial physiology     

Systematic approaches to dissect biological processes in stem cells by image-based screening

High-throughput RNAi or small molecule screens have proven to be powerful methodologies for the systematic dissection of cellular processes. In model organisms and cell lines, large-scale screens have identified key components of many cellular pathways and helped to identify novel targets in disease-relevant pathways. Image-based high-content screening has become an increasingly important tool in high-throughput screening, enabling changes in phenotype characteristics, such as cell morphology and cell differentiation, to be monitored. In this review, we discuss the use of image-based screening approaches to explore the behavior of adult, embryonic, and induced pluripotent stem cells. First, we review how current pluripotency and differentiation assays can be adapted to high-throughput formats. We then describe general aspects of image-based screening of cells and present an outlook on challenges for screening stem cells.     

Molecular and cellular barriers limiting the effectiveness of antisense oligonucleotides

Antisense oligonucleotides present a powerful means to inhibit expression of specific genes, but their effectiveness is limited by factors including cellular delivery, biochemical attack, and poor binding to target. We have developed a systems model of the processes required for an antisense oligonucleotide to enter, gain access to its target mRNA, and exert activity in a cell. The model accurately mimics observed trends in antisense effectiveness with the stability of the oligonucleotide backbone and with the affinity/kinetics of binding to the mRNA over the time course of inhibition. By varying the model parameters within the physically realizable range, we note that the major molecular and cellular barriers to antisense effectiveness are intracellular trafficking, oligonucleotide-mRNA binding rate, and nuclease degradation of oligonucleotides, with a weaker dependence on total cellular uptake than might be expected. Furthermore, the model may serve as a predictive tool to design and test strategies for the cellular use of antisense oligonucleotides. The use of integrated mathematical modeling can play a significant role in the development of antisense and related technologies.     

Morphogenetic and cellular movements that shape the mouse cerebellum; insights from genetic fate mapping

We used the cerebellum as a model to study the morphogenetic and cellular processes underlying the formation of elaborate brain structures from a simple neural tube, using an inducible genetic fate mapping approach in mouse. We demonstrate how a 90 degrees rotation between embryonic days 9 and 12 converts the rostral-caudal axis of dorsal rhombomere 1 into the medial-lateral axis of the wing-like bilateral cerebellar primordium. With the appropriate use of promoters, we marked specific medial-lateral domains of the cerebellar primordium and derived a positional fate map of the murine cerebellum. We show that the adult medial cerebellum is produced by expansion, rather than fusion, of the thin medial primordium. Furthermore, ventricular-derived cells maintain their original medial-lateral coordinates into the adult, whereas rhombic lip-derived granule cells undergo lateral to medial posterior transverse migrations during foliation. Thus, we show that progressive changes in the axes of the cerebellum underlie its genesis.