Emergent stem cell homeostasis in the C. elegans germline is revealed by hybrid modeling

RAP-2 and CNH-MAP4 Kinase MIG-15 confer resistance in bystander epithelium to cell-fate transformation by excess Ras or Notch activity

Induction of cell fates by growth factors impacts many facets of developmental biology and disease. LIN-3/EGF induces the equipotent vulval precursor cells (VPCs) in Caenorhabditis elegans to assume the 3˚-3˚-2˚-1˚-2˚-3˚ pattern of cell fates. 1˚ and 2˚ cells become specialized epithelia and undergo stereotyped series of cell divisions to form the vulva. Conversely, 3˚ cells are relatively quiescent and nonspecialized; they divide once and fuse with the surrounding epithelium. 3˚ cells have thus been characterized as passive, uninduced, or ground state. Based on our previous studies, we hypothesized that a 3˚-promoting program would confer resistance to cell fate-transformation by inappropriately activated 1˚ and 2˚ fate-promoting LET-60/Ras and LIN-12/Notch, respectively. Deficient MIG-15/CNH-MAP4 Kinase meets the expectations of genetic interactions for a 3˚-promoting protein. Moreover, endogenous MIG-15 is required for expression of a fluorescent biomarker of 3˚ cell fate, is expressed in VPCs, and functions cell autonomously in VPCs. The Ras family small GTPase RAP-2, orthologs of which activate orthologs of MIG-15 in other systems, emulates these functions of MIG-15. However, gain of RAP-2 function has no effect on patterning, suggesting its activity is constitutive in VPCs. The 3˚ biomarker is expressed independently of the AC, raising questions about the cellular origin of 3˚-promoting activity. Activated LET-60/Ras and LIN-12/Notch repress expression of the 3˚ biomarker, suggesting that the 3˚-promoting program is both antagonized by as well as antagonizes 1˚- and 2˚- promoting programs. This study provides insight into developmental properties of cells historically considered to be nonresponding to growth factor signals.

Directed cell invasion and asymmetric adhesion drive tissue elongation and turning in C. elegans gonad morphogenesis

Development of the C. elegans gonad has long been studied as a model of organogenesis driven by collective cell migration. A somatic cell named the distal tip cell (DTC) is thought to serve as the leader of following germ cells; yet, the mechanism for DTC propulsion and maneuvering remains elusive. Here, we demonstrate that the DTC is not self-propelled but rather is pushed by the proliferating germ cells. Proliferative pressure pushes the DTC forward, against the resistance of the basement membrane in front. The DTC locally secretes metalloproteases that degrade the impeding membrane, resulting in gonad elongation. Turning of the gonad is achieved by polarized DTC-matrix adhesions. The asymmetrical traction results in a bending moment on the DTC. Src and Cdc42 regulate integrin adhesion polarity, whereas an external netrin signal determines DTC orientation. Our findings challenge the current view of DTC migration and offer a distinct framework to understand organogenesis.

Clonal dominance in excitable cell networks

Clonal dominance arises when the descendants (clones) of one or a few founder cells contribute disproportionally to the final structure during collective growth [1-8]. In contexts such as bacterial growth, tumorigenesis, and stem cell reprogramming [2-4], this phenomenon is often attributed to pre-existing propensities for dominance, while in stem cell homeostasis, neutral drift dynamics are invoked [5,6]. The mechanistic origin of clonal dominance during development, where it is increasingly documented [1,6-8], is less understood. Here, we investigate this phenomenon in the Drosophila melanogaster follicle epithelium, a system in which the joint growth dynamics of cell lineage trees can be reconstructed. We demonstrate that clonal dominance can emerge spontaneously, in the absence of pre-existing biases, as a collective property of evolving excitable networks through coupling of divisions among connected cells. Similar mechanisms have been identified in forest fires and evolving opinion networks [9-11]; we show that the spatial coupling of excitable units explains a critical feature of the development of the organism, with implications for tissue organization and dynamics [1,12,13].

Executable cancer models: successes and challenges

Making decisions on how best to treat cancer patients requires the integration of different data sets, including genomic profiles, tumour histopathology, radiological images, proteomic analysis and more. This wealth of biological information calls for novel strategies to integrate such information in a meaningful, predictive and experimentally verifiable way. In this Perspective we explain how executable computational models meet this need. Such models provide a means for comprehensive data integration, can be experimentally validated, are readily interpreted both biologically and clinically, and have the potential to predict effective therapies for different cancer types and subtypes. We explain what executable models are and how they can be used to represent the dynamic biological behaviours inherent in cancer, and demonstrate how such models, when coupled with automated reasoning, facilitate our understanding of the mechanisms by which oncogenic signalling pathways regulate tumours. We explore how executable models have impacted the field of cancer research and argue that extending them to represent a tumour in a specific patient (that is, an avatar) will pave the way for improved personalized treatments and precision medicine. Finally, we highlight some of the ongoing challenges in developing executable models and stress that effective cross-disciplinary efforts are key to forward progress in the field.

Constructing and Analyzing Computational Models of Cell Signaling with BioModelAnalyzer

BioModelAnalyzer (BMA) is an open-source graphical tool for the development of executable models of protein and gene networks within cells. Based upon the Qualitative Networks formalism, the user can rapidly construct large networks, either manually or by connecting motifs selected from a built-in library. After the appropriate functions for each variable are defined, the user has access to three analysis engines to test the model. In addition to standard simulation tools, BMA includes an interface to the stability-testing algorithm and to a graphical Linear Temporal Logic (LTL) editor and analysis tool. Alongside this, we have developed a novel ChatBot to aid users constructing LTL queries and to explain the interface and run through tutorials. Here we present worked examples of model construction and testing via the interface. As an initial example, we discuss fate decisions in Dictyostelium discoidum and cAMP signaling. We go on to describe the workflow leading to the construction of a published model of the germline of C. elegans. Finally, we demonstrate how to construct simple models from the built-in network motif library. © 2020 by John Wiley & Sons, Inc. Basic Protocol 1: Modeling the signaling network of Dictyostelium discoidum Basic Protocol 2: Modeling the germline progression of Caenorhabditis elegans Basic Protocol 3: Constructing a model of the cell cycle using motifs.

Biology of the Caenorhabditis elegans Germline Stem Cell System

Stem cell systems regulate tissue development and maintenance. The germline stem cell system is essential for animal reproduction, controlling both the timing and number of progeny through its influence on gamete production. In this review, we first draw general comparisons to stem cell systems in other organisms, and then present our current understanding of the germline stem cell system in Caenorhabditis elegans In contrast to stereotypic somatic development and cell number stasis of adult somatic cells in C. elegans, the germline stem cell system has a variable division pattern, and the system differs between larval development, early adult peak reproduction and age-related decline. We discuss the cell and developmental biology of the stem cell system and the Notch regulated genetic network that controls the key decision between the stem cell fate and meiotic development, as it occurs under optimal laboratory conditions in adult and larval stages. We then discuss alterations of the stem cell system in response to environmental perturbations and aging. A recurring distinction is between processes that control stem cell fate and those that control cell cycle regulation. C. elegans is a powerful model for understanding germline stem cells and stem cell biology.

Computer simulations of the signalling network in FLT3 +-acute myeloid leukaemia - indications for an optimal dosage of inhibitors against FLT3 and CDK6

Background

Mutations in the FMS-like tyrosine kinase 3 (FLT3) are associated with uncontrolled cellular functions that contribute to the development of acute myeloid leukaemia (AML). We performed computer simulations of the FLT3-dependent signalling network in order to study the pathways that are involved in AML development and resistance to targeted therapies.

Results

Analysis of the simulations revealed the presence of alternative pathways through phosphoinositide 3 kinase (PI3K) and SH2-containing sequence proteins (SHC), that could overcome inhibition of FLT3. Inhibition of cyclin dependent kinase 6 (CDK6), a related molecular target, was also tested in the simulation but was not found to yield sufficient benefits alone.

Conclusions

The PI3K pathway provided a basis for resistance to treatments. Alternative signalling pathways could not, however, restore cancer growth signals (proliferation and loss of apoptosis) to the same levels as prior to treatment, which may explain why FLT3 resistance mutations are the most common resistance mechanism. Finally, sensitivity analysis suggested the existence of optimal doses of FLT3 and CDK6 inhibitors in terms of efficacy and toxicity.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2145-y) contains supplementary material, which is available to authorized users.

A toolbox for discrete modelling of cell signalling dynamics

We present a library of network motifs for the development of complex and realistic biological network models using the BioModelAnalyzer, and demonstrate their wider value by using them to construct a model of the cell cycle.

How computational models contribute to our understanding of the germ line

Computational models are an invaluable tool in modern biology. They provide a framework within which to summarize existing knowledge, enable competing hypotheses to be compared qualitatively and quantitatively, and to facilitate the interpretation of complex data. Moreover, models allow questions to be investigated that are difficult to approach experimentally. Theories can be tested in context, identifying the gaps in our understanding and potentially leading to new hypotheses. Models can be developed on a variety of scales and with different levels of mechanistic detail, depending on the available data, the biological questions of interest, and the available mathematical and computational tools. The goal of this review is to provide a broad picture of how modeling has been applied to reproductive biology. Specifically, we look at four uses of modeling: (i) comparing hypotheses; (ii) interpreting data; (iii) exploring experimentally challenging questions; and (iv) hypothesis evaluation and generation. We present examples of each of these applications in reproductive biology, drawing from a range of organisms-including Drosophila, Caenorhabditis elegans, mouse, and humans. We aim to describe the data and techniques used to construct each model, and to highlight the benefits of modeling to the field, as complementary to experimental work. Mol. Reprod. Dev. 83: 944-957, 2016 © 2016 Wiley Periodicals, Inc.

A Transport Model for Estimating the Time Course of ERK Activation in the C. elegans Germline

The Caenorhabditis elegans germline is a well-studied model system for investigating the control of cell fate by signaling pathways. Cell signals at the distal tip of the germline promote cell proliferation; just before the loop, signals couple cell maturation to organism-level nutrient status; at the proximal end of the germline, signals coordinate oocyte maturation and fertilization in the presence of sperm. The latter two events require dual phosphorylation and activation of ERK, the effector molecule of the Ras/MAPK cascade. In C. elegans, ERK is known as MPK-1. At this point, none of today's methods for real-time monitoring of dually phosphorylated MPK-1 are working in the germline. Consequently, quantitative understanding of the MPK-1-dependent processes during germline development is limited. Here, we make a step toward advancing this understanding using a model-based framework that reconstructs the time course of MPK-1 activation from a snapshot of a fixed germline. Our approach builds on a number of recent studies for estimating temporal dynamics from fixed organisms, but takes advantage of the anatomy of the germline to simplify the analysis. Our model predicts that the MPK-1 signal turns on ∼30 h into germ cell progression and peaks ∼7 h later.

Multi-class and multi-scale models of complex biological phenomena

Computational modeling has significantly impacted our ability to analyze vast (and exponentially increasing) quantities of experimental data for a variety of applications, such as drug discovery and disease forecasting. Single-scale, single-class models persist as the most common group of models, but biological complexity often demands more sophisticated approaches. This review surveys modeling approaches that are multi-class (incorporating multiple model types) and/or multi-scale (accounting for multiple spatial or temporal scales) and describes how these models, and combinations thereof, should be used within the context of the problem statement. We end by highlighting agent-based models as an intuitive, modular, and flexible framework within which multi-scale and multi-class models can be implemented.

Mechano-logical model of C. elegans germ line suggests feedback on the cell cycle

The Caenorhabditis elegans germ line is an outstanding model system in which to study the control of cell division and differentiation. Although many of the molecules that regulate germ cell proliferation and fate decisions have been identified, how these signals interact with cellular dynamics and physical forces within the gonad remains poorly understood. We therefore developed a dynamic, 3D in silico model of the C. elegans germ line, incorporating both the mechanical interactions between cells and the decision-making processes within cells. Our model successfully reproduces key features of the germ line during development and adulthood, including a reasonable ovulation rate, correct sperm count, and appropriate organization of the germ line into stably maintained zones. The model highlights a previously overlooked way in which germ cell pressure may influence gonadogenesis, and also predicts that adult germ cells might be subject to mechanical feedback on the cell cycle akin to contact inhibition. We provide experimental data consistent with the latter hypothesis. Finally, we present cell trajectories and ancestry recorded over the course of a simulation. The novel approaches and software described here link mechanics and cellular decision-making, and are applicable to modeling other developmental and stem cell systems.