Modular assembly of dynamic models in systems biology

It is widely acknowledged that the construction of large-scale dynamic models in systems biology requires complex modelling problems to be broken up into more manageable pieces. To this end, both modelling and software frameworks are required to enable modular modelling. While there has been consistent progress in the development of software tools to enhance model reusability, there has been a relative lack of consideration for how underlying biophysical principles can be applied to this space. Bond graphs combine the aspects of both modularity and physics-based modelling. In this paper, we argue that bond graphs are compatible with recent developments in modularity and abstraction in systems biology, and are thus a desirable framework for constructing large-scale models. We use two examples to illustrate the utility of bond graphs in this context: a model of a mitogen-activated protein kinase (MAPK) cascade to illustrate the reusability of modules and a model of glycolysis to illustrate the ability to modify the model granularity.

Science Abstract Model Simulation Framework

Meta-researchers increasingly study biases in quantitative study outcomes (effect sizes) that emerge from questionable research practices (QRPs) in designing, running, analyzing, and reporting studies. Here, we introduce an extensible and modular C++ simulation framework called SAM (Science Abstract Model) that enables systematic study of the effects of QRPs and researchers’ degrees of freedom (p-hacking) on a host of outcomes across the different phases of quantitative studies that test hypotheses. SAM achieves this by modular modelling of different entities and processes involved in research, from study designs and inferential criteria, the data collection and analyses, to the submission and acceptance of manuscripts in a journal. We demonstrate the advantages of our approach by reproducing and extending the Bakker, van Dijk, and Wicherts (2012) simulation study that investigated the effects of various p-hacking methods and publication bias on meta-analytic outcomes. We showcase how SAM’s modularity and flexibility makes it possible to easily examine the original study by modifying, adding, or removing different components— e.g., publication bias, different significance levels, or meta-analytic metrics. We focus our illustration on the fundamental question of whether lowering alpha will reduce the biases in the scientific literature.

Modular assembly of dynamic models in systems biology

It is widely acknowledged that the construction of large-scale dynamic models in systems biology requires complex modelling problems to be broken up into more manageable pieces. To this end, both modelling and software frameworks are required to enable modular modelling. While there has been consistent progress in the development of software tools to enhance model reusability, there has been a relative lack of consideration for how underlying biophysical principles can be applied to this space. Bond graphs combine the aspects of both modularity and physics-based modelling. In this paper, we argue that bond graphs are compatible with recent developments in modularity and abstraction in systems biology, and are thus a desirable framework for constructing large-scale models. We use two examples to illustrate the utility of bond graphs in this context: a model of a mitogen-activated protein kinase (MAPK) cascade to illustrate the reusability of modules and a model of glycolysis to illustrate the ability to modify the model granularity.

The impact of modelling decisions in hydrological modelling

<p>Scientific hydrological modellers make multiple decisions during the modelling process, e.g. related to the calibration period and performance metrics. These decisions affect the model results differently. Modelling decisions can refer to several steps in the modelling process. In this project, modelling decisions refer to the decisions made during the whole modelling process, not just the definition of the model structure. Each model output is a hypothesis of the reality; it is an interpretation of the real system underpinned by scientific reasoning and/or expert knowledge. Currently, there is a lack of knowledge and understanding about which modelling decisions are taken and why they are taken. Consequently, the influence of modelling decisions is unknown. Quantifying this influence, which is done in this study, can raise awareness among scientists. This study is based on analysis of interviews with scientific hydrological modellers, thus taking actual practices into account. Different modelling decisions were identified from the interviews, which are subsequently implemented and evaluated in a controlled modelling environment, in our case the modular modelling framework Raven. The variation in the results is analysed to determine which decisions affect the results and how they affect the results. This study pinpoints what aspects are important to consider in studying modelling decisions, and can be an incentive to clarify and improve modelling procedures.</p>