GARMEN: GRN Agent-based Reaction-Diffusion Modeling Environment

INTRODUCTION This document contains information on how to run GARMEN to simulate peri-gastrulation patterns. GARMEN represents an integration of a gene regulatory network (GRN) embedded in an agent-based model (ABM), which, in turn, is linked with a Reaction Diffusion (RD) model. The files made available here simulate a micropatterned human pluripotent stem cell colony of radius 465 µm (colony size based on in vitro measurements, number of colonies measured = 10, refer to Kaul et al., 2022, https://doi.org/10.1016/j.stemcr.2022.10.004). Patterns in the simulations emerge as a result of dynamic interplay between the GRNs and signal gradients via agent (Kaul and Ventikos, 2015, https://doi.org/10.1093/bib/bbt077) activity. Model predictions were validated in vitro via signal-based and transcriptomic perturbations (Kaul et al., 2022, https://doi.org/10.1016/j.stemcr.2022.10.004). GARMEN MODULES  FLAME: The virtual nucleus and virtual cell The virtual nucleus and virtual cell modules were simulated using the Flexible Large-scale Agent-based Modelling Environment (FLAME). Visit www.flame.ac.uk for details regarding installation and usage. To run a model in FLAME, one needs to create an XMML scheme describing the structure of the model files (this is the uploaded GARMEN.xml file). Functions or rule-sets that govern agent interactions are written in C (this is the uploaded final_functions.c file). Lastly, an XMML file containing the initial conditions is required to run the model (the 0.xml file). FLAME Architecture  FLAME works by parsing a model XMML definition into simulation program code via the xparser (xparser version used in Kaul et al., 2022, https://doi.org/10.1016/j.stemcr.2022.10.004 has been uploaded). Subsequently, the message board library (libmboard) is required to compile the model (libmboard version used in this study has been provided here). The libmboard MUST be in the same folder as the model. FLAME Contents  The following files/folders have been provided: - Xparser (v 0.16.2): To parse the model (in our case the GARMEN.xml file), go in the xparser folder and use the following command line xparser> xparser ../model/GARMEN.xml - Libmboard (v 0.2.1): Copy this folder in the model folder. The model will NOT compile, if the libmboard folder is not in the same location as the model - The model files: They contain GARMEN.xml This file contains the model structure. It describes the environment that includes the width of the simulation environment, two user-defined datatypes ‘gen_net’ and ‘chem_sensing’, and the reference to the agent functions file. User-defined Datatypes: The datatype ‘gen_net’ enabled the incorporation of the GRN in each agent, whereas ‘chem_sensing’ enabled the agents to sense signal concentration around them. Whereas gen_net allowed us to define the name of the genes (OCT4, SOX2, etc) and their activation status (OFF, LOW, or HIGH), chem_sensing allowed us to define the signal (BMP4, WNT3, etc) and their concentrations. X-machines: FLAME models agents as communicating X-machines. Here, we defined only one agent (or x-machine): hpsc (human pluripotent stem cell) that had characteristics like id, type (pluripotent, ectodermal, etc), gene activation status (gen_net), signal concentration (chem_sensing), spatial coordinates, number of neighbours (monolayer and edge bonds), and cell cycle. This file also lists the various functions these agents will perform as well as the messages they can exchange with each other.   final_functions.c This file details the agent functions that were specified in the .xml file. It contains the source code for how agent functions will be implemented. Refer to annotations in the .c file for more details. Folder ‘its’ containing the initial file 0.xml This initial file contains information regarding the number of agents at the starting point of the simulation and their id, type, and spatial coordinates, etc. The spatial coordinates of the agents were set randomly.  Multiple .txt files containing concentrations of BMP4, NOGGIN, WNT3, and DKK at various time points (12 h, 24 h, 36 h, and 48 h) These files contain the spatial concentrations of BMP4 & NOGGIN (12 h) and WNT3 & DKK (24 h, 36 h, 48 h) that were computed in COMSOL. When the model was simulated for the first time, these files were generated in COMSOL after every 30 iterations (equivalent to 12 h of physical time) based on the agent phenotype and position by freezing the agent-based model (refer to Kaul et al., 2022, https://doi.org/10.1016/j.stemcr.2022.10.004, for details). However, we are providing these files so that the user can execute the code fully. These .txt files MUST stay in the same folder as the model. Other files  In this folder, we are providing WNT3/DKK concentration profiles equivalent to 12 h when differentiation is induced via WNT3 alone (i.e. the BMP4− simulation). This file (12h_wnt3_dkk_data.txt) MUST stay in the same folder as the model when simulating patterning as triggered by WNT3 (in which case do not use the .txt files specified in the preceding bullet point). Relevant Command Lines To parse the GARMEN.xml file: Go to the xparser folder (wherever you decide to store it), xparser> xparser ../model/GARMEN.xml To compile the files: Go to the model folder, model_folder> make To simulate the model: Stay in the model folder, model_folder> main 180 its\0.xml This command will simulate the model for 180 iterations (equivalent to 72 hours of physical time). This includes the first 60 iterations where agents are undergoing proliferation (equivalent to 24 hours pre-differentiation) followed by 120 iterations capturing differentiation and pattern-emergence (equivalent to 48 hours following induction of differentiation). All output files will be stored in the folder entitled ‘its’. Additional requirements You will need to install MinGW and change the Environment Settings to add MinGW to the path. Refer to www.flame.ac.uk/docs/install.html for instructions. COMSOL: The virtual microenvironment We simulated the signal gradients via the RD paradigm using Gierer-Meinhardt reaction-kinetics (Gierer and Meinhardt, 1972, https://doi.org/10.1007/BF00289234) in COMSOL Multiphysics v5.2a (COMSOL, MA, USA). We used the Coefficient Form PDEs module to simulate the RD equations. In the file, entitled the-virtual-microenvironment-nonDimensional.mph, Analytics 1, 2, 5, and 6 represent the non-dimensional equations that were solved. Refer to Equation 9 in Supplemental Experimental Procedures (Kaul et al., 2022, https://doi.org/10.1016/j.stemcr.2022.10.004). D_NOG, D_BMP, D_Wnt, and D_Dkk represent the effective diffusivities of NOGGIN, BMP4, WNT3, and DKK, respectively. They were quantified such that D_BMP:D_NOG = 0.4 and D_Wnt:D_Dkk = 0.36. Refer to Supplemental Experimental Procedures (Kaul et al., 2022, https://doi.org/10.1016/j.stemcr.2022.10.004) for details. We constructed the non-dimensional virtual twin of a single well of a microtiter plate containing multiple human pluripotent stem cell colonies. We used tetrahedral meshes of size 20 µm inside the cellular colonies and 30 µm outside to discretise the geometry. Initial BMP4, NOGGIN, and DKK concentrations were set randomly with a uniform distribution. The initial concentration of WNT3 was tied to that of BMP4 (for the cases where differentiation was initiated via BMP4). We employed zero-flux boundary conditions. Following simulations, we exported data from COMSOL, assimilated it in a .txt format, and fed it to the agent-based model (relevant files have been uploaded). Data flow between the ABM and COMSOL modules was manual. For details, refer to Kaul et al., 2022, https://doi.org/10.1016/j.stemcr.2022.10.004.  COMSOL Files  We are providing the file entitled the-virtual-microenvironment-nonDimensional.mph that contains modules to simulate BMP4-NOGGIN and WNT3-DKK dynamics. Model steady state is achieved equivalent to 12 h of physical time. The geometry created is non-dimensional. SUPPORT Contact Himanshu Kaul at [email protected] for queries related to this model FLAME related queries should be directed to [email protected]</p

Balancing heterogeneous assembly line with multi-skilled human-robot collaboration via Adaptive cooperative co-evolutionary algorithm

In human-centred manufacturing, deploying collaborative robots (cobots) is recognized as a promising strategy to enhance the inclusiveness and resilience of production systems. Despite notable progress, current production scheduling methods for human-robot collaboration (HRC) still fail to adequately accommodate workforce heterogeneity, significantly reducing their adoption and implementation. To address this gap, we introduce a novel model for the Assembly Line Worker Integration and Balancing Problem considering Multi-skilled Human-Robot Collaboration (ALWIBP-mHRC). This model aims to optimize task scheduling between semi-skilled workers and cobots, aiming to maximize productivity and minimize costs. It features a multi-skilled human-robot collaboration (mHRC) task assignment scheme that selects the optimal assembly/collaboration mode from seven scenarios, based on specific task requirements and resource-skill availability, thus maximizing resource-skill complementarity. To tackle the complexities of this problem, we propose an adaptive multi-objective cooperative co-evolutionary algorithm (a-MOCC) that incorporates a sub-problem decomposition and decoding framework tailored for ALWIBP-mHRC, enhanced by an adaptive evolutionary strategy based on Q-learning (Q-Coevolution). Experimental tests demonstrate the superior performance of the proposed method compared to other established metaheuristic algorithms across various instance sizes, underscoring its effectiveness in enhancing the productivity of production systems for semi-skilled workers. The findings are significant for investment decision-making and resource planning, as they highlight the strategic value of integrating cobots in large-scale heterogeneous workforce production. This work underscores the potential of cobots to mitigate skill gaps in assembly systems, laying the groundwork for future research and industrial strategies focused on enhancing productivity, inclusivity, and adaptability in a dynamically changing labour market.</p

Seven recommendations for scientists, universities, and funders to embrace interdisciplinarity: Practical guidelines to enabling interdisciplinarity

Interdisciplinary research is vital for innovation. Here, we consider interdisciplinarity to mean any form of collaboration between researchers that integrates information, data, techniques, concepts, theories and/or perspectives from two or more disciplines to advance fundamental understanding or solve problems that are beyond the scope of a single discipline (Choi and Pak, 2006; National Academy of Sciences et al, 2005). Increasingly, university leaders, funders and politicians have recognised that the most pressing problems facing the world are too complex to be tackled from a single-disciplinary perspective. Despite this significance and general recognition, a recent report suggests that a high share of academic institutions only pay “lip service” to interdisciplinary research and fail to recognise staff for cross-disciplinary working. Crucially, it states that global research hubs, that is, the USA, UK and Australia, are in reality much less focused on interdisciplinarity versus their Asian counterparts as their research continues to orient itself along disciplinary boundaries and thinking. [Opening paragraph]</p