Biology subjects

Rodriguez-Paton, A.

Publications and source records attributed to Rodriguez-Paton, A..

5 recordsLinked to original sources

bioRxiv2019

The multicellular incoherent feedforward loop motif generates spatial patterns

The multicellular incoherent feedforward loop (mIFFL) is an extension of the traditional intracellular IFFL gene motif where the interacting nodes no longer need to be genes inside the same cell but can be spatially distributed in different cells. We studied for the first time the spatial computing abilities of these mIFFL through in silico simulations done with individual-based models (run in Morpheus and GRO software). We observed that: 1) a genetic circuit working as a mIFFL can behaves as an edge detector of the border of an infection, and 2) a mIFFL can be the inner mechanism generating the complex 7 stripe pattern of eve in D. melanogaster embryogenesis. So, in this work, we show that multicellular IFFL architectures can produce spatial patterns and are a promising spatial computing motif that deserves to be incorporated into the toolbox of pattern generation and multicellular coordination mechanisms. This study opens several future lines of research: multi-agent IFFL applied in ecology as a tool to predict spatial position of interacting animals or in distributed robotics.

synthetic biology↗

bioRxiv2018

Dynamical Task Switching in Cellular Computers

We present a scheme for implementing a version of task switching in engineered bacteria, based on the manipulation of plasmid copy numbers. Our method allows for the embeddingof multiple computations in a cellular population, whilst minimising resource usage inefficiency. We describe the results of computational simulations of our model, and discuss the potential for future work in this area.

synthetic biology↗

bioRxiv2018

Deriving general conditions and mechanisms for division of labor using the cell-based simulator gro

Division of Labor can occur as a consequence of a major evolutionary transition such as multicellularity but is also found in societies of similar individuals like microbes. It has been defined as a process that occurs when cooperating individuals specialize to carry out specific tasks in a distributed manner. This paper analyzes the conditions for division of labor to emerge as a beneficial evolutionary solution and proposes two novel mechanisms for this process to emerge as a consequence of cell communication in an isogenic group of cells. The study is conducted by means of the cell-based model gro that simulates the growth and interaction of cells in a two-dimensional bacterial colony. When the labor is social, like the production of a molecule that is publicly shared, simulation results indicate that division of labor provides higher fitness than individual labor if the benefits of specialization are accelerating. Two genetic networks that generate consensual and reversible specialization are presented and characterized. In the proposed mechanisms, cells self-organize through the exchange of certain molecules and coordinate behaviors at the local level without the requirements of any fitness benefits. In addition, the proposed regulatory mechanisms are able to create de novo patterns unprecedented to this date that can scale with size.

evolutionary biology↗

bioRxiv2016

A new improved and extended version of the multicell bacterial simulator gro

gro is a cell programming language developed in Klavins Lab for simulating colony growth and cell-cell communication. It is used as a synthetic biology prototyping tool for simulating multicellular biocircuits. In this work, we present several extensions made to gro that improve the performance of the simulator, make it easier to use and provide new functionalities. The new version of gro is between one and two orders of magnitude faster than the original version. It is able to grow microbial colonies with up to 105 cells in less than 20 minutes. A new library, CellEngine, accelerates the resolution of spatial physical interactions between growing and dividing cells by implementing a new shoving algorithm. A genetic library, CellPro, based on Probabilistic Timed Automata, simulates gene expression dynamics using simplified and easy to compute digital proteins. We also propose a more convenient language specification layer, ProSpec, based on the idea that proteins drive cell behavior. CellNutrient, another library, implements Monod-based growth and nutrient uptake functionalities. The intercellular signaling management was improved and extended in a library called CellSignals. Finally, bacterial conjugation, another local cell-cell communication process, was added to the simulator. To show the versatility and potential outreach of this version of gro, we provide studies and novel examples ranging from synthetic biology to evolutionary microbiology. We believe that the upgrades implemented for gro have made it into a powerful and fast prototyping tool capable of simulating a large variety of systems and synthetic biology designs.

synthetic biology↗

bioRxiv2016

BioBlocks: Programming protocols in biology made easier

The methods to execute biological experiments are evolving. Affordable fluid handling robots and on-demand biology enterprises are making automating entire experiments a reality. Automation offers the benefit of high-throughput experimentation, rapid prototyping and improved reproducibility of results. However, learning to automate and codify experiments is a difficult task as it requires programming expertise. Here, we present a web-based visual development environment called BioBlocks for describing experimental protocols in biology. It is based on Googles Blockly and Scratch, and requires little or no experience in computer programming to automate the execution of experiments. The experiments can be specified, saved, modified and shared between multiple users in an easy manner. BioBlocks is open-source and can be customized to execute protocols on local robotic platforms or remotely i.e. in the cloud. It aims to serve as a de facto open standard for programming protocols in Biology.\n\n\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=136 SRC=\"FIGDIR/small/081075_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (51K):\norg.highwire.dtl.DTLVardef@108403corg.highwire.dtl.DTLVardef@b15c17org.highwire.dtl.DTLVardef@14ed6forg.highwire.dtl.DTLVardef@1f046c4_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗