Data_Sheet_2_Identification via Numerical Computation of Transcriptional Determinants of a Cell Phenotype Decision Making.ZIP

Complex cellular processes, such as phenotype decision making, are exceedingly difficult to analyze experimentally, due to the multiple-layer regulation of gene expression and the intercellular variability referred to as biological noise. Moreover, the heterogeneous experimental approaches used to investigate distinct macromolecular species, and their intrinsic differential time-scale dynamics, add further intricacy to the general picture of the physiological phenomenon. In this respect, a computational representation of the cellular functions of interest can be used to extract relevant information, being able to highlight meaningful active markers within the plethora of actors forming an active molecular network. The multiscale power of such an approach can also provide meaningful descriptions for both population and single-cell level events. To validate this paradigm a Boolean and a Markov model were combined to identify, in an objective and user-independent manner, a signature of genes recapitulating epithelial to mesenchymal transition in-vitro. The predictions of the model are in agreement with experimental data and revealed how the expression of specific molecular markers is related to distinct cell behaviors. The presented method strengthens the evidence of a role for computational representation of active molecular networks to gain insight into cellular physiology and as a general approach for integrating in-silico/in-vitro study of complex cell population dynamics to identify their most relevant drivers.</p

Data_Sheet_1_Identification via Numerical Computation of Transcriptional Determinants of a Cell Phenotype Decision Making.PDF

Complex cellular processes, such as phenotype decision making, are exceedingly difficult to analyze experimentally, due to the multiple-layer regulation of gene expression and the intercellular variability referred to as biological noise. Moreover, the heterogeneous experimental approaches used to investigate distinct macromolecular species, and their intrinsic differential time-scale dynamics, add further intricacy to the general picture of the physiological phenomenon. In this respect, a computational representation of the cellular functions of interest can be used to extract relevant information, being able to highlight meaningful active markers within the plethora of actors forming an active molecular network. The multiscale power of such an approach can also provide meaningful descriptions for both population and single-cell level events. To validate this paradigm a Boolean and a Markov model were combined to identify, in an objective and user-independent manner, a signature of genes recapitulating epithelial to mesenchymal transition in-vitro. The predictions of the model are in agreement with experimental data and revealed how the expression of specific molecular markers is related to distinct cell behaviors. The presented method strengthens the evidence of a role for computational representation of active molecular networks to gain insight into cellular physiology and as a general approach for integrating in-silico/in-vitro study of complex cell population dynamics to identify their most relevant drivers.</p

A Synthetic Post-transcriptional Controller To Explore the Modular Design of Gene Circuits

The assembly from modular parts is an efficient approach for creating new devices in Synthetic Biology. In the “bottom-up” designing strategy, modular parts are characterized in advance, and then mathematical modeling is used to predict the outcome of the final device. A prerequisite for bottom-up design is that the biological parts behave in a modular way when assembled together. We designed a new synthetic device for post-transcriptional regulation of gene expression and tested if the outcome of the device could be described from the features of its components. Modular parts showed unpredictable behavior when assembled in different complex circuits. This prevented a modular description of the device that was possible only under specific conditions. Our findings shed doubts into the feasibility of a pure bottom-up approach in synthetic biology, highlighting the urgency for new strategies for the rational design of synthetic devices

DataSheet1_Peripheral blood mononuclear cells contribute to myogenesis in a 3D bioengineered system of bone marrow mesenchymal stem cells and myoblasts.pdf

In this work, a 3D environment obtained using fibrin scaffold and two cell populations, such as bone marrow-derived mesenchymal stem cells (BM-MSCs), and primary skeletal muscle cells (SkMs), was assembled. Peripheral blood mononuclear cells (PBMCs) fraction obtained after blood filtration with HemaTrate® filter was then added to the 3D culture system to explore their influence on myogenesis. The best cell ratio into a 3D fibrin hydrogel was 1:1 (BM-MSCs plus SkMs:PBMCs) when cultured in a perfusion bioreactor; indeed, excellent viability and myogenic event induction were observed. Myogenic genes were significantly overexpressed when cultured with PBMCs, such as MyoD1 of 118-fold at day 14 and Desmin 6-fold at day 21. Desmin and Myosin Heavy Chain were also detected at protein level by immunostaining along the culture. Moreover, the presence of PBMCs in 3D culture induced a significant downregulation of pro-inflammatory cytokine gene expression, such as IL6. This smart biomimetic environment can be an excellent tool for investigation of cellular crosstalk and PBMC influence on myogenic processes.</p