Software Development for Simulating and Engineering Gene Circuits

<p>Mathematical modeling has become an increasingly important aspect of biological research. Computer simulations help to improve our understanding of complex systems by testing the validity of proposed mechanisms and generating experimentally testable hypotheses. However, significant overhead is generated by the creation, debugging, and perturbation of these computational models and their parameters, especially for researchers who are unfamiliar with programming or numerical methods. Dynetica 2.0 is a user-friendly dynamic network simulator designed to expedite this process. Models are created and visualized in an easy-to-use graphical interface, which displays all of the species and reactions involved in a graph layout. System inputs and outputs, indicators, and intermediate expressions may be incorporated into the model via the versatile "expression variable" entity. Models can also be modular, allowing for the quick construction of complex systems from simpler components. Dynetica 2.0 supports a number of deterministic and stochastic algorithms for performing time-course simulations. Additionally, Dynetica 2.0 provides built-in tools for performing sensitivity or dose response analysis for a number of different metrics. Its parameter searching tools can optimize specific objectives of the time course or dose response of the system. Systems can be translated from Dynetica 2.0 into MATLAB code or the SBML format for further analysis or publication. Finally, since it is written in Java, Dynetica 2.0 is platform independent, allowing for easy sharing and collaboration between researchers.</p>Thesi

Cascading signaling pathways improve the fidelity of a stochastically and deterministically simulated molecular RS latch

<p>Abstract</p> <p>Background</p> <p>While biological systems have often been compared with digital systems, they differ by the strong effect of crosstalk between signals due to diffusivity in the medium, reaction kinetics and geometry. Memory elements have allowed the creation of autonomous digital systems and although biological systems have similar properties of autonomy, equivalent memory mechanisms remain elusive. Any such equivalent memory system, however, must silence the effect of crosstalk to maintain memory fidelity.</p> <p>Results</p> <p>Here, we present a system of enzymatic reactions that behaves like an RS latch (a simple memory element in digital systems). Using both a stochastic molecular simulator and ordinary differential equation simulator, we showed that crosstalk between two latches operating in the same spatial localization disrupts the memory fidelity of both latches. Crosstalk was reduced or silenced when simple reaction loops were replaced with multiple step or cascading reactions, showing that cascading signaling pathways are less susceptible to crosstalk.</p> <p>Conclusion</p> <p>Thus, the common biological theme of cascading signaling pathways is advantageous for maintaining the fidelity of a memory latch in the presence of crosstalk. The experimental implementation of such a latch system will lead to novel approaches to cell control using synthetic proteins and will contribute to our understanding of why cells behave differently even when given the same stimulus.</p

MetaReg: a platform for modeling, analysis and visualization of biological systems using large-scale experimental data

A new computational tool is presented that allows the integration of high-throughput experimental results with the probabilistic modeling of previously obtained information about cellular systems. The tool (MetaReg) is demonstrated on the leucine biosynthesis system in S.cerevisiae

Sensing and Integration of Erk and PI3K Signals by Myc

The transcription factor Myc plays a central role in regulating cell-fate decisions, including proliferation, growth, and apoptosis. To maintain a normal cell physiology, it is critical that the control of Myc dynamics is precisely orchestrated. Recent studies suggest that such control of Myc can be achieved at the post-translational level via protein stability modulation. Myc is regulated by two Ras effector pathways: the extracellular signal-regulated kinase (Erk) and phosphatidylinositol 3-kinase (PI3K) pathways. To gain quantitative insight into Myc dynamics, we have developed a mathematical model to analyze post-translational regulation of Myc via sequential phosphorylation by Erk and PI3K. Our results suggest that Myc integrates Erk and PI3K signals to result in various cellular responses by differential stability control of Myc protein isoforms. Such signal integration confers a flexible dynamic range for the system output, governed by stability change. In addition, signal integration may require saturation of the input signals, leading to sensitive signal integration to the temporal features of the input signals, insensitive response to their amplitudes, and resistance to input fluctuations. We further propose that these characteristics of the protein stability control module in Myc may be commonly utilized in various cell types and classes of proteins

Desenvolvimento de ferramentas computacionais para a optimização de processos de fermentação em Biotecnologia

Tese de mestrado em InformáticaActualmente, uma larga variedade de produtos tais como antibióticos, proteínas, vacinas e outros compostos químicos são produzidos através de processos fermentativos. Devido à subida dos preços do petróleo e aos fortes incentivos por parte das instituições para substituir os produtos derivados de petróleo por “produtos verdes”, muitos dos processos tradicionais têm vindo a ser substituídos por bioprocessos. Consequentemente, tem existido um esforço para melhorar a produtividade dos processos biológicos. A optimização destes processos pode ser realizada em duas etapas: primeiramente, faz-se uma selecção e uma melhoria genética do microrganismo e num segundo passo são identificadas as melhores condições para realizar o processo fermentativo. Nesta etapa, normalmente são realizados estudos experimentais através de tentativa-erro para obter as condições ambientais que propiciem o melhor crescimento e produtividade do microrganismo, manipulando as concentrações iniciais dos nutrientes, os perfis de alimentação de substrato ao reactor, os modos de operação, bem como a temperatura e o pH. Nos últimos anos, têm sido desenvolvidas várias ferramentas informáticas para simulação e optimização de bioprocessos. Porém, a maioria destas ferramentas está direccionada para estudar as vias metabólicas de um microrganismo de modo a optimizar a produtividade de determinado produto. Numa fase posterior, é efectuada uma optimização genética do microrganismo. Apesar de existir uma grande variedade de ferramentas informáticas verifica-se que nenhuma delas está desenhada especificamente para a optimização e simulação de processos fermentativos. Assim, o objectivo deste trabalho foi desenvolver de raiz uma ferramenta direccionada para simulação, optimização e estimação de parâmetros de processos fermentativos. A aplicação OptFerm foi desenvolvida sobre uma plataforma denominada AIBench, tendo-se utilizado a linguagem Java como linguagem de programação. O OptFerm foi então desenvolvido de modo a ser uma ferramenta de fácil uso, extensível e que pudesse funcionar em qualquer sistema operativo, estando disponível como software livre em http://darwin.di.uminho.pt/optferm/. A aplicação foi desenhada de modo a que o utilizador pudesse realizar várias tarefas de simulação, optimização e estimação de parâmetros com diferentes condições no que se refere a variáveis de estado, parâmetros, perfis de alimentação, etc.. As tarefas de optimização foram focadas na determinação do melhor perfil de alimentação de uma corrente de substrato a alimentar ao reactor, dos melhores valores das variáveis de estado para iniciar uma fermentação e do tempo óptimo de duração para uma fermentação. Foram realizados alguns estudos de optimização e estimação de parâmetros com o objectivo de verificar se a aplicação era suficientemente robusta. Os estudos foram baseados na repetição das experiências por 30 vezes para obter significância estatística. Após os estudos, verificou-se que as operações foram realizadas levando a resultados coerentes, não tendo sido detectados erros relevantes.Nowadays, several products such as antibiotics, proteins, vaccines, aminoacids and other chemicals are produced using fermentation processes. Due to the rise of petroleum prices and the strong incentive to replace petroleum derivatives by “green products”, many traditional processes have been replaced by new biotechnological ones. Consequently, an effort to improve biotechnological techniques has been undertaken. In order to optimize the productivity of a biological process, in the majority of the cases, two different steps have to be addressed: firstly, a selection and genetic improvement of the microbial strain is accomplished; in a second step, the best conditions for the fermentation process are identified, such as the initial nutrient concentrations, operating modes, feeding profiles for fed-batch fermentations, temperature and pH. Over the last few years, several tools have been developed for the simulation and optimization of biological processes. However, the majority of these tools was designed specifically to study metabolic pathways with the aim of increasing the productivity of a certain product. In a subsequent stage, a genetic optimization of the organism is conducted. Although there are several tools available to study simulate and optimize cellular pathways, there is still a clear lack of specific tools to perform the optimization of fermentation processes. Therefore, the aim of this work was to develop a specific computational tool to perform simulation and optimization of fermentation processes and estimation of unknown parameters. The OptFerm software was developed using the Java programming language, with the aim of being a user-friendly, extensible and platform-independent computational tool. OptFerm is freely available in http://darwin.di.uminho.pt/optferm/. The tool was designed in order to allow the user to evaluate and compare several different methods for the tasks of simulation, optimization and parameter estimation, in the context of fermentation processes. The aim is to allow users to improve process productivity, achieving better results in reduced times. The optimization tasks available include the optimization of a substrate feeding trajectory, of the feeding trajectory plus initial conditions or of the feeding trajectory plus the final fermentation time. After developing the OptFerm tool, some studies on optimization and parameter estimation were performed, with the aim of verifying if the tool is sufficiently robust. The studies were based on repeating 30 times each type of experiment. It was verified that the operations were performed with coherent results and no relevant errors have been detected

Mecanismos de protección frente a rotenona por PDGF-BB en un modelo astrocitario

Los astrocitos cumplen un importante papel neuroprotector en las diversas patologías neurodegenerativas del sistema nervioso central (SNC) ya que le brindan a las neuronas soporte trófico, metabólico y antioxidativo, debido a que poseen concentraciones elevadas de la superóxido dismutasa (SOD), la catalasa, el glutatión (GSH) y distintos factores de crecimiento como el BDNF, GDNF y PDGF. En situaciones donde los astrocitos no ejecutan tales funciones, como es el caso de la producción exacerbada de especies reactivas de oxígeno (ERO) y el daño mitocondrial, sobreviene inevitablemente la muerte neuronal. Estudios recientes sugieren que la alteración de las funciones astrocitarias se encuentra involucrada en la progresión de diversas enfermedades como Parkinson, Alzheimer, esclerosis lateral amiotrófica y otras. La producción de ERO conduce a la disfunción mitocondrial en los tejidos del sistema nervioso, acompañada por muerte neuronal excitotóxica y cambios morfológicos en las células del SNC, como son la activación astrocitaria y la inhibición de la regeneración neuronal en el sitio de la lesión. Diversos estudios han mostrado que las neuronas poseen una mayor susceptibilidad al daño oxidativo que los astrocitos, ya que estas poseen un menor número de mecanismos antioxidantes, siendo por lo tanto más propensas a la muerte celular. Por esta razón, los modelos in vitro relativos a la protección de las funciones astrocitarias antes mencionadas han sido considerados como una excelente aproximación en el estudio de las lesiones y enfermedades cerebrales. En este aspecto, se ha demostrado que el uso de factores de crecimiento como el BDNF, el FGF, el GDNF y el VEGF promueven actividades protectoras en neuronas y en células gliales en eventos como la excitotoxicidad, la producción de ERO, y la protección mitocondrial tanto en modelos in vitro como in vivo, incluyendo procesos neurodegenerativos como el Parkinson. Igualmente, el factor de crecimiento derivado de plaquetas, isoforma B (PDGF-BB), el cual expresa su receptor (PDGFR-ß), en distintos tipos celulares del SNC incluyendo los astrocitos, ha demostrado ejercer un efecto neuroprotector en demencia, modelos murinos de Parkinson, en insulto oxidativo por peróxido de hidrogeno y en protección excitotóxica de neuronas.In the present PhD Thesis, we studied the protective effects of PDGF-BB (Platelet Derived Growth Factor B), on oxidative and mitochondrial damage exerted by rotenone, in an astrocytic model (T98G cell line ). We used both experimental and computational methods for study molecular effects such as reactive oxygen species (ROS) production, changes in mitochondrial membrane potential, ultrastructural effects, changes in cell viability, and changes in the expression of proteins like GRP78 and neuroglobin (ngb). We establish the protective effects of PDGF-B in astrocytes, suggesting prospective applications for Parkinson Disease.Doctor en Ciencias BiológicasDoctorad

Modeling biological systems using Dynetica—a simulator of dynamic networks

We present Dynetica, a user-friendly simulator of dynamic networks for constructing, visualizing, and analyzing kinetic models of biological systems. In addition to generic reaction networks, Dynetica facilitates construction of models of genetic networks, where many reactions are gene expression and interactions among gene products. Further, it integrates the capability of conducting both deterministic and stochastic simulations