Biomedical subjects

Simeone Marino

Publications and source records attributed to Simeone Marino.

6 recordsLinked to original sources

PubMed2006

An automated procedure for the extraction of metabolic network information from time series data.

Novel high-throughput measurement techniques in vivo are beginning to produce dense high-quality time series which can be used to investigate the structure and regulation of biochemical networks. We propose an automated information extraction procedure which takes advantage of the unique S-system structure and supports model building from time traces, curve fitting, model selection, and structure identification based on parameter estimation. The procedure comprises of three modules: model Generation, parameter estimation or model Fitting, and model Selection (GFS algorithm). The GFS algorithm has been implemented in MATLAB and returns a list of candidate S-systems which adequately explain the data and guides the search to the most plausible model for the time series under study. By combining two strategies (namely decoupling and limiting connectivity) with methods of data smoothing, the proposed algorithm is scalable up to realistic situations of moderate size. We illustrate the proposed methodology with a didactic example.

Algorithms↗

PubMed2005

Mycobacterium tuberculosis as viewed through a computer.

Mathematical models are emerging as important tools in the study of microbiology. As an illustrative example, we present results from several models each generated to study the interaction of Mycobacterium tuberculosis and the immune system. Different mathematical models were formulated on the basis of assumptions regarding system-component interactions, enabling us to explore specific aspects at diverse biological scales (e.g. intracellular, cell-cell interactions, and cell population dynamics). In addition, we were able to examine both temporal and spatial aspects. At each scale, there were consistent themes that emerged as determinative in infection outcome. Factors we identified include both host and microbial characteristics. The use of the models lies in generating hypotheses that can then be tested experimentally. Here, we outline the primary host and bacterial factors that we have identified as key mechanisms that contribute to the success of M. tuberculosis as a human pathogen. Our goal is to stimulate experimentation and foster collaborations between theoretical and experimental scientists.

Animals↗

PubMed2005

Challenges for the identification of biological systems from in vivo time series data.

Modern methods of high-throughput molecular biology render it possible to generate time series of metabolite concentrations and the expression of genes and proteins in vivo. These time profiles contain valuable information about the structure and dynamics of the underlying biological system. This information is implicit and its extraction is a challenging but ultimately very rewarding task for the mathematical modeler. Using a well-suited modeling framework, such as Biochemical Systems Theory (BST), it is possible to formulate the extraction of information as an inverse problem that in principle may be solved with a genetic algorithm or nonlinear regression. However, two types of issues associated with this inverse problem make the extraction task difficult. One type pertains to the algorithmic difficulties encountered in nonlinear regressions with moderate and large systems. The other type is of an entirely different nature. It is a consequence of assumptions that are often taken for granted in the design and analysis of mathematical models of biological systems and that need to be revisited in the context of inverse analyses. The article describes the extraction process and some of its challenges and proposes partial solutions.

Linear Models↗

PubMed2004

Dendritic cell trafficking and antigen presentation in the human immune response to Mycobacterium tuberculosis.

Mycobacterium tuberculosis (Mtb) is an extraordinarily successful human pathogen, one of the major causes of death by infectious disease worldwide. A key issue for the study of tuberculosis is to understand why individuals infected with Mtb experience different clinical outcomes. To better understand the dynamics of Mtb infection and immunity, we coupled nonhuman primate experiments with a mathematical model we previously developed that qualitatively and quantitatively captures important processes of cellular priming and activation. These processes occur between the lung and the nearest draining lymph node where the key cells mediating this process are the dendritic cells (DC). The nonhuman primate experiments consist of bacteria and cell numbers from tissues of 17 adult cynomolgus macaques (Macaca fascicularis) that were infected with Mtb strain Erdman ( approximately 25 CFU/animal via bronchoscope). The main result of this work is that delays in either DC migration to the draining lymph node or T cell trafficking to the site of infection can alter the outcome of Mtb infection, defining progression to primary disease or latent infection and reactivated tuberculosis. Our results also support the idea that the development of a new generation of treatment against Mtb should optimally elicit a fast DC turnover at the site of infection, as well as strong activation of DCs for maximal Ag presentation and production of key cytokines. This will induce the most protective T cell response.

Animals↗

PubMed2004

The human immune response to Mycobacterium tuberculosis in lung and lymph node.

A key issue for the study of tuberculosis is to understand why individuals infected with Mycobacterium tuberculosis (Mtb) experience different clinical outcomes. To better understand the dynamics of Mtb infection and immunity, we have previously developed a temporal mathematical model that qualitatively and quantitatively characterizes the cellular and cytokine control network during infection. In this work we extend that model to a two compartmental model to capture the important processes of cellular activation and priming that occur between the lung and the nearest draining lymph node. We are able to reproduce typical disease progression scenarios including primary infection, latency or clearance. Then we use the model to predict key processes determining these different disease trajectories (i.e. identify bifurcation parameters), suggesting directions for further basic science study and potential new treatment strategies.

Cytokines↗

PubMed2003

The importance of an inter-compartmental delay in a model for human gastric acid secretion.

In this work we re-examine an existing model of gastric acid secretion. The model is a 2-compartment model of the human stomach accounting for regions where relevant cells (D, G, ECL and parietal cells) and proteins and acid they secrete (somatostatin, gastrin, histamine, and gastric acid, respectively) are found. These proteins compose a positive and negative feedback system that controls the secretion of gastric acid by parietal cells. The original model consists of 18 ordinary differential equations and yields a stable 3-period limit cycle solution. We modify the existing model by introducing a delay into the system and assuming that the cell populations are in steady state over a short-time window (<300 h) and are able to reduce the system to an 8-equation delay differential equation model. In addition to demonstrating congruency between the two models, we also show that a similar stability is only reproducible when the delay in gastrin transport is approximately 30 min. This suggests that gastric acid secretion homeostasis likely depends strongly on the delay in gastrin transport from the antrum to the corpus.

Computer Simulation↗