Automated parameter estimation for biological models using Bayesian statistical model checking.

BACKGROUND: Probabilistic models have gained widespread acceptance in the systems biology community as a useful way to represent complex biological systems. Such models are developed using existing knowledge of the structure and dynamics of the system, experimental observations, and inferences drawn from statistical analysis of empirical data. A key bottleneck in building such models is that some system variables cannot be measured experimentally. These variables are incorporated into the model as numerical parameters. Determining values of these parameters that justify existing experiments and provide reliable predictions when model simulations are performed is a key research problem. RESULTS: Using an agent-based model of the dynamics of acute inflammation, we demonstrate a novel parameter estimation algorithm by discovering the amount and schedule of doses of bacterial lipopolysaccharide that guarantee a set of observed clinical outcomes with high probability. We synthesized values of twenty-eight unknown parameters such that the parameterized model instantiated with these parameter values satisfies four specifications describing the dynamic behavior of the model. CONCLUSIONS: We have developed a new algorithmic technique for discovering parameters in complex stochastic models of biological systems given behavioral specifications written in a formal mathematical logic. Our algorithm uses Bayesian model checking, sequential hypothesis testing, and stochastic optimization to automatically synthesize parameters of probabilistic biological models.

The microenvironment in hepatocyte regeneration and function in rats with advanced cirrhosis.

UNLABELLED: In advanced cirrhosis, impaired function is caused by intrinsic damage to the native liver cells and from the abnormal microenvironment in which the cells reside. The extent to which each plays a role in liver failure and regeneration is unknown. To examine this issue, hepatocytes from cirrhotic and age-matched control rats were isolated, characterized, and transplanted into the livers of noncirrhotic hosts whose livers permit extensive repopulation with donor cells. Primary hepatocytes derived from livers with advanced cirrhosis and compensated function maintained metabolic activity and the ability to secrete liver-specific proteins, whereas hepatocytes derived from cirrhotic livers with decompensated function failed to maintain metabolic or secretory activity. Telomere studies and transcriptomic analysis of hepatocytes recovered from progressively worsening cirrhotic livers suggest that hepatocytes from irreversibly failing livers show signs of replicative senescence and express genes that simultaneously drive both proliferation and apoptosis, with a later effect on metabolism, all under the control of a central cluster of regulatory genes, including nuclear factor κB and hepatocyte nuclear factor 4α. Cells from cirrhotic and control livers engrafted equally well, but those from animals with cirrhosis and failing livers showed little initial evidence of proliferative capacity or function. Both, however, recovered more than 2 months after transplantation, indicating that either mature hepatocytes or a subpopulation of adult stem cells are capable of full recovery in severe cirrhosis. CONCLUSION: Transplantation studies indicate that the state of the host microenvironment is critical to the regenerative potential of hepatocytes, and that a change in the extracellular matrix can lead to regeneration and restoration of function by cells derived from livers with end-stage organ failure.

A two-compartment mathematical model of endotoxin-induced inflammatory and physiologic alterations in swine.

OBJECTIVE: To gain insights into individual variations in acute inflammation and physiology. DESIGN: Large-animal study combined with mathematical modeling. SETTING: Academic large-animal and computational laboratories. SUBJECTS: Outbred juvenile swine. INTERVENTIONS: Four swine were instrumented and subjected to endotoxemia (100 µg/kg), followed by serial plasma sampling. MEASUREMENTS AND MAIN RESULTS: Swine exhibited various degrees of inflammation and acute lung injury, including one death with severe acute lung injury (PaO(2)/FIO(2) ratio µ200 and static compliance µ10 L/cm H(2)O). Plasma interleukin-1ß, interleukin-4, interleukin-6, interleukin-8, interleukin-10, tumor necrosis factor-α, high mobility group box-1, and NO(2)/NO(3) were significantly (p µ .05) elevated over the course of the experiment. Principal component analysis was used to suggest principal drivers of inflammation. Based in part on principal component analysis, an ordinary differential equation model was constructed, consisting of the lung and the blood (as a surrogate for the rest of the body), in which endotoxin induces tumor necrosis factor-α in monocytes in the blood, followed by the trafficking of these cells into the lung leading to the release of high mobility group box-1, which in turn stimulates the release of interleukin-1ß from resident macrophages. The ordinary differential equation model also included blood pressure, PaO(2), and FIO(2), and a damage variable that summarizes the health of the animal. This ordinary differential equation model could be fit to both inflammatory and physiologic data in the individual swine. The predicted time course of damage could be matched to the oxygen index in three of the four swine. CONCLUSIONS: The approach described herein may aid in predicting inflammation and physiologic dysfunction in small cohorts of subjects with diverse phenotypes and outcomes.

Transcriptomic response of murine liver to severe injury and hemorrhagic shock: a dual-platform microarray analysis.

Trauma-hemorrhagic shock (HS/T) is a complex process that elicits numerous molecular pathways. We hypothesized that a dual-platform microarray analysis of the liver, an organ that integrates immunology and metabolism, would reveal key pathways engaged following HS/T. C57BL/6 mice were divided into five groups (n = 4/group), anesthetized, and surgically treated to simulate a time course and trauma severity model: 1) nonmanipulated animals, 2) minor trauma, 3) 1.5 h of hemorrhagic shock and severe trauma (HS/T), 4) 1.5 h HS/T followed by 1 h resuscitation (HS/T+1.0R), 5) 1.5 h HS/T followed by 4.5 h resuscitation (HS/T+4.5R). Liver RNA was hybridized to CodeLink and Affymetrix mouse whole genome microarray chips. Common genes with a cross-platform correlation >0.6 (2,353 genes in total) were clustered using k-means clustering, and clusters were analyzed using Ingenuity Pathways Analysis. Genes involved in the stress response and immunoregulation were upregulated early and remained upregulated throughout the course of the experiment. Genes involved in cell death and inflammatory pathways were upregulated in a linear fashion with elapsed time and in severe injury compared with minor trauma. Three of the six clusters contained genes involved in metabolic function; these were downregulated with elapsed time. Transcripts involved in amino acid metabolism as well as signaling pathways associated with glucocorticoid receptors, IL-6, IL-10, and the acute phase response were elevated in a severity-dependent manner. This is the first study to examine the postinjury response using dual-platform microarray analysis, revealing responses that may enable novel therapies or diagnostics.

How can we improve Science, Technology, Engineering, and Math education to encourage careers in Biomedical and Pathology Informatics?

The Computer Science, Biology, and Biomedical Informatics (CoSBBI) program was initiated in 2011 to expose the critical role of informatics in biomedicine to talented high school students.[1] By involving them in Science, Technology, Engineering, and Math (STEM) training at the high school level and providing mentorship and research opportunities throughout the formative years of their education, CoSBBI creates a research infrastructure designed to develop young informaticians. Our central premise is that the trajectory necessary to be an expert in the emerging fields of biomedical informatics and pathology informatics requires accelerated learning at an early age.In our 4(th) year of CoSBBI as a part of the University of Pittsburgh Cancer Institute (UPCI) Academy (http://www.upci.upmc.edu/summeracademy/), and our 2nd year of CoSBBI as an independent informatics-based academy, we enhanced our classroom curriculum, added hands-on computer science instruction, and expanded research projects to include clinical informatics. We also conducted a qualitative evaluation of the program to identify areas that need improvement in order to achieve our goal of creating a pipeline of exceptionally well-trained applicants for both the disciplines of pathology informatics and biomedical informatics in the era of big data and personalized medicine.

Creating a pipeline of talent for informatics: STEM initiative for high school students in computer science, biology, and biomedical informatics.

This editorial provides insights into how informatics can attract highly trained students by involving them in science, technology, engineering, and math (STEM) training at the high school level and continuing to provide mentorship and research opportunities through the formative years of their education. Our central premise is that the trajectory necessary to be expert in the emergent fields in front of them requires acceleration at an early time point. Both pathology (and biomedical) informatics are new disciplines which would benefit from involvement by students at an early stage of their education. In 2009, Michael T Lotze MD, Kirsten Livesey (then a medical student, now a medical resident at University of Pittsburgh Medical Center (UPMC)), Richard Hersheberger, PhD (Currently, Dean at Roswell Park), and Megan Seippel, MS (the administrator) launched the University of Pittsburgh Cancer Institute (UPCI) Summer Academy to bring high school students for an 8 week summer academy focused on Cancer Biology. Initially, pathology and biomedical informatics were involved only in the classroom component of the UPCI Summer Academy. In 2011, due to popular interest, an informatics track called Computer Science, Biology and Biomedical Informatics (CoSBBI) was launched. CoSBBI currently acts as a feeder program for the undergraduate degree program in bioinformatics at the University of Pittsburgh, which is a joint degree offered by the Departments of Biology and Computer Science. We believe training in bioinformatics is the best foundation for students interested in future careers in pathology informatics or biomedical informatics. We describe our approach to the recruitment, training and research mentoring of high school students to create a pipeline of exceptionally well-trained applicants for both the disciplines of pathology informatics and biomedical informatics. We emphasize here how mentoring of high school students in pathology informatics and biomedical informatics will be critical to assuring their success as leaders in the era of big data and personalized medicine.

A Multiscale Agent-Based in silico Model of Liver Fibrosis Progression.

Chronic hepatic inflammation involves a complex interplay of inflammatory and mechanical influences, ultimately manifesting in a characteristic histopathology of liver fibrosis. We created an agent-based model (ABM) of liver tissue in order to computationally examine the consequence of liver inflammation. Our liver fibrosis ABM (LFABM) is comprised of literature-derived rules describing molecular and histopathological aspects of inflammation and fibrosis in a section of chemically injured liver. Hepatocytes are modeled as agents within hexagonal lobules. Injury triggers an inflammatory reaction, which leads to activation of local Kupffer cells and recruitment of monocytes from circulation. Portal fibroblasts and hepatic stellate cells are activated locally by the products of inflammation. The various agents in the simulation are regulated by above-threshold concentrations of pro- and anti-inflammatory cytokines and damage-associated molecular pattern molecules. The simulation progresses from chronic inflammation to collagen deposition, exhibiting periportal fibrosis followed by bridging fibrosis, and culminating in disruption of the regular lobular structure. The ABM exhibited key histopathological features observed in liver sections from rats treated with carbon tetrachloride (CCl4). An in silico "tension test" for the hepatic lobules predicted an overall increase in tissue stiffness, in line with clinical elastography literature and published studies in CCl4-treated rats. Therapy simulations suggested differential anti-fibrotic effects of neutralizing tumor necrosis factor alpha vs. enhancing M2 Kupffer cells. We conclude that a computational model of liver inflammation on a structural skeleton of physical forces can recapitulate key histopathological and macroscopic properties of CCl4-injured liver. This multiscale approach linking molecular and chemomechanical stimuli enables a model that could be used to gain translationally relevant insights into liver fibrosis.

SPARK: A Framework for Multi-Scale Agent-Based Biomedical Modeling.

Multi-scale modeling of complex biological systems remains a central challenge in the systems biology community. A method of dynamic knowledge representation known as agent-based modeling enables the study of higher level behavior emerging from discrete events performed by individual components. With the advancement of computer technology, agent-based modeling has emerged as an innovative technique to model the complexities of systems biology. In this work, the authors describe SPARK (Simple Platform for Agent-based Representation of Knowledge), a framework for agent-based modeling specifically designed for systems-level biomedical model development. SPARK is a stand-alone application written in Java. It provides a user-friendly interface, and a simple programming language for developing Agent-Based Models (ABMs). SPARK has the following features specialized for modeling biomedical systems: 1) continuous space that can simulate real physical space; 2) flexible agent size and shape that can represent the relative proportions of various cell types; 3) multiple spaces that can concurrently simulate and visualize multiple scales in biomedical models; 4) a convenient graphical user interface. Existing ABMs of diabetic foot ulcers and acute inflammation were implemented in SPARK. Models of identical complexity were run in both NetLogo and SPARK; the SPARK-based models ran two to three times faster.

Agent-based models in translational systems biology.

Effective translational methodologies for knowledge representation are needed in order to make strides against the constellation of diseases that affect the world today. These diseases are defined by their mechanistic complexity, redundancy, and nonlinearity. Translational systems biology aims to harness the power of computational simulation to streamline drug/device design, simulate clinical trials, and eventually to predict the effects of drugs on individuals. The ability of agent-based modeling to encompass multiple scales of biological process as well as spatial considerations, coupled with an intuitive modeling paradigm, suggests that this modeling framework is well suited for translational systems biology. This review describes agent-based modeling and gives examples of its translational applications in the context of acute inflammation and wound healing.