At the Biological Modeling and Simulation Frontier

We provide a rationale for and describe examples of synthetic modeling and simulation (M&S) of biological systems. We explain how synthetic methods are distinct from familiar inductive methods. Synthetic M&S is a means to better understand the mechanisms that generate normal and disease-related phenomena observed in research, and how compounds of interest interact with them to alter phenomena. An objective is to build better, working hypotheses of plausible mechanisms. A synthetic model is an extant hypothesis: execution produces an observable mechanism and phenomena. Mobile objects representing compounds carry information enabling components to distinguish between them and react accordingly when different compounds are studied simultaneously. We argue that the familiar inductive approaches contribute to the general inefficiencies being experienced by pharmaceutical R&D, and that use of synthetic approaches accelerates and improves R&D decision-making and thus the drug development process. A reason is that synthetic models encourage and facilitate abductive scientific reasoning, a primary means of knowledge creation and creative cognition. When synthetic models are executed, we observe different aspects of knowledge in action from different perspectives. These models can be tuned to reflect differences in experimental conditions and individuals, making translational research more concrete while moving us closer to personalized medicine

Prerequisites for Effective Experimentation in Computational Biology

The development of any given biological model is driven by the experimental context in which it will be used. Hence, computer models are often overfitted to a single, unique, experimental context and fail to be useful in other situations. So doing severely limits the model's usefulness, effectively blocking inferential extensions to somewhat different conditions. To solve this problem, multiple, separate models of a biological system are required to adequately represent that system 's behaviors. This is in stark contrast to the biological components to which these models refer. They function in many different situations, which is often not the case for mechanical components. The problem presents a fundamental breakdown in the extent to which any computer model represents its biological referent, especially when considering these functional units in a complex, medically relevant context. In this paper, we present the basics of a modeling method, FURM (Functional Unit Representation Method) that attempts to address this breakdown by enforcing and encouraging the use of some basic methodological principles in the development of any functional unit model

Similarity Measures for Automated Comparison of In Silico and In Vitro Experimental Results

Abstract—The overwhelming complexity of biological systems prevents exhaustive description of the processes and mechanisms that cause system functionality. There are large numbers of processes to be considered with options for manifold hypotheses describing each. The long-term goal of this project, for a particular biological system, is to put the computer to work weeding out the weaker hypotheses and, even, weeding out posited processes that do not pertain directly to specific functionality. An objective towards this goal is to build a computational framework to host an ongoing competition for the most effective structural description of what goes on inside an organ, in this case the liver. In order to do that, one needs robust algorithms for comparing the data taken from biological experiments with the data taken from the simulation. In this paper, we begin to delineate and survey algorithms by which to compare the output of any given simulation with data taken from experiments. Key Words — Computational biology, computer, in silico, liver, model, modeling methodology, simulation

ISL and ISHC parameters descriptions and values for validating experiments.

<p>ISL and ISHC parameters descriptions and values for validating experiments.</p

Virtual Experiments Enable Exploring and Challenging Explanatory Mechanisms of Immune-Mediated P450 Down-Regulation

<div><p>Hepatic cytochrome P450 levels are down-regulated during inflammatory disease states, which can cause changes in downstream drug metabolism and hepatotoxicity. Long-term, we seek sufficient new insight into P450-regulating mechanisms to correctly anticipate how an individual’s P450 expressions will respond when health and/or therapeutic interventions change. To date, improving explanatory mechanistic insight relies on knowledge gleaned from in vitro, in vivo, and clinical experiments augmented by case reports. We are working to improve that reality by developing means to undertake scientifically useful virtual experiments. So doing requires translating an accepted theory of immune system influence on P450 regulation into a computational model, and then challenging the model via in silico experiments. We build upon two existing agent-based models—an in silico hepatocyte culture and an in silico liver—capable of exploring and challenging concrete mechanistic hypotheses. We instantiate an in silico version of this hypothesis: in response to lipopolysaccharide, Kupffer cells down-regulate hepatic P450 levels via inflammatory cytokines, thus leading to a reduction in metabolic capacity. We achieve multiple in vitro and in vivo validation targets gathered from five wet-lab experiments, including a lipopolysaccharide-cytokine dose-response curve, time-course P450 down-regulation, and changes in several different measures of drug clearance spanning three drugs: acetaminophen, antipyrine, and chlorzoxazone. Along the way to achieving validation targets, various aspects of each model are falsified and subsequently refined. This iterative process of falsification-refinement-validation leads to biomimetic yet parsimonious mechanisms, which can provide explanatory insight into how, where, and when various features are generated. We argue that as models such as these are incrementally improved through multiple rounds of mechanistic falsification and validation, we will generate virtual systems that embody deeper credible, actionable, explanatory insight into immune system-drug metabolism interactions within individuals.</p></div

Enzyme-specific parameter values for validating experiments.

<p>Enzyme-specific parameter values for validating experiments.</p

Wet-lab and in silico normalized drug disappearance curves.

<p><b>A.</b> APAP [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0155855#pone.0155855.ref020" target="_blank">20</a>]; <b>B.</b> ANT [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0155855#pone.0155855.ref021" target="_blank">21</a>]; <b>C.</b> CZN [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0155855#pone.0155855.ref022" target="_blank">22</a>]. Red/blue circles: in silico averages of 16 Monte Carlo trials. Gray circles: wet-lab averages. Red/blue lines: additional in silico values between wet-lab time points. The initial spike in drug corresponds to the administered dose. All drug values are normalized by the control value at the first time point. Error bars: ± 25% of the wet-lab value (the similarity criteria).</p

Validation targets for LPS treatment, before or without drug administration.

<p><b>A.</b> Dose-response curve between LPS stimulus and normalized cytokine response. Values were measured after 48 hr (2,880 simulation cycles). Error bars: wet-lab standard deviation. In silico points are averages of 16 Monte Carlo trials. Wet-lab values are from [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0155855#pone.0155855.ref023" target="_blank">23</a>]. <b>B.</b> Time-course levels of enzymes, normalized by the starting value. Error bars: wet-lab standard deviation. In silico points are averages of 16 Monte Carlo trials. Wet-lab values are from [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0155855#pone.0155855.ref024" target="_blank">24</a>]. <b>C.</b> Wet-lab and in silico P450 levels relative to control values. Wet-lab values are relative measures of CYP3A2 (ANT) or CYP2E1 (CZN). In silico values are relative measures of the respective enzyme type. Error bars: standard deviation. Wet-lab values are from [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0155855#pone.0155855.ref020" target="_blank">20</a>] (APAP), [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0155855#pone.0155855.ref021" target="_blank">21</a>] (ANT), and [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0155855#pone.0155855.ref022" target="_blank">22</a>] (CZN). Note [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0155855#pone.0155855.ref020" target="_blank">20</a>] did not provide P450 data for APAP, but we included in silico values for comparison.</p

Scatterplots between enzyme measurements and clearance measurements for both control and LPS experiments.

<p><b>A.</b> APAP [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0155855#pone.0155855.ref020" target="_blank">20</a>]; <b>B.</b> ANT [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0155855#pone.0155855.ref021" target="_blank">21</a>]; <b>C.</b> CZN [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0155855#pone.0155855.ref022" target="_blank">22</a>]. Gray circles: wet-lab data points (when provided). Red/blue circles: in silico data points. Error bars: in silico standard deviation, extending from the mean of 16 Monte Carlo trials. Blue box: area of acceptable similarity (± 1 standard deviation of wet-lab value). Since [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0155855#pone.0155855.ref020" target="_blank">20</a>] did not provide enzyme data, there is no associated validation target (<b>A</b>). Only [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0155855#pone.0155855.ref022" target="_blank">22</a>] provided values for individual wet-lab trials (<b>C</b>).</p