Yaki Setty

Detailed map of a cis-regulatory input function

Most genes are regulated by multiple transcription factors that bind specific sites in DNA regulatory regions. These cis-regulatory regions perform a computation: the rate of transcription is a function of the active concentrations of each of the input transcription factors. Here, we used accurate gene expression measurements from living cell cultures, bearing GFP reporters, to map in detail the input function of the classic lacZYA operon of Escherichia coli, as a function of about a hundred combinations of its two inducers, cAMP and isopropyl β-d-thiogalactoside (IPTG). We found an unexpectedly intricate function with four plateau levels and four thresholds. This result compares well with a mathematical model of the binding of the regulatory proteins cAMP receptor protein (CRP) and LacI to the lac regulatory region. The model is also used to demonstrate that with few mutations, the same region could encode much purer AND-like or even OR-like functions. This possibility means that the wild-type region is selected to perform an elaborate computation in setting the transcription rate. The present approach can be generally used to map the input functions of other genes.

Four-dimensional realistic modeling of pancreatic organogenesis

Organogenesis, the process by which organs develop from individual precursor stem cells, requires that the precursor cells proliferate, differentiate, and aggregate to form a functioning structure. This process progresses through changes in 4 dimensions: time and 3 dimensions of space—4D. Experimental analysis of organogenesis, by its nature, cuts the 4D developmental process into static, 2D histological images or into molecular or cellular markers and interactions with little or no spatial dimensionality and minimal dynamics. Understanding organogenesis requires integration of the piecemeal experimental data into a running, realistic and interactive 4D simulation that allows experimentation and hypothesis testing in silico. Here, we describe a fully executable, interactive, visual model for 4D simulation of organogenic development using the mouse pancreas as a representative case. Execution of the model provided a dynamic description of pancreas development, culminating in a structure that remarkably recapitulated morphologic features seen in the embryonic pancreas. In silico mutations in key signaling molecules resulted in altered patterning of the developing pancreas that were in general agreement with in vivo data. The modeling approach described here thus typifies a useful platform for studying organogenesis as a phenomenon in 4 dimensions.

Concurrency in Biological Modeling: Behavior, Execution and Visualization

Modeling natural systems is a complicated task that involves the concurrent behavior of various processes, mechanisms and objects. Here, we describe an approach that we have been taking in our group for several years, whereby the complexity of the problem is reduced by decomposing a natural system into its basic elements, which are then reassembled and combined to form a comprehensive, simulatable model of the system. Our modeling approach allows one to view a natural system at various levels of abstraction, in a way that makes it possible to zoom in and out between levels. Using statecharts, a high level visual formalism, we specify the behavior of the basic elements of each level and compile these into executable code, which is then linked to an animated front-end. At run-time, the concurrent execution of the basic elements is continuously displayed and provides a dynamic description of the system. We illustrate this approach by modeling aspects of three biological systems: development of the mammalian pancreas; the differentiation of T cells in the thymus; and the dynamic architecture of a lymph node. We compared each models behavior with experimental data and also reproduced genetic experiments in silico. Interestingly, certain behavioral properties that were not explicitly programmed into the model emerge from concurrent execution and correspond well with the experimental observations.

How neurons migrate: a dynamic in-silico model of neuronal migration in the developing cortex

BackgroundNeuronal migration, the process by which neurons migrate from their place of origin to their final position in the brain, is a central process for normal brain development and function. Advances in experimental techniques have revealed much about many of the molecular components involved in this process. Notwithstanding these advances, how the molecular machinery works together to govern the migration process has yet to be fully understood. Here we present a computational model of neuronal migration, in which four key molecular entities, Lis1, DCX, Reelin and GABA, form a molecular program that mediates the migration process.ResultsThe model simulated the dynamic migration process, consistent with in-vivo observations of morphological, cellular and population-level phenomena. Specifically, the model reproduced migration phases, cellular dynamics and population distributions that concur with experimental observations in normal neuronal development. We tested the model under reduced activity of Lis1 and DCX and found an aberrant development similar to observations in Lis1 and DCX silencing expression experiments. Analysis of the model gave rise to unforeseen insights that could guide future experimental study. Specifically: (1) the model revealed the possibility that under conditions of Lis1 reduced expression, neurons experience an oscillatory neuron-glial association prior to the multipolar stage; and (2) we hypothesized that observed morphology variations in rats and mice may be explained by a single difference in the way that Lis1 and DCX stimulate bipolar motility. From this we make the following predictions: (1) under reduced Lis1 and enhanced DCX expression, we predict a reduced bipolar migration in rats, and (2) under enhanced DCX expression in mice we predict a normal or a higher bipolar migration.ConclusionsWe present here a system-wide computational model of neuronal migration that integrates theory and data within a precise, testable framework. Our model accounts for a range of observable behaviors and affords a computational framework to study aspects of neuronal migration as a complex process that is driven by a relatively simple molecular program. Analysis of the model generated new hypotheses and yet unobserved phenomena that may guide future experimental studies. This paper thus reports a first step toward a comprehensive in-silico model of neuronal migration.

Generic Reactive Animation: Realistic Modeling of Complex Natural Systems

Natural systems, such as organs and organisms, are large-scale complex systems with numerous elements and interactions. Modeling such systems can lead to better understanding thereof and may help in efforts to save on resources and development time. In recent years, our group has been involved in modeling and understanding biological systems, which are perhaps the prime example of highly complex and reactive large-scale systems. To handle their complexity, we developed a technique called reactive animation (RA), which smoothly connects a reactive system engine to an animation tool, and which has been described in earlier publications. In the present paper we show how the basic idea of RA can be made generic, by providing a simple general way to link up any number of reactive system engines -- even ones that are quite different in nature -- to an animation tool. This results in natural-looking, fully interactive 3D animations, driven by complex reactive systems running in the background. We illustrate this with two examples that link several tools: Rhapsody for state-based specification, the Play-Engine for scenario-based specification, MATLAB for mathematical analysis and the 3DGameStudio for animation. Our examples are both from biology (pancreatic development) and from everyday activities (e.g., gym training).

Modeling Biology using Generic Reactive Animation

Complex biological systems involve incorporated behaviors of numerous processes, mechanisms and objects. However, experimental analysis, by its nature, divides biological systems into static interactions with little dynamics. To bridge the gap between experimental data and the underlying behavior, our group has been formalizing biological findings into mathematically and algorithmically rigorous specifications, which are then compiled into reactive models. To realistically animate our models, we designed a generic architecture for the earlier idea of reactive animation, in a way that allows it to link up reactive models with animation tools. Here, we describe the reactive animation approach and some of the benefits of employing it to simulate and analyze complex biological systems. We illustrate our approach with a model of pancreatic development, a highly complex system with a unique 3D structure, and also mention more recent work on adding animation to the generic cell project (GemCell).

Executable Modeling of Morphogenesis: A Turing-Inspired Approach

In his pioneering 1952 paper, “The chemical basis of morphogenesis”, Alan Turing introduced, perhaps for the first time, a model of the morphogenesis of embryo development. Central to his theory is the concept of cells with chemical entities that interact with morphogens to drive embryonic development through changes in what he termed ‘the state of the system’. Turings concepts have inspired many mathematical and computational models proposed since then. Here we discuss the way Turings ideas inspired our approach to the state-based modeling of morphogenesis, which results in a fully executable program for the interactions between chemical entities and morphogens. As a representative example we describe our modeling of pancreatic organogenesis, a complex developmental process that develops from a flat sheet of cells into a 3D cauliflower-like shape. We show how we constructed the model and tested the relations between morphogens and cells, and illustrate the analysis of the model against experimental data. Finally, we discuss a variant of the original Turing-Test for a machines ability to demonstrate intelligence as a future means to validate computerized biological models, like the one presented here.

Crafting game-models using reactive system design

This paper presents a game-model of a gym training system, where the behavior of the system is specified using languages developed originally for reactive system design, which drive a game engine. The approach makes it possible to describe behaviors of different parts of the system using different reactive system design languages and tools. It thus provides a framework for integrating the model behavior to obtain an executable game-model of the entire system. Among the advantages of this approach is the ability to use existing analysis tools to understand the game behavior at design time and run time, the ability to easily modify the behavior, and the use of visual languages to allow various stakeholders to be involved in early stages of building the game. Finally, we suggest integrating future games and game design methods into the emerging field of biological modeling, to which reactive system design has recently been successfully applied.