A SystemC Platform for Signal Transduction Modelling and Simulation in Systems Biology

Signal transduction is a class of cell\u2019s biological processes,which are commonly represented as highly concurrent reactive systems. In the Systems Biology community, modelling and simulation of signal transduction require overcoming issues like discrete event-based execution of complex systems, description from building blocks through composition and encapsulation, description at different levels of granularity, methods for abstraction and refinement. This paper presents a signal transduction modelling and simulationplatform based on SystemC, and shows how the platform allows handling the system complexity by modelling it at different abstraction levels. The paper reports the results obtained by applying the platform to model the intracellular signalling network controlling integrin activation mediating leukocyte recruitment from the blood into the tissues. The dynamic simulation of the model has been conducted with the aim of exploring oscillating behaviors of such a biochemical circuit and, more in general, to help better understanding properties of the overall dynamics of leukocyte recruitment

Dynamic modeling and simulation of leukocyte integrin activation through an electronic design automation framework

Model development and analysis of biological systems is recognized as a key requirement for integrating in-vitro and in-vivo experimental data. In-silico simulations of a biochemical model allows one to test different experimental conditions, helping in the discovery of the dynamics that regulate the system. Several characteristics and issues of biological system modeling are common to the electronics system modeling, such as concurrency, reactivity, abstraction levels, as well as state space explosion during verification. This paper proposes a modeling and simulation framework for discrete event-based execution of biochemical systems based on SystemC. SystemC is the reference language in the electronic design automation (EDA) field for modeling and verifying complex systems at different abstraction levels. SystemC-based verification is the de-facto an alternative to model checking when such a formal verification technique cannot deal with the state space complexity of the model. The paper presents how the framework has been applied to model the intracellular signalling network controlling integrin activation mediating leukocyte recruitment from the blood into the tissues, by handling the solution space complexity through different levels of simulation accuracy

In-silico Models of Stem Cell and Developmental Systems

Understanding how developmental systems evolve over time is a key question in stem cell and developmental biology research. However, due to hurdles of existing experimental techniques, our understanding of these systems as a whole remains partial and coarse. In recent years, we have been constructing in-silico models that synthesize experimental knowledge using software engineering tools. Our approach integrates known isolated mechanisms with simplified assumptions where the knowledge is limited. This has proven to be a powerful, yet underutilized, tool to analyze the developmental process. The models provide a means to study development in-silico by altering the model’s specifications, and thereby predict unforeseen phenomena to guide future experimental trials. To date, three organs from diverse evolutionary organisms have been modeled: the mouse pancreas, the C. elegans gonad, and partial rodent brain development. Analysis and execution of the models recapitulated the development of the organs, anticipated known experimental results and gave rise to novel testable predictions. Some of these results had already been validated experimentally. In this paper, I review our efforts in realistic in-silico modeling of stem cell research and developmental biology and discuss achievements and challenges. I envision that in the future, in-silico models as presented in this paper would become a common and useful technique for research in developmental biology and related research fields, particularly regenerative medicine, tissue engineering and cancer therapeutics

Simulating the decentralized processes of the human immune system in a virtual anatomy model

BACKGROUND: Many physiological processes within the human body can be perceived and modeled as large systems of interacting particles or swarming agents. The complex processes of the human immune system prove to be challenging to capture and illustrate without proper reference to the spacial distribution of immune-related organs and systems. Our work focuses on physical aspects of immune system processes, which we implement through swarms of agents. This is our first prototype for integrating different immune processes into one comprehensive virtual physiology simulation. RESULTS: Using agent-based methodology and a 3-dimensional modeling and visualization environment (LINDSAY Composer), we present an agent-based simulation of the decentralized processes in the human immune system. The agents in our model - such as immune cells, viruses and cytokines - interact through simulated physics in two different, compartmentalized and decentralized 3-dimensional environments namely, (1) within the tissue and (2) inside a lymph node. While the two environments are separated and perform their computations asynchronously, an abstract form of communication is allowed in order to replicate the exchange, transportation and interaction of immune system agents between these sites. The distribution of simulated processes, that can communicate across multiple, local CPUs or through a network of machines, provides a starting point to build decentralized systems that replicate larger-scale processes within the human body, thus creating integrated simulations with other physiological systems, such as the circulatory, endocrine, or nervous system. Ultimately, this system integration across scales is our goal for the LINDSAY Virtual Human project. CONCLUSIONS: Our current immune system simulations extend our previous work on agent-based simulations by introducing advanced visualizations within the context of a virtual human anatomy model. We also demonstrate how to distribute a collection of connected simulations over a network of computers. As a future endeavour, we plan to use parameter tuning techniques on our model to further enhance its biological credibility. We consider these in silico experiments and their associated modeling and optimization techniques as essential components in further enhancing our capabilities of simulating a whole-body, decentralized immune system, to be used both for medical education and research as well as for virtual studies in immunoinformatics

Automated requirements analysis for a molecular watchdog timer

Dynamic systems in DNA nanotechnology are often programmed using a chemical reaction network (CRN) model as an intermediate level of abstraction. In this paper, we design and analyze a CRN model of a watchdog timer, a device commonly used to monitor the health of a safety critical system. Our process uses incremental design practices with goal-oriented requirements engineering, software verification tools, and custom software to help automate the software engineering process. The watchdog timer is comprised of three components: an absence detector, a threshold filter, and a signal amplifier. These components are separately designed and verified, and only then composed to create the molecular watchdog timer. During the requirements-design iterations, simulation, model checking, and analysis are used to verify the system. Using this methodology several incomplete requirements and design flaws were found, and the final verified model helped determine specific parameters for biological experiments

Computational Modeling, Formal Analysis, and Tools for Systems Biology.

As the amount of biological data in the public domain grows, so does the range of modeling and analysis techniques employed in systems biology. In recent years, a number of theoretical computer science developments have enabled modeling methodology to keep pace. The growing interest in systems biology in executable models and their analysis has necessitated the borrowing of terms and methods from computer science, such as formal analysis, model checking, static analysis, and runtime verification. Here, we discuss the most important and exciting computational methods and tools currently available to systems biologists. We believe that a deeper understanding of the concepts and theory highlighted in this review will produce better software practice, improved investigation of complex biological processes, and even new ideas and better feedback into computer science

Devices for safety-critical molecular programmed systems

The behavior of matter at the molecular level can be programmed to create nanoscale molecular components that accomplish desired tasks. Many molecular components are developed with intended uses that are safety-critical, such as medical applications. Ensuring the correctness and fault tolerance of such devices is paramount. Techniques to develop robustly correct programs have been widely studied in software systems and many devices have been constructed to aid in the safe operation of systems. We seek to demonstrate the effectiveness of software and safety engineering techniques in the molecular programming domain. In this thesis, we present the design of five new devices to aid in the development of safetycritical molecular programmed systems. We introduce a Runtime Fault Detection device (RFD) to robustly detect faults and initiate recovery actions in response to a failed system. We present the Concentration Monitor, a device that can detect changes, major and minor, in concentrations in real-time and demonstrate its utility. We also describe methods for constructing chemical reaction networks that can robustly simulate any combinational logic gate. Finally, we present two devices to log the state of a molecular program, where the first device logs a state upon receiving a request, and the second device ensures that the current state meets a defined validity property before allowing a log to be taken. All devices have been formally verified using model checking, simulations, or formal proof techniques. The methods used to construct and verify these devices can be adapted to the design of future molecular systems to assist in ensuring their correctness