Jaap Heringa, Ph.D.
Director & Professor of Bioinformatics, IBIVU VU University Amsterdam, The Netherlands
Modeling strategy based on Petri-nets
In my talk I will introduce a formal modeling strategy based on Petri-nets, which are a convenient means of modeling biological processes. I will illustrate the capabilities of Petri-nets as reasoning vehicles using two examples: Haematopoietic stem cell differentiation in mice, and vulval development in C. elegance. The first system was modeled using a Boolean implementation, and the second using a coarse-grained multi-cellular Petri-net model. Concepts such as the model state space, attractor states, and reasoning to adapt the model to the biological reality will be discussed.
Jaydeep Unni, Ph.D.
Sr. Project Manager, Robert Bosch Healthcare Systems, Palo Alto, CA
Remote Patient Monitoring – Challenges and Opportunities
Remote Patient Monitoring (RPM) is gaining importance and acceptance with rising number of chronic disease conditions and with increase in the aging population. As instances of Heart diseases, Diabetes etc are increasing the demand for these technologies are increasing. RPM devices typically collect patient vital sign data and in some case also patient responses to health related questions. Thus collected data is then transmitted through various modalities (wireless/Bluetooth/cellular) to Hospitals/Doctor’s office for clinical evaluation. With these solutions Doctors are able to access patient’s vital data ‘any time any where’ thus enabling them to intervene on a timely and effective manner. For older adult population chronic disease management, post-acute care management and safety monitoring are areas were RPM finds application. That said, there are significant challenges in adoption of Remote Patient Monitoring including patient willingness and compliance for adoption, affordability, availability of simpler/smarter technology to mention a few. But experts contend that if implemented correctly Remote Patient Monitoring can contain healthcare expenditure by reducing avoidable hospitalization while greatly improving quality of care.
Karmeshu, Ph.D.
Dean & Professor, School of Computer & Systems Sciences & School of Computational & Integrative Sciences, Jawaharlal Nehru University, India.
Interspike Interval Distribution of Neuronal Model with distributed delay: Emergence of unimodal, bimodal and Power law
The study of interspike interval distribution of spiking neurons is a key issue in the field of computational neuroscience. A wide range of spiking patterns display unimodal, bimodal ISI patterns including power law behavior. A challenging problem is to understand the biophysical mechanism which can generate the empirically observed patterns. A neuronal model with distributed delay (NMDD) is proposed and is formulated as an integro-stochastic differential equation which corresponds to a non-markovian process. The widely studied IF and LIF models become special cases of this model. The NMDD brings out some interesting features when excitatory rates are close to inhibitory rates rendering the drift close to zero. It is interesting that NMDD model with gamma type memory kernel can also account for bimodal ISI pattern. The mean delay of the memory kernels plays a significant role in bringing out the transition from unimodal to bimodal ISI distribution. It is interesting to note that when a collection of neurons group together and fire together, the ISI distribution exhibits power law.
Pandiaraj Manickam, Niroj Kumar Sethy, Kalpana Bhargava, Vepa Kameswararao and Karunakaran Chandran
Designing electrochemical label free immunosensors for cytochrome c using nanocomposites functionalized screen printed electrodes
Release of cytochrome c (cyt c) from mitochondria into cytosol is a hallmark of apoptosis, used as a biomarker of mitochondrial dependent pathway of cell death (Kluck et al. 1997; Green et al. 1998). We have previously reported cytochrome c reductase (CcR) based biosensors for the measurement of mitochondrial cyt c release (Pandiaraj et al. 2013). Here, we describe the development of novel label-free, immunosensor for cyt c utilizing its specific monoclonal antibody. Two types of nanocomposite modified immunosensing platforms were used for the immobilization of anti-cyt c; (i) Self-assembled monolayer (SAM) functionalized gold nanoparticles (GNP) in conducting polypyrrole (PPy) modified screen printed electrodes (SPE) (ii) Carbon nanotubes (CNT) incorporated PPy on SPE. The nanotopologies of the modified electrodes were confirmed by scanning electron microscopy (SEM). Cyclic voltammetry, electrochemical impedance spectroscopy (EIS) were used for probing the electrochemical properties of the nanocomposite modified electrodes. Method for cyt c quantification is based on the direct electron transfer between Fe3+/Fe2+-heme of cyt c selectively bound to anti-cyt c modified electrode. The Faradaic current response of these nanoimmunosensor increases with increase in cyt c concentration. The procedure for cyt c detection was also optimized (pH, incubation times, and characteristics of electrodes) to improve the analytical characteristics of immunosensors. The analytical performance of anti-cyt c biofunctionalized GNP-PPy nanocomposite platform (detection limit 0.5 nM; linear range: 0.5 nM–2 μM) was better than the CNT-PPy (detection limit 2 nM; linear range: 2 nM-500nM). The detection limits were well below the normal physiological concentration range (Karunakaran et al. 2008). The proposed method does not require any signal amplification or labeled secondary antibodies contrast to widespread ELISA and Western blot. The immunosensors results in simple and rapid measurement of cyt c and has great potential to become an inexpensive and portable device for conventional clinical immunoassays.