Kirti M. Yenkie

Maximizing sustainability of ecosystem model through socio-economic policies derived from multivariable optimal control theory

Current practices in natural resources consumption are unsustainable and may eventually lead to ecosystem extinction. Sustainable ecosystem management is necessary to ensure that human and ecological needs of the present are satisfied without compromising the ability of future generations to meet their own. This paper uses a simple mathematical model of an integrated ecological and economic system representing our planet’s sectors, including, but not limited to, natural, industrial, housing, and energy production sectors. The aim of the project is to maximize the sustainability of this system, using Fisher information as a statistical measure as a measure of sustainability, and derive socio-economic policies using optimal control techniques. By controlling six policy parameters, we were able to sustain all the ecological mass compartments (which were not sustainable in the consumption increase scenario of the future), thus significantly increasing the lifespan of all the species in the model.

Modeling the Superovulation Stage in In Vitro Fertilization

In vitro fertilization (IVF) is the most common technique in assisted reproductive technology and in most cases the last resort for infertility treatment. It has four basic stages: superovulation, egg retrieval, insemination/fertilization, and embryo transfer. Superovulation is a drug-induced method to enable multiple ovulation per menstrual cycle. The success of IVF majorly depends upon successful superovulation, defined by the number and similar quality of eggs retrieved in a cycle. Modeling the superovulation stage can help in predicting the outcomes of IVF before the cycle is complete. In this paper, we developed a model for superovulation stage. The model is adapted from the theory of batch crystallization. The aim of crystallization is to get maximum crystals of similar size and purity, while superovulation aims at eggs of similar quality and size. The rate of crystallization and superovulation are both dependent on the process conditions and varies with time. The kinetics of follicle growth is modeled as a function of injected hormones and the follicle properties are represented in terms of the moments. The results from the model prediction were verified with the known data from Jijamata Hospital, Nanded, India. The predictions were found to be in agreement with the actual observations.

A superstructure-based framework for bio-separation network synthesis

Abstract Modern biotechnologies enable the production of chemicals using engineered microorganisms. However, the cost of downstream recovery and purification steps is high, which means that the feasibility of bio-based chemicals production depends heavily on the synthesis of cost-effective separation networks. To this end, we develop a superstructure-based framework for bio-separation network synthesis. Based on general separation principles and insights obtained from industrial processes for specific products, we first identify four separation stages: cell treatment, product phase isolation, concentration and purification, and refinement. For each stage, we systematically implement a set of connectivity rules to develop stage-superstructures, all of which are then integrated to generate a general superstructure that accounts for all types of chemicals that can be produced using microorganisms. We further develop a superstructure reduction method to solve specific instances, based on product attributes, technology availability, case-specific considerations, and final product stream specifications. A general optimization model, including short-cut models for all technologies, is formulated. The proposed framework enables preliminary synthesis and analysis of bio-separation networks, and thus estimation of separation costs.

A roadmap for the synthesis of separation networks for the recovery of bio-based chemicals: Matching biological and process feasibility

Microbial conversion of renewable feedstocks to high-value chemicals is an attractive alternative to current petrochemical processes because it offers the potential to reduce net CO2 emissions and integrate with bioremediation objectives. Microbes have been genetically engineered to produce a growing number of high-value chemicals in sufficient titer, rate, and yield from renewable feedstocks. However, high-yield bioconversion is only one aspect of an economically viable process. Separation of biologically synthesized chemicals from process streams is a major challenge that can contribute to >70% of the total production costs. Thus, process feasibility is dependent upon the efficient selection of separation technologies. This selection is dependent on upstream processing or biological parameters, such as microbial species, product titer and yield, and localization. Our goal is to present a roadmap for selection of appropriate technologies and generation of separation schemes for efficient recovery of bio-based chemicals by utilizing information from upstream processing, separation science and commercial requirements. To achieve this, we use a separation system comprising of three stages: (I) cell and product isolation, (II) product concentration, and (III) product purification and refinement. In each stage, we review the technology alternatives available for different tasks in terms of separation principles, important operating conditions, performance parameters, advantages and disadvantages. We generate separation schemes based on product localization and its solubility in water, the two most distinguishing properties. Subsequently, we present ideas for simplification of these schemes based on additional properties, such as physical state, density, volatility, and intended use. This simplification selectively narrows down the technology options and can be used for systematic process synthesis and optimal recovery of bio-based chemicals.

Simulation-free estimation of reaction propensities in cellular reactions and gene signaling networks

Abstract Classical reaction kinetics based on deterministic rates laws are not valid for the description of cellular events in which the small number of molecules introduces stochasticity with discrete instead of continuous state transitions. Stochastic models are suitable for simulating transcriptional and translational events inside biological cells, but are impractical for solving inverse problems, which aim to estimate unknown reaction propensities from experimental observations. We introduce a new mathematical framework of Ito stochastic differential equations for the modeling of discrete cellular events and the robust and consistent parameter estimation of cellular dynamics where classical reaction kinetics is invalid. The results supported by case studies on gene expression in B. subtilis cells and viral gene transcription and translation inside non-lytic viral cells demonstrate that the proposed methodology performs as reliable as the gold standard Gillespie algorithm for simulating cellular events. More importantly, the new Ito process framework is ideal for estimating unknown reaction propensities from data as readily as in deterministic parameter estimation by using the novel ‘SPE – simulation free parameter estimation’ approach. Also, the computation time for the stochastic differential equation models is significantly low when compared to discrete event simulations.

Synthesis and analysis of separation networks for the recovery of intracellular chemicals generated from microbial-based conversions

BackgroundBioseparations can contribute to more than 70% in the total production cost of a bio-based chemical, and if the desired chemical is localized intracellularly, there can be additional challenges associated with its recovery. Based on the properties of the desired chemical and other components in the stream, there can be multiple feasible options for product recovery. These options are composed of several alternative technologies, performing similar tasks. The suitability of a technology for a particular chemical depends on (1) its performance parameters, such as separation efficiency; (2) cost or amount of added separating agent; (3) properties of the bioreactor effluent (e.g., biomass titer, product content); and (4) final product specifications. Our goal is to first synthesize alternative separation options and then analyze how technology selection affects the overall process economics. To achieve this, we propose an optimization-based framework that helps in identifying the critical technologies and parameters.ResultsWe study the separation networks for two representative classes of chemicals based on their properties. The separation network is divided into three stages: cell and product isolation (stage I), product concentration (II), and product purification and refining (III). Each stage exploits differences in specific product properties for achieving the desired product quality. The cost contribution analysis for the two cases (intracellular insoluble and intracellular soluble) reveals that stage I is the key cost contributor (>70% of the overall cost). Further analysis suggests that changes in input conditions and technology performance parameters lead to new designs primarily in stage I.ConclusionsThe proposed framework provides significant insights for technology selection and assists in making informed decisions regarding technologies that should be used in combination for a given set of stream/product properties and final output specifications. Additionally, the parametric sensitivity provides an opportunity to make crucial design and selection decisions in a comprehensive and rational manner. This will prove valuable in the selection of chemicals to be produced using bioconversions (bioproducts) as well as in creating better bioseparation flow sheets for detailed economic assessment and process implementation on the commercial scale.

Uncertainty in Clinical Data and Stochastic Model for In-vitro Fertilization

In-vitro Fertilization (IVF) is the most common technique in Assisted Reproductive Technology (ART). It has been divided into four stages; (i) superovulation, (ii) egg retrieval, (iii) insemination/fertilization and (iv) embryo transfer. The first stage of superovulation is a drug induced method to enable multiple ovulation, i.e., multiple follicle growth to oocytes or matured follicles in a single menstrual cycle. IVF being a medical procedure that aims at manipulating the biological functions in the human body is subjected to inherent sources of uncertainty and variability. Also, the interplay of the hormones with the natural functioning of the ovaries make the procedure dependent on several factors like patient’s condition in terms of cause of infertility, actual ovarian function, responsiveness to medication, etc. The treatment requires continuous monitoring and testing and this can give rise to errors in observations. Thus, it becomes essential to look at the process noise and deviations and think of a way to account for them to build better representative models for follicle growth. The purpose of this work is to come up with a robust model which can project the superovulation cycle outcome based on the hormonal doses and patient response in presence of uncertainty. The customized stochastic model results in better projection of the cycle outcome for the patients where the deterministic model has some deviations from the clinical observations and the growth term value is not within the range of ‘0.3 to 0.6’. It was found that the prediction accuracy was enhanced by more than 70% for some patients by using the stochastic model projections.

Modeling the Superovulation stage in IVF

Abstract In-vitro fertilization (IVF) is the most common technique in assisted reproductive technology and in most cases the last resort for infertility treatment. It has four basic stages: superovulation, egg retrieval, insemination/fertilization and embryo transfer. Superovulation is a drug induced method to enable multiple ovulation per menstrual cycle. The success of IVF majorly depends upon successful superovulation, defined by the number and similar quality of eggs retrieved in a cycle. Hence, modeling of this stage in terms of distribution of eggs (oocytes) obtained per cycle involving the chemical interactions of drugs used and the conditions imposed on the patient during the process would provide a basis for predicting the possible outcome. This is the focus of current endeavor. The model will be made more robust by considering uncertainties; like the response of a patient depending upon previous medical history, suitability of medicine and type of protocol used. This model will then be used to decide optimal drug delivery so as to maximize good quality egg formation. Thus, a phenomenon currently based on trial and error will get a strong base. It will help the patient to decide whether to undergo superovulation or start the IVF from donor eggs, which in turn would save the patient from financial loss as well as emotional distress. The aim of crystallization is to get maximum crystals of similar size and purity, while superovulation aims at eggs of similar quality which include the properties of size and number of chromosomes to enlist a few. The rate of crystallization and superovulation are both dependent on the process conditions and varies with time. Thus, model formulation for multiple ovulation will be on parallel lines to crystal formation in a batch process and will be modeled such.