Anshuman Gupta

Managing Demand Uncertainty in Supply Chain Planning

In this work, we provide an overview of our previously published works on incorporating demand uncertainty in midterm planning of multisite supply chains. A stochastic programming based approach is described to model the planning process as it reacts to demand realizations unfolding over time. In the proposed bilevel-framework, the manufacturing decisions are modeled as ‘here-and-now’ decisions, which are made before demand realization. Subsequently, the logistics decisions are postponed in a ‘waitand-see’ mode to optimize in the face of uncertainty. In addition, the trade-off between customer satisfaction level and production costs is also captured in the model. The proposed model provides an effective tool for evaluating and actively managing the exposure of an enterprises assets (such as inventory levels and profit margins) to market uncertainties. The key features of the proposed framework are highlighted through a supply chain planning case study. # 2003 Elsevier Science Ltd. All rights reserved.

Mid-term supply chain planning under demand uncertainty: customer demand satisfaction and inventory management

Abstract This paper utilizes the framework of mid-term, multisite supply chain planning under demand uncertainty to safeguard against inventory depletion at the production sites and excessive shortage at the customer. A chance constraint programming approach in conjunction with a two-stage stochastic programming methodology is utilized for capturing the trade-off between customer demand satisfaction (CDS) and production costs. In the proposed model, the production decisions are made before demand realization while the supply chain decisions are delayed. The challenge associated with obtaining the second stage recourse function is resolved by first obtaining a closed-form solution of the inner optimization problem using linear programming duality followed by expectation evaluation by analytical integration. In addition, analytical expressions for the mean and standard deviation of the inventory are derived and used for setting the appropriate CDS levels in the supply chain. A three-site example supply chain is studied within the proposed framework for providing quantitative guidelines for setting customer satisfaction levels and uncovering effective inventory management options. Results indicate that significant improvement in guaranteed service levels can be obtained for a small increase in the total cost.

A mixed integer linear programming (MILP) framework for inferring time delay in gene regulatory networks.

In this paper, an optimization based modeling and solution framework for inferring gene regulatory networks while accounting for time delay is described. The proposed framework uses the basic linear model of gene regulation. Boolean variables are used to capture the existence of discrete time delays between the various regulatory relationships. Subsequently, the time delay that best fits the expression profiles is inferred by minimizing the error between the predicted and experimental expression values. Computational experiments are conducted for both in numero and real expression data sets. The former reveal that if time delay is neglected in a system a priori known to be characterized with time delay then a significantly larger number of parameters are needed to describe the system dynamics. The real microarray data example reveals a considerable number of time delayed interactions suggesting that time delay is ubiquitous in gene regulation. Incorporation of time delay leads to inferred networks that are sparser. Analysis of the amount of variance in the data explained by the model and comparison with randomized data reveals that accounting for time delay explains more variance in real rather than randomized data.

Design of combinatorial protein libraries of optimal size

In this article we introduce a computational procedure, OPTCOMB (Optimal Pattern of Tiling for COMBinatorial library design), for designing protein hybrid libraries that optimally balance library size with quality. The proposed procedure is directly applicable to oligonucleotide ligation‐based protocols such as GeneReassembly, DHR, SISDC, and many more. Given a set of parental sequences and the size ranges of the parental sequence fragments, OPTCOMB determines the optimal junction points (i.e., crossover positions) and the fragment contributing parental sequences at each one of the junction points. By rationally selecting the junction points and the contributing parental sequences, the number of clashes (i.e., unfavorable interactions) in the library is systematically minimized with the aim of improving the overall library quality. Using OPTCOMB, hybrid libraries containing fragments from three different dihydrofolate reductase sequences (Escherichia coli, Bacillus subtilis, and Lactobacillus casei) are computationally designed. Notably, we find that there exists an optimal library size when both the number of clashes between the fragments composing the library and the average number of clashes per hybrid in the library are minimized. Results reveal that the best library designs typically involve complex tiling patterns of parental segments of unequal size hard to infer without relying on computational means. Proteins 2005.

Elucidation of directionality for co-expressed genes: predicting intra-operon termination sites

MOTIVATION In this paper, we present a novel framework for inferring regulatory and sequence-level information from gene co-expression networks. The key idea of our methodology is the systematic integration of network inference and network topological analysis approaches for uncovering biological insights. RESULTS We determine the gene co-expression network of Bacillus subtilis using Affymetrix GeneChip time-series data and show how the inferred network topology can be linked to sequence-level information hard-wired in the organisms genome. We propose a systematic way for determining the correlation threshold at which two genes are assessed to be co-expressed using the clustering coefficient and we expand the scope of the gene co-expression network by proposing the slope ratio metric as a means for incorporating directionality on the edges. We show through specific examples for B. subtilis that by incorporating expression level information in addition to the temporal expression patterns, we can uncover sequence-level biological insights. In particular, we are able to identify a number of cases where (1) the co-expressed genes are part of a single transcriptional unit or operon and (2) the inferred directionality arises due to the presence of intra-operon transcription termination sites. AVAILABILITY The software will be provided on request. SUPPLEMENTARY INFORMATION http://www.phys.psu.edu/~ralbert/pdf/gma_bioinf_supp.pdf

Large-scale inference of the transcriptional regulation of Bacillus subtilis

This paper addresses the inference of the transcriptional regulatory network of Bacillus subtilis. Two inference approaches, a linear, additive model and a non-linear power-law model, are used to analyze the expression of 747 genes from B. subtilis obtained using Affymetrix GeneChip ® arrays under three different experimental conditions. A robustness analysis is introduced for identifying confidence levels for all inferred regulatory connections. Both the linear and non-linear methods produce candidate networks that share a scale-free or a “huband-spoke” topology with a small number of global regulator genes influencing the expression of a large number of target genes. The two computational approaches in tandem are able to identify known global regulators with a high level of confidence. The linear model is able to identify the interactions of highly expressed genes, particularly those involved in genetic information processing, energy metabolism and signal transduction. Conversely, the non-linear power-law approach tends to capture development regulation and specific carbon and nitrogen regulatory interactions.

Large-scale inference and graph-theoretical analysis of gene-regulatory networks in B. Subtilis

We present the methods and results of a two-stage modeling process that generates candidate gene-regulatory networks of the bacterium B.subtilis from experimentally obtained, yet mathematically underdetermined microchip array data. By employing a computational, linear correlative procedure to generate these networks, and by analyzing the networks from a graph theoretical perspective, we are able to verify the biological viability of our inferred networks, and we demonstrate that our networks’ graph-theoretical properties are remarkably similar to those of other biological systems. In addition, by comparing our inferred networks to those of a previous, noisier implementation of the linear inference process [A. Gupta, J.D. Varner, C.D. Maranas, Comput. Chem. Eng. 29 (2005) 565], we are able to identify trends in graph-theoretical behavior that occur both in our networks as well as in their perturbed counterparts. These commonalities in behavior at multiple levels of complexity allow us to ascertain the level of complexity to which our process is robust to noise.

Real options based approaches to decision making under uncertainty

Abstract Most of the research in the process systems engineering literature has focused within an enterprises boundaries without recognizing the existence of financial markets to mitigate the risks associated with planning decisions made in the face of uncertainty. This paper discusses the incorporation of a market-based technique known as real options valuation (ROV) to models of pharmaceutical portfolio management and pollution abatement planning. The key managerial insights of this approach, that external financial market information can be used for valuing the flexibility of internal planning decisions and that both financial as well as real assets can be used to achieve desired objectives, are presented through planning case studies that demonstrate the capabilities of the models

Correction: Computational Modelling of Genome-Wide Transcription Assembly Networks Using a Fluidics Analogy

The fifth word of the title was misspelled. This also affect the articles citation. The correct title should be: Computational Modelling of Genome-Wide Transcription Assembly Networks Using a Fluidics Analogy. The corrected citation is: Azmy YY, Gupta A, Pugh BF (2008) Computational Modelling of Genome-Wide Transcription Assembly Networks Using a Fluidics Analogy. PLoS ONE 3(8): e3095. doi:10.1371/journal.pone.0003095